Barrenness and Plant-to-Plant Variability in Maize (Zea mays L.)
by
Lin Li
A Thesis
presented to
The University of Guelph
In partial fulfilment of requirements
for the degree of
Doctor of Philosophy
in
Plant Agriculture
Guelph, Ontario, Canada
© Lin Li, April, 2013
ABSTRACT
BARRNENESS AND PLANT-TO-PLANT VARIABILITY IN MAIZE
(ZEA MAYS L.)
Lin Li
University of Guelph, 2013
Advisor:
Professor Elizabeth A. Lee
Co-advisor:
Professor Steven J. Rothstein
This thesis is an investigation of barrenness and plant-to-plant variability (PPV) in ear
development of maize (Zea mays L.). A three-year field experiment was conducted on
homogenous plants with similar initial plant size, development and uniform spatial patterns in
parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102. Physiological processes
underlying barrenness were dissected into plant growth through development and dry matter
partitioning to the ear at canopy, subpopulation, and primarily, individual plant levels. The
growth and development of the ultimately barren individuals were followed from early
vegetative stage to physiological maturity (PM) using a non-destructive allometric methodology.
Plant-to-plant variability in ear development, related to plant development, was measured
destructively from ear initiation to 1 wk after silking and at PM. Results showed that the
individual plants exhibited differential responses to their previous growth and development in the
two parental inbred lines. No physiological traits in growth and development or dry matter
partitioning to the ear during the critical period bracketing silking could characterize individual
barren plants. The F1 hybrid was resistant to barrenness even at 160,000 plants ha-1. At 80,000
plants ha-1, the spikelet number per row (SNPR) and spikelet number per ear (SNPE) exhibited
less PPV around silking than earlier stages of development. For the three genotypes, PPV in
plant morphological traits and ear length was relatively constant throughout development. In
addition, the period around the kernel row number (KRN) formation stage was the only timewindow that the PPV in stem volume, representing PPV in above-ground plant dry matter
(PDM), affected PPV in SNPR and KRN for the three genotypes, with SNPR being more
affected. Although the F1 produced greater PDM at silking and 1 wk after silking, it had shorter
ear length and less ear dry matter than the two parental inbred lines at the corresponding stages.
When the relationships are elucidated among early ear development, plant growth, leaftip
development, and dry matter partitioning to the ear, during the vegetative to silking stages and
under stress conditions, then the physiological processes underlying barrenness of the tested
inbred lines could be further characterized.
ACKNOWLEDGEMENTS
First, I would like to thank my advisor Elizabeth A. Lee for directing me to these thesis
topics. Her acceptance, wisdom and delicate guidance helped me continue my studies and finish
my thesis. I also owe sincere thanks to Dr. Hugh J. Earl for his tremendous time and efforts in
teaching me how to develop allometric models, and for discussing and correcting my thesis.
Without his help, finishing the thesis would have been impossible. I would also like to thank my
previous advisor Matthijs Tollenaar for initiating the thesis project and accepting me as his
student. His passion for crop physiology inspires me. My gratitude also goes to Dr. Steven J.
Rothstein for being my co-advisor and for his kindness, time, and generosity and for providing
me the opportunity to conduct molecular biology research.
I am grateful for valuable feedbacks from Drs. Bao-Luo Ma, Barry J. Shelp and Lewis
Lukens. I also thank Dr. Clarence J. Swanton for his time and commitment as the internal
external examiner. I am thankful to the technical support from Alberto Aguilera, Dr. Weidong
Liu, Hugo Gonzalez, Diego Cerrudo and Sabrina Brugière as well as the friendship from fellow
graduate students, staff and professors in the Department of Plant Agriculture.
Financial support from Syngenta, the Ontario Research Fund, the Ontario Ministry of
Agriculture, Food and Rural Affairs, and the Natural Sciences and Engineering Research Council
of Canada is also greatly appreciated.
I would like to thank my church community for their many prayers and for their spiritual
iv
support. I also like to thank my parents and family for their unconditional love. Last, I humbly
thank my heavenly Father for His love and encouragement, and giving me the wisdom and
strength to finish my PhD study and thesis.
v
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ........................................................................................................... iv TABLE OF CONTENTS............................................................................................................... vi LISTS OF TABLES ........................................................................................................................ x LISTS OF FIGURES .................................................................................................................... xii LIST OF ABBREVIATIONS ...................................................................................................... xvi Chapter 1. General Introduction and Literature Review ................................................................. 1 1.1. Definition and Previous Study of Barrenness ................................................................... 1 1.1.1 Low water potential at pollination ............................................................................ 4 1.1.2 Low light at pollination ............................................................................................ 6 1.1.3 Heat stress ................................................................................................................. 7 1.1.4 Other factors causing barrenness ............................................................................ 10 1.1.5 Crowding stress ...................................................................................................... 11 1.1.5.1 Carbon and nitrogen ...................................................................................... 12 1.1.5.2 Plant growth regulator ................................................................................... 16 1.1.5.3 Barrenness of dominated plants under crowding stress ................................ 18 1.1.6 Secondary tools to characterize barrenness ............................................................ 20 1.2. Top-down Approach and Allometric Methodology ........................................................ 22 1.2.1 Top-down approach ................................................................................................ 22 1.2.2 Allometric methodology ......................................................................................... 25 1.3 Plant-to-Plant Variability in Plant and Ear Development ................................................ 26 1.3.1 Maize life cycle and ear development .................................................................... 27 1.3.2 Yield compensation mechanism associated with plant-to-plant variability ........... 30 1.3.3 Measurement of plant-to-plant variability .............................................................. 31 1.4 Research Hypotheses, Objectives, and Significance of the Research ............................. 32 Chapter 2. Ear Development in Relation to Plant Development in Maize (Zea mays L.) ............ 34 vi
2.0 Abstract ............................................................................................................................ 34 2.1 Introduction ...................................................................................................................... 36 2.2 Materials and Methods ..................................................................................................... 40 2.2.1 Genetic materials and experimental design ............................................................ 40 2.2.2 Developmental stages, morphometric and physiological measurements ............... 42 2.2.3 Statistical analyses .................................................................................................. 45 2.3 Results and Discussion ..................................................................................................... 47 2.3.1 General phenological development in terms of thermal time ................................. 47 2.3.2 Ear development in terms of thermal time and leaftip stage. ................................. 50 2.3.3 Above-ground plant dry matter and ear traits ......................................................... 55 2.3.4 Effects of sampling individual plants that were not competitively bordered ......... 76 2.4 Conclusions ...................................................................................................................... 78 Chapter 3. Physiological Characteristics of Barrenness in Maize (Zea mays L.) ......................... 80 3.0 Abstract ............................................................................................................................ 80 3.1 Introduction ...................................................................................................................... 81 3.2 Materials and Methods ..................................................................................................... 84 3.2.1 Genetic materials .................................................................................................... 84 3.2.2 Cultural practices and experimental design ............................................................ 84 3.2.2.1 Field experiment in 2007 .............................................................................. 85 3.2.2.2 Field experiment in 2008 .............................................................................. 86 3.2.2.3 Morphometric methodology and measurement ............................................ 86 3.2.2.4 Destructive samplings ................................................................................... 87 3.2.2.5 Non-destructive measurements ..................................................................... 88 3.2.2.6 Harvest at physiological maturity ................................................................. 90 3.2.3 Selection of allometric models ............................................................................... 90 3.2.3.1 Comparison of models with an intercept and without an intercept for aboveground plant dry matter ............................................................................................. 90 vii
3.2.3.2 Comparison of combined plant density model to individual plant density
model for above-ground plant dry matter ................................................................. 92 3.2.3.3 Selection of allometric models for ear dry matter......................................... 94 3.2.4 Data analyses .......................................................................................................... 98 3.2.4.1 Classification of four subpopulations according to barrenness and plant
hierarchy.................................................................................................................. 100 3.2.4.2 Frequency distribution of the physiological and morphological traits ....... 101 3.3 Results ............................................................................................................................ 103 3.3.1 Ultimately barren plants at physiological maturity .............................................. 103 3.3.1.1 Occurrence of barrenness ............................................................................ 103 3.3.1.2 The position of ultimately barren plants within a plant population ............ 103 3.3.1.3 Relationships between yield components ................................................... 106 3.3.2 Allometric model development to predict individual barren and non-barren plants
....................................................................................................................................... 106 3.3.2.1 Model comparison for above-ground plant dry matter estimation ............. 106 3.3.2.2 Model comparison for ear dry matter estimation ........................................ 109 3.3.2.3 Allometric model justification .................................................................... 115 3.3.2.4 Allometric model calibration ...................................................................... 123 3.3.2.5 Allometric model evaluation ....................................................................... 124 3.3.3 The history of individual barren plants................................................................. 125 3.3.3.1 Plant growth and development of barren plants.......................................... 125 3.3.3.2 Ear growth and development of barren plants ............................................ 131 3.3.3.3 Efficiency and dry matter partitioning during the critical period bracketing
silking ...................................................................................................................... 139 3.3.4 Barren plants and dominated plants of a plant hierarchy ..................................... 148 3.3.4.1 Difference in growth throughout development and partitioning at the
subpopulation level ................................................................................................. 149 3.3.4.2 Similarity in growth throughout development and partitioning at the
individual plant level ............................................................................................... 153 viii
3.3.5 Barrenness and morphological and physiological traits ....................................... 156 3.4 Discussion ...................................................................................................................... 162 3.4.1 Allometric models to predict individual barren and non-barren plants ................ 162 3.4.1.1 Comparison of allometric models ............................................................... 162 3.4.1.2 Justification of allometric models and methodology .................................. 163 3.4.1.3 Evaluation of allometric models ................................................................. 164 3.4.1.4 Limitation and improvement of allometric models..................................... 166 3.4.2. Characterizing barren plants ................................................................................ 167 3.4.2.1 Approach to study barren plants ................................................................. 168 3.4.2.2 Unpredictability of barrenness in two parental inbred lines ....................... 169 3.4.2.3 Above-ground plant dry matter at physiological maturity of barren plants 171 3.4.2.4 Effects of genotype, plant density and genotype × plant density interaction
................................................................................................................................. 172 3.4.2.5 Barrenness is not totally in relation to dominated plants ............................ 174 3.4.2.6 Barrenness and physiological and morphological traits ............................. 174 3.4.2.7 Research limitation, values and implications .............................................. 176 3.5 Conclusions .................................................................................................................... 178 Chapter 4. General Discussion and Conclusions ........................................................................ 179 4.1 Summary of the Results and Conclusions ...................................................................... 179 4.2 Contributions .................................................................................................................. 181 4.3 Research Limitations and Future Research .................................................................... 182 4.3.1 Effects of sampling individual plants that were not competitively bordered ....... 182 4.3.2. Plant-to-plant variability in ear traits during the kernel row number formation
stage at the canopy level ................................................................................................ 183 4.3.3. Relationships among plant growth and leaftip development as well as dry matter
partitioning to the ear at the subpopulation level........................................................... 184 REFERENCES ........................................................................................................................... 185 APPENDIXES ............................................................................................................................ 213
ix
LISTS OF TABLES
Table 2.1. Summary of numbers of sampled plants CG60, CG102 and their F1 CG60 × CG102
from the 6-leaftip (LT) stage until tassel emergence (TE). .............................................. 43 Table 2.2. Thermal time accumulation in growing degree days (GDD) required for the key
phenological stages, plant and ear morphological characteristics for two inbred lines
CG60, CG102 and their F1 CG60 × CG102 grown at 80,000 plants ha-1 in 2009............ 49 Table 2.3. Spearman’s rank phenotypic correlation coefficients between ear length and spikelet
number per row during development including tassel emergence (TE), silking and 1 wk
after silking for two inbred lines CG60, CG102 and their F1 CG60 × CG102 grown at
80,000 plants ha-1 in 2009. ................................................................................................ 58 Table 2.4. List of phenological stages during which indicated variables that were not normally
distributed. ........................................................................................................................ 60 Table 2.5. Above-ground plant dry matter (PDM) at silking (PDMs), 1 wk after silking (PDMs+1)
and physiological maturity (PM) (PDMPM), the corresponding ear dry matter (EDM) with
husks, cob and shank at silking (EDMs), 1 wk after silking (EDMs+1) and PM (EDMPM)
and dry matter partitioning to the ear during each period for two inbred lines CG60,
CG102 and their F1 CG60 × CG102 grown at 80,000 plants ha-1 in 2009. ...................... 73 Table 2.6. Above-ground plant dry matter (PDMPM), grain yield (GY), harvest index (HI),
kernel number per plant (KNP) as well as primary ear KNP at physiological maturity for
two inbred lines CG60 and CG102 and their F1 CG60 × CG102 grown at 80,000 plants
ha-1 in 2009. ...................................................................................................................... 74 Table 3.1. Relationships between post-silking leaf area per plant (PSLA) and primary ear leaf
area (LAE) for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 ×
CG102 in 2008. ................................................................................................................. 91 Table 3.2. Relationships between above-ground plant dry matter (PDM) and morphological
variables (i.e., stem volume [SV], maximum primary ear diameter [ED]) at different
phenological stages for two parental inbred lines CG60, CG102 and their F1 hybrid CG60
× CG102 with a combination of plant densities in 2007 and 2008................................... 95 Table 3.3. Relationships between ear dry matter (EDM) and maximum primary ear diameter
(ED) at silking and 2 wk after silking for two parental inbred lines CG60, CG102 and
their F1 hybrid CG60 × CG102 with individual plant densities or a combination of
different plant densities in 2007 and 2008. ....................................................................... 97 Table 3.4. Comparison of mean residual percentage (i.e., the difference between measured
above-ground plant dry matter minus estimated above-ground plant dry matter divided by
measured above-ground plant dry matter) between two plant densities within the
combined plant density model for parental inbred lines CG60, CG102 and their F1 hybrid
CG60 × CG102 at five destructive sampling stages in 2007. ......................................... 116 x
Table 3.5. Comparison of mean residual percentage (i.e., the difference between measured
above-ground plant dry matter minus estimated above-ground plant dry matter divided by
measured above-ground plant dry matter) among three plant densities within the
combined plant density model for two parental inbred lines CG60, CG102 and their F1
hybrid CG60 × CG102 at four destructive sampling stages in 2008. ............................. 117 Table 3.6. Comparison of mean residual percentage (i.e., the difference between measured ear
dry matter [EDM] minus estimated EDM divided by measured EDM) within the
combined plant density model between two plant densities in 2007 and among three plant
densities in 2008 for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 ×
CG102 at silking and 2 wk after silking. ........................................................................ 119 Table 3.7. Comparison of mean residual percentage (i.e., the difference between measured ear
dry matter [EDM] minus estimated EDM divided by measured EDM) of a plant density
between combined plant density model and individual plant density model for parental
inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 at two genotype ×
sampling stage combinations in 2007 and four genotype × sampling stage combinations
in 2008 when there was a significant difference in residual percentage between/among
plant densities within a combined plant density model. ................................................. 121 Table 3.8 Threshold values (± standard error) of hyperbolic model fitted to the relationship
between harvest index (HI) and plant growth rate during the critical period bracketing
silking (PGRs) or HI and ear growth rate during the critical period bracketing silking
(EGRs) for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 in
2007 and 2008 grown at various plant densities. ............................................................ 144 Table 3.9. The minimum (Min) and maximum (Max) harvest index (HI) of non-barren plants
when non-barren plants had similar values with barren plants (i.e., final grain yield per
plant = 0 g) in plant growth rate during the critical period bracketing silking (PGRs), or
ear growth rate during the period bracketing silking (EGRs), or .................................... 146 Table 3.10. Comparisons among different subpopulations in above-ground plant dry matter
(PDM), ear dry matter (EDM) throughout development, plant (PGRs) and ear growth rate
during the critical period bracketing silking (EGRs), and partitioning index (i.e.,
EGRs/PGRs) for two parental inbred lines CG60 and CG102 grown at different plant
densities in 2007 and 2008.............................................................................................. 150 Table 3.11. Summary of similarities in combination of above-ground plant dry matter (PDM)
and leaftip (LT) stage, flowering dynamics (anthesis to silking interval (ASI) and
growing degree days from planting to silking (GDDsilking) and the combination of ear dry
matter around silking (EDMs) and flowering dynamics among different subpopulations
for two parental inbred lines CG60 and CG102 grown at different plant densities in 2007
and 2008. ......................................................................................................................... 155 xi
LISTS OF FIGURES
Figure 1.1. Schematic of the developmental progression of meristem identities during ear
development (Vollbrecht and Schmidt, 2009). ................................................................. 28 Figure 2.1. The linear relationships between leaftip (LT) number and the growing degree days
(GDD) accumulated from the day after planting in two parental inbred lines CG60 and
CG102 and their F1 hybrid CG60 × CG102 grown under 80,000 plants ha-1 in 2009. .... 48 Figure 2.2. Dynamics of spikelet number per row from ear initiation to 1 wk after silking in
primary ear of inbred line CG60 grown under 80,000 plants ha-1 in 2009. ...................... 51 Figure 2.3. Dynamics of spikelet number per row from ear initiation to 1 wk after silking in
primary ear of inbred line CG102 grown under 80,000 plants ha-1 in 2009. .................... 52 Figure 2.4. Dynamics of spikelet number per row from ear initiation to 1 wk after silking in
primary ear of the F1 CG60 × CG102 grown under 80,000 plants ha-1 in 2009. .............. 53 Figure 2.5. Frequency distributions of kernel row number (KRN) in inbred lines CG60, CG102
and the F1 hybrid CG60 × CG102 grown under 80,000 plants ha-1 in 2009. ................... 54 Figure 2.6. Mean ear length and standard deviation (SD) of ear length in sampled inbred lines
CG60, CG102 and their F1 CG60 × CG102 plants during different phenological stages
(PS) grown under 80,000 plants ha-1 in 2009. .................................................................. 56 Figure 2.7. Coefficients of variation (CV) and standard deviation (SD) of stem volume (SVmax)
of sampled inbred line CG60 during different phenological stages (PS) when the crop
was grown under 80,000 plants ha-1 in 2009. ................................................................... 61 Figure 2.8. Coefficients of variation (CV) and standard deviation (SD) of stem volume (SVmax)
of sampled inbred line CG102 during different phenological stages (PS) when the crop
was grown under 80,000 plants ha-1 in 2009. ................................................................... 62 Figure 2.9. Coefficient of variation (CV) and standard deviation (SD) of stem volume (SVmax) of
sampled F1 CG60 × CG102 during different phenological stages (PS) when the crop was
grown under 80,000 plants ha-1 in 2009............................................................................ 63 Figure 2.10. Coefficient of variation (CV) and standard deviation (SD) of spikelet number per
row (SNPR), spikelet number per ear (SNPE) and ear length (EL) for inbred line CG60
during different phenological stages (PS) when the crop was grown under 80,000 plants
ha-1 in 2009. ...................................................................................................................... 64 Figure 2.11. Standard deviation (SD) of spikelet number per row (SNPR), spikelet number per
ear (SNPE) and ear length for inbred line CG60 during different phenological stages (PS)
when the crop was grown under 80,000 plants ha-1 in 2009. ............................................ 65 Figure 2.12. Coefficient of variation (CV) and standard deviation (SD) of spikelet number per
row (SNPR), spikelet number per ear (SNPE) and ear length (EL) for inbred line CG102
xii
during different phenological stages (PS) when the crop was grown under 80,000 plants
ha-1 in 2009. ...................................................................................................................... 66 Figure 2.13. Standard deviation (SD) of spikelet number per row (SNPR), spikelet number per
ear (SNPE) and ear length for inbred line CG102 during different phenological stages
(PS) when the crop was grown under 80,000 plants ha-1 in 2009..................................... 67 Figure 2.14. Coefficient of variation (CV) and standard deviation (SD) of spikelet number per
row (SNPR), spikelet number per ear (SNPE) and ear length (EL) for F1 hybrid CG60 ×
CG102 during different phenological stages (PS) when the crop was grown under 80,000
plants ha-1 in 2009. ............................................................................................................ 68 Figure 2.15. Standard deviation (SD) of spikelet number per row (SNPR), spikelet number per
ear (SNPE) and ear length for F1 hybrid CG60 × CG102 during different phenological
stages (PS) when the crop was grown under 80,000 plants ha-1 in 2009. ......................... 69 Figure 2.16. The relationship between plant-to-plant variability in stem volume measured as
coefficient of variation (CV) and plant-to-plant variability in spikelet number per row
(SNPR) and maximum kernel row number (KRNmax) from kernel row number (KRN)
formation to 1 wk after silking and at physiological maturity for two parental inbred lines
CG60, CG102 and their F1 hybrid CG60 × CG102 planted at 80,000 plants ha-1 in 2009.
........................................................................................................................................... 71 Figure 2.17. Individual values of above-ground plant dry matter at silking (x-axis) and 1 wk
after silking (PDMs+1) (primary y-axis, open symbols) and individual values of aboveground plant dry matter at physiological maturity (PM) (x-axis) and its corresponding
grain yield (secondary y-axis, filled symbols) for inbred lines CG60, CG102 and their F1
hybrid CG60 × CG102 grown at 80,000 plants ha-1 in 2009. ........................................... 75 Figure 3.1. Frequency distributions of grain yield (GY)(primary y-axis), and the relationship
between kernel number per plant (KNP) (secondary y-axis) and GY in two parental
inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 grown across different
plant densities in 2007 and 2008. .................................................................................... 104 Figure 3.2. Frequency distributions of above-ground plant dry matter at physiological maturity
(PDMPM) (primary y-axis), and the relationship between harvest index (HI) (i.e., ratio
between grain yield and PDMPM) (secondary y-axis) and PDMPM in two parental inbred
lines CG60, CG102 and their F1 hybrid CG60 × CG102 grown across different plant
densities in 2007 and 2008.............................................................................................. 105 Figure 3.3. Selective comparison of allometric models with an intercept and without an intercept
for F1 hybrid CG60 × CG102 at 160,000 plants ha-1 at the 9-leaftip (LT) stage and inbred
CG60 at 120,000 plants ha-1 at 2 wk after silking in 2007. ............................................ 107 Figure 3.4. Selective comparison of allometric models with an intercept and without an intercept
for inbred CG102 at 160,000 plants ha-1 at the 10-leaftip (LT) stage and inbred CG60 at
160,000 plants ha-1 at 2 wk after silking in 2008. ........................................................... 110 xiii
Figure 3.5. Selected comparisons of allometric models with a power function and with an
exponential function for the F1 hybrid CG60 × CG102 at 160,000 plants ha-1 at silking
and inbred CG60 at 120,000 plants ha-1 at 2 wk after silking in 2007. .......................... 112 Figure 3.6. Selected comparisons of allometric models with a power function and with an
exponential function for inbred CG102 at 80,000 plants ha-1 at 2 wk after silking and F1
hybrid CG60 × CG102 at 120,000 plants ha-1 at 2 wk after silking in 2008. ................. 114 Figure 3.7. Selected presentation of the relationship between measured above-ground plant dry
matter (PDM) in destructive sampling areas using combined plant density models and the
corresponding predicted PDM for inbred CG102 at 11-leaftip (LT) stage and inbred
CG60 at silking in 2007 and CG60 at 16-LT stage and the F1 hybrid CG60 × CG102 at 2
wk after silking in 2008. ................................................................................................. 118 Figure 3.8. Presentation of relationship between measured ear dry matter (EDM) in destructive
sampling areas using combined plant density model and the corresponding predicted
EDM for F1 hybrid CG60 × CG102 at silking, and inbred CG60 at 2 wk after silking in
2007, and CG60 at silking, and CG102 at 2 wk after silking in 2008. ........................... 122 Figure 3.9. Estimated above-ground plant dry matter (PDM) for individual barren plants (i.e.,
grain yield per plant = 0 g), average barren plants, and average individual plants from
early vegetative stage to physiological maturity for one of the parental inbred lines,
CG102, at different plant densities in 2007 and 2008. ................................................... 126 Figure 3.10. Estimated above-ground plant dry matter (PDM) for individual barren plants (i.e.,
grain yield per plant = 0 g), average barren plants, and average individual plants from
early vegetative stage to physiological maturity for one of the parental inbred lines CG60
and its F1 hybrid CG60 × CG102 at different plant densities in 2008. ........................... 127 Figure 3.11. The leaftip stage development of individual barren plants (i.e., grain yield per plant
= 0 g) and average individual plants for two parental inbred lines CG60 and CG102 and
their F1 hybrid CG60 × CG102 at different plant densities in 2007 and 2008. .............. 128 Figure 3.12. The initial ear dry matter (EDM) around silking, and the subsequent growth I of
individual ears between silking and approximately 2 wk after silking, the subsequent
growth II between 2 wk after silking and physiological maturity as well as the
corresponding final grain yield (GY) at physiological maturity for one of the parental
inbred lines, CG102, at different plant densities in 2007 and 2008. ............................... 132 Figure 3.13. The initial ear dry matter (EDM) around silking, and the subsequent growth I of
individual ears between silking and approximately 2 wk after silking, the subsequent
growth II between 2 wk after silking and physiological maturity as well as the
corresponding final grain yield (GY) at physiological maturity for one of the parental
inbred lines, CG60, and its F1 hybrid CG60 × CG102 at different plant densities in 2008.
......................................................................................................................................... 133 Figure 3.14. Relationship between ear dry matter at silking (EDMs) and anthesis to silking
interval (ASI) for ultimately barren and non-barren ears, and the relationship between
xiv
EDMs and growing degree days from planting to silking (GDDsilking) for barren and nonbarren ears for one of the parental inbred lines, CG102, at different plant densities in
2007 and 2008. ................................................................................................................ 137 Figure 3.15. Relationship between harvest index (HI) and plant growth rate during the critical
period bracketing silking (PGRs) expressed as gram per plant per growing degree day (g
plant-1 GDD-1), and between HI and ear growth rate after silking (EGRs) in g plant-1
GDD-1 for parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 across
two plant densities in 2007. ............................................................................................ 140 Figure 3.16. Relationship between harvest index (HI) and plant growth rate during the critical
period bracketing silking (PGRs) expressed as gram per plant per growing degree day (g
plant-1 GDD-1), and between HI and ear growth rate after silking (EGRs) in g plant-1
GDD-1 for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102
across three plant densities in 2008. ............................................................................... 142 Figure 3.17. Relationships between estimated ear growth rate during the critical period
bracketing silking (EGRs) expressed as gram per plant per growing degree day (g plant-1
GDD-1) and estimated plant growth rate during the critical period bracketing silking
(PGRs) in g plant-1 GDD-1 of parental inbred lines CG60, CG102 and their F1 hybrid
CG60 × CG102 grown across different plant densities in 2007 and 2008. .................... 147 Figure 3.18. Relationship between above-ground plant dry matter at physiological maturity
(PDMPM) and maximum plant height (MaxPH) as well as the frequency distributions of
the ratio between PDMPM and MaxPH for two parental inbred lines CG60, CG102 and
their F1 hybrid CG60 × CG102 grown across different plant densities in 2007 and 2008.
......................................................................................................................................... 157 xv
LIST OF ABBREVIATIONS
ASI ––– anthesis to silking interval
Bd plants ––– barren and dominated plants
BID plants ––– barren plants that belong to the intermediate and dominant plants
C ––– Carbon
CV ––– coefficient of variation
d plants ––– dominated plants
D plants ––– dominant plants
ED ––– maximum primary ear diameter
EDM ––– ear dry matter
EDMPM ––– ear dry matter at physiological maturity
EDMs ––– ear dry matter at silking
EDMs+1 ––– ear dry matter at 1 wk after silking
EDMs+2 ––– ear dry matter at 2 wk after silking
EGRs ––– ear growth rate during the critical period bracketing silking
GDD ––– growing degree days
GDDsilking ––– growing degree days from planting to silking
GY ––– grain yield
HI ––– harvest index
KNP ––– kernel number per plant
KRN ––– kernel row number
KRNmax ––– maximum kernel row number
LAE ––– primary ear leaf area
xvi
LT ––– leaftip
MaxPH ––– maximum plant height
N ––– nitrogen
NBd ––– non-barren dominated plants
NBID ––– non-barren plants that belong to the intermediate and dominant plants
NT & NS ––– no anther and silk emergence
PDM ––– above-ground plant dry matter
PDMPM ––– above-ground plant dry matter at physiological maturity
PDMs ––– above-ground plant dry matter at silking
PDMs+1 ––– above-ground plant dry matter at 1 wk after silking
PDMs+2 ––– above-ground plant dry matter at 2 wk after silking
PGRs ––– plant growth rate during the critical period bracketing silking
PH ––– plant height
PM ––– physiological maturity
PPV ––– plant-to-plant variability
PSLA ––– post-silking leaf area per plant
RMSE ––– root mean squared error
RP ––– residual percentage
SD ––– standard deviation
SDb ––– the maximum width of basal stem diameter
SD2 ––– the maximum width of stem diameter 2 cm above the ground level
SNPE ––– spikelet number per ear
SNPR ––– spikelet number per row
xvii
SV ––– stem volume
SVmax ––– stem volume derived from the maximum width of stem diameter 2 cm above the
ground level
TE ––– tassel emergence
V stage ––– fully emerged leaves with visible collars
xviii
Chapter 1. General Introduction and Literature Review
1.1. Definition and Previous Study of Barrenness
Barrenness in maize (Zea mays L.) has been defined according to kernel number per plant
(KNP), grain yield (GY) and the absence of a visible ear (Buren et al., 1974; Olness et al., 1990;
Vega et al., 2001a; Sangoi et al., 2002; Boomsma, 2009). It has been manipulated or
characterized in different research topics using various approaches. Section 1.1. will summarize
previous studies according to research topics and approaches.
Genotype, agronomic practice and the combination of genotype and agronomic practice
affect yield performance. This yield performance includes incidences of barrenness that have
been evaluated at the canopy level. Incidence of barrenness is affected by genotype × plant
density, hybrid × nitrogen (N), plant density × N, and hybrid × plant density × N (Sass and
Loeffel, 1959; Genter and Camper, 1973; Sangoi et al., 2002; Subedi et al., 2006), as well as
tillage × N × hybrid (Olness et al., 1990). In general, parental inbred lines and their F1 hybrid
respond differentially to plant density in terms of incidences of barrenness (Sass and Loeffel,
1959). Prolificacy is defined as the tendency to produce more than one ear per plant at normal
plant densities (Bjarnason, 1994). Prolific germplasms and also more recent hybrids with
increased plant growth rate during the critical period bracketing silking (PGRs), reduce
barrenness at high plant density (Duvick, 1974; Tollenaar et al., 1992).
The incidence of barrenness increases with an increase in plant density (Stringfield and
Thatcher, 1947; Woolley et al., 1962; Genter and Camper, 1973; Daynard and Muldoon, 1983;
1
Hashemi-Dezfouli and Herbert, 1992; Sangoi et al., 2002). Zero N fertilization results in the
highest barrenness (Subedi et al., 2006; Boomsma, 2009). The incidence of barrenness decreases
when the intensity of tillage increases (Olness et al., 1990). Spacing patterns, which are the
number of plants per hill, do not significantly affect barrenness (Stringfield and Thatcher, 1947).
Barrenness is more affected by plant density than by soil N level and hybrid (Lang et al., 1956).
Tillage and application of N fertilizer reduce barrenness by promoting root proliferation and
development (Olness et al., 1990). These studies help to examine the genetic and physiological
mechanisms for yield improvement, as well as to determine the optimum genotype and
agronomic practice at the canopy level; however the exact physiological mechanism behind
barrenness is not addressed.
Whole plant and leaf photosynthesis have been measured to characterize barren hybrid
plants (Moss, 1962; Thiagarajah et al., 1981). Barren plants were induced by preventing
fertilization or removing ears and were compared to non-barren plants. Barren plants tend to
exhibit less post-silking plant photosynthesis under field conditions and less leaf photosynthesis
in plant growth chambers. Moreover, the degree of decrease in plant photosynthesis of barren
plants as a proportion of non-barren plants depends on the hybrid. For example, the degree of
decrease in net CO2 assimilation of barren plants is higher for an early cultivar until 5 d after ear
removal, after which a late cultivar exhibits higher degree of loss (Moss, 1962). The rate of
reduction in leaf photosynthesis of barren plants, as a proportion of the corresponding leaf of
non-barren plants grown under the same photoperiod, depends on leaf age. For example, a leaf
of barren plant (leaf 13), which is one or two nodes above the ear and acts as a major assimilate
source for kernel development, has a relatively slower rate of reduction in leaf photosynthetic
2
rate, compared to the other two lower leaves (leaves 9 and 11) (Thiagarajah et al., 1981). With
the prevention of pollination, barren plants do not consistently exhibit purple leaves and stems in
the two experiments (Moss, 1962; Thiagarajah et al., 1981). Measuring plant and leaf
photosynthesis is a good way to characterize barrenness non-destructively; however, if
barrenness or GY is investigated under field conditions, measuring canopy photosynthesis is
neither an easy nor a complete approach without considering dry matter partitioning (Poorter et
al., 1990). In addition, it is difficult to obtain meaningful leaf photosynthesis data under field
conditions, especially at high plant density, and available data should be interpreted with caution
(Tollenaar and Lee, 2010). Also, it is possible to induce barrenness by preventing fertilization or
removing ears, enabling the study of leaf senescence (Christensen et al., 1981; Crafts-Brandner
et al., 1984; Crafts-Brandner and Poneleit, 1987; Rajcan and Tollenaar, 1999). This provides the
evidence that genotypes are consistent in responses during the grain-filling period, which
displays lower net photosynthesis and dry matter accumulation and higher leaf carbohydrate
content. However, there are genotype-dependent responses of plant and leaf N status to the same
treatments. In summary, researchers can manipulate barrenness instead of allowing barrenness
to occur naturally, and use this as a tool for related physiological mechanisms. Under field
conditions, individual plants can experience abiotic stress, differential extinction of red and farred light and variations in plant emergence, and ultimately they could become barren.
Physiological mechanisms of maize barrenness have been studied under various abiotic
stresses such as low water potential during pollination (Westgate and Boyer, 1985, 1986;
Schussler and Westgate, 1991; Bassetti and Westgate, 1993; Boyer and Westgate, 2004) and low
light (Schussler and Westgate, 1991), heat (Commuri and Jones, 2001; Cicchino et al., 2010) and
3
drought (Bolaños and Edmeades, 1993; Edmeades et al., 1993). The common research approach
is to study the effects of stress on source strength and sink strength together, or on sink strength
alone. Studies however differ in when, how long and how to implement stress, as well as the
selected genotype.
1.1.1 Low water potential at pollination
Complete (100%) inhibition of leaf photosynthesis at pollination by low water potential
causes ovary abortion and complete barrenness. Barrenness, caused by low water potential at
pollination, is related to a reduced assimilate supply to ovaries in terms of source strength
(Schussler and Westgate, 1991; Mäkelä et al., 2005). Low water potential does not reduce ovary
sucrose concentration at pollination compared to normal conditions (Schussler and Westgate,
1991; Zinselmeier et al., 1995). Instead, a reduced assimilate supply, which is the diminution of
rate of sugar (mainly sucrose) transport to ovaries, is the major cause of complete ovary abortion,
irrespective of carbon (C) and N assimilate reserves at pollination (Westgate and Boyer, 1985;
Schussler and Westgate, 1994). In terms of sink strength, barrenness is related to a reduced
capacity for the ovaries to absorb sucrose (Schussler and Westgate, 1991; Mäkelä et al., 2005).
Low water potential decreases sink strength by inhibiting activities of insoluble (cell
wall) and soluble (vacuolar) acid invertases as well as downregulating acid invertase genes (cell
wall invertase 1, cell wall invertase 2, soluble invertase 2) (Zinselmeier et al., 1995; McLaughlin
and Boyer, 2004a). Insoluble and soluble acid invertases hydrolyze sucrose into glucose and
fructose, and these invertases are the dominant enzymes regulating sucrose metabolism during
4
silk emergence, pollination and early kernel growth (Zinselmeier et al., 1995). Insoluble acid
invertases are localized at the apoplast and throughout the pedicel (McLaughlin and Boyer,
2004b). Sucrose is unloaded passively from the sieve tube into the apoplast of the pedicel
parenchyma cell of developing maize ovaries. The hydrolytic function of the enzyme establishes
sink strength by maintaining a favorable sucrose concentration gradient between the two points,
which facilitates continued sucrose unloading from the phloem. In addition, the hydrolytic
function of the invertases inverts sucrose to hexoses for uptake by the ovaries, and prevents
sucrose reloading into the phloem (Eschrich, 1980). Soluble invertases are located at the
nucellus (McLaughlin and Boyer, 2004b). Soluble invertases adjust sink strength by affecting
the capacity to utilize the imported sucrose, and soluble invertases could hydrolyze the rest of the
sucrose that escapes from hydrolysis in the pedicel apoplast after they enter the nucellar cells
(Zinselmeier et al., 1995; McLaughlin and Boyer, 2004b). Low water potential inhibits the
activity of both insoluble and soluble acid invertases and limits the hydrolysis of sucrose into
glucose and fructose, which results in sucrose accumulation in ovary at pollination (Schussler
and Westgate, 1991; Zinselmeier et al., 1995).
Low water potential also changes ovary C status and metabolism between silk emergence
and pollination, and low water potential leads to ovary abortion. Both starch and glucose are
abundant in the ovary at pollination (McLaughlin and Boyer, 2004b). Ovary starch serves as a
buffering reserve C and can be consumed to release glucose in order to sustain glucose for a
short time (Zinselmeier et al., 1999; McLaughlin and Boyer, 2004a). Low water potential
triggers starch breakdown in order to supply glucose. Starch is completely depleted at
pollination, which characterizes ovaries destined to abort at low water potential. Glucose
5
depletion follows starch depletion (McLaughlin and Boyer, 2004b). When ovary glucose is
consumed, genes (ribosome inactivating protein 2 and phospholipase D1) for senescence are
activated and plasma membranes lose their integrity, which probably leads to the irreversible
ovary abortion (McLaughlin and Boyer, 2004a). In summary, barrenness or complete ovary
abortion by low water potential at pollination involves sequential events of C (sucrose) transport
diminution, inhibition of acid invertase activity, starch breakdown and depletion, the onset of
senescence, ovary abortion, and inhibition of dry matter accumulation in the ovary and ear
(Westgate and Boyer, 1985; Schussler and Westgate, 1991; McLaughlin and Boyer, 2004a;
Mäkelä et al., 2005).
1.1.2 Low light at pollination
The physiological mechanism behind barrenness under low light at pollination could be
the same as or different from that of low water potential at pollination. Both stresses cause
complete inhibition of leaf photosynthesis at pollination and deplete C supply to the developing
florets and ear (Schussler and Westgate, 1991; Mäkelä et al., 2005; Hiyane et al., 2010). Effects
of low light on sink strength could be the same as or different from the low water potential at
pollination. Low light caused by shade at pollination reduces sink strength via the same
physiological mechanism as low water potential at pollination (Mäkelä et al., 2005; Hiyane et al.,
2010). Under shade stress, insoluble and soluble acid invertase activities in the ovary are
inhibited, followed by starch depletion to supply glucose, and glucose depletion in ovaries.
However, Schussler and Westgate (1991) indicate no change in sink strength under low light
stress. Low light stress does not change the ovary glucose concentration at pollination, but
6
increases the glucose concentration after pollination. It is unclear whydifferent mechanisms are
required for the impact of low light stress on sink strength. Besides stress in water and light
resources, super-optimal temperature can lead to barrenness.
1.1.3 Heat stress
A common physiological mechanism behind barrenness under heat stress is reduction in
sink strength (Commuri and Jones, 2001; Cicchino et al., 2010). Heat stress applied from the
stage of 11 fully emerged leaves with visible collars (V-stage, V11) to anthesis under field
conditions results in barrenness. The stress reduces above-ground plant dry matter (PDM)
during and after the stress application period. However, the dry matter partitioning to the ear is
only reduced during the stress application period. Most heated plants exhibit zero ear growth
rate during the heat stress application period. The dry matter partitioning to the ear is unaffected
as long as the stress is removed (Cicchino et al., 2010). In the study of Cicchino et al. (2010),
the exact mechanism behind barrenness was unknown, but they reported that heat stress can
affect GY by reducing KNP without changing kernel weight. In contrast, Commuri and Jones
(2001) found that heat stress during endosperm cell division of kernel development causes
barrenness by affecting kernel weight and kernel size.
Commuri and Jones (2001) investigated the effect of heat stress on kernel weight and
kernel size during endosperm cell division of kernel development. Heat stress during the period
reduces kernel sink capacity for hybrids and inbred lines and can cause barrenness. The kernel
sink capacity is the intrinsic ability of the endosperm to attract assimilates, and is mainly a
7
function of the number of endosperm cells and starch granules within them (Jones et al., 1985,
1996; Commuri and Jones, 2001), which both determine the number of potential sites for starch
deposition and kernel dry matter accumulation as well as GY (Jones et al., 1984, 1985). The
kernel sink capacity is established during the endosperm cell division period (Jones et al., 1996).
The heat stress during this period has more detrimental effects on GY than during the subsequent
linear grain filling period (Jones et al., 1984). Heat stress causes 100% kernel abortion for an
inbred line under in vitro and field conditions through reducing endosperm cell numbers and
starch granule numbers (Commuri and Jones, 2001). The reduced starch granule numbers
disturb and reduce kernel dry matter accumulation and cause kernel abortion (Commuri and
Jones, 2001). However, the heat stress-induced reduction in kernel sink capacity is not related to
the ability of kernel to take up sucrose (Cheikh and Jones, 1995).
Heat stress during the endosperm cell division period does not limit C supply to the
kernel nor limit C uptake by the kernel during the period. However, heat stress does change C
utilization and partitioning among sucrose, hexose and starch, and disrupt starch biosynthesis,
resulting in less starch (dry matter) accumulation per kernel (Cheikh and Jones, 1995).
Compelling evidences showed that cytokinins regulate kernel sink capacity under heat stress
(Cheikh and Jones, 1994; Jones and Setter, 2000).
Disruption of kernel development under heat stress involves a precipitous decline in
endogenous cytokinin levels of developing kernels (Cheikh and Jones, 1994). An important
mechanism underlying lower cytokinin levels in heat-stressed kernels is the degradation of
cytokinins as a result of increased cytokinin oxidase activity (Brugière et al., 2003). Cytokinin
8
oxidase irreversibly degradates cytokinins with unsaturated isoprenoid side chains in plants
(Brugière et al., 2003). It cleaves the N6 side chain of the cytokinins and causes cytokinins to
lose all their biological activity (Jones and Setter, 2000). Heat stress increases ABA
concentration of stressed kernels under in vitro and field conditions (Cheikh and Jones, 1994;
Brugière et al., 2003). The increased ABA concentration triggers a premature increase in
cytokinin oxidase transcript and activity. The increased level of cytokinin oxidase transcript
before the natural cytokinin peak (approximately 10 d after anthesis) could prevent cytokinin
accumulation, reduce peak intensity or level, and disrupt kernel development (Cheikh and Jones,
1994; Brugière et al., 2003). Cytokinins determine kernel sink capacity probably by regulating
endosperm cell division and processes associated with amyloplast biosynthesis (Cheikh and
Jones, 1994; Brugière et al., 2003). In addition, because of increased ABA levels and
dramatically decreased cytokinin levels, heat stress shifts the hormone balance between ABA
and cytokinins (Cheikh and Jones, 1994). There is a negative correlation between ABA levels
and cytokinin levels in non-stressed kernels, whereas there is no correlation between levels of the
two plant growth regulators in kernels exposed to heat stress (Cheikh and Jones, 1994).
Therefore, an imbalance of plant growth regulators and a reduction in endogenous cytokinin
level within kernels is the major mechanism disturbing kernel development under heat stress.
Most of studies mentioned above used hybrids or inbreds.
The use of other genotypes suggested the existence of a different physiological
mechanism underlying barrenness. When genotypes were chosen from eight cycles of recurrent
selection for drought tolerance, with major genetic differences in GY and differences in anthesis
to silking interval (ASI), the lower incidence of barrenness in the advanced selection cycles is
9
associated with a genetically-controlled increase in dry matter partitioning to the ear and spikelet
in high plant density, drought and wet conditions (Bolaños and Edmeades, 1993; Edmeades et
al., 1993). The genotypic differences do not result in significant changes in PDM at various
growth stages, and plant growth rate around silking (Bolaños and Edmeades, 1993; Edmeades et
al., 1993). Therefore, the physiological mechanisms behind barrenness or GY could be genotype
dependent. In summary, previous studies have contributed to our understanding of using the
source and sink approach to study barrenness or GY. However, the mechanisms behind
barrenness are complicated and depend on physiological factors such as C supply, endogenous
levels of plant growth regulators, stage of development, and the stressor, as well as genetic
factors and their interactions.
1.1.4 Other factors causing barrenness
In addition, other factors could cause barrenness. Barrenness happens when initial shade
avoidance response is combined with subsequent water and nutrition stress (Page et al., 2011).
Barrenness is also associated with temporal variation in terms of plant emergence. Late-emerged
within-row plants tend to be barren and have a higher percentage of barrenness than earlieremerged plants (Nafziger et al., 1991; Liu et al., 2004a). Moreover, the percentage of barrenness
tends to increase in late-emerged plants as the proportion of within-row early-emerged plants
increases (Nafziger et al., 1991). With one exception, previous studies on barrenness have not
utilized individual plants with similar initial growth and development; Westgate and Boyer (1985)
chose individual plants with similar growth under growth chamber conditions.
10
The studies mentioned above used various research approaches. In terms of
organizational level, most studies investigated barrenness and GY at the canopy level, or at both
canopy and individual plant levels (Cicchino et al., 2010; Page et al., 2011). In order to
manipulate environment and resources, the approaches included termination of light (Schussler
and Westgate, 1991) and water supply (Bolaños and Edmeades, 1993; Edmeades et al., 1993),
delicate control of plant water status (Boyer and Westgate, 2004), the increasing temperature in a
study region or around kernels (Commuri and Jones, 2001; Cicchino et al., 2010), stimulating
low red/far red ratio by the use of turfgrass (Page et al., 2011), and changing planting date and
pattern (Nafziger et al., 1991; Liu et al., 2004a). Instead of specific environmental quo, Vega et
al. (2001) compressed all the factors and created variations in resource availability using
different plant densities. The time windows for treatment implementation focused on a growth
stage or a period such as emergence, pollination, the critical period bracketing silking and early
kernel development. The above studies did follow individual plants during their life cycle.
1.1.5 Crowding stress
Crowding stress above optimum plant density intensifies intra-specific competition for
light, available soil nutrients and water (Connor et al., 2011). Barrenness under crowding stress
is mainly caused by shortages of C, N, some plant growth regulators and other resources. The
physiological mechanisms behind barrenness under crowding stress are rarely reviewed. In
crowding stress section, barrenness is dissected from canopy to plant hierarchy to dominated (d)
plant levels.
11
1.1.5.1 Carbon and nitrogen
At the canopy level, crowding stress causes a shortage of available photosynthetically
active light and nutrients. Each individual plant within a crowding population can be considered
to be an isolated plant from seed emergence until the leaf area index approaches 1. When the
leaf area index is larger than 1, intra-specific competition for light within a canopy begins (TetioKagho and Gardner, 1988; Ballaré, 1999; Page et al., 2010a). When intra-specific competition
starts, supra-optimal plant density reduces leaf area per plant from the vegetative stage to the
critical period bracketing silking (Boomsma et al., 2009; Page et al., 2010a), radiation
interception per plant (Pagano and Maddonni, 2007), individual PDM from the early vegetative
stage to physiological maturity (PM) (Tetio-Kagho and Gardner, 1988; Maddonni and Otegui,
2004), and plant growth rate (Pagano and Maddonni, 2007; Boomsma et al., 2009). Competition
for light under crowding stress causes maize plants to invest in more structural tissue with low N
concentration relative to that of leaves, thereby reducing the N concentration of shoots (Lemaire
et al., 2007, Lemaire and Gastal, 2009). Crowding stress also reduces chlorophyll content and
leaf N concentration from the V9 to the R5 stage, as well as grain N concentrations (Scott et al.,
2006; Pagano and Maddonni, 2007; Boomsma et al., 2009).
Instead of direct competition for available resource under crowding stress, another
mechanism known as ‘growth alteration’ regulates plant resource requirement and changes
resource use efficiency (Clay et al., 2009). For example, when increasing plant density from
74,500 plants ha-1 to 149,000 plants ha-1 using a commercial corn hybrid, the crowding stress
reduces the red/near-infrared ratio by 20% at the V12 stage and reduces photosynthetic capacity
while increasing both N and water use efficiency (Clay et al., 2009). The photosynthetic
12
capacity is reduced by downregulating C metabolism genes such as phosphoenolpyruvate
carboxykinase, phosphoenolpyruvate carboxylase, and pyruvate orthophosphate dikinase.
Although ‘growth alteration’ and direct competition for resources are two different mechanisms
regulating plant competition under crowding stress, they are similar in phenotypic responses,
including reduced leaf area per plant, reduced plant chlorophyll content at V12, reduced grain N
concentrations and GY per plant (Clay et al., 2009). The lower plant chlorophyll and grain N
concentrations are not due to increased N stress (Clay et al., 2009). Therefore, both mechanisms
under crowding stress reduce C supply and N nutrition at the canopy level.
The reduced C and N resource availability at the canopy level cannot represent resource
sharing among different plant hierarchies and individual plants. Previous studies on plant
hierarchy give deeper insights on resource sharing of C, N and plant growth regulators among
different plant hierarchies under crowding stress (Maddonni and Otegui, 2004; Lemaire et al.,
2005; Pagano and Maddonni, 2007; Boomsma et al., 2009; Caviglia and Melchiori, 2011; Mayer
et al., 2102). Plant hierarchy is defined as individual plants from a population of plants being
ranked in ascending order and assigned to different strata. In most maize studies, individual
plants are ranked according to the PDM at PM (PDMPM), the plants in the uppermost 1/3 of the
PDMPM rank position are termed dominant (D) plants in the population, while plants in the
lowermost 1/3 of the PDMPM rank position are termed d plants (Maddonni and Otegui, 2004).
For individual plants within a homogenous genotype, with similar initial plant growth and
development, plant hierarchies are established at early stage of development, such as V4 and
maintained until PM (Maddonni and Otegui, 2004).
13
Individual plants within a plant population exhibit hierarchical C and N resource sharing
(Lemaire and Gastal, 2009), due to the unequal competition in maize monoculture (Maddonni
and Otegui, 2004). For maize, the d plants have lower pre-silking leaf area per plant and shorter
plant height (PH), and intercept less radiation per unit leaf area than D plants at the same date
(Maddonni and Otegui, 2004; Pagano and Maddonni, 2007; Boomsma, 2009; Rossini et al.,
2011). Both light competition and photomorphogenetic adaptations lead to different allocations
of N resource among individual plants. The hierarchical N resource sharing of an individual
plant is determined by its hierarchical position for light interception (Lemaire et al., 2005;
Lemaire and Gastal, 2009). In addition, both light competition and subsequent morphological
changes among individual plants lead to different shoot N concentrations. For example, Lemaire
et al. (2005) used model crop alfalfa (Medicago sativa L.) to develop plant hierarchy according
to PH and light interception. The 20% shortest plants have lower N concentrations than the 20%
tallest plants at the same date. According to the idea developed by Lemaire et al. (2005), the d
plants of maize should have a slightly lower shoot N concentration than D plants at the same date.
Beside the hierarchical N resource sharing driving by light interception, the N nutrition of
different plant hierarchies has to consider the intrinsic N uptake capacity.
Dominated and D plants exhibit different or similar intrinsic N uptake capacities in maize,
depending on the genotype (Caviglia and Melchiori, 2011; Mayer et al., 2102). The d plants
exhibit a greater response to N supply than D plants within a population with a large difference
in PDMPM between d and D plants. This population mimics a crowded population with a high
intra-specific competition (Maddonni and Otegui, 2004; Caviglia and Melchiori, 2011). For
individual plants with similar initial growth and development, Mayer et al. (2012) reported that
14
increased N supply allows higher growth recovery in terms of PGRs for d plants than for D
plants in one of the hybrids tested. The two plant hierarchies exhibit similar growth recovery in
the other hybrid tested. In summary, regardless of the intrinsic N uptake capacity, d plants have
lower N resource sharing and should have below optimum shoot N concentrations than D plants
when plant hierarchies are established. Regardless of plant density and N supply, d plants have
lower leaf area per plant at silking, lower leaf N concentrations at pre-silking (V9, V14) and postsilking (R1, R3 and R5) than D plants (Pagano and Maddonni, 2007; Boomsma, 2009). Therefore,
d plants are more limited in N nutrition than D plants since the establishment of plant hierarchy.
The suboptimal N status in d plants reduces their kernel set.
Shortage of N regulates kernel set in four ways. First, low N reduces KNP by affecting C
assimilation (Uhart and Andrade, 1995a). Nitrogen plays a role in establishing and maintaining
plant photosynthetic capacity (Below, 2002). Low N decreases C assimilation by reducing leaf
area per plant through a decline in leaf area expansion (Muchow, 1988), and by reducing leaf
area duration, light interception (Lemcoff and Loomis, 1986; Uhart and Andrade, 1995b), leaf
chlorophyll content, leaf absorption and leaf CO2 exchange rate from pre-silking to the critical
period bracketing silking (Uhart and Andrade, 1995b; Echarte et al., 2008), pre-silking PGR and
PGRs (McCullough et al., 1994; Uhart and Andrade, 1995b; D’Andrea et al., 2009).
Second, a limiting N supply to the plants during the critical period bracketing silking can
alter the relative sink strength of various organs (Paponov et al., 2005). For example, low N
supply to the plants at 4 d before silking under field conditions reduces the sink strength of cobs,
and the effects are genotype dependent (Paponov et al., 2005). Also, low N application at
15
planting or over the life cycle can reduce the sink strength during the critical period bracketing
silking and the effect depends on genotype, the level of radiation and N stress (Uhart and
Andrade, 1995b; Paponov and Engels, 2005; D’Andrea et al., 2009).
Third, the N supply to the ear directly controls the capacity of kernels to utilize C, as
determined by in vivo and stem infusion studies (Cazetta et al., 1999; Below et al., 2000).
Nitrogen supply determines the kernel sink capacity and promotes activity of enzymes involved
in sucrose and N uptake, including insoluble and soluble invertases, sucrose synthase and
aspartate transaminase (Cazetta et al., 1999; Below et al., 2000). Limited N supply to the ear
causes kernel abortion and reduced dry matter accumulation of kernels. Fouth, N supply in both
quantity and quality can alter the metabolism of plant growth regulators, which will be discussed
in section 1.1.5.2 (Drew et al., 1989; Takei et al., 2001; Engels et al., 2012).
1.1.5.2 Plant growth regulator
Crowding stress results in less endogenous cytokinin and ethylene production. Plant
growth regulators regulate dry matter partitioning, and the dry matter partitioning to the ear is
reduced under crowding stress at the canopy level (Edmeades and Daynard, 1979a; Engels et al.,
2012). Shade-induced imbalance of plant growth regulators within normal and aborted kernels
has been investigated. However, whether different plant growth regulators are the causes or the
effects of kernel abortion cannot be concluded (Reed and Singletary, 1989; Cheng and Lur, 1996;
Setter et al., 2001). A shortage of N reduces cytokinin and their ethylene production and their
endogenous concentrations. Both plant growth regulators possibly affect GY or/and dry matter
16
partitioning to the ear (Drew et al., 1989; Smiciklas and Below, 1992; Takei et al., 2001; Below
et al., 2009; Below and Uribelarrea, 2009).
A shortage of N supply stimulates a change in synthesis and translocation of cytokinins to
leaves. In maize, cytokinin biosynthesis is activated by N, and endogenous cytokinin
concentrations are N dependent in terms of quantity (Takei et al., 2001). The cytokinins are
mainly synthesized in root tips and developing seeds (Davies, 2004). Limited N supply
decreases the amount of cytokinins transported from roots to xylem and to leaves (Takei et al.,
2001). The endogenous concentrations of cytokinins may be associated with dry matter
partitioning to the ear and GY in maize, and the concentrations are influenced by N forms, such
as NH4+ and NO3- (Smiciklas and Below, 1992). At the V7 stage, the concentrations of zeatin
and zeatin riboside in roots tips supplied with both NH4+ and NO3-, are much greater than those
found with the NO3- alone. The supply of NH4+ + NO3- increases dry matter partitioning to the
ear. When the shoot cytokinin concentration of NO3--grown plants is manipulated by applying
exogenous cytokinins during vegetative stages, the dry matter partitioning to the ear, KNP and
GY is increased without affecting PDMPM (Smiciklas and Below, 1992). According to these
results, the d plants with lower N concentrations under crowding stress should produce less
cytokinins and have lower endogenous cytokinin concentrations, which may reduce the dry
matter partitioning to the ear and GY. Similarly, the d plants with suboptimal N status under
crowding stress should exhibit reduced endogenous ethylene levels.
Maize plants with suboptimal N status decrease ethylene biosynthesis in adventitious
roots and enhance their sensitivities of root cortical cells to exogenous ethylene (Drew et al.,
17
1989; He et al., 1992). The reduced biosynthesis of ethylene is due to slowed biosynthetic
pathway ‘malonylamino cyclopropane- 1 -carboxylic acid ↔1-amino cyclopropane-1-carboxylic
acid (ACC) → ethylene’, and due to reduced activities of ACC synthase and the ethylene
forming enzyme (Drew et al., 1989). The effects are not influenced by the N source (NH4+ or
NO3-) (Drew et al., 1989). The reduction in endogenous ethylene level could induce barrenness.
A sudden decrease in ethylene level or a decrease in perception of maize plants to
ethylene during vegetative stages could result in special ear barrenness termed ‘hollow husk’
(Below et al., 2009; Below and Uribelarrea, 2009). The ethylene level could be altered by
fungicides and plant growth regulators, and the sensitivity of ethylene could be altered by the
ethylene binding inhibitor 1-methylcyclopropene (Below et al., 2009; Below and Uribelarrea,
2009). The ‘hollow husk’ earshoots exhibit normal husks; however, ear development ceases and
silks do not emerge (Below et al., 2009). The incidence of hollow husk would be lower under
low C and N conditions than under high C and N conditions (Below et al., 2009).
1.1.5.3 Barrenness of dominated plants under crowding stress
When intra-specific competition starts under field conditions, individual d plants
experience lower radiance and suboptimal N status than the D plants, while the D plants
experience normal radiation and N status. The C and N interaction on GY for both plant
hierarchies under same plant densities could be mimicked by plant populations experiencing
different levels of radiation by shading and different levels of N supply from planting or over the
life cycle under similar plant densities. Uhart and Andrade (1995a) manipulated a maize hybrid
18
with different levels of radiation and N supply. They found that N deficiency affects KNP by
reducing C assimilation. Nitrogen deficiency under low radiation reduces the light-saturated rate
of photosynthesis or photosynthetic capacity, and only slightly diminishes CO2 photoassimilation
or quantum efficiency of CO2 assimilation (Khamis et al., 1990). In summary, for d plants, the
decreased photosynthetic capacity reduces C assimilation and C availability per plant from the
onset of intra-specific competition until silking. Nitrogen deficiency increases N mobilization
from shoot to the ears. When leaves are the source for N mobilization to the ear, leaf N content
is further reduced and leaf duration is shortened, which can decrease post-silking photosynthetic
activity and assimilate supply to the ears, and enhance kernel abortion at the lag phase period
(Below, 1997; Echarte et al., 2008). Therefore, d plants exhibit shortage of C from pre-silking to
the lag-phase period. Barren hybrid plants belong to d plants and the mechanism behind
barrenness is related to C and N shortage (Vega and Sadras, 2003; Maddonni and Otegui, 2004;
Vyn and Boomsma, 2009; Boomsma et al., 2009; Mayer et al., 2012).
Barren plants come from no silk emergence and increased kernel abortion when pollen is
available at silking. Kernel set is determined during the critical period bracketing silking, which
is around 4 wk centered silking (Tollenaar et al., 2000). First, d plants that cannot reach the
minimum C availability for ear growth during the critical period bracketing silking fail to expose
silk and become barren (Borrás et al., 2007). Second, d plants with low current C assimilation at
flowering, exhibit reduced C flux to the ear around silking and increased kernel abortion and
become barren (Boomsma et al., 2009; Mayer et al., 2012; Rossini et al., 2012). The C flux to
the ear during the critical period bracketing silking is further reduced by the decreased C and dry
matter partitioning to the ear under N deficiency (Uhart and Andrade, 1995b; Paponov et al.,
19
2005; Paponov and Engels, 2005). However, instead of reduced C flux to the ears, a recent study
indicated that the enhanced kernel abortion of d plants may be due to deficient and direct N
supply to the ears during the critical period bracketing silking (V12 to R2) (Below, 1997; Below
et al., 2000; Rossini et al., 2012). This effect is greatest during the lag phase period (Below,
1997). Under a greenhouse environment, kernel set under N deficiency is not limited by the C
supply to the ear at the end of lag phase period (Paponov and Engels, 2005). Therefore, the
current C assimilation per plant and C supply to the ear during the critical period bracketing
silking cannot determine barrenness. It is hypothesized that barrenness is caused by the low
current C assimilation and C availability during the vegetative stage.
1.1.6 Secondary tools to characterize barrenness
Threshold values of parameters at various growth stages or during a period have been
estimated to predict barrenness. The threshold values for barrenness, at or below which
barrenness occurs, have been estimated at anthesis or silking, during the critical period
bracketing silking and at PM. The studies modeled kernel set as a function of a physiological
parameter using linear, bilinear or negative exponential or hyperbolic functions (D’Andrea et al.,
2006; Messina et al., 2009). Parameters at anthesis or silking include photosynthesis at 1 d after
anthesis (Edmeades and Daynard, 1979a) and ear dry matter (EDM) for silk emergence (Borrás
et al., 2007). Parameters during the critical period bracketing silking include PGRs (Tollenaar et
al., 1992), daily intercepted photosynthetically active radiation per plant during the critical
period bracketing silking (Andrade et al., 2000), and cumulative intercepted photosynthetically
active radiation per plant during the critical period bracketing silking (Ritchie and Alagarswamy,
20
2003). Parameters at PM included PDMPM (Vega et al., 2000; Echarte and Andrade, 2003) and
N uptake (D’Andrea et al., 2009). The relationship between GY/KNP/harvest index (HI) and
PDMPM is fitted to a function with a positive x-axis intercept (Vega et al., 2000; Echarte and
Andrade, 2003). The value of the positive x-axis intercept determines the threshold value of
PDMPM for barrenness. Similarly, a threshold value of N uptake for barrenness is estimated by
the positive x-axis intercept of the fitted relationship between GY and N absorbed at PM
(D’Andrea et al., 2009). The threshold value of a physiological trait for barrenness sometimes
could not be calculated because no biologically meaningful mathematical function could be
established (Cicchino et al., 2010); therefore, estimating the threshold value of a physiological
trait should work as a secondary tool to characterize barrenness.
Another secondary tool to characterize barrenness is through association of barrenness
with physiological and morphological traits at the canopy and individual plant levels. Studies
used common statistical tools such as correlation, regression and frequency distribution to
characterize barren plants (Buren et al., 1974; Smith et al., 1982; Daynard and Muldoon, 1983;
Vega and Sadras, 2003; Boomsma, 2009). Incidence of barrenness at the canopy level is
associated with ASI, and with days from 25 to 75% silking (Buren et al., 1974; Smith et al.,
1982). At the individual plant level, barren plants are present in the lower values of EDM at the
end of the critical period bracketing silking at 170,000 plants ha-1 (Vega and Sadras, 2003), PDM
at silking (PDMs), PH at various stages, and total green leaf area per plant at silking under the
combination of 54,000 plants ha-1 and 330 kg N ha-1 (Boomsma, 2009). Again, the relationship
between barrenness and physiological and morphological traits could not be established under a
specific field condition (Boomsma, 2009).
21
In summary, insights from previous studies on barrenness help to establish research
approaches to characterize barrenness or GY through a source-sink approach by following
individual plant growth and development during the life cycle. Also, barrenness could be
characterized by calculating threshold values of key physiological traits, as well as measuring
and calculating physiological and morphological traits. The selected individual plants should
have similar initial growth and development.
1.2. Top-down Approach and Allometric Methodology
1.2.1 Top-down approach
A top-down approach is widely used in the literature but not well defined. This approach
dissects a complex trait/phenomenon/system/process into one or several components and/or from
a higher level of biological organization as the entrance point to a lower level of organization.
The top-down or reductionist approach has been applied in crop physiology in three ways. First,
key physiological processes underlying GY improvement in maize and wheat (Triticum aestivum
L.) have been dissected (Tollenaar and Lee, 2006; Miralles and Slafer, 2007). Grain yield is
dissected into PDMPM and HI, which is the proportion of PDMPM partitioning to grains. The
PDMPM is the result of dry matter accumulation throughout the growing season. The processes
of dry matter accumulation are further dissected into key subcomponent processes, such as leaf
area and radiation use efficiency (Tollenaar and Lee, 2006; Miralles and Slafer, 2007). Harvest
index is associated with the capacity of the reproductive sink to accommodate assimilates
(Tollenaar et al., 2004). Similarly, kernel set in maize is a function of PGRs and the dry matter
partitioning to the ear during the spikelet initiation period and during the critical period
22
bracketing silking (Tollenaar et al., 1992; Liu and Tollenaar, 2009a). The spikelet initiation
period is a 3- to 4-wk period starting at about the 12-leaftip (LT) stage. Dry matter partitioning
to the ear during the critical period bracketing silking is a measure of strength of the ear as a sink
for assimilates, and is termed the partitioning index (Pagano and Maddonni, 2007; Rossini et al.,
2011). It is calculated as the ratio between ear growth rate during the critical period bracketing
silking (EGRs) and PGRs (Andrade et al., 1999; Vega et al., 2001a; Echarte et al., 2004; Pagano
and Maddonni, 2007). Barren hybrid plants are characterized by the uncoupling of EGRs with
PGRs (Vega et al., 2001a). For barren plants, the ultimate HI is zero. Therefore, using the topdown approach, physiological processes underlying maize barrenness and GY can be dissected
into season-long dry matter accumulation and partitioning to the ear.
Second, the top-down approach has been used from a higher organizational level to a
lower organizational level (Hammer et al., 2004; Caviglia and Melchiori, 2011). In plant biology,
a system such as a plant population can be described at the organization level of a community of
plants, a whole plant, an organ, a cell, an organelle, biochemical pathway, and gene action
(Hammer et al., 2004). For homogenous crops, recent studies have added a scale of plant
hierarchy between the canopy and individual plant level (Maddonni and Otegui, 2004; Maddonni
and Otegui, 2006; Pagano and Maddonni, 2007; Boomsma, 2009; Caviglia and Melchiori, 2011).
The inclusion of plant hierarchy in the top-down approach brings new insights into
physiological mechanisms. First, a physiological trend at one level can be different from a trend
in the level below or above. For example, dry matter partitioning to the head of sunflowers
(Helianthus annuus L.) increases with the decrease in PDM at the canopy level (Villalobos et al.,
23
1994). However, the opposite trend is observed in certain plant strata in the relationship between
dry matter partitioning to the head and plant growth rate during the critical period for seed set
(Vega et al., 2001a). Second, at the level of plant hierarchy alone, the D and d plants exhibit
differential growth. For individual plants with similar initial plant size and development, the
plant growth during the early life cycle preconditions their future kernel set (Maddonni and
Otegui, 2004; Pagano and Maddonni, 2007). The d plants exhibit lower plant growth rate during
the presilking period (V7 to V13), dry matter partitioning to the ear, and KNP than the D plants.
In addition, the d plants can have similar or lower PGRs compared to the D plants (Maddonni
and Otegui, 2004; Pagano et al., 2007; Pagano and Maddonni, 2007). Third, different plant
hierarchies exhibit various responses to plant density and N level. The D and d plants respond
similarly to plant density in terms of GY and KNP, and differently in terms of kernel weight
(Maddonni and Otegui, 2006). The two plant hierarchies have a similar response or differential
responses to N depending on the response level of GY per unit area (Caviglia and Melchiori,
2011). Most previous studies have examined maize GY in hybrids at the canopy level, canopy
and plant hierarchy levels, or canopy and individual plant levels. Few studies have examined
maize from canopy to plant hierarchy to individual plant level (Boomsma, 2009; Caviglia and
Melchiori, 2011), or barrenness in the context of plant hierarchy (Vyn and Boomsma, 2009). In
addition, no previous studies have focused on characterizing barren plants at the individual plant
level. Therefore, studying all three levels might reveal new physiological characteristics of
barrenness.
Finally, using the top-down approach, crop modelling dissects a complex trait into
component attributes as model input parameters and assesses this complex trait at a higher
24
organizational level by integrating the information at a lower organizational level (Yin et al.,
2004; Messina et al., 2009). Because crop modelling is not an approach used in this study, no
further literature will be reviewed.
1.2.2 Allometric methodology
Allometric methodology is the study of relationships between different parts of an
organization (Kjellsson and Simonsen, 1994). Allometric methodology allows researchers to
follow plant growth throughout the life cycle in a non-destructive manner (Vega et al., 2001). It
combines sequential harvest to estimate PDM by an allometric relationship and non-destructive
measurements of individually tagged plants within a plant population. Naturally occurring
ultimately barren plants can be followed and characterized until PM without destructive
samplings. Allometric relationships are the growth of a part of an organism in relation to the
growth of the whole organism or part of it. Different allometric relationships have been applied
to estimate PDM in maize, soybean (Glycine max L.) and sunflower as well as other crops and
species (Koyama and Kira, 1956; Smith and Brand, 1983; Weiner et al., 1990; Niklas, 1994;
Vega et al., 2001; Echarte and Tollenaar, 2006). Many mathematical equations have been used
to develop allometric relationships in maize (Appendix A.1); however, the published studies
have not shown detailed processes of developing an allometric relationship such as model
comparison, justification and evaluation. In order to develop the best model for an allometric
relationship, detailed and rigid developmental processes are needed.
25
1.3 Plant-to-Plant Variability in Plant and Ear Development
The term ‘plant-to-plant variability’ (PPV) is not well defined in the literature. It usually
refers to the non-uniformity in an investigated character among individual plants within a
population. High yield production in maize is associated with reduced PPV (Glenn and Daynard,
1974; Fasoula and Fasoula, 1997; Tollenaar and Wu, 1999; Tokatlidis and Koutroubas, 2004;
Martin et al., 2005; Boomsma, 2009). Plant-to-plant variability in maize within a population
could be attributed to genetic differences (Francis et al., 1978), temporal differences in seed
emergence (Liu et al., 2004a; Andrade and Abbate, 2005; Boomsma et al., 2010), spatial
differences in plant spacing (Liu et al., 2004b; Andrade and Abbate, 2005), plant growth (Borrás
et al., 2007) or intra-specific competition (Boomsma, 2009). Even for homogenous individual
plants with uniform seed size, planting depth and spacing, and similar initial plant growth and
development, PPV in PDM, measured as coefficient of variation (CV), increases from V3 (6-LT
stage) to V9 (13-LT stage) and maintains a similar magnitude through to PM (Maddonni and
Otegui, 2004; Pagano and Maddonni, 2007).
Plant-to-plant variability in plant growth has been widely studied; however, there are few
studies on PPV in early ear growth and development. The PPV in plant growth has been studied
throughout the life cycle, as well as ear growth and development from the spikelet initiation
period to PM in hybrids (Edmeades and Daynard, 1979b; Vega and Sadras, 2003; Maddonni and
Otegui, 2004; Pagano and Maddonni, 2007; Boomsma, 2009). The only documented PPV in ear
morphological character is spikelet number per row (SNPR) in a hybrid, with the trend of
decreased CVs in SNPR from spikelet initiation period (i.e., V8) to silking (Edmeades and
Daynard, 1979b). Few attempts have been made to quantify the PPV of ear and plant
26
development simultaneously during the vegetative stages, especially in parental inbred lines and
their F1 hybrid.
1.3.1 Maize life cycle and ear development
In order to understand maize ear and plant development, the life cycle of maize
associated with ear development is reviewed. The life cycle can be divided into vegetative,
transitional, reproductive and seed stages (Bonnett, 1954). During the vegetative growth stage,
the apical meristem of a plant first produces a predictable number of leaf primordia and then
initiates a tassel (Tollenaar and Hunter, 1983). Tassel initiation marks the transition from
vegetative growth to reproductive growth, and happens at a LT stagewhich is equal to half of the
final leaf number, and also at the beginning of internode elongation (Bonnett, 1953; Tollenaar
and Hunter, 1983). About three LTs after tassel initiation, the ear initiates from the last formed
and the most developed lateral shoot apical meristem (Bonnett, 1954; Muldoon et al., 1984).
Maize ear development goes through several distinct transitions in terms of meristem
identities (Vollbrecht and Schmidt, 2009) (Fig. 1.1). The lateral shoot apical meristem first
initiates a prophyll, which is the first leaf on a shoot, and approximately eight to 14 husk leaves,
then transits into a female inflorescence meristem (Kaplinsky and Freeling, 2003). The
inflorescence meristem then elongates and produces rows of lateral projections as spikelet pair
meristem (Bonnett, 1966). Each spikelet pair meristem gives rise to a pair of spikelet meristems
that are next to each other. The number of rows of spikelet meristems determines the potential
kernel row number (KRN). Each spikelet meristem produces a floret meristem, which later
27
Shoot
apical
meristem
Inflorescence
meristem
Spikelet
meristem
Spikelet
pair
meristem
Spikelet
meristem
Floret meristem
Staminate
floret
Floret meristem
Pistillate
floret
Floret meristem
Staminate
floret
Floret meristem
Pistillate
floret
Figure 1.1. Schematic of the developmental progression of meristem identities during ear development (Vollbrecht and Schmidt,
2009).
28
produces a spikelet including an upper and a lower floret. The stamens of the upper floret and
the lower floret abort. Ultimately, a functional upper pistillate floret is left and present in every
spikelet (Bonnett, 1966; Kaplinsky and Freeling, 2003; Vollbrecht and Schmidt, 2009).
From ear initiation to form final seeds, maize plants experience the following seven
events: (i) During the period of transitions in female meristem identity, the tassel elongates
rapidly and appears from the leaf whorl, which marks the stage of tassel emergence (TE). (ii)
Right after rapid tassel elongation, the primary ear starts to accelerate elongation and emerges its
prophyll at the same time or later compared to TE (Siemer et al., 1969). (iii) Anthesis happens
when an anther extrudes from the tassel. (iv) Silking happens when a silk emerges from the
primary ear (Siemer et al., 1969). (v) Pollination happens when pollen grains land on hairs of a
silk, which is from an ovule in a pistil of a pistillate floret (Bonnett, 1966). (vi) Fertilization of
egg and endosperm nuclei occurs at 24 to 36 hours after pollination (Goss, 1968). (vii)
Successful fertilization leads to kernel development until all kernels reach their maximum dry
matter accumulation at PM (Daynard and Duncan, 1969).
Maize kernel development can be divided into three periods: endosperm cell division
period or lag phase; linear grain-filling period or effective grain-filling period; and, leveling-off
dry matter accumulation towards PM (Johnson and Tanner, 1972). The endosperm cell division
period starts after ovary fertilization and takes around 15 d (Johnson and Tanner, 1972). The
onset and completion of endosperm cell division and initiation of amyloplasts (the sites of starch
deposition and formation of starch granules) occur during this period. One or multiple starch
granules are formed per amyloplast. The kernel sink capacity is determined around 6 to 12 d
29
after pollination (Jones and Setter, 2000). The kernel accumulates less than 5% of its final kernel
weight during this period (Johnson and Tanner, 1972). The length of linear grain-filling period
depends on the assimilate availability and air temperature (Cirilo and Andrade, 1996). During
the linear grain-filling period, the grains accumulate 90% of their final kernel weight at a
constant rate (Johnson and Tanner, 1972). At the final period, the kernels reach their maximum
dry matter accumulation at PM (Johnson and Tanner, 1972).
1.3.2 Yield compensation mechanism associated with plant-to-plant variability
Under field conditions, individual maize plants have the plasticity to compensate for PPV
in resource availability. Yield compensation at the canopy level depends on plant density.
Under a constant density of below 80,000 plants ha-1, maize plants can compensate for factors
that affect resource capture, such as absorption of solar radiation, water and nutrients, but cannot
compensate for a decrease in resource utilization, such as HI (Tollenaar et al., 2006). Variations
in within-row plant spacing do not significantly affect GY, HI and PDM (Liu et al., 2004;
Tollennar et al., 2006). Variations in plant emergence time decrease GY and HI without
affecting PDM (Nafziger et al., 1991; Liu et al., 2004a; Tollenaar et al., 2006). Variations in
emergence include within-row and between-row PPV in plant emergence, and proportion of
within-row delayed plants (Nafziger et al., 1991; Liu et al., 2004a; Tollenaar et al., 2006). When
soil moisture is the major resource that plants compete for, early emerged plants bordered by
late-emerged plants can compensate for PPV in plant emergence (Nafziger et al., 1991). Under
changing plant densities, the general trend is that as the percentage of stand loss increases, the
GY at the canopy level decreases (Shapiro et al., 1986; Nafziger et al., 1991; Pommel and
30
Bonhomme, 1998; Coulter et al., 2011). When stand loss happens at emergence, GY per plant
increases as the percentage of stand loss increases; however, it can not compensate for the sink
loss (Nafziger et al., 1991; Pommel and Bonhomme, 1998). When stand loss happens at
vegetative stages, remaining plants have the plasticity to compensate for the stand loss and do
not always result in yield loss (Shapiro et al., 1986; Coulter et al., 2011). The developmental
stage of stand loss during vegetative growth does not always affect the ability of the remaining
plants to compensate for GY (Shapiro et al., 1986; Coulter et al., 2011). When dissecting the
final GY into yield components, PPV in within-row plant spacing does not influence KRN, but
affects KNP and kernel weight (Pommel and Bonhomme, 1998; Coulter et al., 2011). Most
studies on yield compensation have focused on results at PM; few have studied the effects of
PPV in resource availability on early sink size establishment and PPV (Smith, 2012).
1.3.3 Measurement of plant-to-plant variability
Statistically, there are several measures of PPV. The CV and the Gini coefficient can
both be used to measure relative precision and assess inequality (Bendel et al., 1989). The Gini
coefficient is defined as the arithmetic average of the absolute values of the differences between
all pairs of individuals in equation 1.1 (Weiner and Solbrig, 1984).
Gini coefficient =
(Weiner and Solbrig, 1984)
[1.1]
The Gini coefficient ranges from zero when all individual plants are identical, to theoretical 1
when all individual plants of an infinite population have a value of zero except one individual
plant (Weiner and Solbrig, 1984). Most studies with regards to PPV in physiological or
morphological parameters use CV (Glenn and Daynard, 1974; Edmeades and Daynard, 1979b;
31
Liu and Tollenaar, 2009b). Several studies chose both CV and Gini coefficient (Vega and
Sadras, 2003; Boomsma, 2009). Both coefficients reflect the ratio of a measure of dispersion
relative to the mean. However, CV is a more sensitive measure of relative precision and
observations in the right tail of the distribution, whereas the Gini coefficient is a more robust
measure of dispersion and very sensitive to the small shift of the mean (Bendel et al., 1989).
Coefficient of variation is calculated as the ratio between standard deviation (SD) and mean and
is affected excessively by the mean. Therefore, the SD should be chosen to describe the by-plant
variability (Boomsma, 2009).
1.4 Research Hypotheses, Objectives, and Significance of the Research
Reduced barrenness and PPV represent two opportunities to increase GY. The
hypotheses of the thesis are:
(i)
Plant-to-plant variability at the whole plant level leads to PPV in ear growth and
development; and,
(ii)
Barrenness within a genotype is caused by either low C assimilation during
vegetative growth or by low C partitioning to the ear during the critical period
bracketing silking.
The major objectives of the study are: (i) to investigate PPV in ear development in
relation to PPV in plant development from ear initiation until 1 wk after silking and at PM, at the
optimal plant density (80,000 plants ha-1) and at the canopy level; and, (ii) to explore C supply,
availability and partitioning processes underlying barrenness throughout the life cycle at the
canopy, subpopulation and primarily, individual plant levels. A special objective is to be able to
32
predict barrenness. These objectives were met by experimenting with homogenous individual
plants of similar initial growth and development, as well as uniform plant spacing, in two
parental inbred lines and their F1 hybrid.
The results from this study could benefit a wide range of researchers. A better
understanding of ear barrenness in maize plant population being caused by PPV could help plant
breeders select for genotypes with reduced barrenness under high plant densities and increase
yield potential. This is the primary benefit of the research. Crop physiologists could understand
the mechanisms and processes of barrenness for the life cycle and the association of ear and plant
development during vegetative and early reproductive growth stages in parental inbred lines and
their F1 hybrid. The results could provide ecologists with quantitative data for theoretical
analysis in reproductive allometry, growth of individual plants and size hierarchy.
33
Chapter 2. Ear Development in Relation to Plant Development in
Maize (Zea mays L.)
2.0 Abstract
Research on maize plants with similar initial plant size and development at optimal plant
density under field conditions could improve our understanding of the nature of plant-to-plant
variability (PPV) for early ear development. The objectives of this study were (i) to follow the
dynamics of PPV in early ear and plant development in a leaftip (LT) stage framework and (ii) to
quantify or establish a quantitative relationship between early ear development parameters and
grain yield (GY). Field experiments were conducted in Ontario, Canada in 2009 in which the F1
hybrid CG60 × CG102 and its two parental inbred lines were planted at 80,000 plants ha-1 with
similar initial plant size at the 4-LT stage. Data on ear traits such as ear length, spikelet number
per row (SNPR), spikelet number per ear (SNPE), maximum kernel row number (KRNmax) and
plant morphological traits such as plant height (PH), the maximum width of stem diameter of the
stalk at 2 cm above the ground level (SD2), and the corresponding stem volume (SV) derived
from SD2 (SVmax) were collected from ear initiation until 1 wk after silking and the collection
was performed on a LT-stage basis during the vegetative growth stages. The three genotypes
exhibited ear development as described previously in the literature. Under optimal plant density,
the ear traits SNPR and SNPE exhibited relatively less PPV around the silking period than early
vegetative growth stages. Plant-to-plant variability in plant morphological traits and ear length
was relatively constant throughout development for each genotype. Plant-to-plant variability in
SNPR was affected more than PPV in KRNmax by PPV in SVmax for the three genotypes. The F1
had shorter ear length and less ear dry matter (EDM) at silking (EDMs) and at 1 wk after silking
34
(EDMs+1) than the two parental inbred lines, although it produced greater above-ground plant dry
matter (PDM) at the corresponding stages. The one-year field experiment suggests that the
lower dry matter partitioning to the ear of the F1 hybrid at the silking and 1 wk after silking is not
a disadvantage for final GY, compared to the two inbred lines.
35
2.1 Introduction
Grain yield in maize is directly proportional to kernel number and has increased nearly 7fold in less than 70 years of intensive breeding efforts (Lee and Tollenaar, 2007). Most
phenomena that increase (e.g., heterosis) or decrease GY (e.g., stress) do so through affecting
kernel number (Lee and Tollenaar, 2007). There are two types of variability in plant stand,
spatial and temporal, that can potentially impact GY. The impact of spatial variability or nonuniform distribution of the plants in a stand on GY is minimal (Liu et al., 2004b; Tollenaar et al.,
2006). The impact of temporal variability or the non-uniform development of plants in a stand
does impact GY (e.g., Liu et al., 2004a). Plant-to-plant variability for both non-uniform (i.e.,
variation in plant emergence) and uniform development has been documented for GY, kernel
number per plant (KNP), harvest index (HI), and shoot dry matter (Edmeades and Daynard,
1979b; Vega and Sadras, 2003; Maddonni and Otegui, 2004).
Ear development has been extensively studied in maize both from a developmental
biology perspective (Vollbrecht and Schmidt, 2009) and from a crop physiology perspective
(e.g., Edmeades et al., 1993; Otegui and Melón, 1997). The developing ear represents a gradient
of tissues at varying stages of differentiation. Developmental biologists describe these ear
development stages in terms of the progression of four types of reproductive meristems (Fig.
1.1). Each ear originates from a lateral shoot apical meristem. The lateral shoot apical meristem
transitions into an inflorescence meristem; the inflorescence meristem produces multiple rows of
spikelet pair meristems; the spikelet pair meristems generate a pair of spikelet meristems that are
aligned in adjacent vertical rows (i.e., kernel rows); spikelet meristems give rise to floret
36
meristems; and soon after initiating floral organ primordia the lower floret aborts as do the
stamens of the upper floret, leaving one pistillate flower per spikelet (Cheng et al., 1983; Irish,
1998; McSteen et al., 2000; Vollbrecht and Schmidt, 2009).
Different methods have been used to characterize tassel and ear development as it relates
to whole plant development. One approach has been to relate it to days after planting or
emergence (Siemer et al., 1969; Edmeades et al., 1993). However, using actual time (i.e., days
or weeks) makes comparing results from different environments impossible (Otegui and
Bonhomme, 1998). An alternative to days is to use plant stage or an environmental variable such
as thermal time to characterize ear development (Otegui and Melón, 1997; Otegui, 1997;
Cárcova et al., 2003). The two most common methods for assessing ear development relative to
crop development in maize are number of LT and fully emerged leaves with visible collars (Vstage) (Stevens et al., 1986), which are both linear functions of thermal time (Carberry, 1991;
Kiniry and Bonhomme, 1991; Lejeune and Bernier, 1996; Otegui and Melón, 1997; Vinocur and
Ritchie, 2001). Counting LT is a simple, non-destructive way to describe the phenological stage
of a maize plant. Several studies have associated inflorescence development with LT stage
(Tollenaar and Hunter, 1983; Stevens et al., 1986; Lejeune and Bernier, 1996). In general tassel
initiation and ear initiation occur at a LT stage, which is equal to approximately 50% of the final
leaf number (Tollenaar and Hunter, 1983; Lejeune and Bernier, 1996).
Studies that have documented the impact of PPV on GY (e.g., Edmeades and Daynard,
1979b; Vega and Sadras, 2003; Maddonni and Otegui, 2004) have not addressed when this
variability arises in the life-cycle. Given that GY is directly proportional to number of kernels,
37
PPV in KNP most likely arises either during the establishment of potential kernel number, or
shortly after pollination. Potential kernel number is a function of SNPR and kernel row number
(KRN). The only study of PPV in ear development during the establishment of potential kernel
number investigated PPV in SNPR of a commercial hybrid from spikelet initiation to silking
(Edmeades and Daynard, 1979b). No studies have investigated PPV in KRN. Moreover, no
studies have investigated PPV in ear development in relation to plant development on parental
inbred lines and their F1 hybrid during the entire period of establishing potential kernel number.
The objective of this paper is to examine PPV in ear development related to plant
development from ear initiation to 1 wk after silking at the optimal plant density. In order to
characterize PPV in early ear development related to plant development, we selected individual
plants with similar initial plant size and development at optimal plant density of 80,000 plants ha1
. Non-uniform initial plant size and development will affect subsequent PPV in ear growth and
development and confound the intrinsic PPV of ear growth and development as well as plant
growth at the optimal plant density. When similar initial plant size and development were
considered, only PPV in plant growth were studied (Maddonni and Otegui, 2004; Pagano and
Maddonni, 2007). We frequently sampled maize plants at the same LT stage or stage of
development in order to relate ear growth and development with plant growth in a LT-stage
framework, and used allometric measurement to estimate plant growth during vegetative growth.
Due to the significant relationship between allometric measurement and PDM in previous studies,
allometric measurement is simple and quick to estimate plant growth without drying and
weighting sampled plants (Pagano et al., 2007). Also, we used a functional approach to examine
the dynamics of PPV in ear growth and development, as well as plant growth. The functional
38
approach utilizes mathematical equations or models to study plant growth and development, and
can generate values such as PPV and show apparent trends in PPV if there are any (Hunt, 1982).
No previous literature has used functional approach to study PPV.
39
2.2 Materials and Methods
2.2.1 Genetic materials and experimental design
Two maize inbred lines, CG60 and CG102, and their single-cross hybrid CG60 × CG102
were planted using a randomized complete block design with three replications at the Elora
Research Station, ON, Canada (43o38’ N, 80o25’ W; London loam soil [Aquic Hapludalf]) in
2009. The three genotypes differ in kernel set dynamics and have been used in previous
physiology and genetics studies (Echarte and Tollenaar, 2006; Khanal et al., 2011; Singh et al.,
2011). CG60 is derived from Pioneer 3902 and represents the Iodent heterotic pattern (Lee et al.,
2001a). CG102 is derived from Cycle 2 of the CG Stiff Stalk Combined population and
represents the Stiff Stalk heterotic pattern (Lee et al., 2001b). The hybrid represents one of the
classic heterotic patterns grown in the Northern Corn Belt and is not a commercial hybrid (Lee
and Tracy, 2009).
The experimental field was moldboard plowed in the fall before the experimental year.
The preceding crop was alfalfa (Medicago sativa L.). The secondary tillage included spring
disking and cultivation twice using a cultivator, and cultipacking for seed-bed preparation. The
experimental plots received 500 kg ha-1 of 20-10-10 (N–P–K) before planting. Ammonium
nitrate (34–0–0) was applied at 100 kg ha-1 N as a single band at the 12-LT stage of the F1 hybrid.
Weeds were controlled with pre-planting herbicides made of a mixture of 3-4 L ha-1 Primestra II
Magnun, which included S-metolachlor [Acetamide, 2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2methoxy-1-methylethyl]-,(S)] and atrazine (2-chloro-4-ethylamino-6-isopropylamino-s-triazine),
40
and 0.3 L ha-1 Callisto, which included mesotrione [2-[4-(methylsulfonyl)-2-nitrobenzoyl]-1,3cyclohexanedione]. No post-emergence herbicide was applied.
Plants were hand-planted on 13 May 2009 at a density of 120,000 plants ha-1 (12 plants
m-2) and thinned to the optimal density of 80,000 plants ha-1 (8 plants m-2) at the 4-LT stage.
Each plot was in a north-south orientation and consisted of six rows, 10 m long and 0.76 m
between rows. Daily minimum and maximum air temperature, rainfall and incident solar
radiation were recorded from planting until final harvest using a weather station at the Elora
Research Station. Growing degree days (GDD) were calculated as:
GDD =
Dm
 [(
i  Dp
T max  T min
)  Tbase]
2
[2.1]
Where Tmax and Tmin are daily maximum and minimum air temperature, Tbase is the base
temperature of 8 oC below which maize plants are assumed to cease development (Wang, 1960;
Major et al., 1983; Ritchie and NeSmith, 1991). Abbreviation Dp is date of planting, and Dm is
date of physiological maturity (PM).
Each six-row plot was sub-divided for sampling into an early and late stage sampling
area. The outer rows of each plot were not part of the sampling areas. At the 4-LT stage,
approximately 150 visually similar plants (i.e., uniform PH and maximum width of basal stem
diameter [SDb]) from the remaining rows were tagged. The early stage sampling area had a three
to four plant border from the start of the plot to the sampling area and was at the front of all
plots. In total approximately 130 plants per plot were tagged at the 4-LT stage in the early stage
sampling area. The late stage sampling area was behind the early stage sampling area in all plots
41
and was bordered from the early stage area by five plants, with a three to four plant border at the
end of the plot. In total approximately 20 plants per plot were tagged at the 4-LT stage in the
late stage sampling area. The early stage sampling area was used to systematically collect plants
for destructive sampling throughout the vegetative portion of the life cycle (Table 2.1). If
possible, six to seven plants per plot were collected at every half LT stage from 6-LT stage until
12-LT stage for CG60 and CG102 and from 6-LT stage until 13-LT stage for the hybrid from the
early sampling area. Following these LT stages plants were collected only from every full LT
stage until tassel emergence (TE) from the early sampling area. Destructive plant samples were
collected from six to seven plants per plot at silking, 1 wk after silking and PM from the late
stage sampling area. The sampled plants were not competitively bordered and were buffered
from prior and subsequent harvests.
2.2.2 Developmental stages, morphometric and physiological measurements
Vegetative development in maize is generally described by the LT stage, defined as the
total number of visible leaves in the leaf whorl including the coleoptile (Lejeune and Bernier,
1996). In an attempt to increase precision, full-LT stage and half-LT stage designations were
used in this study. For example, a plant is considered at the 6-LT stage when the 7-LT is just
visible and has reached the leaf whorl but has not elongated. A plant is designated as at the 6.5LT stage when the 7-LT is the youngest visible leaf in the leaf whorl and the 7-LT has reached
half of the potential maximum length. The potential maximum length of the 7-LT is estimated
by measuring the length of the 7-LT from plants at the 7-LT stage. Tassel emergence is the stage
when the tassel tip emerges within the leaf whorl (Siemer et al., 1969).
42
Table 2.1. Summary of numbers of sampled plants CG60, CG102 and their F1 CG60 × CG102 from the 6-leaftip (LT) stage until
tassel emergence (TE). The F1 has a total of 17 leaves, while CG60 and CG102 have 16 and 14 leaves, respectively.
Genotype
Phenological stages
–––––––––––––––––––––––––––––––––––––LT–––––––––––––––––––––––––––––––––––––––––––––––
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
CG60
20
8
20
9
18 11 20 20 20
20
20
20
CG102
20
4
5
20 19 20 20 20
9
15
20
20
20
7
20
0
14 20 20 17 20
7
20
1
F1
†
NA, samples are not applicable for the genotype since CG102 has 14 leaves in total.
43
12
12.5
13
14
15
16
10
20
4
14
20
20
20
20
20
20
12
NA†
20
20
NA
20
TE
20
20
20
Starting at the 6-LT stage, developing ear shoots from the collected plants were dissected
with the aid of a binocular microscope (Wild M2B Heerbrugg, Heerbrugg, Switzerland). Tassel
initiation and ear initiation were considered to have occurred when 50% of the sampled shoot
apical and axillary meristems respectively, reached 0.4 mm (Stevens et al., 1986). From ear
initiation until 1 wk after silking, all the measurements were conducted on the primary ear of the
plant. Spikelet pair meristem appearance was noted when all plants sampled at a LT stage
reached their final number of kernel row (KRN). Ear length, KRNmax (i.e., from the base to the
tip of a developing ear KRN changes, therefore the largest value was noted) and SNPR were
recorded when spikelet pair meristems were visible (i.e., 10-LT for CG60, 9-LT for CG102 and
10.5-LT for the F1). The change in KRN is defined as difference in row number for at least five
consecutive rows from the KRN at the base of a developing ear. The KRN at the base of a
developing ear is registered as the first number from the base of an ear that holds for at least five
consecutive rows. The two different KRN separates the ‘base’ and ‘tip’ of the ear. Therefore,
the ‘tip’ of an ear is defined as the position exhibiting change in KRN from the base of a
developing ear. When differences in KRN between tip and base occurred, the KRN at the tip
and base as well as the spikelet position of change in KRN were recorded. Spikelet number per
ear was calculated by multiplying SNPR by KRNmax.
Above-ground plant dry matter from the early sampling area was based on morphometric
measurements starting at the 8.5-LT (CG102), 9-LT (CG60), or 9.5-LT (the F1) stage (i.e., at ear
initiation). Stem volume (in cm3) is closely associated with PDM in maize during the vegetative
development (Maddonni and Otegui, 2004). Morphometric measurements consisted of PH (i.e.,
the distance from the ground level to the uppermost leaf collar, in cm) and SD2 (in cm). The
44
SVmax was calculated as:
SVmax = π × PH × (SD2 × 0.5)2
[2.2]
Anthesis and silking dates for each tagged plant were recorded. Anthesis was noted
when at least one anther was extruded from the tassel, and silking was recorded when one silk
had emerged from the primary ear. Ear length, SNPR, KRNmax, and KNP were recorded for each
ear from the plants sampled at silking, 1 wk after silking, and PM. Individual PDM and EDM
for the plants sampled at silking, 1 wk after silking and PM were obtained by oven drying (80
o
C) the tissue to constant weight. Final harvest at PM was determined when all tagged plants
completed black layer formation (Tollennar and Daynard, 1978).
2.2.3 Statistical analyses
Shapiro-Wilk tests, via PROC UNIVARIATE procedure of the SAS software, version 9.2
(SAS Institute, 2008) were conducted to test normality. Due to the non-normality in most of the
data, differences among genotypes in physiological traits were tested by Kruskal-Wallis test, via
PROC NPAR1WAY procedure of SAS. The physiological traits included GDD from ear
initiation to silking, planting to anthesis and planting to silking, KRNmax, SNPR at silking,
spikelet initiation rate (SNPR GDD-1) as well as the agronomic traits at silking, 1 wk after silking
and PM. The sample size of KRNmax for each genotype included individual ears from KRN
formation stage to 1 wk after silking and PM. Spikelet initiation rate was obtained by the
division of SNPR at silking and the corresponding GDD from ear initiation to silking (Allison
and Daynard, 1979).
45
The relationship between LT and GDD was obtained using the PROC REG procedure.
Difference in the slope between LT stage and GDD among genotypes was tested by the Student's
t test. The rate of spikelet initiation (SNPR LT-1) during the rapid spikelet production period on
a LT stage basis was determined by PROC REG procedure of SAS (Otegui and Melón, 1997).
Spearman’s correlation coefficients for each genotype between ear length and SNPR
were computed using SAS PROC CORR procedure, because not all the ear length and SNPR
data at each stage of development fulfilled the assumption of normal distribution on the base of
the Shapiro-Wilk test statistic W of the PROC UNIVARIATE procedure of SAS. In order to
examine the PPV within a genotype, distributions of various traits from raw data for each
phenological stage were tested for normality using the Shapiro-Wilk test statistic W of the PROC
UNIVARIATE procedure. The distribution is normal when the W statistic is significant at P >
0.05. The same procedure was used to calculate mean and standard deviation (SD). Coefficients
of variation (CV) were only used when the distribution was normal.
Nonlinear regression is the one in which the relationship between the response variable
and at least one of its explanatory variables is nonlinear (Ratkowsky, 1990) and non-linear
regression was applied to the relationship between ear length and phenological stage, between
CV of SVmax, SNPR, SNPE and phenological stage using PROC NLIN procedure of SAS. The
relationships between CV of EL, SD of SNPE and phenological stage were estimated by linear
regression analyses using the PROC REG procedure of SAS. Residuals were checked for
normality and homoscedasticity before conducting linear regression analyses.
46
2.3 Results and Discussion
2.3.1 General phenological development in terms of thermal time
From planting to harvest 1032 GDD were accumulated, which is slightly below the 10-yr
average for Elora, ON, Canada (1167 GDD) (OMAFRA, 2009). Leaftip appearance exhibited a
significant linear relationship with GDD for the parental inbred CG60, CG102 and their F1
hybrid (Fig. 2.1). To reach the 6-LT stage the F1 required 215 GDD, CG60 required 228 GDD,
while CG102 needed 269 GDD. The slopes of the linear relationships did not differ among
genotypes, indicating that rate of LT appearance in the two parental inbred lines and the F1
required similar GDD (P > 0.05). To characterize the genotypic differences in LT appearance,
the phyllochron was calculated for each genotype (Fig. 2.1). Phyllochron is defined as the GDD
between the appearances of two successive LT (McMaster and Wilhelm, 1995). The three
genotypes did not differ in phyllochron (inverse of the slope), although CG60, CG102 and the F1
had different final leaf numbers (Table 2.2). The average phyllochron across the three genotypes
was 28.2 GDD leaf-1. The F1 required fewer GDD to reach anthesis and silking than the inbred
parents, with CG102 showing the slowest phenological development of the two parental inbred
lines (Table 2.2). The final leaf number attained by CG60, CG102 and the F1 was 16, 14 and 17,
respectively. Tassel initiation of CG60, CG102 and the F1 occurred at the 7.5-LT, 6.5-LT, and
7.5-LT stage, respectively (Table 2.2). Apical ear initiation occurred at the 9.0-LT, 8.5-LT, and
9.5-LT stage for the CG60, CG102 and the F1, respectively. CG102 required only 42 GDD
between tassel initiation and ear initiation while the other two genotypes required about 75 GDD
(Table 2.2). The three genotypes used in this study exhibited similar phenological development
to other published studies (e.g., Tollenaar and Hunter, 1983; Lejeune and Bernier, 1996).
47
Leaftip number
20
18
CG60
CG102
F1
16
CG60
CG102
F1
14
12
10
8
6
4
0
100
200
300
400
500
600
Growing degree days
Figure 2.1. The linear relationships between leaftip (LT) number and the growing degree days (GDD) accumulated from the day after
planting in two parental inbred lines CG60 and CG102 and their F1 hybrid CG60 × CG102 grown under 80,000 plants ha-1 in 2009.
The relationships between LT appearance and GDD were LT = - 3.3 + 0.038 × GDD (R2 = 0.95, P < 0.0001) for CG60, LT = - 3.2 +
0.033 × GDD for CG102 (R2 = 0.97, P < 0.0001) and LT = - 2.2 + 0.036 × GDD (R2 = 0.99, P < 0.0001) for the F1. There were no
significant differences in the slopes (P = 0.05). CG60, CG102 and the F1 had a phyllochron of 26.3, 30.3 and 27.8 GDD leaf-1
respectively. Growing degree days were calculated from daily mean air temperature above 8 oC (Ritchie and NeSmith, 1991).
48
Table 2.2. Thermal time accumulation in growing degree days (GDD) required for the key
phenological stages, plant and ear morphological characteristics for two inbred lines CG60,
CG102 and their F1 CG60 × CG102 grown at 80,000 plants ha-1 in 2009. The numbers in
parentheses are leaftip (LT) stages of the plants. Maximum kernel row number per ear after
kernel row number formation is derived from 202, 207 and 196 plants for CG60, CG102 and the
F1, respectively.
Growth stage and ear trait
CG60
CG102
F1
Planting to tassel initiation (GDD)
~279 (7.5-LT)
318 (6.5-LT)
~265 (7.5-LT)
Planting to ear initiation (GDD)
355 (9.0-LT)
360 (8.5-LT)
340 (9.5-LT)
Tassel initiation to anthesis (GDD)
~389
363
~390
Ear initiation to silking (GDD)
307b†
330a
286c
Planting to anthesis (GDD)
663b
683a
654b
Planting to silking (GDD)
662b
690a
626c
16
14
17
Kernel row number formation (GDD)
378 (11-LT)
423 (10-LT)
369 (11-LT)
Change in kernel row number (GDD)
378 (10.5-LT)
471 (13-LT)
408 (12.5-LT)
13.6b
14.0a
14.0a
Final leaf number
Maximum kernel row number
Spikelet number per row at silking
33b
39a
38a
Spikelet initiation rate
(spikelet number per row GDD-1)
0.11c
0.12b
0.14a
†
Different letters within a row indicate significant difference (P < 0.05) among genotypes.
49
2.3.2 Ear development in terms of thermal time and leaftip stage.
Appearance of spikelet pair meristems (Fig. 1.1) occurred at the 11-LT, 10-LT, and 11LT stage for the CG60, CG102 and F1, respectively, with kernel rows visible approximately two
LTs after ear initiation for the three genotypes (Table 2.2). From ear initiation to spikelet pair
meristem, CG102 required more GDD than the other two genotypes (63 GDD for CG102,
compared to 23 and 29 GDD for CG60 and the F1, respectively). Both inbred lines and their F1
hybrid exhibited an approximately linear rate of SNPR production after spikelet pair meristem
establishment (Figs. 2.2-2.4).
The linear rapid SNPR production phases were between 11.5-LT and 16-LT for CG60,
and between 10.5-LT and 13-LT for CG102, and between 12.5-LT and 17-LT for the F1. During
this phase, the rate of spikelet initiation was 4.2 SNPR LT-1 for CG60 (R2 = 0.80, n = 101), 7.0
SNPR LT-1 for CG102 (R2 = 0.84, n = 96) and 5.3 SNPR LT-1 for the F1 (R2 = 0.90, n = 115),
respectively. The genotypes differed in the phenological stage at which the maximum SNPR
was established. CG60 and CG102 attained the maximum SNPR at silking, while the F1 reached
the maximum SNPR at 1 wk after silking. From ear initiation until silking, the F1 produced more
SNPR per GDD (0.14 SNPR GDD-1) than the two inbred lines (0.11 SNPR GDD-1 and 0.12
SNPR GDD-1 for CG60 and CG 102, respectively) (Table 2.2). At silking, the inbred line
CG102 and the F1 had a higher SNPR than CG60 (Table 2.2). These two genotypes also formed
more KRNmax than CG60 (Fig. 2.5). Therefore, inbred line CG102 and the F1 had a higher
potential kernel number than CG60.
50
50
45
Spikelet number per row
40
35
30
25
20
15
10
5
0
8
9
10
11
12
13
14
15
16
TE
17
Silking
Silking
18
19
+ 1 wk
20
Leaftip stage
Phenological stage
Figure 2.2. Dynamics of spikelet number per row from ear initiation to 1 wk after silking in primary ear of inbred line CG60 grown
under 80,000 plants ha-1 in 2009. All sampled plants were initially similar plants with uniform plant height and maximum width of
basal stem diameter at the 4-leaftip stage. The points in each corresponding phenological stage represent the range of spikelet number
per row. The line represents the average spikelet number per row at the specific phenological stage. TE, tassel emergence.
51
50
45
Spikelet number per row
40
35
30
25
20
15
10
5
0
8
9
10
11
12
13
Leaftip stage
14
TE
15
Silking
16
Silking
17
+ 1 wk
18
Phenological stage
Figure 2.3. Dynamics of spikelet number per row from ear initiation to 1 wk after silking in primary ear of inbred line CG102 grown
under 80,000 plants ha-1 in 2009. All sampled plants were initially similar plants with uniform plant height, and maximum width of
basal stem diameter at the 4-leaftip stage. The points in each corresponding phenological stage represent the range of spikelet number
per row. The line represents the average number of spikelet number per row at the specific phenological stage. TE, tassel emergence.
52
50
45
Spikelet number per row
40
35
30
25
20
15
10
5
0
8
9
10
11
12
13
14
15
16
17
Silking
18
Silking
19
+ 1 wk
Leaftip stages
Phenological stage
Figure 2.4. Dynamics of spikelet number per row from ear initiation to 1 wk after silking in primary ear of the F1 CG60 × CG102
grown under 80,000 plants ha-1 in 2009. All sampled plants were initially similar plants with uniform plant height, and maximum
width of basal stem diameter at the 4-leaftip stage. The points in each corresponding phenological stage represent the range of
spikelet number per row. The line represents the average spikelet number per row at the specific phenological stage.
53
60
CG60
CG102
F1
50
Frequency (%)
40
30
20
10
0
12
14
16
B14T12
B16T12
B16T14
Mixture
Others
Kernel row number composition
Figure 2.5. Frequency distributions of kernel row number (KRN) in inbred lines CG60, CG102
and the F1 hybrid CG60 × CG102 grown under 80,000 plants ha-1 in 2009. All sampled plants
were initially similar plants with uniform plant height, and maximum width of basal stem
diameter at the 4-leaftip stage. The number of sampled plants is 202, 207 and 196 for CG60,
CG102 and the F1, respectively from KRN formation stage until 1 wk after silking and at
physiological maturity. Changes in KRN from the base (B) to the tip (T) of the ear were
observed during ear development in all three genotypes. The change in KRN is defined as
difference in KRN for at least five consecutive rows from the KRN at the base of a developing
ear. The KRN at the base of a developing ear is registered as the first number from the base of
an ear that holds for at least five consecutive rows. The ‘tip’ of an ear is defined as the position
exhibiting change in KRN from the base of a developing ear. Abbreviation B14T12 represents
ears with 14 KRN from the base and 12 KRN at the tip. Abbreviation B16T12 represents ears
with 16 KRN from the base and 12 KRN at the tip. Abbreviation B16T14 represents ears with
16 KRN from the base and 14 KRN at the tip. Mixture represents ears exhibiting changes in
KRN twice from the base to the tip of the ear. Others represent other KRN composition with
change in KRN once or without change in KRN.
54
2.3.3 Above-ground plant dry matter and ear traits
Average SNPR and SNPE exhibited quadratic increases during vegetative and early
reproductive development for the three genotypes (data not shown). Spikelet number per ear
reached the maximum at silking, silking and 1 wk after silking for CG60, CG102 and the F1,
respectively. Average ear length exhibited an exponential increase during vegetative and early
reproductive development for all three genotypes similar to that observed by Wilson and Allison
(1978) (Fig. 2.6). CG60 (9.5 cm) had ear length longer than CG102 (8.1 cm) and F1 (8.0 cm) at
silking (Fig. 2.6). All genotypes reached the maximum ear length after silking. At PM, F1
exhibited the longest ear length (14.8 cm), followed by CG60 (12.4 cm), with CG102 having the
shortest ear (11.6 cm). Maximum kernel row number of CG60 and the F1 was predominantly 12row, with some 14- and 16-row ears present (Fig. 2.5). However, KRNmax of CG102 was
predominantly 14, with some 12-row and 16-row ears. Changes in KRN from the base to the tip
of the ear were observed during ear development in all three genotypes. However, KRNmax was
the most consistent, from base to tip, in CG102, and the most variable in CG60. About 2% of
CG60 ears exhibited changes in KRN two times from the base to the tip of the ear. Changes in
row number occurred at the 10.5-LT, 13-LT and 12.5-LT stage for CG60, CG102 and the F1,
respectively (Table 2.2).
Significant positive correlations between ear length and SNPR were observed in CG60,
CG102 and the F1 during vegetative development (Table 2.3). However, this relationship did not
hold up at all stages of development. For CG60 it extended from the 10-LT to 16-LT, while for
CG102 this relationship was observed from the 9-LT to 14-LT and through TE. The F1
55
160
12
CG60
Average EL
SD of EL
Estimated average EL
Estimated SD of EL
120
8
80
4
40
Mean ear lengh (mm)
8
9
10
11
12
13
Leaftip stage
14
15
16
TE
wk
17 Silking
18 Silking
19 + 120
12
160
CG102
120
8
80
4
40
0
0
8
9
10
11
12
Leaftip stage
13
14
TE
15
Silking
Silking
+ 18
1 wk
16
17
19
20
12
160
F1
120
Standard deviation of ear length (cm)
0
0
8
80
4
40
0
0
8
9
10
11
12
13
14
15
16
17 Silking
18 Silking
19 + 120wk
Leaftip stage
Phenological stage
Figure 2.6. Mean ear length and standard deviation (SD) of ear length in sampled inbred lines
CG60, CG102 and their F1 CG60 × CG102 plants during different phenological stages (PS)
grown under 80,000 plants ha-1 in 2009. The values of PS in CG60 for tassel emergence (TE),
silking and 1 wk after silking are 17, 18 and 19, respectively. The values of PS in CG102 for
TE, silking and 1 wk after silking is 15, 16 and 17, respectively. The values of PS in the F1 for
silking and 1 wk after silking is 18 and 19, respectively. Both mean ear length and SD of ear
length followed exponential development for the three genotypes. The relationship between ear
length of CG60 and PS is ear length = 0.0005e(0.66PS) (R2 = 0.98). The relationship between SD
56
of ear length and PS for CG60 is SD = 0.0047e(0.41PS) (R2 = 0.99). The relationship between ear
length of CG102 and PS is ear length = 0.0014e(0.68PS) (R2 = 0.99). The relationship between SD
of ear length and PS for CG102 is SD = 0.0256e(0.34PS) (R2 = 0.88). The relationship between ear
length of the F1 and PS is ear length = 0.0023e(0.57PS) (R2 = 0.99). The relationship between SD
of ear length and PS for the F1 is SD = 0.0006e(0.52PS) (R2 = 0.99). All relationships are
significant at P value < 0.0001.
57
Table 2.3. Spearman’s rank phenotypic correlation coefficients between ear length and spikelet number per row during development
including tassel emergence (TE), silking and 1 wk after silking for two inbred lines CG60, CG102 and their F1 CG60 × CG102 grown
at 80,000 plants ha-1 in 2009.
Genotype ______________________________________________ LT _______________________________________________________
CG60
CG102
F1
9
9.5
10
10.5
11
NA†
0.75**
NA
NA
0.61**
NA
0.72**
0.96***
NA
0.54*
0.87***
0.88*
0.71**
0.81***
0.74**
______________________________
13
14
11.5
12
0.97*** 0.88**
0.62** 0.93***
NA
NA
LT ____________________________
15
16
17
TE
Silking
CG60
0.86*** 0.89*** 0.86** 0.81***
NA
-0.22
-0.38
CG102
0.86***
0.48*
NA
NA
NA
0.45*
0.34
F1
0.89*** 0.88*** 0.82***
0.16
-0.65**
NA
0.09
*, **, *** represent significance at probability 0.05, 0.01 and 0.001, respectively.
†
NA means either not applicable or value not available.
58
12.5
NA
NA
0.81**
1 wk after
silking
0.30
0.16
-0.32
displayed this relationship from 10.5-LT to 15-LT; no significant correlation was evident at the
16-LT, and interestingly, there was a significantly negative correlation between SNPR and ear
length in the F1 at the 17-LT. Beyond TE, there were no significant correlations between SNPR
and ear length in any of the genotypes (Table 2.3).
Two metrics are used to assess variability: SD and the CV. Coefficients of variation for
each trait/stage of development were given only when normal distributions were observed (see
Table 2.4 for a list of traits and stages of development that did not follow a normal distribution).
The CVs for all three plant traits, PH, SD2 and SVmax, across the genotypes were relatively
constant across the range of vegetative development (data only presented for CV of SVmax in
Figs. 2.7-2.9). The SD for SVmax increased with plant development across the three genotypes
(Figs. 2.7-2.9). For the ear characteristics, SNPR, SNPE, and ear length, only the CVs of ear
length were relatively constant across the range of vegetative development (Figs. 2.10 and 2.14).
All three genotypes exhibited greater variability in SNPR and SNPE in the earlier stages of
development, but reduced variability in the latter stages of development (Figs. 2.10, 2.12 and
2.14). The SD of SNPE kept increasing until 1 wk after silking for the three genotypes (Figs.
2.11, 2.13 and 2.15).
In order to compare the three genotypes in the CV of plant morphological traits and ear
characteristics, the stages of KRN formation, change in KRN, final LT, silking, 1 wk after
silking and PM were selected to compare the three genotypes when the traits were normally
distributed (Table 2.4). Only larger than 10% difference in CV was considered. At the KRN
formation stage, the F1 had lower CV of SNPR and SNPE (around 21%) than the two parental
59
Table 2.4. List of phenological stages during which indicated variables that were not normally
distributed. All distributions were based on approximately 20 observations in two inbred lines
CG60 and CG102 and their F1 CG60 × CG102 in plant morphological traits, plant height (PH),
the maximum width of stem diameter 2 cm above the ground level (SD2) and stem volume
(SVmax) from the leaftip (LT) stage of ear initiation until tassel emergence (TE) and aboveground plant dry matter (PDM) at silking and 1 wk after silking and at physiological maturity
(PM); and ear characteristics, spikelet number per row (SNPR), spikelet number per ear (SNPE),
ear length at the same corresponding stages. All sampled plants were initially similar plants
with uniform PH, maximum width of basal stem diameter at the 4-LT stage.
Phenological Genotype
Plant morphological traits
stages
Vegetative
stage
Silking, 1
wk after
silking and
PM
CG60
CG102
F1
CG60
CG102
F1
PH
13-LT and TE
8.5- and 14-LT
13-, 15- and 16-LT
Silking
1 wk after silking
SD2
None
8.5- and 11-LT
None
None
None
SVmax or PDM
14-LT
None
10-LT
None
None
None
None
None
Ear characteristics
Vegetative
stage
Silking, 1
wk after
silking and
PM
CG60
CG102
F1
CG60
SNPR
12-LT and TE
9-, 11-LT and TE
15-LT
None
SNPE
10.5-, 12-LT and TE
9-LT
None
None
Ear length
9-LT
None
9.5- and 12.5-LT
None
CG102
Silking and PM
None
1 wk after silking
F1
Silking and PM
Silking
Silking
60
70
160
140
SD of SVmax
60
SD of SVmax
120
50
100
80
40
60
30
40
20
Standard deviation (SD) (cm2)
Coefficient of variation (CV) (%)
CV of SVmax
20
0
10
8
9
10
11
12
13
14
15
16
TE
17
Silking
Silking
+201 wk
18
19
Leaftip stage
Phenological stage
Figure 2.7. Coefficients of variation (CV) and standard deviation (SD) of stem volume (SVmax)
of sampled inbred line CG60 during different phenological stages (PS) when the crop was grown
under 80,000 plants ha-1 in 2009. The tassel emergence (TE), silking and 1 wk after silking are
assigned as PS values of 17, 18 and 19. The relationship between the SD of SVmax and PS is SD
= 0.41e(0.29PS) (R² = 0.81, P < 0.0001). All sampled plants were initially similar plants with
uniform plant height and maximum width of basal stem diameter at the 4-leaftip stage.
61
70
160
CV of SVmax
140
SD of SVmax
120
50
100
40
80
60
30
40
Standard deviation (SD) (cm2)
Coefficient of variation (CV) (%)
SD of SVmax
60
20
20
10
0
8
9
10
11
12
Leaftip stage
13
14
TE
15
Silking
16
Silking
+ 1 18
wk
17
Phenological stage
Figure 2.8. Coefficients of variation (CV) and standard deviation (SD) of stem volume (SVmax)
of sampled inbred line CG102 during different phenological stages (PS) when the crop was
grown under 80,000 plants ha-1 in 2009. The tassel emergence (TE), silking and 1 wk after
silking are assigned as PS values of 15, 16 and 17. The relationship between the SD of SVmax
and PS is SD = 0.17e(0.37PS) (R² = 0.97, P < 0.0001). All sampled plants were initially similar
plants with uniform plant height and maximum width of basal stem diameter at the 4-leaftip
stage.
62
70
180
CV of SVmax
SD of SVmax
140
50
120
40
100
30
80
60
20
40
10
Standard deviation (SD) (cm2)
Coefficient of variation (CV) (%)
160
SD of SVmax
60
20
0
8
9
10
11
12
13
Leaftip stage
14
15
16
17
0
Silking
Silking
+ 20
1 wk
18
19
Phenological stage
Figure 2.9. Coefficient of variation (CV) and standard deviation (SD) of stem volume (SVmax)
of sampled F1 CG60 × CG102 during different phenological stages (PS) when the crop was
grown under 80,000 plants ha-1 in 2009. All sampled plants were initially similar plants with
uniform plant height and maximum width of basal stem diameter at the 4-leaftip stage. The
silking and 1 wk after silking is assigned as PS values of 18 and 19. The relationship between
the SD of SVmax and PS is SD = 0.93 e(0.27PS) (R² = 0.96, P < 0.0001).
63
70
CV of SNPR
CV of SNPE
CV of EL
Estimated CVof SNPR
Estimated CV of SNPE
Estimated CV of EL
Coefficient of variation (CV) (%)
60
50
40
30
20
10
0
8
9
10
11
12
13
Leaftip stage
14
15
16
TE
17
Silking
Silking
+20
1 wk
18
19
Phenological stage
Figure 2.10. Coefficient of variation (CV) and standard deviation (SD) of spikelet number per
row (SNPR), spikelet number per ear (SNPE) and ear length (EL) for inbred line CG60 during
different phenological stages (PS) when the crop was grown under 80,000 plants ha-1 in 2009.
The tassel emergence (TE), silking and 1 wk after silking are assigned as PS values of 17, 18 and
19. The relationship between CV of SNPR and PS is CV = 1.73 × 105 PS-3.53 (R2 = 0.98). The
relationship between CV of SNPE and PS is CV = CV = 1.35 × 105 PS-3.37 (R2 = 0.98). The
relationship between CV of EL and PS is CV = 51.81 – 2.01 × PS (R² = 0.49). . All
relationships are significant at P value < 0.01.
64
120
SD of SNPR × 10
SD of SNPE
SD of EL × 10
Estimated of SD of SNPE
Standard deviation (SD)
100
80
60
40
20
0
8
9
10
11
12
13
Leaftip stage
14
15
16
TE
17
Silking
Silking
+20
1 wk
18
19
Phenological stage
Figure 2.11. Standard deviation (SD) of spikelet number per row (SNPR), spikelet number per
ear (SNPE) and ear length for inbred line CG60 during different phenological stages (PS) when
the crop was grown under 80,000 plants ha-1 in 2009. The tassel emergence (TE), silking and 1
wk after silking are assigned as PS values of 17, 18 and 19. The relationship between SD of
SNPE and PS is SD = -31.66 + 5.04 × PS (R² = 0.73, P =0.0035).
65
70
CV of SNPR
CV of SNPE
CV of EL
Estimated CV of SNPR
Estimated CVof SNPE
Coefficient of variation (CV) (%)
60
50
40
30
20
10
0
8
9
10
11
12
13
14
TE
15
Silking
16
Silking
17 + 1 wk
18
Leaftip stage
Phenological stage
Figure 2.12. Coefficient of variation (CV) and standard deviation (SD) of spikelet number per
row (SNPR), spikelet number per ear (SNPE) and ear length (EL) for inbred line CG102 during
different phenological stages (PS) when the crop was grown under 80,000 plants ha-1 in 2009.
The tassel emergence (TE), silking and 1 wk after silking are assigned as PS values of 15, 16 and
17. The relationship between CV of SNPR and PS is CV = 1.89 × 106 PS-4.75 (R2 = 0.95). The
relationship between CV of SNPE and PS is CV = 1.00 × 106 PS-4.43 (R2 = 0.95). All
relationships are significant at P value < 0.0001.
66
120
SD of SNPR × 10
SD of SNPE
SD of EL × 10
Estimated SD of SNPE
Standard deviation (SD)
100
80
60
40
20
0
8
9
10
11
12
13
14
TE
15
Silking
Silking
16
17 + 1 wk
18
Leaftip stage
Phenological stage
Figure 2.13. Standard deviation (SD) of spikelet number per row (SNPR), spikelet number per
ear (SNPE) and ear length for inbred line CG102 during different phenological stages (PS) when
the crop was grown under 80,000 plants ha-1 in 2009. The tassel emergence (TE), silking and 1
wk after silking are assigned as PS values of 15, 16 and 17. The relationship between SD of
SNPE and PS is SD = -36.40 + 5.53 × PS (R² = 0.72, P = 0.001).
67
CV of SNPR
CV of SNPE
CV of EL
Estimated CV of SNPR
Estimated CV of SNPE
Estimated CV of EL
Coefficient of variation (CV) (%)
60
40
20
0
8
9
10
11
12
13
14
15
16
17
Silking
Silking
+ 20
1 wk
18
19
Leaftip stage
Phenological stage
Figure 2.14. Coefficient of variation (CV) and standard deviation (SD) of spikelet number per
row (SNPR), spikelet number per ear (SNPE) and ear length (EL) for F1 hybrid CG60 × CG102
during different phenological stages (PS) when the crop was grown under 80,000 plants ha-1 in
2009. The silking and 1 wk after silking is assigned as PS values of 18 and 19. The relationship
between CV of SNPR and PS is CV = 3.83 × 104 PS-3.13 (R2 = 0.99). The relationship between
CV of SNPE and PS is CV = 1.18 × 103 PS-1.70 (R2 = 0.97). The relationship between CV of EL
and PS is CV = 30.23 – 1.03 × PS (R² = 0.95). . All relationships are significant at P value <
0.0001.
68
120
SD of SNPR × 10
SD of SNPE
SD of EL × 10
Estimated SD of SNPE
Standard deviation (SD)
100
80
60
40
20
0
8
9
10
11
12
13
14
15
16
17
Silking
Silking
+ 20
1 wk
18
19
Leaftip stage
Phenological stage
Figure 2.15. Standard deviation (SD) of spikelet number per row (SNPR), spikelet number per
ear (SNPE) and ear length for F1 hybrid CG60 × CG102 during different phenological stages
(PS) when the crop was grown under 80,000 plants ha-1 in 2009. The silking and 1 wk after
silking is assigned as PS values of 18 and 19. The relationship between SD of SNPE and PS is
SD = -51.35 + 6.20 × PS (R² = 0.84, P = 0.0015).
69
inbred lines (above 36%) (Figs. 2.10, 2.12 and 2.14). At the stage of change in KRN, the CG60
had a higher CV of SNPR (50%) than CG102 (11%) and the F1 (12%). At PM, the F1 had lower
CV of SNPR (3%) than the two parental inbred lines (above 13%). The three genotypes were
similar in CV of ear characteristics at the other selected stages and similar in CV of plant
morphological traits at each selected stage (Figs. 2.7-2.9).
The KRNmax was not normally distributed at most of the phenological stages for the three
genotypes except of CG60 at 1 wk after silking and CG102 at the 10-LT stage. The CV of
SNPR was more affected than the CV of KRNmax by CV of SVmax at the KRN formation stage
for the three genotypes, which meant that the CV of SNPR was more than 10% larger than CV of
KRNmax (Fig. 2.16). The CG60 and CG102 kept the trend until 12-LT and 11-LT stage,
respectively. Starting from 13-LT (CG60), 11.5-LT (CG102) and 12.5-LT (the F1) stage to 1 wk
after silking and at PM, the CV of SVmax had no effects on CV of SNPR and CV of KRNmax for
the three genotypes. Moreover, the F1 had lower CV of SNPR (21%) than CG60 (37%) and
CG102 (46%) at the KRN formation stage when the three genotypes had similar CV of SVmax
(around 25%) (Fig. 2.16). In addition, the CV of KRNmax was similar among the three genotypes
and was between 7% and 17% except that CG60 had an above 20% CV of KRNmax at the KRN
formation stage (i.e., 11-LT) (Fig. 2.16).
Not unexpectedly, the F1 accumulated significantly more PDM at silking (PDMs), 1 wk
after silking (PDMs+1), PM (PDMPM) and GY (Tables 2.5 and 2.6) and individual values of each
four traits across all three genotypes is present in Fig. 2.17. The two parental inbred lines had
similar PDMs, PDMs+1; however, CG102 had significantly lower PDMPM. Somewhat surprising
70
50
SNPR
KRNmax
40
Coefficient of variation of ear traits (%)
11-LT
SNPR at KRN formation
KRNmax at KRN formation
30
50
12-LT
CG60
CG102
10-LT
40
11.5-LT
30
10.5-LT
20
20
10
10
0
0
0
10
20
30
40
0
10
20
30
40
50
F1
40
30
11-LT
20
10
0
0
10
20
30
40
Coefficient of variation of stem volume (%)
Figure 2.16. The relationship between plant-to-plant variability in stem volume measured as coefficient of variation (CV) and plantto-plant variability in spikelet number per row (SNPR) and maximum kernel row number (KRNmax) from kernel row number (KRN)
formation to 1 wk after silking and at physiological maturity for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 ×
CG102 planted at 80,000 plants ha-1 in 2009. The stem volume was calculated from the maximum width of stem diameter that is 2 cm
71
above the ground level and the plant height. The CV was derived from approximately 20 individual plants of each genotype. All
sampled plants were initially similar with uniform plant height and maximum width of basal stem diameter at the 4-leaftip (LT) stage.
The KRN formation stage indicates approximately 100% plants reached their final KRN. The KRN formation stage for CG60, CG102
and the F1 was 11-LT, 10-LT and 11-LT stage, respectively.
72
Table 2.5. Above-ground plant dry matter (PDM) at silking (PDMs), 1 wk after silking (PDMs+1) and physiological maturity (PM)
(PDMPM), the corresponding ear dry matter (EDM) with husks, cob and shank at silking (EDMs), 1 wk after silking (EDMs+1) and PM
(EDMPM) and dry matter partitioning to the ear during each period for two inbred lines CG60, CG102 and their F1 CG60 × CG102
grown at 80,000 plants ha-1 in 2009.
EDMs
EDMs+1 Partitioning PDMPM
EDMPM Partitioning
PDMs
Partitioning PDMs+1
Genotype
-1
-1
-1
-1
-1
to the ear
to the ear
to the ear
(g plant ) (g plant )
(g plant ) (g plant )
(g plant ) (g plant-1)
Silking
1 wk after silking
CG60
0.07a
85.4b
14.8a
0.17a
67.1b
4.5a
CG102
66.4b
4.4a
0.07a
88.7b
15.7a
0.17a
F1
100.2a
3.5b
0.03b
113.3a
8.7b
0.08b
†
Different letters within a row indicate significant difference at P < 0.05.
†
73
148.0b
131.7c
257.9a
PM
76.7b
66.7c
152.0a
0.52b
0.51b
0.59a
Table 2.6. Above-ground plant dry matter (PDMPM), grain yield (GY), harvest index (HI), kernel number per plant (KNP) as well as
primary ear KNP at physiological maturity for two inbred lines CG60 and CG102 and their F1 CG60 × CG102 grown at 80,000 plants
ha-1 in 2009.
Genotype
PDMPM
(g plant-1)
PDMPM
(Mg ha-1)
GY
(g plant-1)
GY
(kg ha-1)
HI
148.0b†
CG60
11.84b
60.8b
4860b
0.41b
131.7c
CG102
10.54c
44.0c
3523c
0.34c
257.9a
F1
20.63a
117.3a
9386a
0.46a
†
Different letters within a row indicate significant difference at P < 0.05.
74
Total
KNP
Primary
ear KNP
Realized potential KNP
of the primary ear (%)
288b
249b
442a
240b
249b
442a
54
46
76
140
180
160
120
PDMS+1 (g plant-1)
100
120
100
80
80
60
60
40
CG60
CG102
F1
40
CG60 at PM
CG102 at PM
F1 at PM
20
Grain yield (g plant-1)
140
20
0
0
20
60
100
140
180
220
260
300
340
-1
Above-ground plant dry matter (g plant )
Figure 2.17. Individual values of above-ground plant dry matter at silking (x-axis) and 1 wk after silking (PDMs+1) (primary y-axis,
open symbols) and individual values of above-ground plant dry matter at physiological maturity (PM) (x-axis) and its corresponding
grain yield (secondary y-axis, filled symbols) for inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 grown at 80,000
plants ha-1 in 2009. All sampled plants were initially similar with uniform plant height and maximum width of basal stem diameter at
the 4-leaftip stage.
75
partitioning of dry matter to the ear did not reflect the plant dry matter accumulation results at
silking and 1 wk after silking. The F1 exhibited lower EDMs and EDMs+1 than the two parental
inbreds (Table 2.5). However, the F1 exhibited significantly higher EDM at PM (EDMPM).
Consequently, it appears that partitioning of dry matter to the ear in the F1 was lower than the
inbred lines at silking and 1 wk after silking but higher than the inbred lines at PM. Equally as
unexpected was that percentage of potential kernel number realized appears to be more a
function of PDMs rather than EDMs. Average kernel number of the primary ear per plant at PM
was 240, 249, and 442 kernels, for CG60, CG102 and the F1 respectively (Table 2.6). These
reflect 54%, 46%, and 76% of the potential kernel number for CG60, CG102 and the F1,
respectively.
2.3.4 Effects of sampling individual plants that were not competitively bordered
Sampling individual plants that were not competitively bordered will gradually dilute
initial 80,000 plant ha-1. Competitively bordered plants refer to individual plants surrounded by
adjacent plants under a target plant density. However, it will not affect PPV, ear and plant traits
until intra-specific competition starts to limit plant growth. A previous study indicated that
growth of F1 hybrid CG102 × CG108 is not resource-limited before the 14-LT stage at the
80,000 plants ha-1 (Page et al., 2010a). The exact LT-stage when the three genotypes in this
study experience limited resource is unknown, due to their unequal final leaf number,
phenological development and leaf area per plant to the CG102 × CG108. It should start when
leaf area index is larger than 1 (Ballaré et al., 1987; Tetio-Kagho and Gardner, 1988; Page et al.,
2010a).
76
The graduate dilution of initial 80,000 plant ha-1 will affect the results from late
vegetative stage to TE for the three genotypes. Smith (2012) reduced intra-specific competition
by thinning plants at 1 wk before silking from 74,000 to 37,000 plants ha-1. Thinning plants
affected SNPE but did not affect KRN for CG60, CG102 and the F1 hybrid. Moreover, sampling
individual plants at silking and 1 wk after silking will reduce the intra-specific competition at 1
wk after silking and PM for the three genotypes. It would be ideal to sample competitively
bordered plants only. However, frequent samplings of 20 competitively bordered plants at the
same developmental stage need large plot area and more tagged plants with similar initial plant
size and development. Future studies should increase plot area and the number of tagged plants,
and sample 10 competitively bordered plants at each developmental stage.
77
2.4 Conclusions
The parental inbreds CG60, CG102 and their F1 were similar in phyllochron, LT-stage
interval between tassel initiation and ear initiation, between ear initiation and spikelet pair
meristem appearance. However, the F1 produced more SNPR GDD-1, and had a longer ear
length at PM than the two inbred lines. Although the F1 and CG102 formed higher KRNmax,
SNPR and potential primary KNP than CG60, the F1 achieved more KNP than both inbred lines
at PM, indicating that KNP at PM was not limited by the potential KNP at the optimal density
for the three genotypes. Although the F1 produced greater PDMs and PDMs+1, it had shorter ear
length and less EDM than the two inbred lines at the corresponding stages.
Plant-to-plant variability in early ear development from ear initiation until 1 wk after
silking has not been reported in the literature. The CV of morphological traits PH, SD2, SVmax
and ear trait ear length remained relatively constant for each genotype. The CV of ear traits
SNPR and SNPE tended to decrease around the silking period for each genotype. The CV of
SNPR was more affected than the CV of KRNmax by CV of SVmax at the KRN formation stage
for the three genotypes. Starting at 13-LT (CG60), 11.5-LT (CG102) and the 12.5-LT (the F1) to
1 wk after silking and at PM, the CVs of SNPR and KRNmax were not affected by CV of SVmax
for the three genotypes. While the three genotypes had similar CV of SVmax, the F1 hybrid had
lower CV of SNPR and SNPE at the KRN formation stage and lower SNPR at PM than the two
parental inbred lines. Otherwise, the F1 hybrid exhibited similar CVs in plant and ear
morphological traits as the two inbred lines. Future studies could examine whether stress
imposed during early ear development, particularly around the KRN formation stage, might
78
cause a higher PPV in ear length, SNPR, KRNmax, SNPE, which might affect the potential KNP
and GY.
79
Chapter 3. Physiological Characteristics of Barrenness in Maize
(Zea mays L.)
3.0 Abstract
In this study, the physiological characteristics of barren plants were examined in two
parental inbred lines and their F1 hybrid from canopy to subpopulation to individual plant level
during the various stages of plant growth and development and dry matter partitioning. Initial
uniform homogeneous plants in terms of size, development and spatial pattern were selected at
the 8-leaftip (LT) stage in 2007 and 4-LT stage in 2008 in two parental inbred lines CG60,
CG102 and their F1 hybrid CG60 × CG102, grown at different plant densities (40,000; 80,000;
120,000 and 160,000 plants ha-1). Growth and development of the selected individual plants and
their ears were monitored and assessed during the life-cycle using an allometric methodology.
Results showed that for inbred lines tested, barrenness was not caused by low carbon (C)
assimilation during vegetative growth nor by low dry matter partitioning to the ear during the
critical period bracketing silking; the F1 hybrid was resistant to barrenness even at 160,000
plants ha-1; trends for physiological traits or parameters in growth and development or dry matter
partitioning were inconsistent and inadequate to predict the incidence of barren plants;
barrenness was not totally confined to dominated (d) plants of the two parental inbred lines. The
d plants are those plants in the lower one-third of the population for above-ground plant dry
matter (PDM) at physiological maturity (PM) (PDMPM). This study indicates that barrenness at
the individual plant level is related to the coordination among plant growth, LT development and
early ear development during the vegetative growth stage.
80
3.1 Introduction
Maize (Zea mays L.) is a species susceptible to barrenness under intra-specific
competition (Edmeades and Daynard, 1979b; Vega et al., 2001b; Maddonni and Otegui, 2006).
Ear barrenness is defined as either failure of plants to set any kernels with the development of ear
structure or failure of plants to produce viable ear structure (Buren et al., 1974; Vega et al.,
2001a).
Past studies have associated barrenness with other physiological traits measured during
the critical period bracketing silking and at PM, as well as with physiological and morphological
traits (Vega and Sadras, 2003; Boomsma, 2009). The symptoms of barrenness include silk delay
(Edmeades et al., 1993), longer anthesis to silking interval (ASI) (Daynard and Muldoon, 1983;
Boomsma, 2009), failure of silk emergence during the pollen-shedding period (Sass and Loeffel,
1959), as well as purple stems and leaf margins (Moss, 1962). Incidence of barrenness is
associated with threshold values for assimilation flux per plant 1 d after anthesis (Edmeades and
Daynard, 1979a), intercepted photosynthetically active radiation per plant during the critical
period bracketing silking (Andrade et al., 2000), plant growth rate during the critical period
bracketing silking (PGRs) (Tollennar et al., 1992; Andrade et al., 1999) and ear growth rate
during the critical period bracketing silking (EGRs) (Vega et al., 2001a; Echarte et al., 2004), and
threshold values of PDMPM for harvest index (HI) (i.e., the highest PDMPM below which no
kernel set occurs) (Vega et al., 2000; Liu and Tollenaar, 2009b). However, the development of
barrenness in individual plants has never been characterized over an entire growing season.
Barrenness could be explained as a physiological response of individual plants to intra81
specific competition induced by abiotic stress (e.g., plant density, nitrogen [N], drought and
light). Furthermore, intra-specific competition generates plant hierarchy (i.e., plant individuals
are ranked and assigned to different strata) within a homogenous genotype. Plant hierarchy can
be classified according to the PDMPM; the “d” plants have a lower ability to compete and capture
resources, while the dominant (D) plants (i.e., plants in the upper 1/3 of the PDMPM distribution)
have an enhanced competitive ability for resource capture (Maddonni and Otegui, 2004, 2006).
Plant hierarchy is evident at very early stages of development (ca., 4-fully emerged leaves with
visible collars [V-stage, V-4]) (Maddonni and Otegui, 2004). Dominated plants are associated
with reduction in grain yield (GY) and yield components, especially kernel number per plant
(KNP), and lower values of physiological parameters related to growth such as PGRs, EGRs as
well as morphological traits such as plant height (PH) (Maddonni and Otegui, 2004, 2006;
Pagano et al., 2007; Pagano and Maddonni, 2007). Barren hybrid plants belong to approximately
the lower ¼ to 1/3 of the PDMPM distribution (Vega and Sadras, 2003), or to the lower ¼ of the
PDMPM distribution (Vyn and Boomsma, 2009). However, these studies considered only final
PDMPM and did not investigate the development of d plants over time.
Both barrenness and severity of plant hierarchy can be affected by genotype and plant
density (Woolley et al., 1962; Buren, 1970; Francis et al., 1978; Edmeades and Daynard, 1979b;
Iremiren and Milbourn, 1980; Daynard and Muldoon, 1983; Maddonni and Otegui, 2004, 2006;
Pagano et al., 2007; Pagano and Maddonni, 2007). There were genetic variations in barrenness
among hybrids (Lang et al., 1956; Earley et al., 1966; Center and Camper, 1973; Olness et al.,
1990; Tollenaar et al., 1992; Cox, 1996; Westgate et al., 1997; Subedi et al., 2006), and inbreds
(Zaidi et al., 2008), as well as parental inbred lines and their F1 hybrid (Sass and Loeffel, 1959;
82
Liu and Tollenaar, 2009), and variations in magnitudes and the phenological onset of plant
hierarchy among hybrids (Glenn and Daynard, 1974; Bonaparte and Brawn, 1975; Echarte et al.,
2000; Maddonni and Otegui, 2004, 2006). Furthermore, many studies with the exception of
Lang et al. (1956) have shown that incidence of barrenness increases with plant density (Woolley
et al., 1962; Genter and Camper, 1973; Edmeades and Daynard, 1979b; Daynard and Muldoon,
1983; Hashemi-Dezfouli and Herbert, 1992; Sangoi et al., 2002). Higher plant densities result in
increased plant-to-plant variability (PPV) in PDM, greater differential growth in PDM between
D and d plants (Edmeades and Daynard, 1979b; Vega and Sadras, 2003; Maddonni and Otegui,
2004; Pagano and Maddonni, 2007), and increased PPV of yield components (Edmeades and
Daynard, 1979b; Echarte et al., 2000; Maddonni and Otegui, 2004, 2006).
Previous studies have used inbred-hybrid systems to understand the physiological
mechanisms affecting GY (Echarte and Tollenaar, 2006; Liu and Tollenaar, 2009; Smith, 2012).
Published papers to date have not investigated the physiological characteristics of barren plants
in parental inbred lines and their F1 hybrid, especially homogenous plants with similar initial
growth and development, as well as uniform spatial pattern (plant-to-plant spacing). The
advantage of using this system is to create different levels of variability among inbreds and
hybrids. Plant-to-plant variability in traits related to kernel set is large among inbreds, the
heterosis is also large (Echarte and Tollenaar, 2006; Liu and Tollenaar, 2009). The primary
objective of the current work was to follow the growth and development of plants that would
ultimately become barren, from the early vegetative stages through silking to maturity. This was
done for two inbreds and their F1 hybrid.
83
3.2 Materials and Methods
3.2.1 Genetic materials
Inbred lines CG60 and CG102, and their F1 hybrid CG60 × CG102 were used in this
study. These genotypes were chosen as they have been used in other physiology and genetics
studies (Echarte and Tollenaar, 2006; Khanal et al., 2011; Singh et al., 2011). CG60 is derived
from Pioneer 3902 and belongs to the Iodent heterotic pattern (Lee et al., 2001a). CG102 is
derived from Cycle 2 of the CG Stiff Stalk Combined population and belongs to the Stiff Stalk
heterotic pattern (Lee et al., 2001b). The F1 represents one of the classic heterotic patterns
grown in the Northern Corn Belt (Lee and Tracy, 2009).
3.2.2 Cultural practices and experimental design
A field experiment was conducted with the three genotypes by four plant densities at the
Elora Research Station near Ponsonby, ON, Canada (43º38’ N, 80º25’ W) on a London loam soil
(Aquic Hapludalf) in 2007 and 2008. Before each experimental year, the experimental field was
moldboard plowed in the fall of previous year. The preceding crop was red clover (Trifolium
pratense L.) in 2007 and barley (Hordeum vulgare L.) in 2008. Tillage in the following spring
included disking and cultivation twice with a cultivator, as well as cultipacking for seed-bed
preparation. Treatments were arranged in a split-plot design with three replications in both
years. Plant density served as the main plot and genotype as the sub-plot. This design was used
to organize individual plants and make measurement easy, and individual variations in resource
availability were created by varying plant density. Rows were in a northeast to southwest
orientation in both years. In 2007 and 2008, the experimental plots received 500 kg ha-1 of 2084
10-10 (N–P–K) before planting. Ammonium nitrate (34–0–0) at 100 kg ha-1 N was sidedressed
as a single band at the 12-LT stage of the F1 hybrid. Weeds were controlled with pre-planting
herbicides of a mixture of 3-4 L ha-1 Primestra II Magnun, which includes S-metolachlor
[Acetamide, 2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl]-,(S)] and
atrazine (2-chloro-4-ethylamino-6-isopropylamino-s-triazine), and 0.3 L ha-1 Callisto, which
includes mesotrione [2-[4-(methylsulfonyl)-2-nitrobenzoyl]-1,3-cyclohexanedione]. No postemergence herbicide was applied.
The three genotypes were over-sown with hand planters on 11 May 2007 and 16 May
2008. Daily values of minimum and maximum air temperatures, rainfall and incident solar
radiation were recorded from planting until final harvest using a weather station at the Elora
Research Station. Accumulated growing degree days (GDD) were calculated as equation [2.1]
using daily mean air temperature and a base temperature of 8 C from planting to different
phenological stages (Wang, 1960; Major et al., 1983; Ritchie and NeSmith, 1991).
3.2.2.1 Field experiment in 2007
In 2007, each plot consisted of six rows of 12 m in length and 0.76-m row spacing, with a
0.7-m planted border at each end. Plants were thinned to the target plant densities at the 8-LT
stage, resulting in 40,000 plants ha-1 (i.e., half commercial density) and 120,000 plants ha-1 (one
and a half times commercial density) for the two inbreds; and 40,000 plants ha-1 and 160,000
plants ha-1 (i.e., double commercial density) for the F1. The reason to use 160,000 plants ha-1 for
the F1 instead of 120,000 plants ha-1 was to trigger barrenness in the F1 plants. This was based
85
on previous findings that hybrids are less susceptible than their parental inbreds to barrenness
under high plant densities (Liu and Tollenaar, 2009b). Within each plot, the middle four rows
were used for destructive sampling (each 1.00 m2) at the front of the plot and non-destructive
measurement (7.90 m2) at the back of the plot. Destructive samplings were conducted according
to numbers of plants. A 0.7-m long internal plant border separated two subsequent destructive
sampling areas, as well as between the destructive sampling areas and the non-destructive
measurement area.
3.2.2.2 Field experiment in 2008
In 2008, each plot consisted of five rows with 13-m length and 0.76-m row spacing with
1-m planted border at each end. Plants were thinned at the 4-LT stage to achieve 80,000 plants
ha-1, 120,000 plants ha-1 and 160,000 plants ha-1 for each genotype. Within each plot, the second
row from northwest was used for destructive sampling (each 1.33 m2) at the front of the plot and
the middle three rows were used for non-destructive measurement (3.92 m2) at the back of the
plot separated by a 0.6-m internal border. Between two successive destructive sampling areas
was a 0.6-m long plant border.
3.2.2.3 Morphometric methodology and measurement
Dry matter accumulation of individual plants over time can be estimated by making
sequential non-destructive morphometric measurements on those plants, and then using
allometric relationships to calculate PDM at each time point. The allometric relationships
between the morphometric measurements and PDM are derived from destructive harvests of
86
other plants in the population at each time point (Weiner et al., 1990; Vega et al., 2001b).
Incorporating destructive and non-destructive measurements in this way, PDM and ear dry
matter (EDM) have been predicted in previous studies (Vega et al., 2001b; Maddonni and
Otegui, 2004; Echarte and Tollenaar, 2006; Borrás et al., 2007). The destructive sampling area
and the non-destructive measurement area were marked and separated with border plants, and
uniform plants in the non-destructive area could be selected and tagged at a certain growth stage.
Individual plants from the destructive area were measured and harvested to generate
morphometric data and PDM or/and EDM, and tagged plants from the non-destructive area were
measured without harvest until PM. Subsequently, the best allometric models between
PDM/EDM and morphometric data were developed from the destructive samplings, and then
used to estimate PDM/EDM for the corresponding genotype and phenological stage. The
destructive samplings and non-destructive measurements for each genotype × plant density
combination took place on the same day during maize development including approximately 8LT, 12-LT, 16-LT, silking and 2 wk after silking in 2007, and 12-LT, 15-LT, silking and 2 wk
after silking in 2008.
3.2.2.4 Destructive samplings
At each sampling, LT stage, PH (i.e., the distance from the ground level to the uppermost
leaf collar, in cm), the maximum width of basal stem diameter (SDb, in cm), the maximum width
of stem diameter 2 cm above the ground level (SD2, in cm) (only in 2008) and maximum primary
ear diameter including husks (ED, in cm) at silking and 2 wk after silking for each sampled plant
were measured and recorded. Maximum PH (MaxPH) was determined at 2 wk after silking. All
87
SDb, SD2 and ED measurements were taken using RK 97231-34 digital callipers (Cole-Parmer
Canada Inc., Montreal, QC). After measurement, the above-ground parts of the plants were cut
and oven dried at 80 C until reaching a constant weight to quantify PDM. At silking and 2 wk
after silking, the primary ear including husks and shank was separated and individually weighed
for EDM. In summary, the following numbers of plants were harvested at each sampling: four
plants per replicate at 40,000 plants ha-1, 12 plants per replicate for the two inbreds at 120,000
plants ha-1, and 16 plants per replicate for the F1 at 160,000 plants ha-1 in 2007. In 2008, 10, 16
and 20 plants per replicate were sampled at 80,000; 120,000 and 160,000 plants ha-1,
respectively. In 2008, the harvested plant samples including ears at 120,000 plants ha-1 were lost
for CG60 and the F1 at silking and CG102 at 2 wk after silking. The sampled plants were used to
develop morphometric relationships between PDM and stem volume (SV, in cm3) as a cylinder
before silking. At silking and 2 wk after silking, the relationships predicted PDM from SV and
ED, as well as EDM from ED. In 2008, the selection of the SDb or SD2 depended on the higher
coefficient of determination (R2) of the relationship. The SV derived from SDb and SV derived
from SD2 (SVmax) were calculated as:
SV = π × PH× (0.5 × SDb)2
(Maddonni and Otegui, 2004)
SVmax = π × PH × (0.5 × SD2)2
[3.1]
[3.2]
3.2.2.5 Non-destructive measurements
In the non-destructive measurement area, plants at the same LT stage and visually similar
in terms of PH and SDb were chosen and tagged at the 8-LT stage in 2007 and 4-LT stage in
2008 and followed until PM. In 2007, eight plants per replicate were tagged for each genotype at
40,000 plants ha-1, 16 plants per replicate for the two inbreds at 120,000 plants ha-1 and 20 plants
88
per replicate for the F1 at 160,000 plants ha-1. In 2008, eight, 20 and 24 plants per replicate per
genotype were tagged at 80,000; 120,000 and 160,000 plants ha-1, respectively. For each
genotype × plant density combination, the same measurements were conducted on the tagged
plants on the same day.
Anthesis and silking dates for each tagged plant were recorded. Anthesis date was noted
when at least one anther extruded from a tassel, and silking was recorded when one silk emerged
from a primary ear. Antheis to silking interval (i.e., the difference in days between anthesis and
silking) and the GDD from planting to silking (GDDsilking) were calculated for each tagged plant
when it was available. Average flowering dates for each genotype × plant density × year
combination were based on the date when 50% of the plants in the plots across three replications
were at anthesis and silking.
Length and the maximum width of each green leaf were measured at approximately 1 wk
after silking on each tagged plant in 2007. The individual leaf area was calculated as leaf length
multiplied by maximum leaf width and 0.75 (Montgomery, 1911). Post-silking leaf area per
plant (PSLA) was the sum of each individual leaf area. In 2008, the same measurements were
taken at approximately 2 wk after silking on each tagged plant in replication 2, whereas only the
primary ear leaf (ca., the leaf subtending the primary ear) of the tagged plant was measured in
replications 1 and 3. The largest leaf is usually within one rank of the primary ear leaf (Fournier
and Andrieu, 1998), and the maximum plant leaf area is strongly correlated with mature leaf area
of the largest leaf per plant (Dwyer and Stewart, 1986). Therefore, the PSLA in replication 1 and
3 was estimated by using allometric functions between the primary ear leaf area (LAE) and PSLA
89
determined in replication 2 (Table 3.1). The ratio between length and the maximum width of the
largest leaf was calculated for each tagged plant.
3.2.2.6 Harvest at physiological maturity
At PM, each tagged plant in the non-destructive measurement areas was harvested and
oven dried at 80 ºC until the plant reached a constant weight to quantify PDMPM. Physiological
maturity is determined when a black layer is developed in kernels (Daynard and Duncan, 1969).
Both the primary and secondary ears were shelled to obtain GY. Plants with a GY of zero g
were considered barren. Total KNP including the secondary ear kernel number was counted by
seed counter. In 2008, the PDM without husks and shank was weighed for eared plants. The
PDMPM was adjusted by adding a husk percentage (including the shank) of PDMPM into the
PDM without husks for eared plants of each genotype. Ear dry matter at PM (EDMPM) is
PDMPM minus the vegetative PDM at PM. In 2007 and 2008, the PDMPM was partitioned into
same components, and HI was calculated as the ratio between GY and PDMPM.
3.2.3 Selection of allometric models
3.2.3.1 Comparison of models with an intercept and without an intercept for above-ground
plant dry matter
Most published allometric models in maize (Borrás and Otegui, 2001; Maddonni and
Otegui, 2004; Pagano and Maddonni, 2007; Pagano et al., 2007) have estimated PDM as a linear
function of SV in equation [3.3] during vegetative stages and as non-linear function of SV and
ED in equation [3.4] during reproductive stages.
90
Table 3.1. Relationships between post-silking leaf area per plant (PSLA) and primary ear leaf
area (LAE) for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 in
2008.
Genotype
Relationship
n
R2
P value
CG60
PSLA = 6.82 × LAE + 634.33
50
0.55
<.0001
CG102
PSLA = 8.73 × LAE – 1254.15
52
0.55
<.0001
F1
PSLA = 7.82 × LAE – 9.82
49
0.88
<.0001
91
PDM = a + b × (SV or SVmax) (Maddonni and Otegui, 2004)
[3.3]
PDM = a + b × (SV or SVmax) + c × ED2
[3.4]
(Borrás and Otegui, 2001)
Where a, b and c are model parameters and PDM has units of g plant-1.
However, they did not explain reasons for including an intercept in the model. When estimating
plant growth in a natural manner, zero SV means zero PDM. Therefore, a model with an
intercept is not expected. In this study, the model with an intercept was compared with a model
without an intercept for each genotype × plant density combination in 2007 and 2008. The
model selection criteria were R2 (models with an intercept) and Efron's pseudo-R2 in equation
[3.5] (models without an intercept) as the primary criterion and the root mean squared error
(RMSE) (i.e., the square root of mean squared error which is the average of the squared
differences between each predicted value and its corresponding measured value) as the
secondary criterion.
Efron's pseudo-R2 = 1 –
Sum of squares for residuals
(Efron, 1978)
Corrected total sum of squares
[3.5]
The advantage of Efron's pseudo-R2 statistic is that it is same as R2 based on the ratio between
explained sum of squares and total sum of squares for both linear and non-linear regression.
3.2.3.2 Comparison of combined plant density model to individual plant density model for
above-ground plant dry matter
Before analyzing each genotype × plant density combination for allometric relationship,
the genotype × plant density interaction was tested in terms of the allometric relationship for
estimating PDM by performing analysis of variance using PROC GLM procedure of SAS. The
SV/SVmax was used as a covariate when the allometric relationships fit linear function. The
92
SV/SVmax and ED2 were used as covariates when allometric relationship fit non-linear function.
The interaction was tested at each sampling stage for CG60 and CG102 in 2007 and for the three
genotypes in 2008. The F1 hybrid in 2007 could not be used due to the unequal plant density
with the two inbred lines.
Almost all the published allometric models (Appendix A.1) (Borrás and Otegui, 2001;
Vega et al., 2001b; Maddonni and Otegui, 2004; Echarte and Tollenaar, 2006; Pagano and
Maddonni, 2007; Pagano et al., 2007; D’Andrea et al., 2008) have combined plant densities to
estimate PDM without justification. In order to examine whether combined plant density is
statistically better than individual plant densities, an allometric relationship with an intercept was
developed for each genotype × year combination by using data combined across plant densities
and also for each individual plant density. Year was considered as a random factor. The
selection criteria were R2 and RMSE. If combined plant density models had better performance
than individual plant density models, models with combined plant density were further checked
to examine whether they caused statistically significant bias; that is, whether there was a
significant tendency to overestimate or underestimate biomass at one plant density compared to
another plant density. This was done in two steps. The first step was to calculate the residual
percentage (RP) using equation [3.6] and check whether the mean RP of individual plant
densities within the combined plant density model differ significantly by using the Lsmeans
statement with the pdiff option in the PROC GLM procedure.
RP =
Measured PDM - predicted PDM
× 100%
Measured PDM
[3.6]
If there was no significant difference in RP between/among individual plant densities within the
93
combined plant density model, the plant densities could be combined for model estimation.
Second, when the RP between/among different plant densities was significantly different, the RP
of a plant density derived from the combined plant density model was compared to that of the
same plant density derived from an individual plant density model by using the Lsmeans
statement with the pdiff option in the PROC GLM procedure.
In addition, in some cases in 2007 the range of data derived from destructive harvests,
used to develop the morphometric relationships, was not sufficient to permit prediction of PDM
for all non-destructively-sampled plants without extrapolation. In these cases additional plants
from border rows were also included in the development of the morphometric relationships, to
increase the range of PDM values. The models using plants in central rows only were compared
with the corresponding models using plants in both central and border rows based on R2 and
RMSE; in two cases the new model was rejected because of a reduction in R2. After model
justification, the PDM for each tagged plant from the corresponding genotype × sampling stage ×
year combination was estimated using the allometric relationship in Table 3.2.
3.2.3.3 Selection of allometric models for ear dry matter
When estimating the relationship between EDM and ED, most of the published studies
have either used an exponential function [3.7] (Maddonni and Otegui, 2004; Pagano and
Maddonni, 2007; Pagano et al., 2007) or a power function [3.8] (Vega et al., 2001a; D’Andrea et
al., 2008) without justification.
EDM = a × eb × ED (Maddonni and Otegui, 2004)
94
[3.7]
Table 3.2. Relationships between above-ground plant dry matter (PDM) and morphological
variables (i.e., stem volume [SV], maximum primary ear diameter [ED]) at different
phenological stages for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 ×
CG102 with a combination of plant densities in 2007 and 2008. A combination of 40,000 and
120,000 plants ha-1 for the two parental inbred lines and 40,000 and 160,000 plants ha-1 for the F1
hybrid was analysed in 2007. In 2008, a combination of 80,000, 120,000, and 160,000 plants ha1
for the three genotypes was analysed with three exceptions of a combination of 80,000 and
160,000 plants ha-1 marked by symbol †. Stem volume calculated from maximum width of basal
stem diameter and the maximum width of stem diameter that is 2 cm above the ground level are
presented by SV and SVmax, respectively. Vegetative phases are assessed by leaftip (LT) stage.
All relationships are significant at P value ≤0.0001.
Stage
Genotype
PDM (g plant-1)
n
R2
RMSE‡
(g plant-1)
2007
8-LT
CG60
CG102
PDM = 0.1313 + 0.1406 × SV
PDM = 0.7051 + 0.0767 × SV
49
46
0.87
0.67
0.31
0.25
F1
CG60
CG102
PDM = 1.3827 + 0.0864 × SV
PDM = 3.8361 + 0.1114 × SV
PDM = 1.7306 + 0.1424 × SV
59
78
81
0.79
0.62
0.82
0.37
2.11
1.50
F1
CG60
CG102
PDM = 3.0492 + 0.1666 × SV
PDM = 6.6085 + 0.1543 × SV
PDM = 15.6901 + 0.0921 × SV
89
124
47
0.87
0.80
0.60
2.27
5.86
4.43
F1
PDM = 16.1478 + 0.1037 × SV
59
0.79
6.99
47
0.91
6.96
48
0.78
8.03
Year
12-LT
16-LT
Silking
2 wk
after
silking
2008
12-LT
‡
15-LT
Silking
2 wk
after
silking
2
CG60
PDM = 7.7169 + 0.0823 × SV + 5.2570 × ED
CG102
F1
2
PDM = 26.3628 + 0.0568 × SV + 3.4244 × ED
PDM = 9.7473 + 0.1179 × SV + 2.0079 × ED
2
59
0.92
10.98
PDM = -13.2830 + 0.1517 × SV + 3.2674 × ED
2
48
0.88
13.02
PDM = -10.6016 + 0.1150 × SV + 4.2827 × ED
2
47
0.94
12.91
F1
CG60
CG102
PDM = -15.2232 + 0.1177 × SV + 3.9640 × ED
PDM = 1.6726 + 0.1708 × SV
PDM = 2.7286 + 0.1451 × SV
2
59
130
138
0.97
0.90
0.77
9.19
0.89
1.68
F1
PDM = 5.1469 + 0.1267 × SV
137
0.88
2.03
CG60
CG102
PDM = 5.8417 + 0.0987 × SVmax
PDM = 6.5137 + 0.1269 × SV
123
129
0.90
0.83
3.64
5.00
F1
PDM = 10.0872 + 0.0868 × SVmax
137
0.85
7.29
CG60
CG102
CG60
†
CG102
†
F1
2
PDM = 14.5622 + 0.1010 × SV + 1.7029 × ED
90
0.86
8.31
2
129
0.77
8.78
2
87
0.88
9.41
98
0.94
7.82
64
0.87
8.45
123
0.94
10.69
PDM = 28.8064 + 0.0665 × SVmax + 1.9316 × ED
PDM = -2.9125 + 0.1028 × SVmax + 1.5995 × ED
2
CG60
PDM = 2.2597 + 0.1495 × SV + 1.6293 × ED
CG102†
PDM = 25.0479 + 0.1122 × SVmax + 1.7011 × ED2
F1
PDM = -18.0256 + 0.1104 × SVmax + 3.6589 × ED
RMSE, root mean squared error.
95
2
EDM = a × EDb (Vega et al., 2001a)
[3.8]
Where a and b are model parameters and EDM has units of g plant-1.
Moreover, they used combined plant densities to estimate EDM. In order to choose the
allometric relationship between EDM and ED, the exponential function and power function were
compared for each genotype × plant density × year combination. After selection between the
exponential and power function, the genotype × plant density interaction was tested in terms of
the allometric relationship for EDM estimation by comparing parameters a and b using PROC
NLIN of SAS procedure. Significant interaction is indicated when 95% confident intervals for a
parameter (a or b) among different treatments do not overlap. The interaction was tested at
silking and 2 wk after silking for CG60 and CG102 in 2007, and for the three genotypes in 2008.
When there was significant genotype × plant density interaction, each genotype × plant density
combination was analyzed. Comparison in performance was made between models using
combined plant density and models using each individual plant density for each genotype × year
combination based on R2 and RMSE. If using the combined plant density model was better than
using each individual plant density model, the same two justification steps were taken as for the
PDM estimation to check whether models using combined plant density caused statistically
significant bias for EDM estimation between/among individual plant densities. After model
justification, the EDM for each tagged plant from the corresponding genotype × sampling stage
× year combination was estimated using the allometric relationship in Table 3.3.
96
Table 3.3. Relationships between ear dry matter (EDM) and maximum primary ear diameter
(ED) at silking and 2 wk after silking for two parental inbred lines CG60, CG102 and their F1
hybrid CG60 × CG102 with individual plant densities or a combination of different plant
densities in 2007 and 2008. When using combined plant densities, a combination of 40,000 and
120,000 plants ha-1 for CG60 and 40,000 and 160,000 plants ha-1 for the F1 hybrid was analysed
in 2007. In 2008, a combination of 80,000 and 160,000 plants ha-1 for the three genotypes was
analysed. All relationships are significant at P value ≤0.0001.
Year
Stage
2007 Silking
Genotype
Plant density EDM (g plant-1)
(plants ha-1)
n
R2
RMSE†
(g plant-1)
CG60
40,000
EDM = 0.0844 × ED4.0395
11
0.95
0.10
3.5412
34
0.95
0.14
4.3106
12
0.95
0.10
EDM = 0.0450 × ED
5.4919
34
0.83
0.40
EDM = 0.2344 × ED
3.4047
58
0.93
0.29
3.7680
48
0.89
0.17
2.3582
12
0.91
0.13
EDM = 0.2735 × ED
3.2521
36
0.87
0.20
EDM = 0.1960 × ED
3.5478
60
0.92
0.18
EDM = 0.3403 × ED
2.6014
26
0.52
0.48
Combined
EDM = 0.4204 × ED
2.4611
80
0.78
0.32
80,000
EDM = 0.1630 × ED3.4952
26
0.91
0.24
EDM = 0.1448 × ED
3.9286
42
0.93
0.29
160,000
EDM = 0.2274 × ED
3.4832
52
0.94
0.33
Combined
EDM = 0.4131 × ED2.8400
89
0.63
0.21
80,000
EDM = 0.1110 × ED
4.0868
28
0.52
0.41
120,000
EDM = 0.0511 × ED4.6142
31
0.82
0.30
160,000
EDM = 0.2826 × ED
2.7966
32
0.89
0.29
Combined
EDM = 0.2743 × ED3.4125
63
0.93
0.26
80,000
EDM = 0.5838 × ED
2.8448
29
0.80
0.10
120,000
EDM = 0.2614 × ED3.3146
40
0.84
0.16
160,000
3.2902
56
0.97
0.12
120,000
CG102
40,000
120,000
2 wk after
silking
F1
CG60
CG102
Combined
Combined
40,000
120,000
F1
2008 Silking
CG60
CG102
Combined
80,000
120,000
2 wk after
silking
F1
CG60
CG102
F1
†
EDM = 0.1743 × ED
EDM = 0.0844 × ED
EDM = 0.1162 × ED
EDM = 1.2309 × ED
EDM = 0.3144 × ED
RMSE, root mean squared error.
97
3.2.4 Data analyses
In some genotype × year combinations (CG60 in 2007 and F1 in 2007) no barren plants
were observed among the non-destructively harvested plants. Data from these genotype × year
combinations were not further examined. In all other cases, data within a genotype × year were
combined across the different plant densities and the final GY, PDMPM, HI and KNP were tested
for normality using the Shapiro-Wilk test statistic W of the PROC UNIVARIATE procedure of
SAS package version 9.2 (SAS Institute, 2008, Cary, NC, USA). The same procedure was used
to calculate mean, coefficient of variation (CV), skewness and kurtosis. Before estimating the
linear relationship (i.e., equation [3.3]) between PDM before silking and SV (and SVmax) by
using the PROC REG procedure of SAS, the data points with studentized residuals (i.e., the
unstandardized residual divided by its standard deviation [SD]) equal to or larger than 3 were
identified and removed as outliers. Similarly, outliers were identified and removed with the
same criterion before estimating the relationship between PDM at silking (PDMs) or 2 wk after
silking (PDMs+2) and corresponding morphometric variables in equation [3.4] by nonlinear
regression using the PROC REG procedure of SAS. Similarly using the PROC REG procedure,
the outliers were identified and removed before estimating the relationships between EDM and
ED (i.e., equation [3.8]) for each genotype × sampling stage × year combination. Plant dry
matter ratio is calculated as
PDM ratio =
PDM of an individualbarren plant
PDM of the average of a plant stand
[3.9]
Due to the relatively small number of barren plants within each genotype × plant density
× year combination and the potential violation of the normal distribution assumption for
98
parametric tests, non-parametric tests were conducted for mean comparisons. Comparison
between barren and non-barren plants in PDMPM, PDM, EDMPM and EDM throughout
development for CG102 at 120,000 and 160,000 plants ha-1 in 2008 and CG60 at 160,000 plants
ha-1 in 2008 were conducted by using the Wilcoxon rank sum test, via the PROC NPAR1WAY
procedure of SAS. Using the same SAS procedure, comparisons between barren and non-barren
plants in the LT stage and PH at each sampling stage were conducted. The comparison between
average barren plants and average plants of a population could not be conducted due to
relatedness of barren plants to the whole population.
Plant growth rate during the critical period bracketing silking was calculated for each
individual plant within a genotype by regressing estimated PDM (dependent variable) against the
corresponding thermal time (independent variable) in the linear portion of PDM accumulation
during the period bracketing silking. Each individual plant had three data points to fit the linear
regression. The linear portions of PDM accumulation during the period bracketing silking were
between 524 and 850 GDD for the three genotypes in 2007, and between 550 and 846 GDD for
CG60 and the F1, and between 550 and 976 GDD for CG102 in 2008, respectively. Ear growth
rate during the period bracketing silking for each individual plant was calculated as the quotient
of EDM from silking to 2 wk after silking and the corresponding duration in GDD. The linear
relationships between EDM at silking (EDMs) and ASI, and between EDMs and GDDsilking for
individual plants were established by using the PROC REG procedure of SAS. The normality of
residuals and heteroscedasticity were tested before conducting regression analyses.
Nonlinear relationships between HI and PGRs, and between HI and EGRs were analyzed
99
by using the PROC NLIN procedure of SAS based on a previous publication (Tollenaar et al.,
2006). By comparing the two models presented by Echarte and Andrade (2003), the hyperbolic
function was chosen between HI and PGRs and HI and EGRs because the other function
estimated biologically non-meaningful parameters for each genotype × year combination.
HI =
a (PGRs  PGRt)
1  b (PGRs  PGRt)
[3.10]
HI =
a (EGRs  EGRt)
1  b (EGRs  EGRt)
[3.11]
Where a is the initial slope of the curve, b the degree of curvilinearity of the model and PGRt and
EGRt represent the threshold values of PGRs and EGRs for HI, respectively (i.e., the values of
PGRs or EGRs below which the model predicts HI to be zero).
3.2.4.1 Classification of four subpopulations according to barrenness and plant hierarchy
For each genotype × plant density combination where at least one barren plant was
present, two methods could be used to classify individual plants. In one method, GY can be used
to classify barren and non-barren plants. The other method is that PDMPM are sorted in
ascending order, then the D plants belong to the upper 1/3 of the PDMPM distribution, the d
plants belong to the lower 1/3 of the PDMPM distribution (Maddonni and Otegui, 2004, 2006)
and the intermediate plants belong to the middle 1/3 of the PDMPM distribution. By combining
the two classification methods, individual plants within a genotype × plant density were divided
into barren d (Bd) plants, non-barren d (NBd) plants, barren plants that belonged to the
intermediate and D groups (BID), and non-barren plants that belonged to the intermediate and D
groups (NBID).
100
Differences in GY and MaxPH among d, intermediate and D plants were compared by
using Kruskal-Wallis test, via PROC NPAR1WAY procedure of SAS, due to the violation of
normal distribution of GY and MaxPH data in some d, intermediate and D groups. Differences
in various physiological traits between Bd and NBd plants, and among Bd, BID and NBd plants
were compared by using the Wilcoxon rank–sum test and Kruskal-Wallis test, respectively.
Both statistics used the PROC NPAR1WAY procedure of SAS. The physiological traits
included PDMPM, EDMPM, and PDM throughout development, phenological differences as well
as EDM, ASI, GDDsilking, PGRs, EGRs and the ratio between EGRs and PGRs as partitioning
index (Pagano and Maddonni, 2007). Similarities in plant and ear growth between Bd and NBd,
between BID and NBd and between Bd and BID were checked. The criteria for similarity between
two individual plants in a physiological trait was a difference equal to or less than 0.3 g, 0.15 g,
0.01 g GDD-1, 0.005 g GDD-1 and 0.01 for PDM, EDM, PGRs, EGRs and partitioning index,
respectively. The chosen criteria were arbitrary.
3.2.4.2 Frequency distribution of the physiological and morphological traits
Similarly to yield components at PM, physiological and morphological traits were
selected to characterize barrenness. They included the ratio between PDMPM and MaxPH;
MaxPH; stem elongation rate; ear diameter expansion rate; PSLA; the largest leaf area; and the
ratio between leaf length and maximum leaf width (leaf length/leaf width) of the largest leaf.
The stem elongation rate was calculated for each individual plant within a genotype by
regressing PH (dependent variable) against the corresponding thermal time (independent
variable) in the linear portion of stem elongation. Stem elongation was between 409 and 680
101
GDD for the three genotypes in 2007, and between 377 and 759 GDD for CG60 and the F1 in
2008 and between 377 and 830 GDD for CG102 in 2008. Ear diameter expansion rate was
calculated as the difference in the ED at silking and 2 wk after silking divided by the
corresponding GDD in 2007, and the difference in ED between 1 wk after silking and 3 wk after
silking divided by the corresponding GDD in 2008. The data were analyzed by the PROC
UNIVARIATE procedure of SAS to test normality and calculate mean, CV, skewness and
kurtosis. Due to the non-normality in leaf length/leaf width of the largest leaf for CG102 in
2008, differences in this trait among genotypes were tested by Kruskal-Wallis test, via PROC
NPAR1WAY procedure of SAS. Differences among plant densities as main plot effects were
tested by analysis of variance of leaf length/leaf width of the largest leaf using the PROC GLM
procedure of SAS for CG60 and the F1 hybrid with replication × plant density as the error term.
Differences in the same trait were tested by Kruskal-Wallis test, via PROC NPAR1WAY
procedure of SAS for CG102.
102
3.3 Results
3.3.1 Ultimately barren plants at physiological maturity
3.3.1.1 Occurrence of barrenness
Barrenness in this study refers to the plants with zero final GY. Occurrence of barrenness
varied by year and genotype. Barrenness occurred in both years only for CG102 (Fig. 3.1).
Barrenness occurred in 2008 for CG60 and the F1 hybrid as well. In 2008, even though the three
genotypes were planted across the same three plant densities, CG102 exhibited an exceptionally
high percentage of barrenness (56%) compared to CG60 (10%) and the F1 hybrid (1%). In terms
of barrenness at each genotype × plant density × year combination, barren plants occurred in one
out of the six genotype × plant density combinations in 2007, which was CG102 at 120,000
plants ha-1 (three out of 70 plants). Barren plants occurred in six out of nine genotype × plant
density combinations in 2008, which were CG102 at 80,000 plants ha-1 (six out of 24 plants),
120,000 plants ha-1 (23 out of 59 plants) and 160,000 plants ha-1 (58 out of 72 plants); CG60 at
120,000 plants ha-1 (four out of 60 plants) and 160,000 plants ha-1 (14 out of 71 plants); and the
F1 at 160,000 plants ha-1 (one out of 70 plants).
3.3.1.2 The position of ultimately barren plants within a plant population
The barren CG102 plants were present only in the below-average (mean = 139.7 g plant1
) categories of PDMPM in 2007 (Fig. 3.2), and in both above- and below-average (mean = 82.4 g
plant-1) categories of PDMPM in 2008. Barren CG60 plants were present in below-average (mean
= 88.7 g plant-1) categories of PDMPM in 2008. The barren F1 plant was in the lowest categories
of PDMPM in 2008.
103
120
CG102 in 2007
Grain yield frequency
Frequency of barren plants
KNP
100
500
40
80
400
30
60
300
20
40
200
10
20
100
50
Number of individual plants
600
CG102 in 2008
0
0
0
60
28
56
84
112
0
0
140
60
CG60 in 2008
13 26 40 53 66 79 92 106 119 132
600
F1 in 2008
50
50
500
40
40
400
30
30
300
20
20
200
10
10
100
0
0
0
22
44
66
88
110
0
0
132
KNP
60
17
34
51
68
85
102 119 136
Grain yield (g plant-1)
Figure 3.1. Frequency distributions of grain yield (GY)(primary y-axis), and the relationship
between kernel number per plant (KNP) (secondary y-axis) and GY in two parental inbred lines
CG60, CG102 and their F1 hybrid CG60 × CG102 grown across different plant densities in 2007
and 2008. Dark grey bars indicate the frequency of ultimately barren plants (i.e., GY per plant =
0 g). Circles indicate KNP. The plant densities in 2007 were 40,000 and 120,000 plants ha-1 for
the two inbred lines and 40,000 and 160,000 plants ha-1 for the F1 hybrid. The plant densities in
2008 were 80,000; 120,000 and 160,000 plants ha-1 for the three genotypes. Within each
genotype, plants had similar initial plant size at the 8-leaftip (LT) stage in 2007 and 4-LT stage
in 2008. In terms of frequency distribution of GY, for CG102 in 2007 (n = 70): Shapiro-Wilk
normality test (Pr < W) = 0.00, skewness = 0.08, kurtosis = -0.92, coefficient of variation (CV) =
70%, and mean (M) = 38.4 g plant-1. For CG60 in 2008 (n = 154): Pr < W = < 0.0001, skewness
= 0.03, kurtosis = -0.90, CV = 68% and M = 29.3 g plant-1. For CG102 in 2008 (n = 155): Pr <
W = <0.0001, skewness = 1.58, kurtosis = 1.04, CV = 176%, and M = 8.2 g plant-1. For the F1 in
2008 (n = 153): Pr < W = 0.08, skewness = 0.14, kurtosisK = -0.23, CV = 32%, and M = 78.1 g
plant-1.
104
0.6
CG102 in 2008
CG102 in 2007
Number of individual plants
50
PDMPM frequency
Frequency of barren plants
HI
0.5
40
0.4
30
0.3
20
0.2
10
0.1
0.0
0
3
60
53
103
153
203
253
20
303
56
92 128 164 200 236 272 308
F1 in 2008
CG60 in 2008
0.6
50
0.5
40
0.4
30
0.3
20
0.2
10
0.1
0
HI (g g-1)
60
0.0
20
64
108
152
196
240
284
29 59 89 119 149 179 209 239 269 299
Above-ground plant dry matter at physiological maturity (g plant-1)
Figure 3.2. Frequency distributions of above-ground plant dry matter at physiological maturity
(PDMPM) (primary y-axis), and the relationship between harvest index (HI) (i.e., ratio between
grain yield and PDMPM) (secondary y-axis) and PDMPM in two parental inbred lines CG60,
CG102 and their F1 hybrid CG60 × CG102 grown across different plant densities in 2007 and
2008. Dark grey bars indicate the frequency of ultimately barren plants (i.e., grain yield per
plant = 0 g). Circles indicate HI. The plant densities in 2007 were 40,000 and 120,000 plants ha1
for the two inbred lines and 40,000 and 160,000 plants ha-1 for the F1 hybrid. The plant
densities in 2008 were 80,000; 120,000 and 160,000 plants ha-1 for the three genotypes. Within
each genotype, plants had similar initial plant size at the 8-leaftip (LT) stage in 2007 and 4-LT
stage in 2008. In terms of frequency distribution of PDMPM, for CG102 in 2007 (n = 70):
Shapiro-Wilk normality test (Pr < W) = 0.00, skewness = 0.35, kurtosis = -1.08, coefficient of
variation (CV) = 40%, and mean (M) = 139.7 g plant-1. For CG60 in 2008 (n = 154): Pr < W =
0.00, skewness = 0.61, kurtosis = 0.17, CV = 39% and M = 88.7 g plant-1. For CG102 in 2008 (n
= 155): Pr < W = 0.57, skewness = 0.30, kurtosis = 0.21, CV = 28%, and M = 82.4 g plant-1. For
the F1 in 2008 (n = 153): Pr < W = 0.01, skewness = 0.46, kurtosis = -0.25, CV = 30%, and M =
166.8 g plant-1.
105
3.3.1.3 Relationships between yield components
The two inbred lines had linear relationships between GY and KNP (R2 ≥ 0.95) (Fig. 3.1),
with the GY of the F1 hybrid following a quadratic function (R2 ≥ 0.75). The F1 was
characterized by greater fidelity in HI compared to the two parental inbred lines which had more
plants with similar PDMPM but contrasting HI (Fig. 3.2). When comparing the barren inbred
plants to the non-barren inbred plants with similar PDMPM, the range of HI was from 0.04 to
0.29 for non-barren CG102 plants in 2007, from 0.002 to 0.45 for non-barren CG102 in 2008,
and from 0.01 to 0.51 for non-barren CG60 in 2008. In extreme cases, a barren (75.1 g) and a
non-barren (76.7 g) plant had similar PDMPM but 0.00 and 0.29 HI respectively for CG102 in
2007, as also observed for CG102 in 2008 (86.2 g with 0.00 HI vs. 86.3 g with 0.45 HI), and
CG60 in 2008 (72.4 g with 0.00 HI vs. 72.0 g with 0.51 HI). Both barren and non-barren plants
within a genotype had similar initial plant size and development and uniform spatial pattern, but
it is unclear what resulted in the barrenness and variations in dry matter partitioning (i.e., HI).
Understanding season-long physiological processes of plant growth and dry matter partitioning
will help us understand the physiological mechanisms underlying barrenness. First, allometric
models were developed to estimate individual plant and ear growth throughout the life cycle.
3.3.2 Allometric model development to predict individual barren and non-barren plants
3.3.2.1 Model comparison for above-ground plant dry matter estimation
The models with intercepts outperformed the models without intercepts, with higher R2
and lower RMSE in 16 out of 30 genotype × plant density combinations in 2007 (e.g., F1 at
160,000 plants ha-1 at 9-LT stage [Fig. 3.3]) and 14 out of 33 combinations using SV in 2008
106
With intercept
10
Without intercept
-1
-1
F1 at 160,000 plants ha at 9-LT in 2007
PDM = 0.1515 × SV
PDM = 1.2966 + 0.0926 × SV
2
n = 48 R = 0.78 RMSE = 0.36
-1
Measured PDM or predicted PDM in non-destructive measurement area (g plant )
F1 at 160,000 plants ha at 9-LT in 2007
2
n = 48 Efron's pseudo-R = 0.42 RMSE = 0.58
8
6
4
Destructive area
Non-destructive area
1:1 line
2
0
0
200
2
4
6
8
10 0
2
4
6
8
10
-1
-1
CG60 at 120,000 plants ha at 2 wk after silking in 2007
CG60 at 120,000 plants ha at 2 wk after silking in 2007
2
PDM = -7.2935 + 0.1183 × SV + 3.4951 × ED
2
PDM = 0.1198 × SV + 2.9901 × ED
2
n = 36 Efron's pseudo-R = 0.86 RMSE = 9.02
2
n = 36 R = 0.86 RMSE = 9.03
150
100
50
0
0
50
100
150
200 0
50
100
150
200
-1
Predicted PDM in destructive sampling or non-destructive measurement area (g plant )
Figure 3.3. Selective comparison of allometric models with an intercept and without an
intercept for F1 hybrid CG60 × CG102 at 160,000 plants ha-1 at the 9-leaftip (LT) stage and
inbred CG60 at 120,000 plants ha-1 at 2 wk after silking in 2007. The allometric model estimates
the relationship between above-ground plant dry matter (PDM) and stem volume (SV) at
vegetative stages or between PDM and SV and maximum primary ear diameter (ED) at
reproductive stages. The round symbols (○) represent the relationship between measured PDM
in destructive sampling areas and corresponding predicted PDM. The triangle symbols (Δ)
present the values of predicted PDM in non-destructive measurement areas in the 1:1 line. The
vegetative stages are presented as the average LT stage across sampled plants. Number of
sampled plants (n), coefficient of determination (R2), Efron's pseudo-R2 and root mean squared
107
error (RMSE) in g plant-1 are presented. Efron's pseudo-R2 = 1 – (sum of squares for
residuals/corrected total sum of squares).
108
(e.g., CG102 at 160,000 plants ha-1 at 10-LT stage [Fig. 3.4]). The two models had similar R2
and RMSE in seven out of 30 combinations in 2007 (e.g., CG60 at 120,000 plants ha-1 at 2 wk
after silking [Fig. 3.3]) and 11 out of 33 combinations in 2008 (e.g., CG60 at 160,000 plants ha-1
at 2 wk after silking [Fig. 3.4]). There were significant genotype × plant density interactions in
the allometric relationship for estimating PDM in more than 50% sampling stage × year
combinations (Appendix B.1). Allometric relationships with intercepts were established for each
genotype × plant density combination and further compared to the models with intercepts using
combined plant density. Combined plant density models showed better performance, with higher
R2 in 11 out of 15 genotype × sampling stage combinations in 2007 and four out of 12
combinations in 2008 compared to average R2 of individual plant density models. The R2 of the
combined models was similar to the average R2 of the individual models for the rest of the
genotype × sampling stage × year combinations. The RMSE of the combined plant density
models were lower than the average RMSE of individual plant density models in nine out of 15
genotype × sampling stage combinations in 2007 and two out of 12 combinations in 2008.
Because the R2 was chosen as the primary criteria of accuracy, the combined plant density model
was selected for model justification.
3.3.2.2 Model comparison for ear dry matter estimation
The power function outperformed the exponential function with higher R2 and lower
RMSE in three out of 27 genotype × plant density × reproductive stage × year combinations
(e.g., F1 at 160,000 plants ha-1 at silking in 2007 [Fig. 3.5]), had better performance in R2 and
similar performance in RMSE in three out of 27 combinations (e.g., CG102 at 80,000 plants ha-1
109
With intercept
Measured PDM or predicted PDM in non-destructive measurement area (g plant-1)
25
Without intercept
-1
-1
CG102 at 160,000 plants ha at 10-LT in 2008
PDM = 4.6548 + 0.1300 × SV
2
n = 59 R = 0.84 RMSE = 1.16
CG102 at 160,000 plants ha at 10-LT in 2008
PDM = 0.1983 × SV
2
n = 60 Efron's pseudo-R = 0.61 RMSE = 1.87
20
15
10
Destructive area
Non-destructive area
1:1 line
5
0
0
150
5
10
15
20
25 0
-1
5
10
15
20
25
-1
CG60 at 160,000 plants ha at 2 wk after silking in 2008
2
PDM = 3.9715 + 0.1577 × SV + 1.2862 × ED
2
n = 36 R = 0.91 RMSE = 6.13
CG60 at 160,000 plants ha at 2 wk after silking in 2008
2
PDM = 0.1706 × SV + 1.2891 × ED
2
n = 36 Efron's pseudo-R = 0.91 RMSE = 6.17
100
50
0
0
50
100
150 0
50
100
150
-1
Predicted PDM in destructive sampling or non-destructive measurement area (g plant )
Figure 3.4. Selective comparison of allometric models with an intercept and without an
intercept for inbred CG102 at 160,000 plants ha-1 at the 10-leaftip (LT) stage and inbred CG60 at
160,000 plants ha-1 at 2 wk after silking in 2008. The allometric model estimates the relationship
between above-ground plant dry matter (PDM) and stem volume (SV) at vegetative stages or
between PDM and SV and maximum primary ear diameter (ED) at the reproductive stages. The
round symbols (○) represent the relationship between measured PDM in destructive sampling
areas and corresponding predicted PDM. The triangle symbols (Δ) present the values of
predicted PDM in non-destructive measurement areas in the 1:1 line. The vegetative stages are
presented as the average LT stage across sampled plants. Number of sampled plants (n),
coefficient of determination (R2), Efron's pseudo-R2 and root mean squared error (RMSE) in g
110
plant-1 are presented. Efron's pseudo-R2 = 1 – (sum of squares for residuals/corrected total sum
of squares).
111
Power function
10
Exponential function
-1
-1
F1 at 160,000 plants ha at silking in 2007
3.8738
1.9851 × ED
EDM = 0.1745 × ED
2
n = 45 R = 0.94 RMSE = 0.21
-1
Measured EDM or predicted EDM in non-destructive measurement area (g plant )
F1 at 160,000 plants ha at silking in 2007
EDM = 0.0454 × e
2
n = 48 R = 0.86 RMSE = 0.30
8
6
4
Destructive area
Non-destructive area
1:1 line
2
0
0
50
2
4
6
8
10 0
-1
2
4
6
8
10
-1
CG60 at 120,000 plants ha at 2 wk after silking in 2007
3.8423
EDM = 0.1042 × ED
2
n = 36 R = 0.89 RMSE = 0.18
CG60 at 120,000 plants ha at 2 wk after silking in 2007
1.1281 × ED
EDM = 0.2343 × e
2
n = 36 R = 0.85 RMSE = 0.20
40
30
20
10
0
0
10
20
30
40
50 0
10
20
30
40
50
-1
Predicted EDM in destructive sampling or non-destructive measurement area (g plant )
Figure 3.5. Selected comparisons of allometric models with a power function and with an
exponential function for the F1 hybrid CG60 × CG102 at 160,000 plants ha-1 at silking and
inbred CG60 at 120,000 plants ha-1 at 2 wk after silking in 2007. The allometric model estimates
the relationship between ear dry matter (EDM) and maximum primary ear diameter (ED) at
reproductive stages. The round symbols (○) represent the relationship between measured EDM
in destructive sampling areas and corresponding predicted EDM. The triangle symbols (Δ)
present the values of predicted EDM in non-destructive measurement areas in the 1:1 line.
Number of sampled plants (n) and root mean squared error (RMSE) in g plant-1 are presented.
112
at 2 wk after silking in 2008 [Fig. 3.6]), had similar R2 and lower RMSE in four of 27
combinations (e.g., CG60 at 120,000 plants ha-1 at 2 wk after silking in 2007 [Fig. 3.5]) as well
as similar performance in both parameters as the exponential function in 10 out of 27
combinations (e.g., the F1 at 120,000 plants ha-1 at 2 wk after silking in 2008 [Fig. 3.6]).
Therefore, the power function with individual plant density was chosen and further compared to
the power function with combined plant density. Significant genotype × plant density interaction
for EDM estimation was observed in 50% sampling stage × year combinations (data not shown).
Therefore, allometric relationships in EDM were established for each genotype × plant density
combination and compared to the combined plant density models. The combined plant density
models outperformed the individual plant density models, with higher R2 in three out of six
genotype × reproductive stage combinations in 2007 and three out of six genotype ×
reproductive stage combinations in 2008 compared to the average R2 of the individual plant
density models. Otherwise, the R2 of the combined plant density model were similar to the
average R2 of the individual plant density models. The RMSE of the combined plant density
model was higher than average RMSE of individual plant density models in zero out of six
combinations in 2007 and three out of six combinations in 2008, and similar RMSE as individual
plant density model in one out of six combinations in 2008. Using individual plant density
models for EDM estimation showed low R2 (R2 < 0.53) in one of six genotype × plant density ×
reproductive stage combinations in both 2007 and 2008. Therefore, the combined plant density
model was selected for further justification.
113
Power function
40
Exponential function
-1
-1
CG102 at 80,000 plants ha at 2 wk after silking in 2008
0.8683 × ED
EDM = 0.9127 × e
3.0153
EDM = 0.4479 × ED
2
n = 29 R = 0.94 RMSE = 0.16
-1
Measured EDM or predicted EDM in non-destructive measurement area (g plant )
CG102 at 80,000 plants ha at 2 wk after silking in 2008
2
n = 28 R = 0.86 RMSE = 0.15
30
20
10
Destructive area
Non-destructive area
1:1 line
0
0
60
10
20
30
40 0
F1 at 120,000 plants ha-1 at 2 wk after silking in 2008
10
20
30
40
-1
F1 at 120,000 plants ha at 2 wk after silking in 2008
3.3146
0.8522 × ED
EDM = 0.2614 × ED
n = 40 R2 = 0.84 RMSE = 0.16
EDM = 0.8382 × e
2
n = 40 R = 0.84 RMSE = 0.16
50
40
30
20
10
10
20
30
40
50
60 10
20
30
40
50
60
-1
Predicted EDM in destructive sampling or non-destructive measurement area (g plant )
Figure 3.6. Selected comparisons of allometric models with a power function and with an
exponential function for inbred CG102 at 80,000 plants ha-1 at 2 wk after silking and F1 hybrid
CG60 × CG102 at 120,000 plants ha-1 at 2 wk after silking in 2008. The allometric model
estimates the relationship between ear dry matter (EDM) and maximum primary ear diameter
(ED) at reproductive stages. The round symbols (○) represent the relationship between measured
EDM in destructive sampling areas and corresponding predicted EDM. The triangle symbols (Δ)
present the values of predicted EDM in non-destructive measurement areas in the 1:1 line.
Number of sampled plants (n) and root mean squared error (RMSE) in g plant-1 are presented.
114
3.3.2.3 Allometric model justification
A two-step justification procedure was developed to detect significant bias
between/among individual plant densities within combined plant density models. Instead of
using residual value, RP as a ratio was used to compare bias between/among plant densities in
both magnitude and direction (i.e., overestimation or underestimation). According to the
equation [3.6], negative RP indicated overestimation. Differences in the RP between/among
individual plant densities within the combined plant density models were significant in six
genotype × sampling stage × year combinations in 2007 and 2008 (Tables 3.4 and 3.5).
However, after comparing RP of a plant density derived from combined plant density model and
that of the same plant density derived from its individual plant density model, the RP between
the combined plant density model and the individual plant density model was significantly
different only for CG102 at the first sampling stage and for the F1 at 2 wk after silking in 2008
(data not shown). Therefore, combined plant density models did not cause significant bias in
most cases and were chosen to estimate PDM (Fig. 3.7).
In terms of EDM estimation, the RPs were statistically similar between/among individual
plant densities within the combined plant density models in six out of 12 genotype ×
reproductive stage × year combinations (Table 3.6). Although the RP was not significantly
different between 40,000 and 120,000 plants ha-1 by using the combined plant density model for
CG102 at silking in 2007 (Table 3.6), numerically the EDM overestimation was very large at
21%. At 40,000 plants ha-1 the RPs of the combined plant density model and the individual plant
density model were significantly different (21% vs. 0.4%). When the RPs between/among
115
Table 3.4. Comparison of mean residual percentage (i.e., the difference between measured
above-ground plant dry matter minus estimated above-ground plant dry matter divided by
measured above-ground plant dry matter) between two plant densities within the combined plant
density model for parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 at five
destructive sampling stages in 2007. The vegetative stages are presented by the average leaftip
(LT) stage across sampled plants.
Genotype
CG60
CG102
CG60 × CG102
Plant density
(plants ha-1)
40,000
120,000
40,000
120,000
40,000
160,000
Residual percentage –––––––––––––––––––––––– % ––––––––––––––––––––––––
-3.88a
-3.12a
-6.27a
-1.66a
8-LT
-10.58a† -2.28a
12-LT
10.29a
-7.55b
-4.81a
-1.26a
-2.60a
-2.69a
16-LT
1.97a
-5.79a
0.43a
-4.53a
2.74a
-8.66a
Silking
1.69a
-2.39a
-5.77a
-0.30a
0.36a
-3.30a
2 wk after silking
1.54a
-1.66a
0.61a
-1.41a
1.82a
-0.71a
†
Means followed by the same letter between two plant densities within a genotype × sampling
stage combination are not significantly different using the LSMEANS/PDIFF (P > 0.05)
procedure of SAS.
116
Table 3.5. Comparison of mean residual percentage (i.e., the difference between measured above-ground plant dry matter minus
estimated above-ground plant dry matter divided by measured above-ground plant dry matter) among three plant densities within the
combined plant density model for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 at four destructive
sampling stages in 2008. The vegetative stages are presented by the average leaftip (LT) stage across sampled plants.
Genotype
CG60
CG102
CG60 × CG102
Plant density
(plants ha-1)
80,000
120,000
160,000
80,000
120,000
160,000
80,000
120,000
160,000
Residual percentage
–––––––––––––––––––––––––––––––––––––––– % ––––––––––––––––––––––––––––––––––––––––
12-LT
2.81a†
0.72a
-6.79b
-9.55b
-14.67b
7.98a
2.38a
-2.65ab
-2.98b
15-LT
-0.75a
-3.09a
-2.04a
-10.04a
0.67a
-6.79a
1.43a
-5.44a
-3.75a
‡
Silking
-1.22a
NA
-2.61a
-8.48b
-0.20a
0.27a
-0.05a
NA
-1.32a
2 wk after silking
-0.37a
-3.07a
0.93a
-1.49a
NA
-0.73a
0.32b
-5.18c
2.32a
†
Means followed by the same letter between/among two or three plant densities within a genotype × sampling stage are not
significantly different using the LSMEANS/PDIFF (P > 0.05) procedure of SAS.
‡
NA, not available.
117
Measured PDM or predicted PDM in non-destructive measurement area (g plant-1)
30
150
CG102 at 11-LT in 2007
PDM = 1.8051 + 0.1373 × SV
2
n = 47 R = 0.81 RMSE = 1.37
25
CG60 at silking in 2007
2
PDM = 7.7169 + 0.0823 × SV + 5.2570 × ED
2
n = 48 R = 0.91 RMSE = 6.96
120
20
90
15
60
10
-1
40,000 plants ha at destructive area
-1
120,000 plants m at destructive area
Non-destructive area
1:1 line
5
30
0
0
0
80
5
10
15
20
25
30
0
300
CG60 at 16-LT in 2008
PDM = 5.8417 + 0.0987 × SVmax
30
60
90
120
150
F1 at 2 wk after silking in 2008
2
PDM = -18.0256 + 0.1104 × SVmax + 3.6589 × ED
2
n = 123 R = 0.90 RMSE = 3.64
2
n = 123 R = 0.94 RMSE = 10.69
250
60
200
150
40
100
20
-1
80,000 plants ha at destructive area
-1
120,000 plants ha at destructive area
-1
160,000 plants ha at destructive area
Non-destructive area
1:1 line
0
0
20
40
60
80
50
0
0
50
100
150
200
250
300
Predicted PDM in destructive sampling or non-destructive measurement area (g plant-1)
Figure 3.7. Selected presentation of the relationship between measured above-ground plant dry
matter (PDM) in destructive sampling areas using combined plant density models and the
corresponding predicted PDM for inbred CG102 at 11-leaftip (LT) stage and inbred CG60 at
silking in 2007 and CG60 at 16-LT stage and the F1 hybrid CG60 × CG102 at 2 wk after silking
in 2008. The round (○), diamond (◊), triangle (Δ) and square (□) symbols represent the
relationship between measured PDM in destructive sampling areas and corresponding predicted
PDM at different plant densities. The values of predicted PDM in non-destructive measurement
areas are presented by the cross (×) symbols in the 1:1 ratio line. Stem volume calculated from
maximum width of basal stem diameter and maximum width of stem diameter that is 2 cm above
the ground level are presented by SV and SVmax, respectively. Number of sampled plants (n) and
root mean squared error (RMSE) in g plant-1 are presented.
118
Table 3.6. Comparison of mean residual percentage (i.e., the difference between measured ear dry matter [EDM] minus estimated
EDM divided by measured EDM) within the combined plant density model between two plant densities in 2007 and among three plant
densities in 2008 for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 at silking and 2 wk after silking.
Year
Genotype
CG60
CG102
CG60 × CG102
2007
Plant density
40,000
120,000 40,000
120,000 40,000
160,000
-1
(plants ha )
Residual percentage
Silking
2 wk after silking
–––––––––––––––––––––––––––––––––– % –––––––––––––––––––––––––––––––––
11b†
2a
-21a
-3a
0a
-3a
2a
-3a
10a
-6b
4a
-3a
2008
Plant density
80,000 120,000 160,000 80,000 120,000 160,000 80,000 120,000 160,000
-1
(plants ha )
Residual percentage
––––––––––––––––––––––––––––––––– % –––––––––––––––––––––––––––––––––
Silking
-21a
NA‡
2b
-17a
-7ab
5b
-4a
NA
-1a
2 wk after silking
20a
-7b
-43c
-1a
NA
-6a
2a
-10b
4a
†
Means followed by the same letter between/among two or three plant densities within a genotype × sampling stage × year are not
significantly different using the LSMEANS/PDIFF (P > 0.05) procedure of SAS.
‡
NA, not available.
119
individual plant densities within the combined plant density models were significantly different,
the RPs for an individual plant density between the combined plant density model and its
individual plant density model were significantly different in three out of six genotype ×
reproductive stage × year combinations (Table 3.7). For example, the differences in RPs
between the combined plant density model and the individual 80,000 plants ha-1 model, and
between the combined plant density model and the individual 160,000 plants ha-1 model were
28% and 39%, respectively, for CG60 at 2 wk after silking in 2008. Furthermore, for the other
three out of six genotype × reproductive stage × year combinations with similar RP for an
individual plant density between a combined plant density model and its individual plant density
model (Table 3.7), even though the RP at 80,000 plants ha-1 between the combined plant density
model and the individual 80,000 plants ha-1 model was not significantly different for CG102 at
silking in 2008, more than 20% of difference in RP for EDM estimation at silking could not be
ignored. Therefore, whether statistically significant or not, the combined plant density models
could cause large deviations from measured EDM in some genotype × reproductive stage × year
combinations. Both combined plant density models (Fig. 3.8) and individual plant density
models, as well as the combination of combined plant density model and individual plant density
model were used to estimate EDM. The criteria to use individual plant density models were (i)
When RPs of individual plant densities were not significantly different within the combined
plant density model, but the difference in RP between/among plant densities was larger than
10%, and the RP of an individual plant density with relatively large overestimation or
underestimation by using the combined plant density model was both significantly different and
larger in absolute value than its individual plant density model. The case in this study was
CG102 at silking in 2007. Comparing to the combined plant density model, the individual
120
Table 3.7. Comparison of mean residual percentage (i.e., the difference between measured ear dry matter [EDM] minus estimated
EDM divided by measured EDM) of a plant density between combined plant density model and individual plant density model for
parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 at two genotype × sampling stage combinations in 2007 and
four genotype × sampling stage combinations in 2008 when there was a significant difference in residual percentage between/among
plant densities within a combined plant density model.
Year Genotype
Plant density (plants ha-1)
40,000
120,000
2007
Model
Combined
–––––––––––––––––––––––––––– % ––––––––––––––––––––––––––––
-11.34b†
-0.36a
1.80a
-0.88b
9.99a
-0.71a
-6.13a
-1.84a
CG60
CG102
Residual percentage
Silking
2 wk after silking
2008
Genotype
Plant density (plants ha-1)
Model
Individual
80,000
Combined
Individual
120,000
Combined Individual
160,000
Combined Individual Combined Individual
Residual percentage
––––––––––––––––––––––––––––% ––––––––––––––––––––––––––––
CG60
Silking
-20.62a
-13.40a
NA‡
NA
1.91a
-1.35a
2 wk after silking
19.62a
-8.55b
-7.00a
-4.23a
-42.88b
-3.80a
CG102
Silking
-16.81a
4.46a
-7.26a
-4.22a
5.49a
-5.97a
CG60 × CG102 2 wk after silking
1.81a
-0.46a
-9.59b
-1.20a
3.65a
-0.71b
†
Means followed by the same letter between/among two or three plant densities within a genotype × sampling stage are not
significantly different using the LSMEANS/PDIFF (P > 0.05) procedure of SAS.
‡
NA, not available.
121
Measured EDM or predicted EDM in non-destructive measurement area (g plant-1)
30
50
F1 at silking in 2007
CG60 at 2 wk after silking in 2007
3.7680
EDM = 0.1162 × ED
2
n = 48 R = 0.89 RMSE = 0.17
3.4047
EDM = 0.2344 × ED
2
n = 58 R = 0.93 RMSE = 0.29
25
40
20
30
15
20
10
-1
40,000 plants ha at destructive area
-1
120,000 plants m at destructive area
Non-destructive area
1:1 line
5
10
0
0
0
30
5
10
15
20
25
30
40
CG60 at silking in 2008
2.4611
EDM = 0.4204 × ED
2
n = 80 R = 0.78 RMSE = 0.32
25
0
10
20
30
40
50
CG102 at 2 wk after silking in 2008
3.4125
EDM = 0.2743 × ED
2
n = 63 R = 0.93 RMSE = 0.26
30
20
20
15
10
-1
80,000 plants ha at destructive area
-1
160,000 plants ha at destructive area
Non-destructive area
1:1 line
5
10
0
0
0
5
10
15
20
25
30
0
10
20
30
40
Predicted EDM in destructive sampling or non-destructive measurement area (g plant-1)
Figure 3.8. Presentation of relationship between measured ear dry matter (EDM) in destructive
sampling areas using combined plant density model and the corresponding predicted EDM for F1
hybrid CG60 × CG102 at silking, and inbred CG60 at 2 wk after silking in 2007, and CG60 at
silking, and CG102 at 2 wk after silking in 2008. The round (○), and diamond (◊), triangle (Δ)
and square (□) symbols represent the relationship between measured EDM in destructive
sampling areas and corresponding predicted EDM at different plant densities. The values of
predicted EDM in non-destructive measurement area are presented by the cross (×) symbols in
the 1:1 ratio line. Number of sampled plants (n) and root mean squared error (RMSE) in g plant1
are presented.
122
plant density model improved the absolute RP from 21% to 0.4% (data not shown). (ii) When
RPs between individual plant densities within the combined plant density model were
significantly different and the RPs for an individual plant density between the combined plant
density model and its individual plant density model was significantly different. The cases in
this study were CG60 at silking in 2007, CG60 at 2 wk after silking in 2008 and the F1 at 2 wk
after silking in 2008. (iii) When RPs between individual plant densities within the combined
plant density model were significantly different and RPs for an individual plant density between
the combined plant density model and its individual plant density model were statistically
similar, but the difference in RP between the combined plant density model and its individual
plant density model were larger than 10%. The cases in this study were CG102 at 2 wk after
silking in 2007 and CG102 at silking in 2008.
For CG60 at silking in 2008, although the RPs between the combined plant density model
and an individual plant density model were not significantly different (Table 3.7), the 21%
overestimation of EDM by the combined plant density model at 80,000 plants ha-1 was deemed
unacceptable. Therefore, the EDM at 80,000 plants ha-1 was estimated by using the individual
plant density model with absolute RP of 13%, but using the combined plant density model to
estimate EDM at 120,000 and 160,000 plants ha-1.
3.3.2.4 Allometric model calibration
After allometric model development, the PDM and EDM values in the non-destructive
measurement areas were calculated to check extrapolation. When the measured values in the
123
destructive sampling areas could not cover ranges of predicted values in the non-destructive
measurement areas, extrapolation occurred, such as the F1 at 160,000 plants ha-1 at silking in
2007 (Fig. 3.5). The extrapolation was addressed for most of the genotype × vegetative sampling
stage combinations in 2007 and achieved higher R2 and lower RMSE by adding border plants to
the models except for CG102 and the F1 hybrid at the third vegetative sampling stage. No
additional data was available to be added to address extrapolation for the rest of the dataset. For
the rest of the dataset, extrapolation was observed at 16 genotype × plant density × sampling
stage × year combinations for PDM and 14 combinations for EDM estimation (Appendix B.2).
Extrapolation was observed mainly (12 out of 15 combinations) in CG60 and the F1 and at
40,000 plants ha-1 (nine out of 14 combinations) in 2007. Covering the lower end of ED could
help estimate PDM and EDM of the potential barren plants. In terms of ED range at the
reproductive stages, only the F1 hybrid in the destructive sampling areas at silking did not have
an ear with zero ED when there were plants with zero ED in the non-destructive measurement
areas in 2007 and 2008.
3.3.2.5 Allometric model evaluation
Overall, the allometric models with intercepts and combined plant density for PDM
estimation were significant (P ≤ 0.0001) with R2 ranging from 0.60 to 0.97 (Table 3.2). The
allometric models with power function with the combination of combined and individual plant
density for EDM estimation were significant (P ≤ 0.0001) with R2 varying between 0.52 and 0.97
(Table 3.3). With the same structure of developed allometric models, differences in magnitude
of bias for PDM were found among genotypes, plant densities and sampling stages. When
124
comparing genotypes at the same plant density × sampling stage × year combination, the three
genotypes exhibited similar magnitude of bias in PDM estimation in 2007. However, CG60 and
the F1 had less magnitude of bias compared to CG102 for PDM estimation in 2008 except at the
stage of 2 wk after silking (Table 3.5). No other obvious trends could be found between/among
plant densities in PDM estimation. The bias was less in PDM estimation at 2 wk after silking for
CG60 at 160,000 plants ha-1 and CG102 at 80,000 and 160,000 plants ha-1 in 2008 compared to
the earlier sampling stages for the same genotype × plant density combination (Table 3.5). The
F1 hybrid in most cases exhibited less that 5% of absolute RP in PDM estimation across different
years, plant densities and sampling stages. For EDM estimation, due to the use of combined
plant density models and individual plant density models as well as the combination of both
models at different genotype × reproductive stage combinations, difference in the magnitude of
bias among genotype, plant density and reproductive stage could not be compared.
3.3.3 The history of individual barren plants
Individual plants that had similar initial plant growth and development and uniform
spatial pattern could have different destinies in terms of growth (e.g., PDM, PDM ratio and
EDM), development (e.g., LT stage, ASI and GDDsilking) and dry matter partitioning and could
ultimately become barren or non-barren plants.
3.3.3.1 Plant growth and development of barren plants
Average barren plants had below-average PDMPM (Figs. 3.9 and 3.10), and similar final
leaf number as average plants at each inbred × plant density × year combination (Fig. 3.11).
125
150
-1
-1
CG102 at 80,000 plants ha in 2008
CG102 at 120,000 plants ha in 2007
Individual barren plants
Average barren plants
Average plants
Estimated PDM (g plant-1)
100
50
0
150
-1
-1
CG102 at 120,000 plants ha in 2008
CG102 at 160,000 plants ha in 2008
Barren plants
Average barren plants
Average non-barren plants
100
Average plants
50
0
0
200
400
600
800
1000
0
200
400
600
800
1000
1200
Growing degree days after planting
Figure 3.9. Estimated above-ground plant dry matter (PDM) for individual barren plants (i.e., grain yield per plant = 0 g), average
barren plants, and average individual plants from early vegetative stage to physiological maturity for one of the parental inbred lines,
CG102, at different plant densities in 2007 and 2008. Each sub-figure represents a plant density × year combination that had at least
one barren plant. The estimated PDM for average non-barren CG102 plants at 120,000 and 160,000 plants ha-1 in 2008 are presented.
The first and second arrows indicate the timing of ear initiation and silking, respectively.
126
200
-1
CG60 at 120,000 plants ha in 2008
Barren plants
Average barren plants
Average plants
150
100
Estimated PDM (g plant-1)
50
0
-1
CG60 at 160,000 plants ha in 2008
Barren plants
Average barren plants
Average non-barren plants
Average plants
150
100
50
0
-1
F1 at 160,000 plants ha in 2008
150
100
50
0
0
200
400
600
800
1000
1200
Growing degree days after planting
Figure 3.10. Estimated above-ground plant dry matter (PDM) for individual barren plants (i.e.,
grain yield per plant = 0 g), average barren plants, and average individual plants from early
vegetative stage to physiological maturity for one of the parental inbred lines CG60 and its F1
hybrid CG60 × CG102 at different plant densities in 2008. Each sub-figure represents a
genotype × plant density combination that had at least one barren plant. The estimated PDM for
average non-barren CG60 plants at 160,000 plants ha-1 is presented. The first and second arrows
indicate the timing of ear initiation and silking, respectively.
127
16
-1
-1
CG102 at 120,000 plants ha in 2007 CG102 at 80,000 plants ha in 2008
-1
CG102 at 120,000 plants ha in 2008
-1
CG102 at 160,000 plants ha in 2008
14
12
10
Barren plants
Average plants
Leaftip stage
8
6
200
20
400
600
800
200
-1
CG60 at 120,000 plants ha in 2008
400
600
800
200
400
600
800
200
400
600
800
1000
-1
-1
F1 at 160,000 plants ha in 2008
CG60 at 160,000 plants ha in 2008
18
16
14
12
10
8
200
400
600
800
200
400
600
800
200
400
600
800
1000
Growing degree days after planting
Figure 3.11. The leaftip stage development of individual barren plants (i.e., grain yield per plant = 0 g) and average individual plants
for two parental inbred lines CG60 and CG102 and their F1 hybrid CG60 × CG102 at different plant densities in 2007 and 2008. Each
sub-figure represents a genotype × plant density × year combination that had at least one barren plant. The first and second arrows
indicate the timing of ear initiation and silking, respectively.
128
The differences in PDM between average barren plants and average plants tended to increase
through development (Figs. 3.9 and 3.10). When there were more than 10 barren plants for
CG102 at 120,000 and 160,000 plants ha-1 and CG60 at 160,000 plants ha-1 in 2008, the average
barren plants were compared to the average non-barren plants. The barren plants had
significantly lower PDMs, PDMs+2 and PDMPM only for CG102 at 120,000 and 160,000 plants
ha-1 in 2008 (Fig. 3.9). When barren plants were present at a plant density in 2008, there was
less PDM at PM than at 2 wk after silking and at silking for average barren CG102 and CG60
plants, respectively (Figs. 3.9 and 3.10).
When comparing individual barren plants with the average value, barren plants did not
necessarily have below-average PDMPM, especially for CG102 at 120,000 and 160,000 plants ha1
in 2008 (Fig. 3.9) and CG60 at 160,000 plants ha-1 in 2008 (Fig. 3.10). Individual barren plants
had above-average, average, or below-average final leaf number (Fig. 3.11). An individual
inbred plant with above-average, average or below-average PDM and LT stage in a sampling
stage before PM could become a barren plant (Figs. 3.9-3.11). The exception is that all the
individual barren plants had a below-average PDMs+2 for CG102 at 120,000 plants ha-1 in 2007
(Fig. 3.9). For the F1 hybrid in 2008, the ultimately barren plant had a slightly above-average
PDM and LT stage at the first sampling stage (i.e., 11-LT) and then exhibited below-average
PDM and the same LT stage as the average until PM (Figs. 3.10 and 3.11). Although at the
canopy level, the parental inbred plants and the F1 plants maintained linear growth throughout
phenological development before PM in 2007 and 2008, the individual ultimately barren plants
did not always maintain linear growth (Figs. 3.9 and 3.10). Linear growth of individual plants
with similar initial growth and development indicates rank stability within a genotype × plant
129
density × year combination. For barren inbred plants, their previous rank in PDM within an
inbred × plant density × year combination, either above or below average or close to the average,
could not determine the position of the same individual in the next sampling stage (Figs. 3.9 and
3.10).
The LT stage development of the barren plants could be grouped into several patterns,
especially when there were more than 10 barren plants (Fig. 3.11). Compared to the average LT
stage within an inbred × plant density × year combination, a barren inbred plant that was either
in advance of LT stage development, or was the same as the average or slower in LT stage
development could later be in any rank in LT stage (Fig. 3.11). When tracking the history of
barren and non-barren plants within an inbred × plant density × year combination, plants with
almost the same PDM and in the same LT stage at a sampling stage could eventually become
either barren or non-barren (data not shown). When comparing within the ultimately barren
plants within an inbred × plant density × year combination, the inbreds with similar initial
growth and development could maintain similar PDM and LT stage, or have similar PDM but
different LT stage; mostly as many as one LT in each vegetative sampling stage with the
exception of CG60 at 120,000 plants ha-1 in 2008 (data not shown). Interestingly, two barren
plants had similar PDM but different final leaf number at silking; as many as two LTs for CG102
at 160,000 plants ha-1 in 2008.
Another physiological parameter to characterize the history of barren plants is PDM ratio
between an individual barren plant and the average of a plant stand throughout development.
The PDM ratio could indicate the dynamics of differential growth of an individual barren plant
130
relative to the average plants (Appendixes B.3 and B.4). The average barren plants tended to be
smaller than the average plants, with the difference increasing as development progressed until
PM in each inbred × plant density × year combination, except for CG102 at 160,000 plants ha-1
in 2008. For CG102 at 160,000 plants ha-1 in 2008, the average barren plants had a PDM ratio
close to 1.0 throughout development. At the individual plant level, barren plants all had belowaverage PDMPM ratio for CG102 at 120,000 plants ha-1 in 2007, 80,000 plants ha-1 in 2008 and
CG60 at 120,000 plants ha-1 and the F1 at 160,000 plants ha-1 in 2008, while in the other three
inbred × plant density × year combinations, barren plants had both above- and below-average
PDMPM ratio.
3.3.3.2 Ear growth and development of barren plants
Average barren plants had below-average EDMPM for each inbred × plant density × year
combination (Figs. 3.12 and 3.13). The average barren plants had significantly lower EDMs,
EDM at 2 wk after silking (EDMs+2) and EDMPM than average non-barren plants for CG102 at
120,000 and 160,000 plants ha-1 in 2008 and CG60 at 160,000 plants ha-1 in 2008.
At the individual plant level, genotype and plant density affected the value of EDMs,
EDMs+2 and EDMPM of individual barren plants relative to the average value of a genotype ×
plant density × year combination. All barren inbred plants had below-average EDMPM for each
inbred × plant density × year combination with the exception of CG102 at 160,000 plants ha-1 in
2008 (Figs. 3.12 and 3.13). Barren CG102 plants accumulated below-average EDMs and
EDMs+2 at 80,000 plants ha-1 in 2008 and 120,000 plants ha-1 in 2007 and 2008, while at 160,000
131
GY (g plant-1) EDM (g plant-1)
80
GY (g plant-1) EDM (g plant-1)
80
-1
CG102 at 80,000 plants ha in 2008
-1
CG102 at 160,000 plants ha in 2008
-1
CG102 at 120,000 plants ha in 2007
60
40
Initial EDM
Growth I
Growth II
20
0
20
40
60
GY
80
60
-1
CG102 at 120,000 plants ha in 2008
40
20
0
10
20
30
40
50
Figure 3.12. The initial ear dry matter (EDM) around silking, and the subsequent growth I of
individual ears between silking and approximately 2 wk after silking, the subsequent growth II
between 2 wk after silking and physiological maturity as well as the corresponding final grain
yield (GY) at physiological maturity for one of the parental inbred lines, CG102, at different
plant densities in 2007 and 2008. Each sub-figure represents a plant density × year combination
that had at least one barren plant (i.e., GY per plant = 0 g). In the upper half of each panel, the
black vertical bars indicate the initial EDM, the grey and dark grey bars indicate the subsequent
growth I and II, respectively. In the lower half of each panel, the vertical bars indicate the final
GY of the corresponding individual ears. The dash line separates barren and non-barren plants.
The initial EDM of the barren and non-barren plants are sorted by an ascending order, separately.
132
-1
CG60 at 120,000 plants ha in 2008
80
60
Initial EDM
Growth I
Growth II
40
20
0
GY (g plant-1)
EDM (g plant-1)
100
-20
20
40
60
GY
80
100
-1
EDM (g plant-1)
CG60 at 160,000 plants ha in 2008
80
60
40
20
-1
GY (g plant-1) EDM (g plant )
GY (g plant-1)
0
20
40
60
120
100
80
60
40
20
0
-1
F1 at 160,000 plants ha in 2008
20
40
60
80
100
Figure 3.13. The initial ear dry matter (EDM) around silking, and the subsequent growth I of
individual ears between silking and approximately 2 wk after silking, the subsequent growth II
between 2 wk after silking and physiological maturity as well as the corresponding final grain
yield (GY) at physiological maturity for one of the parental inbred lines, CG60, and its F1 hybrid
CG60 × CG102 at different plant densities in 2008. Each sub-figure represents a genotype ×
133
plant density combination that had at least one barren plant (i.e., GY per plant = 0 g). In the
upper half of each panel, the black vertical bars indicate the initial EDM and the grey and dark
grey bars indicate the subsequent growth I and II, respectively. In the lower half of each panel,
the vertical bars indicate the final GY of the corresponding individual ears. The dash line
separates barren and non-barren plants. The initial EDM of the barren and non-barren plants are
sorted by an ascending order, separately.
134
plants ha-1 in 2008, the ultimately barren plants had both above- and below-average EDMs,
EDMs+2 and EDMPM. Similarly the ultimately barren CG60 plants had above- and belowaverage EDMs and below-average EDMs+2 at 120,000 plants ha-1 in 2008, while at 160,000
plants ha-1 in 2008, CG60 barren plants accumulated both above- and below-average EDMs and
EDMs+2. However, the ultimately barren F1 plant had below-average EDMs, EDMs+2 and
EDMPM. For ultimately barren ears, the rank in EDMs within a plant density did not determine
the rank in the EDMs+2 and EDMPM (Figs. 3.12 and 3.13).
Barrenness seemed related with EDMPM for the three genotypes. For CG102 in 2007, the
three barren ears at 120,000 plants ha-1 belonged to the lowest four EDMPM in rank. Similarly,
for CG102 in 2008, the six barren ears at 80,000 plants ha-1 originated from the six lowest
EDMPM. All the 23 barren ears at 120,000 plants ha-1 belonged to the lowest 25 EDMPM in rank.
However, this trend could not be observed for CG102 at the 160,000 plants ha-1 in 2008.
Similarly, for CG60 in 2008, all the four barren ears at 120,000 plants ha-1 had the lowest four
EDMPM. Thirteen out of 14 barren ears at 160,000 plants ha-1 were present in the lowest 13
EDMPM in rank. The F1 hybrid barren ear had the lowest EDMPM at 160,000 plants ha-1 in 2008.
In addition, barrenness for CG102 was associated with EDMs in 2008. Five out of six barren
ears at 80,000 plants ha-1 had the lowest five EDMs. Twenty-three out of 23 barren ears at
120,000 plants ha-1 belonged to the lowest 26 EDMs in rank. The other three out of 26 lowest
EDMs had a close to barren GY at PM (GY ≈ 1 g plant-1). Fifty-three out of 58 barren ears at
160,000 plants ha-1 were present in the lowest 56 EDMs in rank. The other three out of 56 lowest
EDMs had a close to barren GY at PM (GY ≈ 1 g plant-1). However, this trend could not be
observed for CG102 at 120,000 plants ha-1 in 2007. The F1 barren ear had no initial EDMs. This
135
general trend could not be observed in CG60.
Flowering dynamics of individual plants in this study includes both ASI and GDDsilking.
In terms of the relationship between GDDsilking and ASI, the GDDsilking increased as the ASI
increased for the three genotypes (Appendix B.5). The relationship between barrenness and
flowering dynamics varied by year and genotype. In 2007, the ultimately barren CG102 plants at
120,000 plants ha-1 had ASI with a range of 7 to 11. While in 2008, the ultimately barren CG102
plants across three plant densities could be grouped into three categories: ASI with a range of 4
to 11, no silk emergence as well as no anther and silk emergence (NT & NS) (Appendix B.5).
Moreover, barrenness was mainly related with no silk emergence for CG102 in 2008. Five out
of six (83%), 21 out of 23 (91%), and 56 out of 58 (97%) ultimately barren plants had no silk
emergence for CG102 at 80,000; 120,000 and 160,000 plants ha-1 in 2008, respectively.
However, only two out of four (50%), and three out of 14 (21%) barren plants had no silk
emergence for CG60 at 120,000 and 160,000 plants ha-1 in 2008. The ultimately barren F1 plant
had a relatively long ASI of 3 compared to the rest of the plants at 160,000 plants ha-1 in 2008.
When there was silk emergence for the ultimately barren plants, some of the ultimately barren
plants took the longest GDDsilking (Appendix B.5). However, the plants with the longest
GDDsilking did not necessarily become barren plants.
The three genotypes differed in the relationships between EDMs and ASI, and between
EDMs and GDDsilking. Only CG102 exhibited a linear relationship between EDMs and ASI in
two out of four plant density × year combinations (Fig. 3.14). Similarly, CG102 exhibited a
linear relationship between EDMs and GDDsilking at each plant density in 2008 (Fig. 3.14) and the
136
20
-1
-1
CG102 at 120,000 plants ha in 2007
CG102 at 120,000 plants ha in 2007
Barren ears
Non-barren ears
EDMs vs. GDDsilking
Barren ears
Non-barren ears
EDMs vs. ASI
15
10
5
0
-1
-1
20
CG102 at 80,000 plants ha in 2008
CG102 at 80,000 plants ha in 2008
No silk emergence
15
10
NT
0
20
-1
-1
CG102 at 120,000 plants ha in 2008
CG102 at 120,000 plants ha in 2008
15
No silk emergence
EDMs (g ear-1)
5
10
5
NT
0
-1
-1
CG102 at 160,000 plants ha in 2008
No silk emergence
CG102 at 160,000 plants ha in 2008
15
10
5
NT & NS
20
NT
0
0
5
10
15
20
640
ASI (d)
680
720
760
800
840
GDDsilking
Figure 3.14. Relationship between ear dry matter at silking (EDMs) and anthesis to silking
interval (ASI) for ultimately barren and non-barren ears, and the relationship between EDMs and
growing degree days from planting to silking (GDDsilking) for barren and non-barren ears for one
137
of the parental inbred lines, CG102, at different plant densities in 2007 and 2008. The EDMs for
no silk emergence, no anther and silk emergence (NT & NS) and no anther emergence (NT)
were present in the right-hand side of each panel when available, and, respectively. Each subfigure represents a plant density × year combination that had at least one barren plant (i.e., grain
yield per plant = 0 g). The linear relationships between EDMs and ASI, between EDMs and
GDDsilking were as followings: For CG102 at 80,000 plants ha-1 in 2008: EDMs = 5.11 – 0.49 ×
ASI (R2 = 0.59, n = 18, P = 0.0002); EDMs = 57.88 – 0.08 × GDDsilking (R2 = 0.80, n = 19, P <
0.0001). For CG102 at 120,000 plants ha-1 in 2008: EDMs = 94.73 – 0.12 × GDDsilking (R2 =
0.81, n = 37, P = < 0.0001). For CG102 at 160,000 plants ha-1 in 2008: EDMs = 14.78 – 1.08 ×
ASI (R2 = 0.50, n = 11, P = 0.01); EDMs = 143.41 – 0.19 × GDDsilking (R2 = 0.74, n = 12, P =
0.0003).
138
F1 had a linear relationship between EDMs and GDDsilking at 160,000 plants ha-1 in 2008
(Appendix B.6). However, no linear relationship between EDMs and ASI or GDDsilking could be
established for CG60 at the two plant densities in 2008. Plants with similar EDMs and the same
ASI or similar EDMs and the same GDDsilking could eventually became a barren or a non-barren
plant for CG102 at 120,000 plants ha-1 in 2008 and CG60 at 160,000 plants ha-1 in 2008 (Fig.
3.14 and Appendix B.6).
3.3.3.3 Efficiency and dry matter partitioning during the critical period bracketing silking
Both PGRs and EGRs are considered as critical physiological traits for GY formation
(Tollenaar et al., 1992; Echarte and Tollenaar, 2006). The relationships between HI and PGRs,
and between HI and EGRs indicate the relationship between the dry matter partitioning at PM
and the efficiency of plant and ear growth during the period bracketing silking. Plant growth rate
during the critical period bracketing silking for individual plants was the slope of the linear
regression between PDM and the corresponding GDD. The linear regression calculated PGRs
for individual plants successfully according to the minimum, maximum and average R2
(Appendix B.7).
Threshold values of PGRs for HI were 0.06 g plant-1 GDD-1 and 0.05 g plant-1 GDD-1 for
CG102 in 2007 and CG60 in 2008, respectively (Figs. 3.15, 3.16 and Table 3.8). The threshold
value of EGRs for HI was 0.01 g plant-1 GDD-1 and 0.03 g plant-1 GDD-1 for CG60 and
CG102 in 2007 (Fig. 3.15). The threshold value of PGRs and EGRs for HI was 0.05 and 0.04 g
plant-1 GDD-1 for the F1 in 2008, respectively (Fig. 3.16). All the other PGRs and EGRs
139
1.0
CG60 in 2007
CG60 in 2007
CG102 in 2007
CG102 in 2007
F1 in 2007
F1 in 2007
0.8
0.6
0.4
0.2
0.0
Harvest index (g g-1)
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.6
EGRs (g plant-1 GDD-1)
PGRs (g plant-1 GDD-1)
Figure 3.15. Relationship between harvest index (HI) and plant growth rate during the critical
period bracketing silking (PGRs) expressed as gram per plant per growing degree day (g plant-1
GDD-1), and between HI and ear growth rate after silking (EGRs) in g plant-1 GDD-1 for parental
inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 across two plant densities in
2007. The period bracketing silking for PGRs was from 2 wk before silking to 2 wk after silking.
140
The period bracketing silking for EGRs was from silking to 2 wk after silking. The plant
densities were 40,000 and 120,000 plants ha-1 for the two inbred lines and 40,000 and 160,000
plants ha-1 for the F1 hybrid.
141
1.0
CG60 in 2008
CG60 in 2008
CG102 in 2008
CG102 in 2008
F1 in 2008
F1 in 2008
0.8
0.6
0.4
0.2
0.0
Harvest index (g g-1)
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
0.0
0.1
0.2
0.3
0.4
PGRs (g plant-1 GDD-1)
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
EGRs (g plant-1 GDD-1)
Figure 3.16. Relationship between harvest index (HI) and plant growth rate during the critical
period bracketing silking (PGRs) expressed as gram per plant per growing degree day (g plant-1
GDD-1), and between HI and ear growth rate after silking (EGRs) in g plant-1 GDD-1 for two
parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 across three plant
142
densities in 2008. The period bracketing silking for PGRs was from 2 wk before silking to 2 wk
after silking. The period bracketing silking for EGRs was from 1 wk after silking to 3 wk after
silking. The plant densities were 80,000; 120,000 and 160,000 plants ha-1.
143
Table 3.8 Threshold values (± standard error) of hyperbolic model fitted to the relationship between harvest index (HI) and plant
growth rate during the critical period bracketing silking (PGRs) or HI and ear growth rate during the critical period bracketing silking
(EGRs) for two parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 in 2007 and 2008 grown at various plant
densities. The plant densities in 2007 were 40,000 and 120,000 plants ha-1 for the two inbred lines and 40,000 and 160,000 plants ha-1
for the F1. The plant densities in 2008 were 80,000; 120,000 and 160,000 plants ha-1 for the three genotypes.
R2
Threshold EGRs for HI
R2
n
Year
Genotype Threshold PGRs for HI
(g plant-1 GDD-1)
2007
2008
†
(g plant-1 GDD-1)
CG60
NA†
NA
0.014 ± 0.036
0.95
69
CG102
0.063 ± 0.012
0.85
0.025 ± 0.004
0.87
70
F1
NA
NA
NA
NA
NA
CG60
0.045 ± 0.014
0.79
NA
NA
147
CG102
NA
NA
NA
NA
154
0.051 ± 0.001
0.99
0.035 ± 0.000
0.99
146
F1
NA, not available.
144
threshold values for HI could not be estimated (Table 3.8). The only F1 barren plant had the
lowest PGRs and EGRs in 2008 (Fig. 3.16). Within barren inbred plants, the PGRs ranged from
0.04 to 0.11 g plant-1 GDD-1, from 0.08 to 0.28 g plant-1 GDD-1, and from 0.04 to 0.25 g plant-1
GDD-1 for CG102 in 2007 and in 2008 and CG60 in 2008, respectively. The EGRs ranged from
0.02 to 0.05 g plant-1 GDD-1, from 0.00 to 0.07 g plant-1 GDD-1, and from -0.12 to 0.18 g plant-1
GDD-1 for CG102 in 2007 and in 2008 and CG60 in 2008, respectively. When a barren inbred
plant had similar PGRs or EGRs (i.e., difference in PGRs and EGRs ≤ 0.01 and 0.005 g plant-1
GDD-1) as a non-barren inbred plant, the non-barren plant had HI in different ranges (Figs. 3.15,
3.16 and Table 3.9).
Dry matter partitioning to the ear is equivalent to the ratio between EGRs and PGRs and
is termed partitioning index (Pagano and Maddonni, 2007). The ultimately barren inbred plants
had above- and below-average partitioning index (Fig. 3.17). The values of partitioning index of
barren inbred plants ranged from 0.34 to 0.45, from 0.00 to 0.51, and from -0.87 to 0.97 for
CG102 in 2007 and in 2008 and CG60 in 2008, respectively. The ultimately barren F1 hybrid
had an above-average partitioning index (0.70 vs. 0.62). Furthermore, there was no relationship
between HI and partitioning index for each genotype × year combination irrespective of the
existence of a barren plant (Appendix B.8). The ultimately barren plants could have similar
partitioning index (difference in partitioning index ≤ 0.01) during the period bracketing silking as
the non-barren plants which had HI in different ranges (Table 3.9). Barren plants having similar
PGRs, EGRs and partitioning index as non-barren plants were found for CG102 in 2008 and
CG60 in 2008. These non-barren plants had HI with a range of 0.002 to 0.36, and 0.01 to 0.45
for CG102 in 2008 and CG60 in 2008, respectively (Table 3.9, Fig. 3.16 and Appendix B.8).
145
Table 3.9. The minimum (Min) and maximum (Max) harvest index (HI) of non-barren plants
when non-barren plants had similar values with barren plants (i.e., final grain yield per plant = 0
g) in plant growth rate during the critical period bracketing silking (PGRs), or ear growth rate
during the period bracketing silking (EGRs), or partitioning index (i.e., EGRs/PGRs) or all the
three traits for parental inbred lines CG102 and CG60 growing across different plant densities in
2007 and 2008. The plant densities were 40,000 and 120,000 plants ha-1 in 2007, and 80,000;
120,000 and 160,000 plants ha-1 in 2008. The criteria for similar value in PGRs, EGRs and
partitioning index is less than or equal to 0.01 gram per plant per growing degree day (g plant-1
GDD-1), 0.005 g plant-1 GDD-1 and 0.01, respectively.
Year Traits with similar value Genotype
HI of non-barren plants
Min
Max
2007 PGRs
CG102
None
None
EGRs
CG102
0.023
0.374
partitioning index
CG102
0.023
0.504
PGRs, EGRs and
partitioning index
CG102
None
None
2008 PGRs
CG102
0.002
0.454
EGRs
CG102
0.002
0.356
partitioning index
CG102
0.002
0.370
PGRs, EGRs and
partitioning index
CG102
0.002
0.356
PGRs
CG60
0.010
0.492
EGRs
CG60
0.013
0.461
partitioning index
CG60
0.013
0.509
PGRs, EGRs and
partitioning index
CG60
0.013
0.453
146
0.4
2007
2008
EGRs (g plant-1 GDD-1)
0.3
0.2
0.1
0.0
CG60
CG102
F1
-0.1
Barren CG60
Barren CG102
Barren F1
-0.2
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.0
0.1
-1
0.2
0.3
0.4
0.5
0.6
0.7
-1
PGRs (g plant GDD )
Figure 3.17. Relationships between estimated ear growth rate during the critical period
bracketing silking (EGRs) expressed as gram per plant per growing degree day (g plant-1 GDD-1)
and estimated plant growth rate during the critical period bracketing silking (PGRs) in g plant-1
GDD-1 of parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 grown across
different plant densities in 2007 and 2008. The plant densities in 2007 were 40,000 and 120,000
plants ha-1 for the two inbred lines and 40,000 and 160,000 plants ha-1 for the F1 hybrid. The
plant densities in 2008 were 80,000; 120,000 and 160,000 plants ha-1 for the three genotypes.
For each genotype, each dark grey symbol represents an individual plant. The black symbol
represent the ultimately barren plants (i.e., grain yield per plant = 0 g). The number of plants
was 69, 70 and 82 for CG60, CG102 and the F1 in 2007, respectively. The number of plants was
141,149 and 146 for CG60, CG102 and the F1 in 2008, respectively.
147
3.3.4 Barren plants and dominated plants of a plant hierarchy
When at least one barren plant is present within a genotype × plant density × year
combination, two ways could be used to categorize individual plants. Grain yield is one way to
class barren and non-barren plants. Above-ground plant dry matter at PM is the other way to
class d plants (i.e., those plants in the lower 1/3 of the PDMPM distribution), D plants (i.e., those
plants in the upper 1/3 of the PDMPM distribution) (Maddonni and Otegui, 2004, 2006), and
intermediate plants (i.e., those plants in the middle 1/3 of the PDMPM distribution). According to
both classifications, there were two situations in this study (Appendix B.9). The barren plants
totally belonged to the d plants for CG102 at 120,000 plants ha-1 in 2007 and CG60 at 120,000
plants ha-1 in 2008. In such situation, the barren and d plants could be divided into Bd plants and
NBd plants (Appendix B.9). In the other situation, barren plants partially belonged to the d
plants for CG102 at 80,000; 120,000 and 160,000 plants ha-1 in 2008 and CG60 at 160,000 plants
ha-1 in 2008. The barren and d plants could be further divided into three subpopulations: Bd
plants, NBd plants and BID plants (Appendix B.9). The F1 at 160,000 plants ha-1 in 2008 had
only one barren plant. Therefore, the results in this section (3.3.4) will mainly present the six
inbred × plant density × year combinations (Appendix B.9).
The reasons to study the relationship between barren plants and d plants and within
barren plants are to further characterize barren plants in analytical and comparable ways.
Barrenness is the extreme case of dry matter partitioning into the GY and the d plants are in the
proportion of low PDMPM. Within barren plants, the Bd and BID plants might experience
different physiological processes of becoming barren. In addition, the three subpopulations bring
148
three scenarios of growth and dry matter partitioning: First, Bd plants with low PDMPM and zero
dry matter partitioning; second, NBd plants with low PDMPM and having dry matter partitioning;
third, BID plants with relatively high PDMPM but zero dry matter partitioning. Comparing these
three subpopulations could help us understand mechanisms behind plant growth and dry matter
partitioning.
3.3.4.1 Difference in growth throughout development and partitioning at the subpopulation
level
The three genotypes differed in the relationship between HI and PDMPM (Appendix
B.10). Both the d, intermediate and D CG60 plants exhibited variation in HI. However, there
seemed no relationship between HI and PDMPM for d CG102 plants in 2007 and 2008. The d F1
hybrid plants maintained stable HI similar to the NBID plants. The minimum PDMPM of barren
CG102 plants declined with the increase of plant density, however, this trend could not be found
in CG60. Higher plant density did not widen the range in PDMPM for both inbred lines.
However, the higher the plant density was, the more likely barren plants appeared among the
intermediate and D plants for both inbred lines.
To determine differences among subpopulations, phenological differences, PDM
throughout development, EDM throughout development, flowering dynamics, as well as PGRs,
EGRs and partitioning index were examined. When barren plants belonged to the d plants, the
Bd and NBd plants did not exhibit differences in LT stage and PDM throughout development
(Table 3.10; Appendixes B.11 and B.12). The only two exceptions were CG102 at 120,000
plants ha-1 in 2007 where Bd plants had a subtle advance in LT stage development than NBd
149
Table 3.10. Comparisons among different subpopulations in above-ground plant dry matter (PDM), ear dry matter (EDM) throughout development, plant (PGRs) and ear growth
rate during the critical period bracketing silking (EGRs), and partitioning index (i.e., EGRs/PGRs) for two parental inbred lines CG60 and CG102 grown at different plant densities
in 2007 and 2008. Presented inbred × plant density × year combinations had at least one barren plant (i.e., grain yield per plant = 0 g). Plant hierarchies within a combination are
viewed according to PDM at physiological maturity (PDMPM) as d plants (i.e., plants in the lower 1/3 PDMPM distribution), intermediate plants (i.e., those plants in the middle 1/3
of the PDMPM distribution) and dominant (D) plants (i.e., those plants in the upper 1/3 of the PDMPM distribution). When assessing both barren plants and dominated (d) plants,
the plants could be barren d (Bd) plants, non-barren d (NBd) plants when barren plants were only present in the d plants. When barren plants were present in the intermediate and D
plants, barren plants and d plants could be grouped into Bd plants, non-barren d (NBd) plants, barren intermediate and D (BID) plants, respectively. The six PDM sampling stages in
2007 were 8-leaftip (LT) stage (PDM1), 12-LT stage (PDM2), 14-LT stage (PDM3), silking (PDMs), 2 wk after silking (PDMs+2) and PM (PDMPM), respectively. The five PDM
sampling stages in 2008 were 11-LT stage (PDM2), 15-LT stage (PDM3), silking (PDMs), 2 wk after silking (PDMs+2) and PM (PDMPM), respectively. The three EDM sampling
stages in 2007 and 2008 were silking (EDMs), 2 wk after silking (EDMs+2) and PM (EDMPM), respectively.
Year
Inbred
Plant density
(plants ha-1)
Subpopulation
PDM1
PDM2
PDM3
PDMs
2007
CG102
120,000
Bd
1.4 a†
7.3 a
24.7 a
NBd
1.2 a
7.2 a
Bd
NA
‡
NBd
2008
CG60
120,000
160,000
2008
CG102
80,000
120,000
160,000
†
PDMs+2
PDMPM
EDMs
EDMs+2
EDMPM
48.7 a
53.2 a
74.6 a
0.4 a
6.5 a
23.9 a
47.7 a
64.5 a
73.3 a
0.4 a
9.2 a
27.0 a
65.9 a
66.6 a
50.3 b
NA
8.6 a
29.5 a
63.7 a
71.0 a
Bd
NA
11.6 a
29.9 a
51.8 a
BID
NA
13.1 a
39.1 a
NBd
NA
9.5 a
Bd
NA
BID
Partitioning
index
PGRs
EGRs
5.4 b
0.09 a
0.04 a
0.40 a
8.6 a
15.9 a
0.12 a
0.05 a
0.40 a
5.0 a
9.3 a
1.0 b
0.21 a
0.17 a
0.83 a
71.7 a
5.2 a
12.4 a
29.2 a
0.18 a
0.08 b
0.49 b
58.3 a
41.0 b
3.5 a
5.1 b
2.1 b
0.11 a
0.02 b
0.15 b
65.0 a
69.8 a
66.0 a
7.2 a
6.8 ab
4.5 ab
0.13 a
0.00 ab
-0.01 ab
25.9 a
47.7 a
57.1 a
47.5 b
4.2 a
8.4 a
17.2 a
0.12 a
0.05 a
0.39 a
11.1 a
45.6 a
76.6 a
96.8 a
91.1 a
0.6 a
4.2 a
7.7 a
0.19 a
0.04 a
0.20 a
NA
11.7 a
44.4 a
82.8 a
111.5a
104.0 a
0.5 a
4.2 a
6.0 a
0.24 a
0.04 a
0.15 a
NBd
NA
10.3 a
39.8 a
78.4 a
99.6 a
91.8 a
1.1 a
8.0 a
14.3 a
0.22 a
0.07 a
0.31 a
Bd
NA
10.0 b
40.8 a
73.0 a
88.7 a
73.3 b
0.6 b
2.5 b
6.5 b
0.20 a
0.03 b
0.14 b
BID
NA
10.9 a
45.9 a
76.3 a
96.5 a
90.0 a
0.7 b
2.4 b
2.5 b
0.20 a
0.03 b
0.14 b
NBd
NA
8.7 c
32.0 b
73.2 a
88.2 a
76.0 b
2.8 a
7.3 a
23.7 a
0.23 a
0.07 a
0.30 a
Bd
NA
9.7 b
24.1 b
53.5 b
66.5 b
49.3 b
1.1 a
4.2 a
2.5 b
0.11 a
0.02 a
0.17 a
BID
NA
13.3 a
37.1 a
64.7 a
84.5 a
72.1 a
1.7 a
6.3 a
5.9 a
0.12 a
0.03 a
0.25 a
NBd
NA
10.1 ab
26.4 ab
57 ab
68.6 b
49.8 b
2.8 a
7.7 a
10.1 a
0.11 a
0.03 a
0.29 a
Different letters in bold within a row of inbred × plant density × year combination indicate significant difference (P < 0.05) using non-parametric tests.
‡
NA, not applicable.
150
plants (7.3-LT vs. 7.0-LT) and CG60 at 120,000 plants ha-1 in 2008 where Bd plants exhibited
lower PDMPM values. When barren plants did not totally belong to the d plants, no difference
between Bd and NBd plants was present with one interesting exception. Compared to the NBd
plants, the Bd plants had significantly higher PDM and similar LT stages at the first two
measurement periods and then similar PDM as NBd plants for CG102 at 120,000 plants ha-1 in
2008. When comparing BID to NBd plants, no difference was evident at 80,000 plants ha-1. At
120,000 plants ha-1, the BID CG102 plants had significantly advanced development in LT stage
and higher PDM than NBd plants at the first two vegetative measurement periods (10.1-LT vs.
9.2-LT and 14.0-LT vs. 13.2-LT). The difference disappeared around silking and was reestablished at PM. At 160,000 plants ha-1, the BID CG102 plants exhibited higher final leaf
number and PDMs+2, while no difference was present for CG60. When comparing the Bd and
BID plants, at 80,000 plants ha-1, no significant difference was evident. At 120,000 plants ha-1,
the BID plants had higher LT stage (10.1-LT vs. 9.4-LT) and PDM than Bd and then similar
growth and development through 2-wk post-silking. At 160,000 plants ha-1, the differences in
PDM between Bd and BID CG102 plants was evident at the first measurement period and
maintained through 2-wk post-silking, while no differences were evident for CG60 at the
corresponding period.
When barren plants totally belonged to the d plants, EDM had similar trends as PDM
with the exception that the NBd plants always had higher EDMPM than Bd plants (Table 3.10 and
Appendix B.13). When barren plants did not totally belong to the d plants, the trends in EDM
reflected the ones in PDM with three exceptions. First, in the comparison between Bd and NBd
plants, although no difference in PDM was evident from silking to PM for CG102 at 120,000
151
plants ha-1, NBd plants had higher EDM from silking to PM. Similarly, at 160,000 plants ha-1,
NBd plants had higher EDMs+2 than Bd and similar PDMs+2 as the Bd plants. Both NBd CG60
and CG102 had higher EDMPM than Bd although they had similar PDMPM. Second, in the
comparison between BID and NBd CG102, regardless of the significance of PDM, NBd had
higher EDM than BID from silking to PM at 120,000 plants ha-1 in 2008 and similar EDM as BID
at 160,000 plants ha-1 in 2008. Third, in the comparison between Bd and BID plants, regardless of
the difference in PDM, no difference in EDM was present with one exception for CG102 at
160,000 plants ha-1 in 2008 where BID had higher EDMPM than Bd plants.
In terms of the flowering dynamics, when no silk emergence was present within a
subpopulation (Appendix B.14), percentage of no silk emergence was selected as the comparison
criterion. When barren plants totally belonged to the d plants, the Bd plants had significantly
longer ASI (9 d vs. 5 d) and GDDsilking (806 vs. 759) than NBd plants for CG102 at 120,000
plants ha-1 in 2007. The percentage of no silk emergence for Bd CG102 plants at 80,000;
120,000 and 160,000 plants ha-1 in 2008 and Bd CG60 plants at 160,000 plants ha-1 in 2008 were
75%, 91%, 100% and 18%, respectively. The percentage of no silk emergence for BID CG102
plants at 80,000; 120,000 and 160,000 plants ha-1 in 2008 and BID CG60 plants at 160,000 plants
ha-1 in 2008 were 100%, 92%, 95% and 33%, respectively. When comparing the Bd plants to BID
plants, they had similar percentage of no silk emergence for each inbred × plant density
combination.
Interestingly, no significant differences in PGRs were observed among the three
subpopulations no matter whether barren plants totally belonged to d plants or not (Table 3.10
152
and Appendix B.15), which reflected the PDM dynamics during the period bracketing silking.
However, the difference in EGRs only reflected the difference in EDMs and EDMs+2 for CG102
at 120,000 plants ha-1 and CG60 at 160,000 plants ha-1 in 2008, but did not reflect the similarity
in EDM dynamics for CG60 at 120,000 plants ha-1 in 2008. The difference in partitioning index
reflected the underlying relationship between EGRs and PGRs for CG102 at 120,000 plants ha-1
and CG60 at 160,000 plants ha-1 in 2008, but not for CG60 at 120,000 plants ha-1 in 2008. For
CG60 at 160,000 plants ha-1 in 2008, the BID plants were distinguished from d plants in that they
exhibited zero EGRs and negative partitioning index value (Table 3.10).
3.3.4.2 Similarity in growth throughout development and partitioning at the individual
plant level
Examining the occurrence of similarity in growth, development and dry matter
partitioning could reveal how distinguishable are the three subpopulations before PM and the
chance of two individual plants with similar physiological trait(s) ultimately belonging to
different subpopulations.
To determine whether similarities were present between two individual plants from
different subpopulations, phenological development, PDM throughout development, EDM
throughout development, flowering dynamics, as well as PGRs, EGRs and partitioning index
were examined at the individual plant level. When barren plants totally belonged to the d plants
for CG102 at 120,000 plants ha-1 in 2007 and CG60 at 120,000 plants ha-1 in 2008, similarity in
PDM (i.e., difference in PDM ≤ 0.03 g plant-1), EDM (i.e., difference in EDM ≤ 0.015 g plant-1),
same LT stage, combination of PDM and LT stage and flowering dynamics were present in
153
several sampling stages (Table 3.11 and Appendix B.16). The same LT stage were present
between Bd and NBd plants at each sampling stage, however, they did not all go through the
same LT stage development (Appendix B.12). Barren d and NBd plants with similar EDMs and
same ASI (or GDDsilking) were not found (Table 3.11).
When barren plants did not totally belong to the d plants, both genotype and plant density
appeared to influence similarities in PDM, EDM, the combination of PDM and LT stage, as well
as the combination of PGRs, EGRs and partitioning index (Table 3.11 and Appendix B.16).
Comparing to CG102 at 160,000 plants ha-1, similarities in traits mentioned above among
individual plants from different subpopulations were less frequently observed in CG60 at the
same plant density in 2008 (Appendix B.16). Similarities in the traits were more frequently
observed for CG102 at 120,000 and 160,000 plants ha-1 in 2008, less frequently observed for
CG102 at 80,000 plants ha-1 in 2008 (Appendix B.16). Comparing to the results in PDMs,
PDMs+2 and PDMPM, similarities in EDMs, EDMs+2 and EDMPM between plants from different
subpopulations were more frequently observed at the corresponding stages (Appendix B.16).
The same LT stage development over different sampling stages was present among plants
from different subpopulations in each genotype × plant density combination (Appendix B.12).
However, not all Bd, BID and NBd plants went through the same LT stage development over
different sampling stages for CG102 at 120,000 and 160,000 plants ha-1 in 2008 and CG60 at 160
,000 plants ha-1 in 2008 (Appendix B.12). Only BID and NBd plants went through the same LT
stage development over different sampling stages for CG102 at 80,000 plants ha-1 in 2008.
Genotype seemed to affect the occurrence of the same flowering dynamics among different
154
Table 3.11. Summary of similarities in combination of above-ground plant dry matter (PDM) and leaftip (LT) stage, flowering dynamics (anthesis to silking
interval (ASI) and growing degree days from planting to silking (GDDsilking) and the combination of ear dry matter around silking (EDMs) and flowering
dynamics among different subpopulations for two parental inbred lines CG60 and CG102 grown at different plant densities in 2007 and 2008. Presented inbred
× plant density × year combinations had at least one barren plant (i.e., grain yield per plant = 0 g). Plant hierarchies within a combination are classed according
to PDM at physiological maturity (PDMPM) as d plants (i.e., plants in the lower 1/3 PDMPM distribution), intermediate plants (i.e., those plants in the middle 1/3
of the PDMPM distribution) and dominant (D) plants (i.e., those plants in the upper 1/3 of the PDMPM distribution). When assessing both barren plants and
dominated (d) plants, the plants could be barren d (Bd) plants, non-barren d (NBd) plants when barren plants were only present in the d plants. When barren
plants were present in the intermediate and D plants, barren plants and d plants could be grouped into Bd plants, non-barren d (NBd) plants, barren intermediate and
D (BID) plants, respectively. The five PDM sampling stages before PM in 2007 were 8-LT stage (S1), 12-LT stage (S2), 14-LT stage (S3), silking (S4), and 2 wk
after silking (S5), respectively. The four PDM sampling stages in 2008 were 11-LT stage (S2), 15-LT stage (S3), silking (S4) and 2 wk after silking (S5),
respectively. The similarity in PDM and EDM between two individual plants is defined as less than or equal to 0.3 and 0.15 g differences, respectively. The
existence of at least two plants with similarity in growth and same in development is marked with symbol.
Year
Genotype
Plant
S2
S3
S4
S5
ASI GDDsilking
ASI and
EDMs and EDMs and
Subpopulations
S1
density
GDDsilking
ASI
GDDsilking
(plants ha-1)
2007
2008
2008
CG102
CG60
CG60
120,000
120,000
160,000
CG102
80,000
CG102
120,000
CG102
160,000
Bd
Bd
Bd
BID
BID
Bd
BID
BID
Bd
BID
BID
Bd
BID
NBd
NBd
NBd
NBd
Bd
NBd
NBd
Bd
NBd
NBd
Bd
NBd
NBd

BID
Bd





















155




































subpopulations. The exact same ASI or GDDsilking or both were more frequently observed in
CG60 than CG102 (Table 3.11). Regardless of plant density, same flowering dynamics among
different CG102 subpopulations were rare. Plants with similar ear growth and the same
flowering dynamics (ASI or GDDsilking) from different subpopulations were few with the
following two exceptions. Similar EDMs and same ASI, as well as similar EDMs and GDDsilking
were found between BID and NBd plants for CG102 at 120,000 plants ha-1 in 2008, between Bd
plants and NBd plants for CG60 at 160,000 plants ha-1 in 2008, respectively (Table 3.11).
3.3.5 Barrenness and morphological and physiological traits
The size structure of physiological and morphological traits within a genotype × year
combination could give insight into how initially similar plants differentiate. In order to
minimize the artefacts of the chosen number of categories, the same number of categories was
carefully chosen for all traits in each genotype × year combination when a barren plant was
present (Koyama and Kira, 1956; Mack and Happer, 1977).
The ratio between PDMPM and MaxPH (PDMPM/MaxPH) exhibited normal distribution
for CG102 in 2008, but non-normal distribution for the other three genotype × year combinations
(Fig. 3.18). Maximum PH was normally distributed for CG102 in 2008 and the F1 hybrid in
2008, with the other two genotype × year combinations following non-normal distributions
(Appendix B.17). The PSLA and the largest leaf area were normally distributed for three
genotypes in the four genotype × year combinations (Appendixes B.18 and B.19). The leaf
length/leaf width of the largest leaf was normal distributed for CG60 in 2008 and the F1 hybrid in
156
350
30
250
200
Frequency of barren plants
20
10
0
CG102 in 2008
20
10
0
0.24 0.62 1.00 1.38 1.76 2.14
-1
Ratio (g cm )
150
0.11 0.48 0.83 1.19 1.56 1.92
-1
Ratio (g cm )
100
50
0
30
300
250
30
Plant number
CG60 in 2008
Plant number
PDMPM (g plant-1)
30
PDMPM/MaxPH ratio frequency
Plant number
Barren plants
Non-barren plants
300
Plant number
CG102 in 2007
20
10
F1 in 2008
20
10
200
0
0
0.11 0.56 1.01 1.46 1.91
150
0.21
-1
0.76
1.30
1.84
-1
Ratio (g cm )
Ratio (g cm )
100
50
0
100
150
200
250
100
150
200
250
300
MaxPH (cm)
Figure 3.18. Relationship between above-ground plant dry matter at physiological maturity (PDMPM) and maximum plant height
(MaxPH) as well as the frequency distributions of the ratio between PDMPM and MaxPH for two parental inbred lines CG60, CG102
and their F1 hybrid CG60 × CG102 grown across different plant densities in 2007 and 2008. The plant densities in 2007 were 40,000
and 120,000 plants ha-1 for the two inbred lines, 40,000 and 160,000 plants ha-1 for the F1 hybrid. The plant densities in 2008 were
80,000; 120,000 and 160,000 plants ha-1 for the three genotypes. Within each genotype, plants had similar initial plant size at the 8157
leaftip (LT) stage in 2007 and 4-LT stage in 2008. The dark grey bars indicate the position of ultimately barren plants (i.e., grain yield
per plant = 0 g). In terms of frequency distribution of the ratio, for CG102 in 2007 (n = 70): Shapiro-Wilk normality test (Pr < W) =
0.00, skewness = 0.50, kurtosis = -0.99, coefficient of variation (CV) = 40%, and mean (M) = 0.99. For CG60 in 2008 (n = 154): Pr <
W = 0.01, skewness = 0.52, kurtosis = -0.14, CV = 37% and M = 0.56. For CG102 in 2008 (n = 155): Pr < W = 0.07, skewness =
0.33, kurtosis = -0.44, CV = 29%, and M = 0.48. For the F1 in 2008 (n = 153): Pr < W = 0.00, skewness = 0.68, kurtosis = 0.04, CV =
32%, and M = 0.72.
158
2008, non-normally distributed for CG102 in 2007 and 2008 (Appendix B.20). The stem
elongation rate and ear diameter expansion rate followed non-normal distributions for the three
genotypes in the four genotype × year combinations (Appendixes B.21 and B.22). The stem
elongation rate of CG102 and the F1 exhibited bimodality in 2008. Moreover, the three
genotypes exhibited similar CV values in PDMPM/MaxPH, MaxPH, PSLA, the largest leaf area
and its leaf length/leaf width and stem elongation rate with the exception of ear diameter
expansion rate.
The relationship between PDMPM and MaxPH could give insight into the overall PDMPM
distribution along the stem (Fig. 3.18). The ultimately barren plants could not be found in the
several categories with the highest PDMPM/MaxPH for the three genotypes in the four genotype
× year combinations. Barren plants were most concentrated in the categories with the lowest
PDMPM/MaxPH for CG102 in 2007, CG60 in 2008 and the F1 in 2008 with the exception of
CG102 in 2008. In 2008, plants with the lowest PDMPM/MaxPH were the ultimately barren
plants for the three genotypes.
The ultimately barren plants were present in the close-to- and below-average categories
of the MaxPH for CG102 in 2007, and in a wide range of MaxPH for CG102 in 2008 and CG60
in 2008 (Appendix B.17). The ultimately barren F1 plant was in the below-average category of
MaxPH (the third lowest category). The differences between the tallest and the shortest MaxPH
for barren CG60 in 2008 and CG102 in 2008 were 57 and 68 cm, respectively. The ultimately
barren CG102 plants were present in the above- and below-average categories of PLSA, the
largest leaf area and its leaf length/leaf width in 2007 and in a wide range of PSLA, the largest
159
leaf area and its leaf length/leaf width in 2008 (Appendixes B.18-B.20). The ultimately barren
CG60 plants in 2008 were also distributed in a wide range of PSLA, the largest leaf area and in
the above- and below-average categories of leaf length/leaf width. The ultimately barren F1
plant was in the below-average category of PSLA (the fourth lowest category), the largest leaf
area (the fourth lowest category), and above-average categories of leaf length/leaf width.
Comparing within the barren plants of an inbred × year combination, the largest PSLA (i.e., 3815
cm2) of a barren CG102 plant doubled the size of the smallest PSLA (i.e., 1703 cm2) of another
barren CG102 plant in 2008 (Appendix B.18). In 2008, the leaf length/leaf width of the largest
leaf was significantly different among the three genotypes, with CG102 being the largest, the F1
hybrid being the middle and CG60 being the smallest.
Stem elongation rate for an individual plant is equivalent to the slope of the linear
relationship between PH and thermal time during the linear stem elongation period. Coefficients
of determination of the linear regressions were between 0.86 and 0.97 for CG102 in 2007 and
between 0.94 and 1.00 for the three genotypes in 2008. The ultimately barren CG102 plants
were found in the average and below-average stem elongation rate in 2007 and in almost every
category of stem elongation rate except for the highest stem elongation rate in 2008 (Appendix
B.21). The ultimately barren CG60 plants were found in a wide range of stem elongation rate.
The only F1 barren plant was in the lowest category of stem elongation rate. The highest stem
elongation rate of a barren plant could be 1.3, 1.7, and 1.9 times the lowest stem elongation rate
of a barren plant for CG102 in 2007, CG102 in 2008 and CG60 in 2008, respectively.
Ear growth could be measured as a permanent increase in physical dimensions including
160
ED and ear length. In this study, only the ED was used to estimate ear growth. The ear diameter
expansion rate for two inbred lines in 2008 varied from negative to positive values with CG102
in 2007 and the F1 in 2008 having positive values of ear diameter expansion rate (Appendix
B.22). The ultimately barren CG102 plants had above- and below-average values of ear
diameter expansion rate in 2007 and a wide range of ear diameter expansion rate in 2008.
Within an inbred × year combination, an ultimately barren ear expanded as much as 1.1 and 1.4
mm per day for CG102 in 2008 and CG60 in 2008, respectively.
161
3.4 Discussion
Research on barrenness could be the most extreme way to study yield as a complex trait.
Few studies have examined the physiological processes underlying maize barrenness under field
conditions, especially for homogenous plants with similar initial plant size and development and
uniform spatial pattern. Understanding the dynamics of growth and development throughout the
life cycle for individual barren plants could give insight into yield loss mechanisms compared to
the rest of the non-barren plants which had similar initial growth and development. Therefore,
the main objective of the study was to characterize barren plants in growth dynamics through
development and plant dry matter partitioning at the canopy, subpopulation and mainly
individual plant levels in two parental inbred lines and their F1 hybrid.
3.4.1 Allometric models to predict individual barren and non-barren plants
3.4.1.1 Comparison of allometric models
After selecting models with intercepts for PDM estimation in equation [3.3] and the
power function for EDM estimation in equation [3.8], the PDM should be the sum of vegetative
parts and reproductive parts. The PDM equation during reproductive stages should be
PDM = a + b × (SV or SVmax) + c × EDd
[3.11]
Where a, b, c and d are model parameters and PDM has units of g plant-1.
However, the equation [3.11] formula had similar R2 as the equation [3.4] formula. In addition,
Proc REG to calculate R2 for equation [3.4] is a well-established SAS procedure with outlier
detection function and directly computing R2 and RMSE, compared to the indirect calculating R2
for equation [3.11] by Proc NLIN. Therefore, the allometric relationship in equation [3.4] was
162
used to estimate PDM.
3.4.1.2 Justification of allometric models and methodology
Although the combined plant density models outperformed individual plant density
models, significant bias happened between/among individual plant densities within the combined
plant density models. Significant bias between/among individual plant densities within the
combined plant density models was found in two situations in this study (Tables 3.4 and 3.5). In
50% of cases for PDM estimation, the underestimation (i.e., positive RP) in one plant density
was significantly different from overestimation (i.e., negative RP) in another plant density within
a combined plant density model. In the other 50% of cases, the combined plant density model fit
well with a plant density but exhibited lack of fit for another plant density. For both situations,
the bias between/among plant densities within a combined plant density model could be due to
the following two scenarios. First, using a combined plant density model and an individual plant
density model would have similar bias. Second, using a combined plant density model exhibited
lack of fit of model compared to using an individual plant density model for a not-fitting-well
plant density in the combined plant density model. In fact 67% of significant bias was due to the
first scenario for PDM estimation. Thirty-three percent of significant bias was due to using
combined plant density model for PDM estimation. Therefore, in 17% cases, combined plant
density models exhibited lack of fit of model for PDM estimation. Using the similar rationale as
combining plant density, all the allometric models that combined different factors in the
literature (Appendix A.1) should be justified to avoid significant bias before any further data
analyses. Due to using both combined plant density models and individual plant density models
163
as well as the combination of both models, there were no significant bias in EDM estimation in
this study.
3.4.1.3 Evaluation of allometric models
In order to evaluate whether the developed models have improved the prediction of plant
and ear growth, the models here were compared to previous published models using the same
genotypes during similar measurement periods (Echarte and Tollenaar, 2006). The study here
had larger sample size and used different allometric models compared to their study (Appendix
A.1). The average R2 of their allometric models for PDM estimation across different stress
treatments, reproductive measurement stages and years were 0.82, 0.88 and 0.79 for CG60,
CG102 and the F1, respectively (Echarte and Tollenaar, 2006). The average R2 here across two
reproductive stages and two years were 0.90, 0.84, and 0.93 for CG60, CG102 and the F1,
respectively (Table 3.2). Similarly, the average R2 of their models for EDM estimation were
0.81, 0.93 and 0.96 for CG60, CG102 and the F1, respectively (Echarte and Tollenaar, 2006).
The average R2 here were 0.79, 0.91, and 0.85 for CG60, CG102 and the F1, respectively (Table
3.3). Therefore, the models used in this study have improved the accuracy of CG60 and the F1
PDM estimation and decreased the accuracy of CG102 PDM estimation. The models decreased
the accuracy of EDM estimation for the three genotypes slightly. On the one hand, inclusion of
intercepts in their models for EDM estimation is not biologically meaningful (Appendix A.1).
When ED and ear length is zero, EDM should be zero. On the other hand, based on R2 values,
their allometric models including ear length seemed to increase the accuracy of EDM estimation
compared to the models in this study. Whether models that are consistent across genotype,
164
reproductive growth stage and year, as well as inclusion of ear length will outperform the models
here is questionable.
Three areas of bias could affect the accuracy of PDM and EDM estimation, which
included extrapolation, significant bias by using combined plant density models and the absolute
RP larger than 5% (Appendix B.2). Previous studies indicate that individual plants of a genotype
are homogenous with respect to the allometric relationship across a wide range of resource
availability per plant within a growing season. A wide range of resource availability per plant is
created by different plant densities, water and N availability or their combinations. Studies from
Argentina, USA and Canada have shown this pattern. For examples, across different plant
densities from 20,000 to 200,000 plants ha-1, same allometric relationships for PDM and EDM at
2 wk before/after silking are fit for a hybrid (Vega et al., 2001). Across different levels of water
availability during the critical period bracketing silking, same allometric relationships for PDM
are fit within a genotype at silking and 2 wk after silking for CG60, CG102 and their F1 hybrid
(Echarte and Tollenaar, 2006). The same pattern is shown for EDM at 2 wk after silking
(Echarte and Tollenaar, 2006). Using different plant densities and N rates, Boomsma et al.
(2009) assume a general allometric relationship across different plant densities and N availability
for two commercial hybrids. Therefore, the PDM of a plant density or a N rate at V14, R1, and R3
are deduced by the average PH and SDb non-destructively. They conclude that plants should
have the lowest PDM at these stages for the treatment of the highest plant density × lowest N
availability (Boomsma et al., 2009). In addition, same allometric relationships are fit over
different growth stages across different plant densities (Maddonni and Otegui, 2004; Pagano and
Maddonni, 2007; Pagano et al., 2007) or contrasting plant density × N (Rossini et al., 2011). The
165
growth stages are from vegetative stages to silking for PDM estimation, and from silking to 2 wk
after silking for EDM estimation (Rossini et al., 2011). Therefore, the allometric relationships
developed in this thesis could represent a wider range of data than sampled data range. When
focusing on genotype × plant density × year combinations where barrenness occurred, only
seven combinations were considered. The F1 hybrid at 160,000 plants ha-1 had better
performance compared to the two inbred lines.
3.4.1.4 Limitation and improvement of allometric models
The only one dimensional measurement of ED could be a potential problem for EDM
estimation in inbred plants. Maize ear growth is at least in two dimensions: ED and ear length.
Genotypes might be different in how ears expand and elongate. In this study, the ears of two
parental inbred lines and the F1 hybrid are in the shape between cone and cylinder. However,
only the two inbred lines exhibited shrinkage in ED across 80,000; 120,000 and 160,000 plants
ha-1 in 2008 (8% and 6% for CG60 and CG102, respectively) (Appendix B.22). The decrease in
ED might be compensated by the increase in post-silking ear length (Chapter 2 and Otegui and
Bonhomme, 1998) or be related to other physiological mechanisms which will be discussed in
the section 3.4.2.3. Because ears are covered by husks at the silking and post-silking stages and
the unstraightness of shanks, the exact ear length is difficult to measure. When the ear elongates
with a decrease in ED, the equation in Table 3.3 might estimate a decrease in EDMs+2 compared
to EDMs and a negative EGRs during the period bracketing silking. Besides, CG60 plants
sometimes have two ears (i.e., one primary ear and one small secondary ear) covered by the
same husks, which might overestimate the primary EDM and bring estimation error. Similarly,
166
the shrinkage of SDb was observed in this study, as in a hybrid study by Boomsma (2009), and
could underestimate SV. Therefore, the SD2 was introduced to improve accuracy of PDM
estimation in six out of 12 genotype × sampling stage combinations in 2008. Therefore,
common equations on allometric models might not fit best for all genotypes due to different
plant and ear morphology. A preliminary study on plant and ear morphological change over time
could improve the morphometric variable selection and formula formation for allometric models.
In addition, to avoid extrapolation, the range of a morphometric variable such as SV (SVmax) and
ED in the destructive sampling areas should cover the range of the same variable in the nondestructive measurement areas at the same sampling stage. Especially, plants with zero ED at
reproductive stages should be included to improve the prediction of the zero ED plants at high
plant density in the non-destructive measurement areas. With the increasing application of
allometric models on plant research, standardized sets of criteria for allometric model selection
and evaluation should be established to help professionals assess models effectively (Alexandrov
et al., 2011).
3.4.2. Characterizing barren plants
Barrenness is the most extreme condition for GY as a complex trait. Characterizing
barren plants under field conditions could improve our understanding of yield loss mechanisms
as well as identify the ultimately barren plants. Possible prediction of barren plants before PM
could facilitate researchers to identify the ultimately barren plants for further investigation.
167
3.4.2.1 Approach to study barren plants
Maize barrenness has been studied at the canopy (Buren et al., 1974) and individual plant
level (Vega and Sadras, 2003; Boomsma, 2009). In this study, barren plants were characterized
by the physiological processes underlying GY formation in a top-down approach from canopy
level to subpopulation level, to individual plant level. The physiological processes were
dissected into PDM throughout development, EDM throughout development and partitioning
during the critical period bracketing silking and at PM. The top-down approach included (1)
comparing average barren plants to average plants of a plant population; (2) comparing average
barren plants to average non-barren plants of a plant population; (3) comparing individual barren
plants to average plants of a plant population; (4) comparing among individual barren plants; (5)
comparing Bd to NBd plants; (6) comparing BID to NBd plants; and (7) comparing Bd to BID
plants.
The genetic materials of the study were two parental inbred lines CG60, CG102 and their
F1 hybrid CG60 × CG102 which showed genotypic difference in kernel set mechanisms (Echarte
and Tollennar, 2006). Within a genotype, the individual plants had similar initial plant growth
and development before the stage of ear initiation, and grown under uniform spatial pattern.
Plant density on the one hand, was used to create a broad range of physiological and
morphological values to characterize barren plants within a genotype, and on the other hand was
used to examine the difference among genotypes and within a genotype in response to plant
density at the canopy, subpopulation and individual plant level.
168
3.4.2.2 Unpredictability of barrenness in two parental inbred lines
Due to there being only one F1 barren plant, the discussion of unpredictability of
barrenness is focused on the two parental inbred lines. Barrenness was unpredictable in terms of
plant growth and phenological development from early vegetative to post-silking stages for the
two inbred lines. It was predicted that the ultimately barren plants were preconditioned by their
relatively lower vegetative growth compared to the non-barren plants and ultimately became
barren. However, the results showed that the individual barren plants did not always follow
linear growth and exhibited differential responses to their previous growth and development
(Figs. 3.9–3.11; Appendixes B.3 and B.4). At the individual plant level, it was found that barren
and non-barren plants, Bd and NBd plants or/and BID and NBd plants had similar PDM and were
at the same LT stage at a phenological stage before PM (Table 3.11 and Appendix B.16).
Similarly, barrenness could not be predicted by ear growth and flowering dynamics.
For CG102, EDMs seemed to be a good predictor for barrenness because barren plants were
located at the lowest EDMs frequency distribution in 2008. However, the trend was not
consistent between the two years. Even if the association between EDMs and barrenness exists
without experimental year effect, the exact frequency distribution that barren ears belong to
within a plant population needs to be studied to possibly predict barrenness. In addition, no silk
emergence could be a good predictor for barrenness and it explained most barrenness for CG102
in 2008. A ‘threshold’ value of EDMs for silk emergence seemed to exist in CG102 at each plant
density in 2008 but not in 2007 (Fig. 3.14). However, non-barren plants with EDMs within the
‘threshold’ values exhibited silk emergence. Therefore, a threshold EDMs could not condition
no silk emergence for CG102. For CG60, neither EDM before PM nor ASI and GDDsilking were
169
associated with barrenness. And similar EDM, ASI and the combination of ASI and GDDsilking
were found between Bd and NBd plants or/and BID and NBd plants (Table 3.11 and Appendix
B.16).
Barrenness could not be predicted according to PGRs, EGRs or partitioning index.
Although a threshold value of PGRs for HI was estimated for CG102 in 2007 and CG60 in 2008
(Table 3.8), and a threshold value of EGRs for HI was estimated for CG60 and CG102 in 2007,
there was no consistent estimation between two years for either inbred line. Therefore, barren
inbred plants were still unpredictable. Compared to previous estimations of the PGRs threshold
for kernel set using the same genotypes (Echarte and Tollenaar, 2006), the threshold values here
(PGRs threshold values of 0.05 g plant-1 GDD-1 for CG60 and 0.06 g plant-1 GDD-1 for CG102)
were much smaller than theirs (PGRs threshold values of 0.09 - 0.10 g plant-1 GDD-1 for CG60
and 0.17 - 0.20 g plant-1 GDD-1 for CG102). Echarte and Tollenaar (2006) used 60,000 plants
ha-1 and had consistent estimation of PGRs threshold values for kernel set across experimental
year and water stress and plant density treatment. The inconsistent performance here was
probably due to the plant density as an experimental factor instead of water stress. For plant
density treatment under field conditions, Ritchie and Wei (2000) also found few conclusive
PGRs for estimating barrenness. For the two inbred lines, the lack of threshold values for
barrenness may be due to the involvement of other mechanisms in determining kernel number
(Ritchie and Wei, 2000). If there are other threshold values of plant growth for barrenness
associated with those mechanisms, it is likely that another threshold value of a physiological
parameter could be used together with PGRs and EGRs to predict barrenness. For the F1 hybrid,
threshold values were lacking because almost all the PGRs and EGRs are above the threshold
170
values and the one or two data points of PGRs (EGRs) with zero HI can determine the x-axis of
the hyperbolic function, which might not be the real threshold value of PGRs (EGRs) for the F1.
Barrenness for a genotype could be predicted if (i) there is a common pattern between PGRs
(EGRs) and HI (KNP) and (ii) the estimation of PGRs (EGRs) threshold values for HI or kernel
set is well established across growing seasons and different levels of an experimental factor that
causes variation in PGRs and EGRs. There was no association between HI and partitioning
index; therefore, dry matter partitioning during the period bracketing silking could not condition
barrenness.
3.4.2.3 Above-ground plant dry matter at physiological maturity of barren plants
For average barren plants, trends of less PDMPM than PDMs+2 for two parental inbred
lines in 2008 were similar to the results of PDM for three hybrids after bagging ears or removing
unpollinated ears (Crafts-Brandner et al., 1984). The study presented in this thesis in a natural
way, and the study of Crafts-Brandner et al. (1984), both produced barren plants at PM. The
three hybrids in their study exhibited decrease of PDMPM compared to PDM at 8 d and 30 d after
anthesis at the canopy level, when pollination was prevented by bagging the ears. In the present
work at the individual plant level in both inbred lines, there were barren plants that exhibited
higher PDMPM than PDMs+2; plants had no silk emergence similar to the effects of bagging ears
from pollination. The zero EGRs of BID plants for CG60 at 160,000 plants ha-1 might indicate
the early onset of feedback inhibition during the critical period bracketing silking. The
allometric relationship is based on EDM and fresh ED. The negative partitioning index is
possibly due to two reasons. First, ED at 2 wk after silking was less than at silking, and low
171
moisture content and change in ear shape could cause ear shrinkage. Second, they were
measurement errors.
3.4.2.4 Effects of genotype, plant density and genotype × plant density interaction
Genotype, plant density and genotype × plant density interaction had different effects on
plant growth and development. CG102 exhibited association between plant growth and
development at the subpopulation level and ear growth (EDMs) and development (ASI or
GDDsilking) at the canopy level (Fig. 3.14). The association of plant growth and development
were exhibited between d and BID plants only at 120,000 plants ha-1 in 2008. Existence of plant
hierarchy in development has rarely been reported compared to PDM dynamics through thermal
time by past plant hierarchy studies (Maddonni and Otegui, 2004; Rossini et al., 2011). The
association of ear growth and development in CG102 were consistent across plant densities in
2008. Strong association between EDMs and GDDsilking happened in the F1 at 160,000 plants ha-1
(Appendix B.6). Similarly the association between plant growth and ear development (Rossini et
al., 2012), as well as ear growth and development (Borrás et al., 2007) were found in previous
studies. However, association between growth and development was not found in CG60.
CG60 and CG102 varied in response of barrenness to plant density. At the canopy level
across same plant densities, CG102 was more susceptible to barrenness than CG60 (Figs. 3.1 and
3.2). At 120,000 and 160,000 plants ha-1, barren CG102 plants could extend to the D plant
hierarchy and barren CG60 kept in d plant hierarchy or extended to the intermediate plant
hierarchy (Appendix B.9). At the subpopulation level, the onset of differences among Bd, BID
172
and NBd plants in plant and ear growth happened later than 2 wk after silking in CG60, which
was later than CG102 (Table 3.10). In addition, in contrast to CG102, CG60 had no response to
plant density in terms of comparing plant growth among Bd, BID and NBd plants (Table 3.10).
There were more similar plants in physiological traits regarding growth and development among
individual Bd, BID and NBd plants in CG102 than CG60 (Table 3.11 and Appendix B.16).
Plant density did not always increase PPV in plant and ear growth at the canopy and
subpopulation (i.e., combination of barren and d plants) levels for CG60 and CG102 in 2008
(data not shown). However, at the individual plant level, plant density increased similarity in
plant and ear growth among Bd, BID and NBd plants for both genotypes (Table 3.11 and
Appendix B.16). At the individual plant level, the fewer similarities among Bd, BID and NBd
plants in the combination of plant growth and development (Table 3.11) than in plant growth
alone (Appendix B.16) suggested that plant development is also important to be considered.
Since Bd, BID and NBd included plants with similar initial growth and development and at a later
growth point, they were similar again in growth and different in development. So growth and
development should be considered together. But the same logic could not apply for the ear
growth and development, since the initial ear growth and development were different (Appendix
B.13).
Genotype × plant density interaction happened on plant growth at the subpopulation level
(Table 3.10). Increased plant density only caused significant difference in plant growth at each
measurement stage for CG102 at 160,000 plants ha-1 in 2008. At the same time, similarities in
plant growth among individual Bd, BID and NBd plants were found almost at each measurement
173
period (Appendix B.16). Therefore, the trend in plant growth at the subpopulation level could
not indicate the trend in plant growth at the individual plant level for CG102 at 160,000 plants
ha-1 in 2008.
3.4.2.5 Barrenness is not totally in relation to dominated plants
In this study, barrenness was not totally confined to d plants for the two parental inbred
lines. The relationship between barrenness and d plants varied by environmental year, genotype
and plant density. Whether barrenness is totally related to d plants depends on the threshold
value of PDMPM for HI. When the threshold value of PDMPM for HI is below 1/3 of the PDMPM
distribution, the barrenness should be confined to d plants.
3.4.2.6 Barrenness and physiological and morphological traits
In this study, physiological and morphological traits were used to characterize barrenness
and the possible mechanism behind plant growth because all the traits are easier to measure and
calculate non-destructively than the traits derived from allometric models. Generally, the
ultimately barren plants were the thinnest or had the least PDMPM for their PH in 2008 (Fig.
3.18). No other physiological and morphological traits alone could explain barrenness for
individual plants in the field. The study here did not compare to the previous studies on the
frequency distribution of the same traits due to their analyses on a separate plant density basis
(Daynard and Muldoon, 1983; Vega and Sadras, 2003; Boomsma, 2009).
Shade avoidance response should not be the major cause for barrenness in this study. At
174
the canopy level, one of the most consistent effects of shade avoidance response on maize is the
reduction in LT appearance from 6-LT to 11-LT with the standard of control plants with no
shade avoidance response under no resource competition conditions (Page et al., 2009, 2010b,
2011). In the present study, the average barren plants had similar LT as non-barren plants at
each sampling stage from 7-LT stage to final leaf number for CG102, and from 11-LT stage to
final leaf number for CG60 (data not shown). Another constant characteristic of shade
avoidance response is the significant increase in PH that happened before and around 10-LT
stage under field environments (Page et al., 2009, 2010b, 2011). In the present study, barren and
non-barren plants had similar PH at each sampling stages from 7-LT stage to 2 wk after silking
for CG102 and from 11-LT stage to 2 wk after silking for CG60 except that barren plants
exhibited significant higher PH from 13.7-LT stage to 2 wk after silking for CG102 at 120,000
plants ha-1 in 2008 (data not shown). The leaf length/leaf width of the largest leaf at post-silking
increased with increase in plant density for all three genotypes in 2008 (data not shown). The
longer and narrower leaf for 5-wk-old corn seedlings was regarded as the adaptive response to
lower red/far-red ratio (Kasperbauer and Karlen, 1994). However, whether results here at postsilking could compare to their early seedling stage needs more consideration. Another typical
shade avoidance response is taller plants producing lower yield (Smith and Whitelam, 1997).
However, at the subpopulation level, the D plants always had a higher GY and MaxPH when
there was a significance difference between D and d plants in MaxPH (data not shown).
175
3.4.2.7 Research limitation, values and implications
The physiological characteristics of barrenness in the F1 hybrid could not be fully
examined due to the low occurrence of barrenness at 160,000 plants ha-1 in both years. A higher
plant density should be implemented to characterize barren F1 plants. Also, consistency of plant
densities across years would make the results between two years more comparable. In order to
overcome the experimental limitations and comprehend the mechanisms behind barrenness,
broader genotypes should be collected and tested at broader plant densities and over multiple
years. First, with the assumption of normal early ear development, three types of genotypes with
different mechanisms related to intra-specific competition under crowding stress should be
included: (i) genotypes that are dominated by direct competition for resource such as C, N and
water with minimum sensitivity to changes in light quality; (ii) genotypes that respond to
crowding stress with growth alteration mechanism instead of direct resource competition, such as
a commercial hybrid reported by Clay et al. (2009); and (iii) genotypes that are affected by both
direct resource competition and growth alteration mechanisms in different degrees. Second,
however, when early ear growth and development prior to silking is inhibited or impaired such as
embryo sac abortion (Moss and Downey, 1971) and atypical spikelets (Smith, 2012), genotypes
differing in production and sensitivities of plant growth regulators should be tested. For example,
Below et al. (2009) reported ten commercial hybrids could differ in their responses to ethylene
and the incidence of ‘hollow husk’ barrenness. Due to the methodology of selecting similar
initial plant in size and development, the initial variability of plant size and development would
be relatively smaller compared to variation in the commercial field conditions.
For a complex trait such as GY, the research used barrenness as an entrance to examine
176
the physiological processes underlying GY formation in maize. In addition, a top-down
approach from canopy level to individual plant level was demonstrated and used to dissect
physiological processes underlying barrenness into growth and dry matter partitioning. Partial
barrenness, such as an ear with more than 1/3 unfilled kernels, is perhaps more important than
complete barrenness. This could be examined in future studies. In addition, the association
between growth and development indicates that future research could explore the cause-andeffect or coordination relationships between growth and development in field crops.
177
3.5 Conclusions
In this study, the physiological characteristics of barren plants with similar initial growth
and development and uniform spatial pattern were examined at the canopy, subpopulation and
mainly individual plant levels. (i) Individual plants had differential responses to their previous
growth and development in the two parental inbred lines. The physiological traits related to
plant and ear growth and development, PGRs and EGRs as well as dry matter partitioning during
the period bracketing silking could not characterize ultimately barren plants. Barrenness could
not be predicted accurately for the two parental inbred lines. (ii) Barrenness is not totally
confined to d plants for the two parental inbred lines. (iii) Physiological and morphological traits
used in this study could not characterize barren inbred plants. (iv) In general, the allometric
models could accurately estimate PDM and EDM at the individual plant level. The performance
of allometric models will be improved after rigid development and justification.
178
Chapter 4. General Discussion and Conclusions
4.1 Summary of the Results and Conclusions
The results in Chapter 2 highlight the period around the kernel row number (KRN)
formation stage and less dry matter partitioning to the ear of the F1 hybrid than its parental inbred
lines. At the KRN formation stage, plant-to-plant variability (PPV) in stem volume (SV) derived
from the maximum width of stem diameter 2 cm above the ground level (SDb) (SVmax), which
represents PPV in above-ground plant dry matter (PDM), affected PPV in spikelet number per
row (SNPR) and maximum kernel row number (KRNmax). At this stage, SNPR exhibited a
higher coefficient of variation (CV) than KRNmax for the three genotypes. Otherwise, there were
no effects of PPV in SVmax on PPV in SNPR and KRNmax. These applied from 1.5-leaftip (LT)
stages after the KRN formation for the three genotypes. In addition, the F1 exhibited less CV in
SNPR and spikelet number per ear (SNPE), at the KRN formation stage, than the two parental
inbred lines. Otherwise, the three genotypes generally had similar magnitude of CV in ear traits.
Around the silking period, even though the F1 had higher PDM at silking (PDMs) and 1 wk after
silking (PDMs+1), it exhibited significantly shorter ear length, less ear dry matter (EDM), and
less dry matter partitioning to the primary ear (EDM/PDM) than the two inbred lines at the
corresponding stages.
The results in Chapter 3 highlight that the ultimate barren inbred individuals exhibited
differential responses to their previous growth from vegetative stages to the critical period
bracketing silking, and they exhibited differential responses to their previous phenological stage.
The differential responses of barren plants to their previous growth indicated that the resource
179
availability at the individual plant level changed during these periods. Therefore, the ultimately
barren plants could not be preconditioned by the vegetative PDM. In 2008, only some of the
barren CG102 could be explained by low dry matter partitioning to the ear. The differential LT
development rates might result in changes in the resource availability per plant.
There were some interesting features in ear development, plant growth and development
around the KRN formation stage for inbred CG102. The one-year field study at 80,000 plants
ha-1 at the canopy level showed that (i) from 1.0-LT before KRN formation to 1.0-LT after KRN
formation, CG102 exhibited relatively higher PPV in SNPR than following phenological stages,
until 1 wk after silking (Fig. 2.12). (ii) Only during a 1.5-LT interval from KRN formation, the
PPV in SVmax had effects on PPV in SNPR, and it affected PPV in SNPR more than PPV in
KRNmax (Fig. 2.16). (iii) From ear initiation to KRN formation stage, CG102 needs more
growing degree days (GDD) than the other two genotypes, but a similar LT interval as the other
two genotypes (section 2.3.2). Furthermore, the two-year field study on CG102 at 120,000
plants ha-1 at the subpopulation level had consistently shown that (i) whether barren or not, the
dominated (d) plants exhibited similar LT stage throughout development. (ii) Also, the
intermediate and dominant (D) plants, whether barren or not, exhibited advanced LT stage and
significantly higher PDM during the KRN formation stage (9-LT to 11-LT). (iii) Finally, the
intermediate and D plants had significantly higher plant height (PH), indicating this was not a
shade avoidance response.
180
4.2 Contributions
This is the first study on PPV in ear development that couples with plant development
from ear initiation until 1 wk after silking. It is also the first study on barrenness from the
canopy, to the subpopulation, and to the individual plant level by dissecting barrenness with
plant growth through development and dry matter partitioning in parental inbred lines and their
F1 hybrid throughout the life cycle.
For crop physiologists, the results differ with previous literature in hybrid studies and
broaden the understanding of yield formation processes and intra-specific competition for inbred
lines. For maize researchers, following ear and plant development from ear initiation to
physiological maturity (PM) could contribute to (i) the general understanding of quantitative
dynamics of PPV in early ear development related to PPV in plant development, as well as the
similarities and differences among parental inbred lines and their F1 hybrid in these traits. (ii) It
could also contribute to the knowledge of the possible importance of the period around KRN
formation stage on PPV in SNPR and KRNmax for inbred lines. (iii) It contributes to the use of
the top-down approach from the canopy to the subpopulation and to the individual plant level to
study physiological processes underlying grain yield (GY) formation. (iv) Finally it can benefit
the evaluation and demonstration of the allometric methodology to estimate PDM and EDM at
the individual plant level. For plant ecologists, the results of the study could be used to develop
new models regarding size inequality using individual plants with a similar initial size and
development.
181
4.3 Research Limitations and Future Research
4.3.1 Effects of sampling individual plants that were not competitively bordered
It would be best to select competitively bordered plants with the consideration of intraspecific competition effects on PPV and other ear and plant traits at a constant 80,000 plants ha-1.
Competitively bordered plants refer to individual plants surrounded by adjacent plants under a
target plant density. However, frequent selections of 20 competitively bordered plants at the
same LT/developmental stage with similar initial plant size and development need more tagged
plants and larger plot areas. Therefore, sampling plants that were not competitively bordered
gradually dilutes plant density.
The gradual reduced plant density had little impact on early growth and development for
both plant and ear, but affected the results from late vegetative stages to tassel emergence (TE).
Page et al. (2010a) showed that the onset of intra-specific competition of CG102 × CG108 at
80,000 plants ha-1 starts after 14-LT stage. Before the 14-LT stage, individual F1 plants within
the population are not resource-limited. The inbred plants could start the intra-specific
competition later than the F1 plants due to their smaller leaf area per plant. Smith (2012) reduced
plant competition at 1 wk before silking by removing plants from 74,000 to 37,000 plants ha-1.
The genetic material included a group of parental inbred lines and their F1 hybrid such as CG60,
CG102 and CG60 × CG102 used in this study. Reduced plant competition did not affect the
KRN, but did affect SNPE of the three genotypes, compared to the initial 74,000 plants ha-1. In
this study, at the late stage sampling area, destructive samplings at silking and 1 wk after silking
decreased plant density and reduced intra-specific competition at 1 wk after silking and PM.
Future study should increase tagged plants and experimental area, and sample 10 competitively
182
border plants from ear initiation until post-silking and at PM.
4.3.2. Plant-to-plant variability in ear traits during the kernel row number formation stage
at the canopy level
The field study of PPV in SNPR, KRNmax and SNPE was only conducted for one year. A
multiple-year field study should be conducted to test the consistency of the results. During the
multiple-year field study, the following three questions could be investigated. First, the one-year
field study showed that the two parental inbred lines had a 1.5-LT period of high PPV in SNPR
than KRNmax since the KRN formation stage and it was not clarified whether the F1 has a similar
or a short period of high PPV in SNPR and KRNmax. A longer period of high PPV during the
KRN formation stage could be associated with being more subject to environmental stress.
Second, the dynamics of CV in SNPR for a F1 hybrid (United 106) was similar across 50,000;
100,000; 150,000 and 200,000 plants ha-1 after the KRN formation stage and the magnitude is
similar to the CV of CG60 × CG102 at the corresponding stages in this study (Edmeades and
Daynard, 1979b). The PPV in SNPR and KRNmax during the KRN formation stage could be
subject to high plant density stress, results in higher PPV in SNPR and KRNmax, and has effects
on PPV in GY and harvest index (HI). Third, there should be genotypic difference in these traits
among parental inbred lines and the F1 hybrid, which could be investigated for future studies.
183
4.3.3. Relationships among plant growth and leaftip development as well as dry matter
partitioning to the ear at the subpopulation level
The two-year field results showed that the LT development of the intermediate and D
plants was more advanced than the d plants and the possible association of higher PDM at the
KRN formation stage for CG102 at 120,000 plants ha-1. Future study could compare CG102 at
120,000 plants ha-1 to 80,000 plants ha-1 as a control from ear initiation (8.5-LT) to post-KRN
formation stage (11-LT) and sample at a 0.5-LT interval. A 0.5-LT interval is about 2 d. The
following questions could be clarified: (i) When do the advanced LT of the intermediate and D
plants start compared to the d plants? (ii) Is there an association of LT and PDM for the
intermediate and D plants? (iii) Do the advanced LT stage of the intermediate and D plants
around the KRN formation stage have effects on the PPV in SNPR and KRNmax or dry matter
partitioning to the ear (measured as ear length) at the 120,000 plants ha-1 compared to the 80,000
plants ha-1? (iv) Do the PPV in SNPR, KRNmax or dry matter partitioning to the ear of the d
plants around the KRN formation stage respond to the 120,000 plants ha-1 compared to the
80,000 plants ha-1? (v) Do the PPV in SNPR, KRNmax or dry matter partitioning to the ear of the
intermediate and D plants respond similarly as the d plants to the 120,000 plants ha-1?
184
REFERENCES
Alexandrov, G.A., D. Ames, G. Bellocchi, M. Bruen, N. Crout, M. Erechtchoukova, and et al.
2011. Technical assessment and evaluation of environmental models and software:
Letter to the editor. Environ. Model Softw: 26:328–336.
Allison, J.C.S., and T.B. Daynard. 1979. Effect of change in time of flowering, induced by
altering photoperiod or temperature, on attributes related to yield in maize. Crop Sci. 19:
1–4.
Andrade, F.H., and P.A. Abbate. 2005. Response of maize and soybean to variability in stand
uniformity. Agron. J. 97:1263–1269.
Andrade, F.H., M.E. Otegui, and C. Vega. 2000. Intercepted radiation at flowering and kernel
number in maize. Agron. J. 92:92–97.
Andrade, F.H., C. Vega, S. Uhart, A. Cirilo, M. Cantarero, and O. Valentinuz. 1999. Kernel
number determination in maize. Crop Sci. 39:453–459.
Ballaré, C.L. 1999. Keeping up with the neighbours: Phytochrome sensing and other signaling
mechanisms. Trends Plant Sci. 4:97–102.
Ballaré, C.L., R.A. Sánchez, A.L. Scopel, J.J. Casal, and C.M. Ghersa. 1987. Early detection of
neighbour plants by phytochrome perception of spectral changes in reflected sunlight.
Plant Cell Environ. 10:551–557.
Bassetti, P., and M.E. Westgate. 1993. Water deficit affects receptivity of maize silks. Crop Sci.
33:279–282.
185
Below, F.E. 1997. Growth and productivity of maize under nitrogen stress. In: G.O. Edmeades et
al., editors, Developing drought and low N-tolerant maize. CIMMYT, Mexico. p. 235–
240.
Below, F.E. 2002. Nitrogen metabolism and crop productivity. In: M. Pessarakli, editor,
Handbook of plant and crop physiology. Marcel Dekker, New York. p. 385–406.
Below, F.E., J.O. Cazetta, and J.R. Seebauer. 2000. Carbon/nitrogen interactions during ear and
kernel development of maize. In: M. Westgate and K. Boote, editors, Physiology and
modeling kernel set in maize. CSSA Spec. Publ. 29. CSSA and ASA, Madison, WI. p.
15–24.
Below, F.E., K.A. Duncan, M. Uribelarrea, and T.B. Ruyle. 2009. Occurrence and proposed
cause of hollow husk in maize. Agron. J. 101:237–242.
Below, F.E., and M. Uribelarrea. 2009. Stress control for achieving high yiels: the quest for 300
bushel per acre corn. Paper presented at: the 36th Annual Meeting of the Plant Growth
Regulation Society of America, Asheville, NC, USA. 2–6 Aug. p. 45–52.
Bendel, R.B., S.S. Higgins, J.E. Teberg, and D.A. Pyke. 1989. Comparison of skewness
coefficient, coefficient of variation, and Gini coefficient as inequality measures within
populations. Oecologia 78:394–400.
Bjarnason, M. 1994. The Subtropical, midaltitude, and highland maize subprogram. Maize
program special report. CIMMYT, Mexico. p. 77.
Bolaños, J., and G.O. Edmeades. 1993. Eight cycles of selection for drought tolerance in lowland
tropical maize. I. Responses in yield, biomass and radiation utilization. Field Crops Res.
31:233–252.
186
Bonaparte, E.E.N.A., and R.I. Brawn. 1975. The effect of intraspecific competition on the
phenotypic plasticity of morphological and agronomic characters of four maize hybrids.
Ann. Bot. (Lond.) 39:863–869.
Bonnett, O.T. 1953. Developmental morphology of the vegetative and floral shoots of maize.
Univ. of Illinois Col. of Agric. Agric. Exp. Stn. Bull. 568. Univ. of Illinois, Col. of Agric.,
Agric. Exp. Stn., Urbana-Champaign, IL.
Bonnett, O.T. 1954. The inflorescences of maize. Science 120:77–87.
Bonnett, O.T. 1966. Inflorescences of maize, wheat, rye, barley and oats: Their initiation and
development. Univ. of Illinois Col. of Agric. Agric. Exp. Stn. Bull. 721. Univ. of Illinois,
Col. of Agric., Agric. Exp. Stn., Urbana-Champaign, IL.
Boomsma, C.R. 2009. Resource availability and intra-specific competition in maize: A per-plant
and canopy-level morpho- and eco-physiological analysis. Ph.D diss., Purdue University.,
West Lafayette.
Boomsma, C.R., J.B. Santini, M. Tollenaar, and T.J. Vyn. 2009. Maize morphophysiological
responses to intense crowding and low nitrogen availability: An analysis and review.
Agron.J. 101:1426–1452.
Boomsma, C.R., J.B. Santini, T.D. West, J.C. Brewer, L.M. McIntyre, and T.J. Vyn. 2010.
Maize grain yield responses to plant height variability resulting from crop rotation and
tillage system in a long-term experiment. Soil Tillage Res. 106:227–240.
Borrás, L., and M.E. Otegui. 2001. Maize kernel weight response to postflowering source-sink
ratio. Crop Sci. 41:1816–1822.
Borrás, L., M.E. Westgate, J.P. Astini, and L. Echarte. 2007. Coupling time to silking with plant
growth rate in maize. Field Crops Res. 102:73–85.
187
Boyer, J.S., and M.E. Westgate. 2004. Grain yield with limited water. J. Exp. Bot. 55:2385–
2394.
Brugiere, N., S.P. Jiao, S. Hantke, C. Zinselmeier, J.A. Roessler, X.M. Niu, R.J. Jones, and J.E.
Habben. 2003. Cytokinin oxidase gene expression in maize is localized to the vasculature,
and is induced by cytokinins, abscisic acid, and abiotic stress. Plant Physiol. 132:1228–
1240.
Buren, L.L. 1970. Plant characteristics associated with barrenness in maize. Ph.D diss., Iowa
State University., Ames.
Buren, L.L., J.J. Mock, and I.C. Anderson. 1974. Morphological and physiological traits in
maize associated with tolerance to high plant density. Crop Sci. 14:426–429.
Carberry, P.S. 1991. Test of leaf-area development in CERES-Maize: a correction. Field Crops
Res. 27:159–167.
Cárcova, J., B. Andrieu, and M.E. Otegui. 2003. Silk elongation in maize: Relationship with
flower development and pollination. Crop Sci. 43:914–920.
Caviglia, O.P., and R.J.M. Melchiori. 2011. Contribution of contrasting plant hierarchies to the
response to N fertilizer in maize. Field Crops Res. 122:131–139.
Cazetta, J.O., J.R. Seebauer, and F.E. Below. 1999. Sucrose and nitrogen supplies regulate
growth of maize kernels. Ann. Bot. (Lond.) 84:747–754.
Center, C.F., and H.M. Camper, Jr. 1973. Component plant part development in maize as
affected by hybrids and population density. Agron. J. 65:669–671.
Cheikh, N., and R.J. Jones. 1994. Disruption of maize kernel growth and development by heat
stress. Role of cytokinins/abscisic acid balance. Plant Physiol. 106:45–51.
188
Cheikh, N., and R.J. Jones. 1995. Heat stress effects on sink activity of developing maize kernels.
Physiol. Plant. 95:59–66.
Cheng, C.Y., and H.S. Lur. 1996. Ethylene may be involved in abortion of the maize caryopsis.
Physiol. Plant. 96:245–252.
Cheng, P.C., R.I. Greyson, and D.B. Walden. 1983. Organ initiation and the development of
unisexual flowers in the tassel and ear of Zea mays. Am. J. Bot. 70:450–462. 2
Christensen, L.E., F.E. Below, and R.H. Hageman. 1981. The effects of ear removal on
senescence and metabolism of maize. Plant Physiol. 68:1180–1185.
Cicchino, M., J.I. Rattalino Edreira, M. Uribelarrea, and M.E. Otegui. 2010. Heat stress in field
grown maize: Response of physiological determinants of grain yield. Crop Sci. 50:1438–
1448.
Cirilo, A.G., and F.H. Andrade. 1996. Sowing date and kernel weight in maize. Crop Sci.
36:325–331.
Clay, S.A., D.E. Clay, D. Horvath, J. Pullis, C.G. Carlson, S. Hansen, and G. Reicks. 2009. Corn
(Zea mays) responses to competition: Growth alteration vs. limiting factor. Agron. J.
101:1522–1529.
Commuri, P.D., and R.J. Jones. 2001. High temperatures during endosperm cell division in
maize: A genotypic comparison under in vitro and field conditions. Crop Sci. 41:1122–
1130.
Connor, D.J., R.S. Loomis, and K.G. Cassman. 2011. Community concept. In: Connor et al.,
editors, Crop ecology: productivity and management in agricultural systems. 2nd ed.
Cambridge University Press, Cambridge, UK. p. 44–70.
189
Coulter, J.A., E.D. Nafziger, L.J. Abendroth, P.R. Thomison, R.W. Elmore, and M.E. Zarnstorff.
2011. Agronomic responses of corn to stand reduction at vegetative growth stages. Agron.
J. 103:577–583.
Cox, W.J. 1996. Whole-plant physiological and yield responses of maize to plant density. Agron.
J. 88:489–496.
Crafts-Brandner, S.J., F.E. Below, J.E. Harper, and R.H. Hageman. 1984. Differential
senescence of maize hybrids following ear removal. I. Whole plant. Plant Physiol.
74:360–367.
Crafts-Brandner, S.J., and C.G. Poneleit. 1987. Effect of ear removal on CO2 exchange and
activities of ribulose bisphosphate carboxylase/oxygenase and phosphoenolpyruvate
carboxylase of maize hybrids and inbred lines. Plant Physiol. 84:261–265.
D’Andrea, K.E., M.E. Otegui, A.G. Cirilo, and G. Eyherabide. 2006. Genotypic variability in
morphological and physiological traits among maize inbred lines—Nitrogen responses.
Crop Sci. 46:1266–1276.
D’Andrea, K.E., M.E. Otegui, and A.G. Cirilo. 2008. Kernel number determination differs
among maize hybrids in response to nitrogen. Field Crops Res. 105:228–239.
D’Andrea, K.E., M.E. Otegui, A.G. Cirilo, and G.H. Eyherabide. 2009. Ecophysiological traits in
maize hybrids and their parental inbred lines: Phenotyping of responses to contrasting
nitrogen supply levels. Field Crops Res. 114:147–158.
Davies, P.J. 2004. Plant hormones: Biosynthesis, signal transduction, and action. Kluwer
Academic Pub. Dordrecht, the Netherlands.
Daynard, T.B., and W.G. Duncan. 1969. The black layer and grain maturity in corn. Crop Sci.
9:473–476.
190
Daynard, T.B., and J.F. Muldoon. 1983. Plant-to-plant variability of maize plants grown at
different densities. Can. J. Plant Sci. 63:45–59.
Drew, M.C., C.J. He, and P.W. Morgan. 1989. Decreased ethylene biosynthesis and induction of
aerenchyma by nitrogen-or phosphate-starvation in adventitious roots of Zea mays L.
Plant Physiol. 91:266–271.
Duvick, D.N. 1974. Continuous backcrossing to transfer prolificacy to a single-eared inbred line
of corn. Crop Sci. 14:69–71.
Dwyer, L.M., and D.W. Stewart. 1986. Leaf area development in field grown maize. Agron. J.
78:334–343.
Earley, E.B., R.J. Miller, G.L. Reichert, R.H. Hageman, and R.D. Seif. 1966. Effects of shade on
maize production under field conditions. Crop Sci. 6:1–7.
Echarte, L., and F.H. Andrade. 2003. Harvest index stability of Argentinean maize hybrids
released between 1965 and 1993. Field Crops Res. 82:1–12.
Echarte, L., F.H. Andrade, C.R.C. Vega, and M. Tollenaar. 2004. Kernel number determination
in Argentinean maize hybrids released between 1965 and 1993. Crop Sci. 44:1654−1661.
Echarte, L., S. Luque, F.H. Andrade, V.O. Sadras, A. Cirilo, M.E. Otegui, and C.R.C. Vega.
2000. Response of maize kernel number to plant density in Argentinean hybrids released
between 1965 and 1993. Field Crops Res. 68:1–8.
Echarte, L., S. Rothstein, and M. Tollenaar. 2008. The response of leaf photosynthesis and dry
matter accumulation to nitrogen supply in an older and a newer maize hybrid. Crop Sci.
48:656–665.
Echarte, L., and M. Tollenaar. 2006. Kernel set in maize hybrids and their inbred lines exposed
to stress. Crop Sci. 46:870–878.
191
Edmeades, G.O., J. Bolaños, M. Hernández, and S. Bello. 1993. Causes for silk delay in a
lowland tropical maize population. Crop Sci. 33:1029–1035.
Edmeades, G.O., and T.B. Daynard. 1979a. The relationship between final yield and
photosynthesis at flowering in individual maize plants. Can. J. Plant Sci. 59:585–601.
Edmeades, G.O., and T.B. Daynard. 1979b. The development of plant-to-plant variability in
maize at different planting densities. Can. J. Plant Sci. 59:561–576.
Efron, B. 1978. Regression and ANOVA with zero-one data: Measures of residual variation. J.
Am. Stat. Assoc. 73:113–121.
Engels, C., E. Kirkby, and P.J. White. 2012. Mineral nutrition, yield and source-sink
relationships. In: P. Marschner, editor, Mineral nutrition of higher plants. 3rd ed.
Academic Press, London. p. 85–131.
Eschrich, W. 1980. Free space invertase, its possible role in phloem unloading. Ber. Dtsch. Bot.
Ges. 93:363–378.
Fasoula, D.A., and V.A. Fasoula. 1997. Competitive ability and plant breeding. In: J. Janick,
editor, Plant Breed. Rev. Vol. 14. John Wiley & Sons, Inc, Hoboken, NJ. p. 89–138.
Fournier, C., and B. Andrieu. 1998. A 3D architectural and process-based model of maize
development. Ann. Bot. (Lond.) 81:233–250.
Francis, C.A., S.R. Temple, C.A. Flor, and C.O. Grogan. 1978. Effects of competition on yield
and dry matter distribution in maize. Field Crops Res. 1:51–63.
Genter, C.F., and H.M. Camper. 1973. Component plant part development in maize as affected
by hybrids and population density. Agron. J. 65:669–671.
Glenn, F.B., and T.B. Daynard. 1974. Effects of genotype, planting pattern, and plant density on
plant-to-plant variability and grain yield of corn. Can. J. Plant Sci. 54:323–330.
192
Goss, J.A. 1968. Development, physiology, and biochemistry of corn and wheat pollen. Bot.
Rev. 34:333–358.
Hammer, G.L., T.R. Sinclair, S.C. Chapman, and E. Van Oosterom. 2004. On systems thinking,
systems biology, and the in silico plant. Plant Physiol. 134:909–911.
Hashemi-Dezfouli, A., and S.H. Herbert. 1992. Intensifying plant density response of corn with
artificial shade. Agron. J. 84:547–551.
He, C.J., P.W. Morgan, and M.C. Drew. 1992. Enhanced sensitivity to ethylene in nitrogenstarved or phosphate-starved roots of Zea mays L. during aerenchyma formation. Plant
Physiol. 98:137–142.
Hiyane, R., S. Hiyane, A.C. Tang, and J.S. Boyer. 2010. Sucrose feeding reverses shade-induced
kernel losses in maize. Ann. Bot. (Lond.) 106:395–403.
Hunt, R. 1982. Plant growth curves: an introduction to the functional approach to plant growth
analysis. Edward Arnold, London.
Iremiren, G.O., and G.M. Milbourn. 1980. Effects of plant density on ear barrenness in maize.
Exp. Agric. 16:321–326.
Irish, E.E. 1998. Grass spikelets: a thorny Problem. BioEssays 20:789–793.
Johnson, D.R., and J.W. Tanner. 1972. Calculation of the rate and duration of grain filling in
corn (Zea mays L.). Crop Sci. 12:485–486.
Jones, R.J., S. Quattar, and R.K. Crookston. 1984. Thermal environment during endosperm and
grain filling in maize: Effects on kernel growth and development in vitro. Crop Sci.
24:133–137.
Jones, R.J., J.A. Roessler, and S. Ouattar. 1985. Thermal environment during endosperm cell
division in maize: Effects on number of endosperm cells and starch granules. Crop Sci.
193
25:830–834.
Jones, R.J., B.M.N. Schreiber, and J.A. Roessler. 1996. Kernel sink capacity in maize: Genotypic
and maternal regulation.Crop Sci. 36:301–306.
Jones, R.J., and T.L. Setter. 2000. Hormonal regulation of early kernel development. In: M.
Westgate and K. Boote, editors, Physiology and modeling kernel set in maize. CSSA
Spec. Publ. 29. CSSA and ASA, Madison, WI. p. 25–42.
Kaplinsky, N.J., and M. Freeling. 2003. Combinatorial control of meristem identity in maize
inflorescences. Development 130:1149–1158.
Kasperbauer, M.J., and D.L. Karlen. 1994. Plant spacing and reflected far-red light effects on
phytochrome-regulated photosynthate allocation in corn seedlings. Crop Sci. 34:1564–
1569.
Khamis, S., T. Lamaze, Y. Lenoine, and C. Foyer. 1990. Adaptation of the photosynthetic
apparatus in maize leaves as a result of nitrogen limitation. Relationships between
electron transport and carbon assimilation. Plant Physiol. 94:1436–1443.
Khanal, R., H. Earl, E.A. Lee, and L. Lukens. 2011. The genetic architecture of flowering time
and related traits in two early flowering maize lines. Crop Sci. 51:146–156.
Kiniry, J.R., and R. Bonhomme. 1991. Predicting maize phenology. In: T. Hodges, editor,
Predicting crop phenology. CRC Press, Boca Raton, FL. p. 115–132.
Kjellsson, G., and V. Simonsen. 1994. Methods for risk assessment of transgenic plants. I.
Competition, establishment and ecosystem effects. Birkhäuser Basel, Basel.
Koyama, H., and T. Kira. 1956. Intraspecific competition among higher plants. VIII. Frequency
distribution of individual plant weight as affected by the interaction between plants. J.
Inst. Polytech. Osaka City Univ. Ser. D7:73–94.
194
Lang, A.L., J.W. Pendleton, and G.H. Dungan. 1956. Influence of population and nitrogen levels
on yield and protein and oil contents of nine corn hybrids. Agron. J. 48:284–289.
Lee, E.A., B. Good, R. Chakravarty, and L. Kannenberg. 2001a. Corn inbred lines CG60 and
CG62. Can. J. Plant Sci. 81:453–454.
Lee, E.A., B. Good, R. Chakravarty, and L. Kannenberg. 2001b. Corn inbred line CG102. Can. J.
Plant Sci. 81:455–456.
Lee, E.A., and M. Tollenaar. 2007. Physiological basis of successful breeding strategies for
maize grain yield. Crop Sci. 47(S3):S202–S215.
Lee, E.A., and W.F. Tracy. 2009. Modern maize breeding. In: J.L. Bennetzen and S. Hake,
editors, Handbook of maize: Genetics and genomics. Springer-Verlag, New York. p.
141–160.
Lejeune, P., and G. Bernier. 1996. Effect of environment on the early steps of ear initiation in
maize (Zea mays L.). Plant Cell Environ. 19:217–224.
Lemaire, G., J.C. Avice, T.H. Kim, and A. Ourry. 2005. Developmental changes in shoot N
dynamics of lucerne (Medicago sativa L.) in relation to leaf growth dynamics as a
function of plant density and hierarchical position within canopy. J. Exp. Bot. 56:935–
943.
Lemaire, G., and F.F. Gastal. 2009. Quantifying crop responses to nitrogen deficiency and
avenues to improve nitrogen use efficiency. In: V.O. Sadras and D.F. Calderini, editors,
Crop physiology: Applications for genetic improvement and agronomy. Academic Press,
London. p. 171–211.
195
Lemaire, G., E. van Oosterom, J. Sheehy, M.H. Jeuffroy, A. Massignam, and L. Rossato. 2007.
Is crop N demand more closely related to dry matter accumulation or leaf area expansion
during vegetative growth? Field Crops Res. 100:91–106.
Lemcoff , J.H., and R.S. Loomis. 1986. Nitrogen influences on yield determination in maize.
Crop Sci. 26:1017–1022.
Liu, W., M. Tollenaar, G. Stewart, and W. Deen. 2004a. Response of corn grain yield to spatial
and temporal variability in emergence. Crop Sci. 44:847–854.
Liu, W., M. Tollenaar, G. Stewart, and W. Deen. 2004b. Within-row spacing variability does not
affect corn yield. Agron. J. 96:275–280.
Liu, W., and M. Tollenaar. 2009a. Physiological mechanisms underlying heterosis for shade
tolerance in maize. Crop Sci. 49:1817–1826.
Liu, W., and M. Tollenaar. 2009b. Response of yield heterosis to increasing plant density in
maize. Crop Sci. 49:1807–1816.
Mack, R., and J.L. Harper. 1977. Interference in dune annuals: spatial pattern and neighbourhood
effects. J. Ecol. 65:345–363.
Maddonni, G.A., and M.E. Otegui. 2004. Intra-specific competition in maize: Early
establishment of hierarchies among plants affects final kernel set. Field Crops Res. 85:1–
13.
Maddonni, G.A., and M.E. Otegui. 2006. Intra-specific competition in maize: Contribution of
extreme plant hierarchies to grain yield, grain yield components and kernel composition.
Field Crops Res. 97:155–166.
196
Major, D.J., D.M. Brown, A. Bootsma, G. Dupuis, N.A. Fairley, E.A. Grant and et al. 1983. An
evaluation of the corn heat unit system for the short-season growing regions across
Canada. Can. J. Plant Sci. 63:121–130.
Mäkelä, P., J.E. McLaughlin, and J.S. Boyer. 2005. Imaging and quantifying carbohydrate
transport to the developing ovaries of maize. Ann. Bot. (Lond.) 96:939–949.
Martin, K.L., P.J. Hodgen, K.W. Freeman, R. Melchiori, D.B. Arnall, R.K. Teal, and et al. 2005.
Plant-to-plant variability in corn production. Agron. J. 97:1603–1611.
Mayer, L.I., M.A. Rossini, and G.A. Maddonni. 2012. Inter-plant variation of grain yield
components and kernel composition of maize crops grown under contrasting nitrogen
supply. Field Crops Res. 125:98–108.
McCullough, D.E., Ph. Girardin, M. Mihajlovic, A. Aguilera, and M. Tollenaar. 1994. Influence
of N supply on development and dry matter accumulation of an old and a new maize
hybrid. Can. J. Plant Sci. 74:471–477.
McLaughlin, J.E., and J.S. Boyer. 2004a. Sugar-responsive gene expression, invertase activity,
and senescence in aborting maize ovaries at low water potentials. Ann. Bot. (Lond.)
94:675–689.
McLaughlin, J.E., and J.S. Boyer. 2004b. Glucose localization in maize ovaries when kernel
number decreases at low water potential and sucrose is fed to the stems. Ann. Bot. (Lond.)
94:75–86.
McMaster, G.S., and W.W. Wilhelm. 1995. Accuracy of equations predicting the phyllochron of
wheat. Crop Sci. 35:30–36.
McSteen, P., D. Laudencia-Chingcuanco, and J. Colasanti. 2000. A floret by any other name:
control of meristem identity in maize. Trends Plant Sci. 5:61–66.
197
Messina, C., G. Hammer, Z. Dong, D. Podlich, and M. Cooper. 2009. Modelling crop
improvement in a G × E × M framework via gene-trait-phenotype relationships. In: V.O.
Sadras and D. Calderini, editors, Crop physiology: Applications for genetic improvement
and agronomy. Academic Press, Elsevier, London. p. 235–265.
Miralles, D.J., and G.A. Slafer. 2007. Sink limitations to yield in wheat: how could it be
reduced? J. Agric. Sci. (Cambridge) 145:139–149.
Montgomery, F.G. 1911. Correlation studies in corn. Nebr. Agric. Exp. Stn. Annu. Rep. 24.
Nebr. Agric. Exp. Stn., Col. of Agric., Univ. of Nebraska, Lincoln, NE.
Moss, D.N. 1962. Photosynthesis and barrenness. Crop Sci. 2:366–367.
Moss, G.I., and L.A. Downey. 1971. Influence of drought stress on female gametophyte
development in corn (Zea-mays L.) and subsequent grain yield. Crop Sci. 11:368–372.
Muchow, R.C. 1988. Effect of nitrogen supply on the comparative productivity of maize and
sorghum in a semi-arid tropical environment. I. Leaf growth and leaf nitrogen. Field
Crops Res. 18:1–16.
Muldoon, J.F., T.B. Daynard, B. van Duinen, and M. Tollenaar. 1984. Comparisons among rates
of appearance of leaf tips, collars, and leaf area in maize (Zea mays L.). Maydica 29:109–
120.
Nafziger, E.D., P.R. Carter, and E.E. Graham. 1991. Response of corn to uneven emergence.
Crop Sci. 31:811–815.
Niklas, K.J. 1994. Plant allometry: The scaling of form and process. University of Chicago Press,
Chicago, IL.
Olness, A., S.D. Evans, K.R. Harris, J.L. Rinke, and S. Gooch. 1990. Postemergence mortality
and barrenness of two maize hybrids affected by tillage and nitrogen. Crop Sci. 30:261–
198
266.
OMAFRA. 2009. Corn: Hybrid selection. OMAFRA Publication 811: Agronomy Guide for
Field Crops. Ontario Ministry of Agriculture Food and Rural Affairs.
http://www.omafra.gov.on.ca/english/crops/pub811/1hybrid.htm (accessed 18 Jan. 2013).
Otegui, M.E. 1997. Kernel set and flower synchrony within the ear of maize: II. Plant population
effects. Crop Sci. 37:448–455.
Otegui, M.E., and R. Bonhomme. 1998. Grain yield components in maize: I. Ear growth and
kernel set. Field Crops Res. 56:247–256.
Otegui, M.E., and S. Melón. 1997. Kernel set and flower synchrony within the ear of maize: I.
Sowing date effects. Crop Sci. 37:441–447.
Pagano, E., S. Cela, G.A. Maddonni, and M.E. Otegui. 2007. Intra-specific competition in maize:
Ear development, flowering dynamics and kernel set of early-established plant
hierarchies. Field Crops Res. 102:198–209.
Pagano, E., and G.A. Maddonni. 2007. Intra-specific competition in maize: Early established
hierarchies differ in plant growth and biomass partitioning to the ear around silking. Field
Crops Res. 101:306–320.
Page, E.R., W. Liu, D. Cerrudo, E.A. Lee, and C.J. Swanton. 2011. Shade avoidance influences
stress tolerance in maize. Weed Sci. 59:326–334.
Page, E.R., M. Tollenaar, E.A. Lee, L. Lukens, and C.J. Swanton. 2009. Does shade avoidance
underlie the critical period for weed control in maize (Zea mays L.)? Weed Res. 49:563–
571.
Page, E.R., M. Tollenaar, E.A. Lee, L. Lukens, and C.J. Swanton. 2010a. Timing, effect, and
recovery from intraspecific competition in maize. Agron. J. 102:1007–1013.
199
Page, E.R., M. Tollenaar, E.A. Lee, L. Lukens, and C.J. Swanton. 2010b. Shade avoidance: an
integral component of crop-weed competition. Weed Res. 50:281–288.
Paponov, I.A., and C. Engels. 2005. Effect of nitrogen supply on carbon and nitrogen
partitioning after flowering in maize. J. Plant Nutr. Soil Sci. 168:447–453.
Paponov, I.A., P. Sambo, G. Schulte auf’m Erley, T. Presterl, H.H. Geiger, and C. Engels. 2005.
Kernel set in maize genotypes differing in nitrogen use efficiency in response to resource
availability around flowering. Plant Soil 272:101–110.
Pommel, B., and R. Bonhomme. 1998. Variations in the vegetative and reproductive systems in
individual plants of an heterogeneous maize crop. Eur. J. Agron. 8:39–49.
Poorter, H., C. Remkes, and H. Lambers. 1990. Carbon and nitrogen economy of 24 wild species
differing in relative growth rate. Plant Physiol. 94:621–627.
Rajcan, I., and M. Tollenaar. 1999a. Source: sink ratio and leaf senescence in maize. I. Dry
matter accumulation and partitioning during grain filling. Field Crops Res. 60:245–253.
Rajcan, I., and M. Tollenaar. 1999b. Source: sink ratio and leaf senescence in maize. II. Nitrogen
metabolism during grain filling. Field Crops Res. 60:255–265.
Ratkowsky, D.A. 1990. Handbook of nonlinear regression models. Marcel Dekker Inc. New
York, NY.
Reed, A.J., and G.W. Singletary. 1989. Roles of carbohydrate supply and phytohormones in
maize kernel abortion. Plant Physiol. 91:986–992.
Ritchie, J.T., and G. Alagarswamy. 2003. Model concepts to express genetic differences in maize
yield components. Agron. J. 95:4–9.
Ritchie, J.T., and D.S. NeSmith. 1991. Temperature and crop development. In: R.J. Hanks and
J.T. Ritchie, editors, Modeling plant and soil systems. Amer. Soc. Agron. Monograph 31.
200
Madison, WI. p. 5–29.
Ritchie, J.T., and J. Wei. 2000. Models of kernel number in maize. In: M. Westgate and K.
Boote, editors, Physiology and modeling kernel set in maize. CSSA Spec. Publ. 29.
CSSA and ASA, Madison, WI. p. 75–88.
Rossini, M.A., G.A. Maddonni, and M.E. Otegui. 2011. Inter-plant competition for resources in
maize crops grown under contrasting nitrogen supply and density: variability in plant and
ear growth. Field Crops Res. 121:373–380.
Rossini, M.A., G.A. Maddonni, and M.E. Otegui. 2012. Inter-plant variability in maize crops
grown under contrasting N × stand density combinations: Links between development,
growth and kernel set. Field Crops Res. 133:90–100.
Sangoi, L., M.A. Gracietti, C. Rampazzo, and P. Bianchetti. 2002. Response of Brazilian maize
hybrids from different eras to changes in plant density. Field Crops Res. 79:39–51.
SAS Institute. 2008. The SAS system for Windows. Release 9.2. SAS Inst., Cary, NC.
Sass, J.E., and F.A. Loeffel. 1959. Development of axillary buds in maize in relation to
barrenness. Agron. J. 51:481–486.
Schussler, J.R., and M.E. Westgate. 1991. Maize kernel set at low water potential: II. Sensitivity
to reduced assimilate supply at pollination. Crop Sci. 31:1196–1203.
Schussler, J.R., and M.E. Westgate. 1994. Increasing assimilate reserves does not prevent kernel
abortion at low water potential in maize. Crop Sci. 34:1569–1576.
Scott, M.P., J.W. Edwards, C.P. Bell, J.R. Schussler, and J.S. Smith. 2006. Grain composition
and amino acid content in maize cultivars representing 80 years of commercial maize
varieties. Maydica 51:417–423.
Setter, T.L., B.A. Flannigan, and J. Melkonian. 2001. Loss of kernel set due to water deficit and
201
shade in maize: Carbohydrate supplies, abscisic acid, and cytokinins. Crop Sci. 41:1530–
1540.
Shapiro, C.A., T.A. Peterson, and A.D. Flowerday. 1986. Yield loss due to simulated hail
damage on corn: A comparison of actual and predicted values. Agron. J. 78:585–589.
Siemer, E.G., E.R. Leng, and O.T. Bonnett. 1969. Timing and correlation of major
developmental events in maize, Zea mays L. Agron. J. 61:14–17.
Singh, A.K., T.K. Coleman, M. Tollenaar, and E.A. Lee. 2011. Nature of the genetic variation in
an elite maize breeding cross. Crop Sci. 51:75–83.
Smiciklas, K.D., and F.E. Below. 1992. Role of cytokinin in enhanced productivity of maize
supplied with NH4+ and NO3-. Plant Soil 142:307–313.
Smith, C.S., J.J. Mock, and T.M. Crosbie. 1982. Variability for morphological and
physiological traits associated with barrenness and grain yield in maize population, Iowa
Upright Leaf Synthetic #1. Crop Sci. 22:828–832.
Smith, H., and G.C. Whitelam. 1997. The shade avoidance syndrome: multiple responses
mediated by multiple phytochromes. Plant Cell Environ. 20:840–844.
Smith, N.C. 2012. Dynamic nature of heterosis and determination of sink size in maize. Ph.D
diss., University of Guelph., Guelph.
Smith, W.B., and G.J. Brand. 1983. Allometric biomass equations for 98 species of herbs,
shrubs, and small trees. North Central Forest Exp. Stn. Res. Note NC-299. Forest
Service-USDA, St. Paul, MN.
Stevens, S.J., E.J. Stevens, K.W. Lee, A.D. Flowerday, and C.O. Gardner. 1986. Organogenesis
of the staminate and pistillate inflorescences of pop and dent corns: Relationship to leaf
stages. Crop Sci. 26:712–718.
202
Stringfield, G.H., and L.E. Thatcher. 1947. Stands and methods of planting corn hybrids. Agron.
J. 39:995–1010.
Subedi, K.D., B.L. Ma, and D.L. Smith. 2006. Response of a leafy and non-leafy maize hybrid
to population densities and fertilizer nitrogen levels. Crop Sci. 46:1860–1869.
Takei, K., H. Sakakibara, M. Taniguchi, and T. Suglyama. 2001. Nitrogen-dependent
accumulation of cytokinins in root and the translocation to leaf: Implication of cytokinin
species that induces gene expression of maize response regulator. Plant Cell Physiol.
42:85–93.
Tetio-Kagho, F., and F.P. Gardner. 1988. Responses of maize to plant population density. I.
Canopy development, light relationships and vegetative growth. Agron. J. 80:930–935.
Thiagarajah, M.R., L.A. Hunt, and J.D. Mahon. 1981. Effects of position and age on leaf
photosynthesis in corn (Zea mays). Can. J. Bot. 59:28–33.
Tittonell, P., B. Vanlauwe, P.A. Leffelaar, and K.E. Giller. 2005. Estimating yields of tropical
maize genotypes from non-destructive, on-farm plant morphological measurements.
Agric. Ecosys. Environ. 105:213–220.
Tokatlidis, I.S., and S.D. Koutroubas. 2004. A review of maize hybrid dependence on high plant
populations and its implications for crop yield stability. Field Crops Res. 88:103–114.
Tollenaar, M., A. Ahmadzadeh, and E.A. Lee. 2004. Physiological basis of heterosis for grain
yield in maize. Crop Sci. 44:2086–2094.
Tollenaar, M., and T.B. Daynard. 1978. Effect of defoliation on kernel development in maize.
Can. J. Plant Sci. 58:207–212.
Tollenaar, M., W. Deen, L. Echarte, and W. Liu. 2006. Effect of crowding stress on dry matter
accumulation and harvest index in maize. Agron. J. 98:930–937.
203
Tollenaar, M., L.M. Dwyer, and D.W. Stewart. 1992. Ear and kernel formation in maize hybrids
representing three decades of grain yield improvement in Ontario. Crop Sci. 32:432–438.
Tollenaar, M., L.M. Dwyer, and D.W. Stewart. 2000. Physiological parameters associated with
differences in kernel set among maize hybrids. In: M. Westgate and K. Boote, editors,
Physiology and modeling kernel set in maize. CSSA Spec. Publ. 51. CSSA and ASA,
Madison, WI. p.115–130.
Tollenaar, M., and R.B. Hunter. 1983. A photoperiod and temperature sensitive period for leaf
number of maize. Crop Sci. 23:457–460.
Tollenaar, M., and E.A. Lee. 2006. Physiological dissection of grain yield in maize by examining
genetic improvement and heterosis. Maydica 51:399–408.
Tollenaar, M., and E.A. Lee. 2010. Strategies for enhancing grain yield in maize. In: J. Janick,
editor, Plant Breed. Rev. Vol. 34. John Wiley & Sons, Inc, Hoboken, NJ. p. 37–82.
Tollenaar, M., and J. Wu. 1999. Yield improvement in temperate maize is attributable to greater
stress tolerance. Crop Sci. 39:1597–1604.
Uhart, S.A., and F.H. Andrade. 1995a. Nitrogen deficiency in maize: II. Carbon-nitrogen
interaction effects on kernel number and grain yield. Crop Sci. 35:1384–1389.
Uhart, S.A., and F.H. Andrade. 1995b. Nitrogen deficiency in maize: I. Effects on crop growth,
development, dry matter partitioning, and kernel set. Crop Sci. 35:1376–1383.
Vega, C.R.C., F.H. Andrade, and V.O. Sadras. 2001a. Reproductive partitioning and seed set
efficiency in soybean, sunflower and maize. Field Crops Res. 72:163–175.
Vega, C.R.C., F.H. Andrade, V.O. Sadras, S. Uhart, and O. Valentinuz. 2001b. Seed number as a
function of growth. A comparative study in soybean, sunflower, and maize. Crop Sci.
41:748–754.
204
Vega, C.R.C., V.O. Sadras, F.H. Andrade, and S.A. Uhart. 2000. Reproductive allometry in
soybean, maize and sunflower. Ann. Bot. (Lond.) 85:461–468.
Vega, C.R.C., and V.O. Sadras. 2003. Size-dependent growth and the development of inequality
in maize, sunflower and soybean. Ann. Bot. (Lond.) 91:795–801.
Villalobos, F.J., V.O. Sadras, A. Soriano, and E. Fereres. 1994. Planting density effects on dry
matter partitioning and productivity of sunflower hybrids. Field Crops Res. 36:1–11.
Vinocur, M.G., and J.T. Ritchie. 2001. Maize leaf development biases caused by air-apex
temperature differences. Agron. J. 93:767–772.
Vollbrecht, E., and R.S. Schmidt. 2009. Development of the Inflorescences. In: J.L. Bennetzen
and S.C. Hake, editors, Handbook of maize: Its biology. Springer Science+Business
Media, New York, NY. p. 13–40.
Vyn, T.J., and C.R. Boomsma. 2009. The when and why of barren plants in high yield corn.
2009 ASA-CSSA-SSSA International Annual Meetings. ASA-CSSA-SSSA. http://a-cs.confex.com/crops/2009am/webprogram/Paper55241.html (accessed 18 Jan. 2013).
Wang, J.Y. 1960. A critique of the heat unit approach to plant response studies. Ecology 4:785–
790.
Weiner, J., E.B. Mallory, and C. Kennedy. 1990. Growth and variability in crowded and
uncrowded populations of dwarf marigolds (Tagetes patula). Ann. Bot. (Lond.) 65:513–
524.
Weiner, J., and O.T. Solbrig. 1984. The meaning and measurement of size hierarchies in plant
populations. Oecologia 61:334–336.
Westgate, M.E., and J.S. Boyer. 1985. Carbohydrate reserves and reproductive development at
low leaf water potentials in maize. Crop Sci. 25:762–769.
205
Westgate, M.E., and J.S. Boyer. 1986. Reproduction at low silk and pollen water potentials in
maize. Crop Sci. 26:951–956.
Westgate, M.E., F. Forcella, D.C. Reicosky, and J. Somsen. 1997. Rapid canopy closure for
maize production in the northern US Corn Belt: Radiation use efficiency and grain yield.
Field Crops Res. 49:249–258.
Wilson, J.H., and J.C.S. Allison. 1978. Effect of plant population on ear differentiation and
growth in maize. Ann. Appl. Biol. 90:127–132.
Woolley, D.G., N.P. Baracco, and W.A. Russell. 1962. Performance of four corn inbreds in
single-cross hybrids as influenced by plant density and spacing patterns. Crop Sci. 2:951–
956.
Yin, X., P.C. Struik, and M.J. Kropff. 2004. Role of crop physiology in predicting gene-tophenotype relationships. Trends Plant Sci. 9:426–432.
Zaidi, P.H., M. Yadav, D.K. Singh, and R.P. Singh. 2008. Relationship between drought and
excess moisture tolerance in tropical maize (Zea mays L.). Aust. J. Crop Sci. 1:78–96.
Zinselmeier, C., B.R. Jeong, and J.S. Boyer. 1999. Starch and the control of kernel number in
maize at low water potentials. Plant Physiol. 121:25–36.
Zinselmeier, C., M.E.Westgate, J.R. Schussler, and R.J. Jones. 1995. Low water potential
disrupts carbohydrate metabolism in maize ovaries. Plant Physiol. 107:385–391.
206
APPENDIXES
Appendix A.1 Summary of allometric models on maize crops. Expression on right part of allometric models for above-ground plant
dry matter (PDM) at vegetative stages and reproductive stages, primary ear dry matter (EDM) and the factors combined to develop
allometric models.
No.
Reference
PDM at vegetative stages
PDM at reproductive stages
Primary EDM
Combined factors
1
Vega et al., 2001a
NA†
NA
a + b × EDc
Plant density
a × EDb
2
Vega et al., 2001b
a + b × SDbc + d × (ED ×
EL)1.25
NA
Plant density
NA
a + b × SV‡ + c × ED2
NA
Genotype and plant density
a + b × SV
a + b × SV + c × ED2
a × e(b × ED)
Genotype, row spacing and plant
density
a + b × SV
a + b × SV + c × ED2
a × e(b × ED)
Plant density, thinning, shading and
growth stage
Nitrogen level with statistical testing
a + b × SDb2
a × SDbb
3
4
5,6
Borrás and Otegui,
2001
Maddonni and Otegui,
2004
Pagano et al., 2007;
Pagano and Maddonni,
2007
7
D’Andrea et al., 2008
a + b × SV
a + b × SV + c × ED2
a × EDb
8
Rossini et al., 2011
a × SV for SV ≤ b
a + b × SV
a × e(b × ED)
a × b + c × (SV − b)
for SV > b
Nitrogen stress, plant density, growth
stage and year
9
Boomsma, 2009
NA
a + b × SV§ (modified)
NA
NA
10
Tittonell et al., 2005
NA
a + b × PH¶
NA
Location
a + b × PH + c × ED
a + b × PH + c × EL
207
Appendix A.1 Continued
No.
Reference
11
Echarte et al., 2004
PDM at vegetative
stages
a + b × SDb
Combined
factors
Plant density
PDM at reproductive stages
Primary EDM
a + b × SDb + c × (ED × EL)d
[a + b × (SDb × ED)2 + c × EL + d ×
SDb]2
(a + b × SDb × ED + c × EL2)2
(a + b × ED + c × ED × EL)2
(a + b × ED + c × ED × EL2)2
(a + b × SDb + c × ED + d × EL)2
[a + b × ED × EL + c ×( ED × EL)2]2
(a + b × ED + c × EL)2
a + b × ED + c × (SDb × EL) + d ×
(SDb × ED) + f × EL × SDb2
12
Echarte and Tollenaar,
2006
a × SDbb
a + b × (SDb × EL)2 + c × ED × SDb2
a × e(b × SDb)
a + b × SDb × ED
a + b × SDb
a + b × SDb2 + c × ED × SDb2
a + b × ED + c × ED × EL + d ×
(ED × EL)2 + f × EL × ED2
a + b × (ED × EL)2 + c × ED ×
EL2
a + b × ED + c × ED2
a + b × SDb + c × ED + d × EL × ED2
a + b × (ED × EL)2
a + b × SDb + c × ED2 + d × EL × ED2
a + b × EL2 + c × (ED × EL)2 +
d × ED × EL2
a + b × (ED × SDb)2
a + b × (SDb × ED)2 + c × EL × SDb2
Water stress
a + b × EL2 + c × (ED × EL)2
a + b × ED × EL + c × (ED ×
EL)2
a + b × SDb + c × SDb2 + d × (ED ×
EL)2 + f × ED × EL2
a + b × SDb × ED + c × (SDb × EL)2
a + b × ED × SDb2
†
NA, not available;
SV = π × (0.5 × SDb)2 × PH; plant height was measured as distance from ground level to the uppermost leaf collar.
§
SV (modified) = π × (0.5 × SDb)2 × PH ; plant height was measured as the distance from the soil surface to the uppermost extended
leaf tip.
¶
PH, plant height was measured as the distance from the ground level to the top of the tassel.
No., number; ED, maximum primary ear diameter; EL, ear length; PH, plant height; SDb, maximum width of basal stem diameter; e
indicates exponential function; a, b, c, d, f are model parameters.
‡
208
Appendix B.1. Analysis of variance of effects of genotype, plant density and genotype × plant
density on allometric relationship for estimating above-ground plant dry matter (PDM) at
different phenological stages in 2007 and 2008. A combination of 40,000 and 120,000 plants ha1
for the two parental inbred lines CG60 and CG102 was analyzed in 2007. In 2008, a
combination of 80,000; 120,000, and 160,000 plants ha-1 was analysed during the vegetative
phases and a combination of 80,000 and 160,000 plants ha-1 was analyzed during the
reproductive stages for CG60, CG102 and their F1 hybrid CG60 × CG102. The allometric
relationship was derived from maximum width of basal stem diameter (SDb) in 2007 and from
either SDb or the maximum width of stem diameter that is 2 cm above the ground level (SD2) in
2008 and maximum primary ear diameter (ED). Vegetative phases are assessed by leaftip (LT)
stage.
Year
Source of d.f.†
8-LT 12-LT 16-LT Silking
2 wk after
variation
silking
2007 with
SDb
2008 with
SDb
2008 with
SD2
†
‡
Plant density
Genotype
1 0.7536
1 0.1846
0.1427 0.0145
0.0745 0.7903
0.1226
0.0199
0.0096
0.5383
Interaction
1 0.9954
0.0048 0.0745 <.0001
0.1946
Stem volume
1 <.0001
<.0001 <.0001 <.0001
<.0001
ED2
Source of
variation
d.f.
12-LT
15-LT
silking
<.0001
2 wk after
silking
Plant density
Genotype
Interaction
Stem volume
1
1
1
1
0.002
<.0001
<.0001
<.0001
<.0001 0.0204
0.0041 0.1747
<.0001 0.0531
<.0001 <.0001
0.0256
0.0275
<.0001
<.0001
ED2
Source of
variation
Plant density
Genotype
Interaction
Stem volume
1
NA
NA <.0001
d.f.
1
1
1
1
15-LT silking
0.4342 0.0496
0.009 0.0016
<.0001 <.0001
<.0001 <.0001
<.0001
2 wk after
silking
0.7577
<.0001
0.0009
<.0001
ED2
1
NA <.0001
<.0001
1
NA‡
NA
d.f. degrees of freedom.
NA, not applicable;
209
NA <.0001
Appendix B.2. Summary of extrapolation (E), significant bias (B) by using combined plant
density models and the residual percentage larger (L) than 5% for above-ground plant dry matter
(PDM) estimation and primary ear dry matter (EDM) estimation for two parental inbred lines
CG60, CG102 and their F1 hybrid CG60 × CG102 grown at different plant densities and sampled
at different phenological stages in 2007 and 2008. Vegetative phase are assessed by leaftip (LT)
stage. Bold letter indicates the genotype × plant density × phenological stage × year
combination that had at least one barren plant.
Year
Genotype
Growth stage
2007
PDM
40,000
EDM
120,000
_______________________
CG60
CG102
F1
8-LT
12-LT
16-LT
Silking
2 wk after
silking
8-LT
12-LT
16-LT
Silking
2 wk after
silking
8-LT
12-LT
16-LT
Silking
2 wk after
silking
2008
E/L
E/L
40,000
120,000
-1 _______________________
plants ha
L
L
E
E
E
E/L
E
E
E
E
E/L
E
L
E
E
E
80,000
120,000
160,000
80,000
120,000
160,000
_______________________
CG60
CG102
F1
12-LT
15-LT
Silking
2 wk after
silking
12-LT
15-LT
Silking
2 wk after
silking
12-LT
15-LT
Silking
2 wk after
silking
plants ha-1 _______________________
E/L
E
E
E
L/E
E
L/E
L
L
L
L/B
E
L/B/E
L
L
E
E/L
L
E
L/B
210
E
E
E
2.0
CG102 at 80,000 plants ha-1 in 2008
-1
CG102 at 120,000 plants ha in 2007
Average plants
Barren plants
Average barren plants
1.5
1.0
PDM Ratio
0.5
0.0
0
200
400
2.0
600
800
1000
0
200
400
600
800
1000
1200
800
1000
1200
-1
-1
CG102 at 160,000 plants ha in 2008
CG102 at 120,000 plants ha in 2008
1.5
1.0
0.5
0.0
0
200
400
600
800
1000
0
200
400
600
Growing degree days after planting
Appendix B.3. Dynamics of above-ground plant dry matter (PDM) ratio between PDM of
individual barren plants (i.e., grain yield per plant = 0 g) and average PDM of a plant density
from early vegetative stage to physiological maturity for one of the parental inbred lines, CG102,
at different plant densities in 2007 and 2008. Each sub-figure represents a plant density × year
combination that had at least one barren plant. The first and second arrows indicate the timing of
ear initiation and silking, respectively.
211
2.0
-1
CG60 at 120,000 plants ha in 2008
Average plants
Barren plants
Average barren plants
1.5
1.0
0.5
0.0
2.0
-1
PDM Ratio
CG60 at 160,000 plants ha in 2008
1.5
1.0
0.5
0.0
2.0
-1
F1 at 160,000 plants ha in 2008
1.5
1.0
0.5
0.0
0
200
400
600
800
1000
1200
Growing degree days after planting
Appendix B.4. Dynamics of above-ground plant dry matter (PDM) ratio between PDM of
individual barren plants (i.e., grain yield per plant = 0 g) and average PDM of a plant density
from early vegetative stage to physiological maturity for one of the parental inbred lines CG60
and its F1 hybrid CG60 × CG102 at different plant densities in 2008. Each sub-figure represents
a genotype × plant density combination that had at least one barren plant. The first and second
arrows indicate the timing of ear initiation and silking, respectively.
212
40
-1
800
CG102 at 80,000 plants ha-1 in 2008
CG102 at 120,000 plants ha in 2007
30
750
ASI frequency
Barren plants
GDDsilking vs. ASI
for barren plants
GDDsilking vs. ASI
for non-barren plants
20
10
700
650
0
600
40
0
4
8
12
16
-4
60
-1
CG102 at 120,000 plants ha in 2008
30
0
4
8
16NS 20NT 24
12
800
-1
CG102 at 160,000 plants ha in 2008
750
50
20
700
GDDsilking
Number of individual plants
-4
20
10
650
10
0
0
-4
0
4
NT
12 >14
16NS 20
8
40
-1
CG60 at 120,000 plants ha in 2008
24
600
-4
0
-1
CG60 at 160,000 plants ha in 2008
4
8
12
>11
16S NS20NT 24
N
&
NT
-1
F1 at 160,000 plants ha in 2008
850
800
30
750
20
700
10
650
0
600
-4
0
4
8
12
16
NS -4
0
4
8
12 >14
16NS
-4
0
4
8 >11
12 16 20
Anthesis to silking intervel (d)
Appendix B.5. Frequency distribution of anthesis to silking interval (ASI) in day and the
relationship between growing degree days from planting to silking (GDDsilking) (secondary yaxis) and ASI for parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 at
213
different plant densities in 2007 and 2008. Each sub-figure represents a genotype × plant density
× year combination that had at least one barren plant (i.e., grain yield per plant = 0 g). The grey
bars indicate the frequency distribution of the ultimately barren plants. The triangle and round
symbols indicate the barren and non-barren plants, respectively. No anther and silk emergence
are represented by NT & NS, respectively.
214
30
-1
-1
CG60 at 120,000 plants ha in 2008
25
20
No silk emergence
Barren ears
Non-barren ears
15
10
CG60 at 120,000 plants ha in 2008
Barren ears
Non-barren ears
EDMs vs. GDDsilking
5
0
-5
0
5
10
15
560
20
600
640
680
720
760
800
720
760
800
720
760
800
30
-1
-1
CG60 at 160,000 plants ha in 2008
CG60 at 160,000 plants ha in 2008
20
No silk emergence
EDMs (g ear-1)
25
15
10
5
0
-5
0
5
10
15
560
20
600
640
680
30
25
-1
-1
F1 at 160,000 plants ha in 2008
F1 at 160,000 plants ha in 2008
20
15
10
5
0
-5
0
5
10
15
560
20
ASI (d)
600
640
680
GDDsilking
Appendix B.6. Relationship between ear dry matter at silking (EDMs) and anthesis to silking
interval (ASI) and the relationship between EDMs and growing degree days from planting to
silking (GDDsilking) for barren and non-barren ears for one of parental inbred lines CG60 and its
F1 hybrid CG60 × CG102 at different plant densities in 2008. The values of EDMs for no silk
emergence were present in the right-hand side of each panel. Each sub-figure represents a
genotype × plant density combination that had at least one barren plant (i.e., grain yield per plant
= 0 g). The linear relationship between EDMs and GDDsilking for the F1 at 160,000 plants ha-1 in
215
2008: EDMs = 74.95 – 0.10 × GDDsilking (R2 = 0.69, n = 68, P < 0.0001).
216
Appendix B.7. Mean, minimum and maximum coefficient of determination for plant growth
rate during the critical period bracketing silking for individual plants of two parental inbred lines
CG60, CG102 and their F1 hybrid CG60 × CG102 grown across different plant densities in 2007
and 2008. The plant densities in 2007 were 40,000 and 120,000 plants ha-1 for the two parental
inbred lines and 40,000 and 160,000 plants ha-1 for the F1 hybrid. The plant densities in 2008
were 80,000; 120,000 and 160,000 plants ha-1 for the three genotypes.
Year
Genotype
Coefficient of determination
Mean
Minimum
Maximum
2007
CG60
0.97
0.79
1.00
CG102
0.97
0.46
1.00
F1
0.94
0.12
1.00
2008
CG60
0.94
0.14
1.00
CG102
0.98
0.67
1.00
F1
0.97
0.64
1.00
217
1.0
CG60 in 2007
CG60 in 2008
CG102 in 2007
CG102 in 2008
F1 in 2007
F1 in 2008
0.8
0.6
0.4
0.2
0.0
Harvest index (g g-1)
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -1.0
-0.6
-0.2
0.2
0.6
1.0
EGRs/PGRs
Appendix B.8. Relationship between harvest index and dry matter partitioning to the ear (i.e.,
the ratio between ear growth rate after silking (EGRs) and plant growth rate during the critical
period bracketing silking (PGRs) for parental inbred lines CG60, CG102 and their F1 hybrid
CG60 × CG102 across different plant densities in 2007 and 2008. The PGRs was from 2 wk
218
before silking to 2 wk after silking in both years. The EGRs was from silking to 2 wk after
silking in 2007 and from 1 wk after silking to 3 wk after silking in 2008. The plant densities in
2007 were 40,000 and 120,000 plants ha-1 for two inbred lines and 40,000 and 160,000 plants ha1
for the F1 hybrid. The plant densities in 2008 were 80,000; 120,000 and 160,000 plants ha-1.
219
CG102 at 120,000 plants ha-1 in 2007
CG102 at 80,000 plants ha-1 in 2008
d
d
NBd = 4
B
NBd = 13
Bd = 3
Bd = 4
CG102 at 120,000 plants ha-1 in 2008
d
Intermediate
NBd = 9
B
Bd = 11
d
Intermediate
NBd = 4
BD = 2
Bd = 20
CG60 at 120,000 plants ha-1 in 2008
D
B
BI = 22
BD = 16
CG60 at 160,000 plants ha-1 in 2008
d
d
NBd = 12
B
NBd = 16
BI = 2
CG102 at 160,000 plants ha-1 in 2008
D
BI = 10
Intermediate
B
Bd = 4
Bd = 11
Intermediate
B
BI = 3
Appendix B.9. Schematic of the relationship between ultimately barren (B) plants (i.e., grain
yield per plant = 0 gram) and plant hierarchies for two parental inbred lines CG60 and CG102 at
different plant densities in 2007 and 2008. Plant hierarchies are classed according to the aboveground plant dry matter at physiological maturity (PDMPM) as dominated (d) plants (i.e., those
plants in the lower 1/3 of PDMPM distribution), intermediate plants (i.e., those plants in the
middle 1/3 of the PDMPM distribution) and dominant (D) plants (i.e., those plants in the upper
1/3 of the PDMPM distribution). The number of plants that belonged to non-barren d (NBd)
plants and barren d (Bd) plants are presented for each inbred × plant density × year combination
where there was a barren plant. When barren plants were present in the intermediate and D
plants, the numbers of barren intermediate (BI) plants and barren D (BD) plants are presented.
220
0.8
-1
CG102 at 120,000 plants ha in 2007
-1
Bd plants
0.6
Bd plants
CG102 at 80,000 plants ha in 2008
NBd plants
NBd plants
NBID plants
BID plants
NBID plants
0.4
0.2
0.0
0
Harvest index (g g-1)
0.8
40
80
120
160
0
-1
40
80
120
160
200
160
200
-1
CG102 at 120,000 plants ha in 2008
CG102 at 160,000 plants ha in 2008
0.6
0.4
0.2
0.0
0
40
80
0.8
120
160
-1
0
40
80
-1
-1
CG60 at 120,000 plants ha in 2008
120
F1 at 160,000 plants ha in 2008
CG60 at 160,000 plants ha in 2008
0.6
0.4
0.2
0.0
0
40
80
120
160
0
40
80
120
160
0
40
80
120
160
200
Above-ground plant dry matter at physiological maturity (g plant-1)
Appendix B.10. The relationship between harvest index (i.e., ratio between grain yield and the
above-ground plant dry matter at physiological maturity [PDMPM]) and PDMPM of individual
barren plants (i.e., grain yield per plant = 0 g) and individual dominated (d) plants (i.e., plants in
221
the lower 1/3 PDMPM distribution) of a plant hierarchy for two parental inbred lines CG60,
CG102 and their F1 hybrid CG60 × CG102 grown at different plant densities in 2007 and 2008.
Within each genotype, plants had similar initial plant size at the 8-leaftip (LT) stage in 2007 and
4-LT stage in 2008. Each sub-figure represents a genotype × plant density × year combination
that had at least one barren plant. Plant hierarchies are classed according to PDMPM as d plants,
intermediate plants (i.e., those plants in the middle 1/3 of the PDMPM distribution) and dominant
(D) plants (i.e., those plants in the upper 1/3 of the PDMPM distribution). When barren plants
were only present in the d plants, plants within a combination could be grouped into barren d
(Bd) plants, non-barren d (NBd) plants and non-barren intermediate and D (NBID) plants,
respectively. When barren plants were present in the intermediate and D plants, plants within a
combination could be grouped into Bd, NBd, barren intermediate and D (BID) and NBID plants,
respectively.
222
150
-1
-1
CG102 at 120,000 plants ha in 2007
CG102 at 80,000 plants ha in 2008
Barren but not d plants
Average barren plants
Non-barren d plants
Average d plants
Barren and d plants
Average barren plants
Non-barren d plants
Average d plants
Barren and d plants
100
50
0
Estimated PDM (g plant-1)
150
-1
-1
CG102 at 160,000 plants ha in 2008
CG102 at 120,000 plants ha in 2008
100
50
0
150
-1
-1
CG60 at 120,000 plants ha in 2008
CG60 at 160,000 plants ha in 2008
100
50
0
200
400
600
800
1000
1200
400
600
800
1000
1200
Growing degree days after planting
Appendix B.11. Dynamics of estimated above-ground plant dry matter (PDM) for individual
barren plants (i.e., grain yield per plant = 0 g), average barren plants, as well as individual
dominated (d) plants (i.e., plants in the lower 1/3 PDM at physiological maturity distribution)
and average d plants from early vegetative stage to physiological maturity for two parental
inbred lines CG60 and CG102 at different plant densities in 2007 and 2008. The barren plants
223
either totally belonged to d plants, or did not totally belong to d plants. Therefore, the barren
plants could be grouped into barren and d plants, barren but not d plants. Each sub-figure
represents an inbred × plant density × year combination that had at least one barren plant. The
first and second arrow indicates the timing of ear initiation and silking, respectively.
224
20
18
16
CG102 at 120,000 plants ha-1 in 2007
Average barren plants
Non-barren d plants
Average d plants
Barren and d plants
-1
CG102 at 80,000 plants ha in 2008
14
12
Barren but not d plants
Average barren plants
Non-barren d plants
Average d plants
Barren and d plants
10
8
6
20
-1
-1
CG102 at 120,000 plants ha in 2008
CG102 at 160,000 plants ha in 2008
CG60 at 120,000 plants ha-1 in 2008
CG60 at 160,000 plants ha in 2008
18
Leaftip
16
14
12
10
8
6
20
-1
18
16
14
12
10
8
6
200
400
600
800
1000
1200
400
600
800
1000
1200
Growing degree days after planting
Appendix B.12. Dynamics of leaftip stage development for individual barren plants (i.e., grain
yield per plant = 0 g), average barren plants, as well as individual dominated (d) plants (i.e.,
plants in the lower 1/3 above-ground plant dry matter at physiological maturity distribution) and
average d plants from early vegetative stage to physiological maturity for two parental inbred
lines CG102 and CG60 at different plant densities in 2007 and 2008. The barren plants either
225
totally belonged to d plants, or did not totally belong to d plants. Therefore, the barren plants
could be grouped into barren and d plants, barren but not d plants. Each sub-figure represents an
inbred × plant density × year combination that had at least one barren plant. The first and second
arrow indicates the timing of ear initiation and silking, respectively.
226
-1
GY (g plant-1) EDM (g plant )
40
30
20
10
-1
-1
CG102 at 120,000 plants ha in 2007
CG102 at 80,000 plants ha in 2008
Initial EDM
Growth I
Growth II
0
-100
10
20
BID
NBd
Bd
Bd
NBd
GY
GY (g plant-1) EDM (g plant-1)
30
40
30
-1
-1
CG102 at 120,000 plants ha in 2008
CG102 at 160,000 plants ha in 2008
20
10
0
-100
BID
Bd
NBd
10
BID
20
NBd
Bd
GY (g plant-1) EDM (g plant-1)
30
50
40
30
20
10
0
-10
-200
-1
-1
CG60 at 160,000 plants ha in 2008
CG60 at 120,000 plants ha in 2008
BID
10
20
30
Bd
Bd
NBd
NBd
40
Appendix B.13. The initial ear dry matter around silking (EDMs) and subsequent growth of
individual ultimately barren ears (i.e., grain yield [GY] per plant = 0 g) and ears from the
dominated (d) plants (i.e., plants in the lower 1/3 above-ground plant dry matter at physiological
maturity distribution [PDMPM]) as well as the corresponding final GY for two parental inbred
lines CG60 and CG102 at different plant densities in 2007 and 2008. The subsequent growth
227
included growth I from silking to approximately 2 wk after silking and growth II from
approximately 2 wk after silking to physiological maturity. Each sub-figure represents an inbred
× plant density × year combination that had at least one barren plant. In the upper panel, the
black vertical bars indicate the initial EDMs, the grey and dark grey bars indicate the subsequent
growth I and II. In the lower panel, the vertical bars indicate the final GY of the corresponding
individual ears. The individual ears that belonged to either barren or d plants could be divided
into (i) ears that belonged to barren d (Bd) plants; (ii) ears that belonged to non-barren d (NBd)
plants and (iii) ears that belonged to barren intermediate (middle 1/3 of PDMPM) and dominant
(D)(upper 1/3 of PDMPM) (BID) plants. The dotted line separates barren and non-barren ears.
The dash line separates ears from BID and Bd plants. The initial EDMs of the BID, Bd and NBd
plants are sorted by an ascending order, separately.
228
CG102 at 120,000 plants ha-1 in 2007
CG102 at 80,000 plants ha-1 in 2008
d
d
ASI = 7 – 8 d
B
ASI = 3 – 8 d
ASI = 7 – 11 d
ASI = 2 – >14 d
d
Intermediate and D
ASI > 11, NT
B
B
NT & NS,
no silk emergence
ASI = 8 d,
no silk emergence
CG60 at 120,000 plants ha-1 in 2008
ASI = 4 d, 8 d,
no silk emergence
CG60 at 160,000 plants ha-1 in 2008
d
d
ASI = 0 – 5 d
B
ASI = -1 - 6 d
no silk emergence
CG102 at 160,000 plants ha-1 in 2008
Intermediate and D
NT
no silk emergence,
B
ASI = 11 d,
no silk emergence
CG102 at 120,000 plants ha-1 in 2008
d
Intermediate
ASI > 14 d
ASI = 1 – 10 d,
no silk emergence
ASI = 3 d, 5 d,
no silk emergence
Intermediate
B
ASI = 0 d, 1 d,
no silk emergence
Appendix B.14. Schematic of ear development characteristics for ultimately barren (B) plants
(i.e., grain yield per plant = 0 gram) and dominated (d) plants (i.e., those plants in the lower 1/3
of above-ground plant dry matter at physiological maturity [PDMPM] distribution) for two
parental inbred lines CG60 and CG102 at different plant densities in 2007 and 2008. Plant
hierarchies are classed according to PDMPM as d plants, intermediate plants (i.e., those plants in
the middle 1/3 of the PDMPM distribution) and dominant (D) plants (i.e., those plants in the upper
1/3 of the PDMPM distribution). The ASI, NT, and NT & NS represent anthesis to silking
interval (ASI) in day, no anther emergence, no anther and silk emergence, respectively.
229
0.2
-1
-1
CG102 at 80,000 plants ha in 2008
-1
CG102 at 160,000 plants ha in 2008
CG102 at 120,000 plants ha in 2007
Barren and d plants
Barren but not d plants
Non-barren d plants
0.1
0.0
0.2
-1
EGRs (g plant-1 GDD-1)
CG102 at 120,000 plants ha in 2008
0.1
0.0
-0.1
0.3
-1
-1
CG60 at 120,000 plants ha in 2008
CG60 at 160,000 plants ha in 2008
0.2
0.1
0.0
-0.1
-0.2
0.0
0.1
0.2
0.3
0.1
-1
0.2
0.3
-1
PGRs (g plant GDD )
Appendix B.15. Relationships between estimated ear growth rate during the period bracketing
silking (EGRs) expressed as gram per plant per growing degree day (g plant-1 GDD-1) and
estimated plant growth rate during the critical period bracketing silking (PGRs) in g plant-1 GDD1
of individual barren plants (i.e., grain yield per plant = 0 g) and individual dominated (d) plants
(i.e., plants in the lower 1/3 above-ground plant dry matter at physiological maturity distribution)
230
for two parental inbred lines CG60 and CG102 at different plant densities in 2007 and 2008. The
barren plants either totally belonged to the d plants, or did not totally belong to the d plants.
Therefore, the barren plants could be grouped into barren d plants, barren but not d plants. Each
sub-figure represents an inbred × plant density × year combination that had at least one barren
plant.
231
Appendix B.16. Summary of similarities among different sub-populations in above-ground plant dry matter (PDM) and ear dry matter (EDM) throughout
development, plant growth rate during the critical period bracketing silking (PGRs), ear growth rate during the period bracketing silking (EGRs), and partitioning
index (i.e., EGRs/PGRs) for two parental inbred lines CG60 and CG102 grown at different plant densities in 2007 and 2008. Presented inbred × plant density ×
year combinations had at least one barren plant (i.e., grain yield per plant = 0 g). Plant hierarchies within a combination are viewed according to PDM at
physiological maturity (PDMPM) as d plants (i.e., plants in the lower 1/3 PDMPM distribution), intermediate plants (i.e., those plants in the middle 1/3 of the
PDMPM distribution) and dominant (D) plants (i.e., those plants in the upper 1/3 of the PDMPM distribution). When assessing both barren plants and dominated
(d) plants, the plants could be barren d (Bd) plants, non-barren d (NBd) plants when barren plants were only present in the d plants. When barren plants were
present in the intermediate and D plants, barren plants and d plants could be grouped into Bd plants, non-barren d (NBd) plants, barren intermediate and D (BID)
plants, respectively. The six PDM sampling stages in 2007 were 8-leaftip (LT) stage (PDM1), 12-LT stage (PDM2), 14-LT stage (PDM3), silking (PDMs), 2 wk
after silking (PDMs+2) and PM (PDMPM), respectively. The five PDM sampling stages in 2008 were 11-LT stage (PDM2), 15-LT stage (PDM3), silking (PDMs),
2 wk after silking (PDMs+2) and PM (PDMPM), respectively. The three EDM sampling stages in 2007 and 2008 were silking (EDMs), 2 wk after silking
(EDMs+2) and PM (EDMPM), respectively. The similarity in PDM and EDM between two individual plants is defined as less than or equal to 0.3 g and 0.15 g
differences, respectively. The similarity in PGRs, EGRs and partitioning index between two individual plants is defined as less than or equal to 0.01 gram per
plant per growing degree day (g plant-1 GDD-1), 0.005 g plant-1 GDD-1 and 0.01 differences, respectively. The similarity is marked with symbol.
Year
Inbred
Plant
Subpopulation
PDM1 PDM2 PDM3 PDMs PDMs+2 PDMPM EDMs EDMs+2 EDMPM PGRs, EGRs and
density
partitioning index
(plants ha-1)
2007
CG102
120,000
Bd
NBd
2008
CG60
120,000
Bd
NBd
CG60
160,000
Bd
BID
BID







NBd
NBd
Bd






CG102
80,000
Bd
NBd
BID
BID
NBd
Bd













NBd


 BID
BID
Bd
BID
BID
NBd
Bd
NBd
NBd
Bd













Bd
160,000

120,000
CG102

CG102




232




































30
CG102 in 2007
CG102 in 2008
Post-silking ligule height frequency
Frequency of barren plants
Number of individual plants
20
10
0
110 125 140 155 170 185 200 215 230 245 260 103 121 139 157 175 193 211 229 247 265
30
CG60 in 2008
F1 in 2008
20
10
0
114 130 146 162 178 194 210 226 242 258
110 125 140 155 170 185 200 215 230 245 260
Maximum plant height (cm)
Appendix B.17. Frequency distributions of the maximum plant height in two parental inbred
lines CG60, CG102 and their F1 hybrid CG60 × CG102 grown across different plant densities in
2007 and 2008. The plant densities in 2007 were 40,000 and 120,000 plants ha-1 for the two
inbred lines, 40,000 and 160,000 plants ha-1 for the F1 hybrid. The plant densities in 2008 were
80,000; 120,000 and 160,000 plants ha-1 for the three genotypes. Within each genotype, plants
had similar initial plant size at the 8-leaftip (LT) stage in 2007 and 4-LT stage in 2008. Dark
grey bars indicate the position of ultimately barren plants (i.e., grain yield per plant = 0 g). In
terms of frequency distribution, for CG102 in 2007 (n = 70): Shapiro-Wilk normality test (Pr <
W) = 0.04, skewness = -0.22, kurtosis = -0.91, coefficient of variation (CV) = 9%, and mean (M)
= 141 cm. For CG60 in 2008 (n = 154): Pr < W = 0.02, skewness = -0.56, kurtosis = 0.49, CV =
7% and M = 157 cm. For CG102 in 2008 (n = 155): Pr < W = 0.87, skewness = 0.03, kurtosis =
-0.08, CV = 8%, and M = 173 cm. For the F1 in 2008 (n = 153): Pr < W = 0.08, skewness = 0.42, kurtosis = -0.03, CV = 5%, and M = 232 cm.
233
30
CG102 in 2008
CG102 in 2007
Post-silking leaf area per plant
Frequency of barren plants
Number of individual plants
20
10
0
21
00
0
27
0
33
00
00 500 100 700 300 700 300 900 500 100 700 300 900 500
39
2
4
1
2
5
3
5
6
4
4
5
5
6
30
F1 in 2008
CG60 in 2008
20
10
0
174
0
0
270
0
366
462
0
0
558
0
166
0
246
0
326
406
0
486
0
566
0
0
646
Post-silking leaf area (cm2)
Appendix B.18. Frequency distributions of the post-silking leaf area in two parental inbred lines
CG60, CG102 and their F1 hybrid CG60 × CG102 grown across different plant densities in 2007
and 2008. The plant densities in 2007 were 40,000 and 120,000 plants ha-1 for the two inbred
lines, 40,000 and 160,000 plants ha-1 for the F1 hybrid. The plant densities in 2008 were 80,000;
120,000 and 160,000 plants ha-1 for the three genotypes. Within each genotype, plants had
similar initial plant size at the 8-leaftip (LT) stage in 2007 and 4-LT stage in 2008. Dark grey
bars indicate the position of ultimately barren plants (i.e., grain yield per plant = 0 g). In terms
of frequency distribution, for CG102 in 2007 (n = 70): Shapiro-Wilk normality test (Pr < W) =
0.69, skewness = 0.14, kurtosis = -0.37, coefficient of variation (CV) = 16%, and mean (M) =
3235 cm2. For CG60 in 2008 (n = 154): Pr < W = 0.87, skewness = -0.05, kurtosis = 0.16, CV =
9% and M = 3669 cm2. For CG102 in 2008 (n = 155): Pr < W = 0.30, skewness = -0.30, kurtosis
= 0.15, CV = 15%, and M = 3021 cm2. For the F1 in 2008 (n = 153): Pr < W = 0.29, skewness =
-0.15, kurtosis = 0.06, CV = 12%, and M = 4723 cm2.
234
30
CG102 in 2008
CG102 in 2007
Largest leaf area frequency
Frequency of barren plants
Number of individual plants
20
10
0
300 360 420 480 540 600 660 720 780
30
CG60 in 2008
330
394
458
522
586
650
714
778
F1 in 2008
20
10
0
310 370 430 490 550 610 670 730 790
315 365 415 465 515 565 615 665 715 765 815
Largest leaf area (cm2)
Appendix B.19. Frequency distributions of the largest leaf area in two parental inbred lines
CG60, CG102 and their F1 hybrid CG60 × CG102 grown across different plant densities in 2007
and 2008. The plant densities in 2007 were 40,000 and 120,000 plants ha-1 for the two inbred
lines, 40,000 and 160,000 plants ha-1 for the F1 hybrid. The plant densities in 2008 were 80,000;
120,000 and 160,000 plants ha-1 for the three genotypes. Within each genotype, plants had
similar initial plant size at the 8-leaftip (LT) stage in 2007 and 4-LT stage in 2008. Dark grey
bars indicate the position of ultimately barren plants (i.e., grain yield per plant = 0 g). In terms
of frequency distribution, for CG102 in 2007 (n = 70): Shapiro-Wilk normality test (Pr < W) =
0.11, skewness = -0.43, kurtosis = -0.45, coefficient of variation (CV) = 11%, and mean (M) =
496 cm2. For CG60 in 2008 (n = 154): Pr < W = 0.49, skewness = -0.21, kurtosis = -0.06, CV =
10% and M = 445 cm2. For CG102 in 2008 (n = 155): Pr < W = 0.00, skewness = -0.71, kurtosis
= 0.45, CV = 10%, and M = 489 cm2. For the F1 in 2008 (n = 153): Pr < W = 0.70, skewness = 0.16, kurtosis = -0.20, CV = 12%, and M = 603 cm2.
235
40
CG102 in 2007
CG102 in 2008
Leaf length/leaf width frequency
Frequency of barren plants
30
Number of individual plants
20
10
0
4.3
5.5
6.7
7.9
9.1 10.3 11.5 12.7 13.9 4.5
40
6.0
7.5
9.0
10.5
12.0
13.5
F1 in 2008
CG60 in 2008
30
20
10
0
5.0
6.2
7.4
8.6
9.8 11.0 12.2 13.4 4.2
5.3
6.5
7.6
8.8
9.9 11.1 12.2 13.4
Leaf length/leaf width
Appendix B.20. Frequency distributions of the ratio between leaf length and maximum leaf
width (leaf length/leaf width) of the largest leaf area in two parental inbred lines CG60, CG102
and their F1 hybrid CG60 × CG102 grown across different plant densities in 2007 and 2008. The
plant densities in 2007 were 40,000 and 120,000 plants ha-1 for the two inbred lines, 40,000 and
160,000 plants ha-1 for the F1 hybrid. The plant densities in 2008 were 80,000; 120,000 and
160,000 plants ha-1 for the three genotypes. Within each genotype, plants had similar initial
plant size at the 8-leaftip (LT) stage in 2007 and 4-LT stage in 2008. Dark grey bars indicate the
position of ultimately barren plants (i.e., grain yield per plant = 0 g). In terms of frequency
distribution, for CG102 in 2007 (n = 70): Shapiro-Wilk normality test (Pr < W) = 0.02, skewness
= -0.25, kurtosis = -0.98, coefficient of variation (CV) = 14%, and mean (M) = 7.85. For CG60
in 2008 (n = 154): Pr < W = 0.07, skewness = 0.23, kurtosis = 1.42, CV = 9% and M = 7.39. For
CG102 in 2008 (n = 155): Pr < W = 0.04, skewness = 0.41, kurtosis = -0.20, CV = 12%, and M =
10.26. For the F1 in 2008 (n = 153): Pr < W = 0.06, skewness = 0.49, kurtosis = 0.46, CV = 8%,
and M = 9.26.
236
50
CG102 in 2007
CG102 in 2008
Stem elongation rate frequency
Frequency of barren plants
40
Number of individual plants
30
20
10
0
0.24 0.30 0.36 0.42 0.48 0.54 0.60 0.66 0.72 0.78
50
CG60 in 2008
0.28
0.36
0.44
0.52
0.60
0.68
0.76
F1 in 2008
40
30
20
10
0
0.25
0.35
0.46
0.56
0.67
0.77
0.31
0.39
0.47
0.55
0.63
0.71
0.79
Stem elongation rate (cm GDD-1)
Appendix B.21. Frequency distributions of stem elongation rate (in cm per growing degree day
[GDD]) in two parental inbred lines CG60, CG102 and their F1 hybrid CG60 × CG102 grown
across different plant densities in 2007 and 2008. The plant densities in 2007 were 40,000 and
120,000 plants ha-1 for the two inbred lines, 40,000 and 160,000 plants ha-1 for the F1 hybrid.
The plant densities in 2008 were 80,000; 120,000 and 160,000 plants ha-1 for the three
genotypes. Within each genotype, plants had similar initial plant size at the 8-leaftip (LT) stage
in 2007 and 4-LT stage in 2008. Dark grey bars indicate the position of ultimately barren plants
(i.e., grain yield per plant = 0 g). In terms of frequency distribution, for CG102 in 2007 (n = 70):
Shapiro-Wilk normality test (Pr < W) = 0.02, skewness = -0.62, kurtosis = -0.05, coefficient of
variation (CV) = 11%, and M = 0.44 cm GDD-1. For CG60 in 2008 (n = 154): Pr < W =
<0.0001, skewness = -0.89, kurtosis = 1.65, CV = 8% and mean (M) = 0.38 cm GDD-1. For
CG102 in 2008 (n = 155): Pr < W = <0.0001, skewness = 1.16, kurtosis = 0.92, CV = 16%, and
M = 0.41 cm GDD-1. For the F1 in 2008 (n = 153): Pr < W = <0.0001, skewness = 1.37, kurtosis
= 1.80, CV = 10%, and M = 0.57 cm GDD-1.
237
80
60
CG102 in 2007
CG102 in 2008
Ear diameter extension rate frequency
Frequency of barren plants
Number of individual plants
40
20
0
-28
80
-20
-12
-4
4
12
20
28
CG60 in 2008
-30 -25 -20 -15 -10 -5 0
5 10 15 20 25 30
F1 in 2008
60
40
20
0
-30 -23 -16
-9
-2
5
12
19
26
-30 -24 -18 -12 -6
0
6
12 18 24 30
Ear diameter expansion rate (mm × 10-2 GDD-1)
Appendix B.22. Frequency distributions of ear diameter expansion rate (in mm × 10-2 per
growing degree day [GDD]) in two parental inbred lines CG60, CG102 and their F1 hybrid CG60
× CG102 grown across different plant densities in 2007 and 2008. The plant densities in 2007
were 40,000 and 120,000 plants ha-1 for the two inbred lines, 40,000 and 160,000 plants ha-1 for
the F1 hybrid. The plant densities in 2008 were 80,000; 120,000 and 160,000 plants ha-1 for the
three genotypes. Within each genotype, plants had similar initial plant size at the 8-leaftip (LT)
stage in 2007 and 4-LT stage in 2008. Dark grey bars indicate the position of ultimately barren
plants (i.e., grain yield per plant = 0 g). In terms of frequency distribution, for CG102 in 2007 (n
= 70): Shapiro-Wilk normality test (Pr < W) = <0.0001, skewness = 1.26, kurtosis kurtosis =
0.88, coefficient of variation (CV) = 35%, and mean (M) = 9.61 mm × 10-2 GDD-1. For CG60 in
2008 (n = 154): Pr < W = <0.0001, skewness = -2.01, kurtosis = 10.45, CV = 97% and M = 6.07
mm × 10-2 GDD-1. For CG102 in 2008 (n = 155): Pr < W = <0.0001, skewness = -0.57, kurtosis
= 4.34, CV = 71%, and M = 5.81 mm × 10-2 GDD-1. For the F1 in 2008 (n = 153): Pr < W =
<0.0001, skewness = 1.10, kurtosis = 4.17, CV = 43%, and M = 7.13 mm × 10-2 GDD-1.
238