Published September 24, 2014
Crop Ecology & Physiology
CharacterizationofaSpringWheatAssociationMapping
PanelforRootTraits
SruthiNarayananandP.V.VaraPrasad*
ABSTRACT
Improved root traits are important for increased nutrient and water uptake and productivity in wheat (Triticum spp.). The objectives of this research were to characterize genetic variability for root traits in a Spring Wheat Association Mapping (AM) Panel
and determine whether root traits are related to shoot dry weight, tiller number, and plant height. Rooting depth, root dry
weight, root/shoot ratio, and shoot traits were determined for 250 genotypes of the AM panel. The remaining root traits were
measured for a subset of 30 genotypes selected based on rooting depth. Significant genetic variability was observed for root traits.
Genotypes Treasure and IDO686 were ranked high and genotypes MN08106-6 and MT1016 were ranked low for most root
traits in the AM panel or its subset. Shoot dry weight had positive relationships (correlation coefficient, r ≥ 0.50) with rooting
depth and root dry weight in the AM panel, and with total root length, total root surface area, root length density (30–60-cm
soil depth), fine root length, and fine root surface area in the subset. Tiller number also had positive relationships (r ≥ 0.50) with
all the above root traits except rooting depth and root dry weight. Plant height had no correlations with most root traits. Plant
height had only weak negative relationships (|r| ≤ 0.36) with root dry weight and root/shoot ratio in the AM panel. The genetic
variability identified in this research for root traits offers useful information for wheat improvement programs for choosing
genotypes with contrasting root characteristics.
Wheatisthemostwidelygrowncropin the world
and it provides 19% of the daily calories and 21% of the daily
proteins to 4.5 billion people (FAO, 2011). To feed the nine billion people in the world by 2050, wheat production needs to be
increased by 60% (FAO, 2012). Root traits play a critical role in
improving wheat productivity as they determine soil exploration
and therefore, water and nutrient absorption (Manschadi et al.,
2006, 2008; Lynch, 2007; Hammer et al., 2009). Considering
the potential role of root traits in capturing resources, Lynch
(2007) suggested that identifying genetic variability for root
traits will be critical for increasing crop productivity.
Root traits improve resource capture under water limiting
and non-limiting conditions. Sivamani et al. (2000) identified
transgenic wheat lines with increased root dry weight, which
were also associated with increased biomass productivity. Deeper
roots can improve water absorption from deep soil layers at critical
crop growth stages resulting in higher yield and harvest indices
(reviewed by Ludlow and Muchow, 1990). Kashiwagi et al. (2006)
reported that root length density (RLD) in the top soil layers
(0–30 cm) and deeper soil layers (30–60 cm) are associated with
Supplemental material available online. Dep. of Agronomy, 2004
Th rockmorton Plant Sciences Center, Kansas State Univ., Manhattan, KS
66506. Received 9 Jan. 2014. *Corresponding author ([email protected]).
Published in Agron. J. 106:1593–1604 (2014)
doi:10.2134/agronj14.0015
Available freely online through the author-supported open access option.
Copyright © 2014 by the American Society of Agronomy, 5585 Guilford
Road, Madison, WI 53711. All rights reserved. No part of this periodical
may be reproduced or transmitted in any form or by any means, electronic or
mechanical, including photocopying, recording, or any information storage and
retrieval system, without permission in writing from the publisher.
seed yield in chickpea (Cicer arietinum L.). Caassen and Barber
(1976) considered root surface area as the primary determinant
of ion flux from soil to plant roots. Increased root dry weight,
RLD, and total root surface area were found to be associated with
improved water use efficiency and grain yield of wheat in Mediterranean environments (Rebetzke and Richards, 1999; Liao et al.,
2004). Roots with increased diameter had large xylem vessels,
increased axial conductance, and improved root penetration ability in rice ([Oryza sativa L.] Fukai and Cooper, 1995; Clark et al.,
2008). Huang and Fry (1998) suggested that fine root production in response to soil drying could improve water and nutrient
absorption in tall fescue (Festuca arundinacea Schreb.) under
drought conditions. Such traits are also valuable under optimal
growth conditions for improving resource capture.
Most crop improvement programs have concentrated on
aboveground components in wheat, especially for decreasing plant
height and increasing harvest index, but their impacts on root traits
are not clearly understood. One of the early studies that evaluated
root traits in relation to plant height reported that a tall wheat plant
tends to produce a deep root system and a short wheat plant tends
to produce a shallow root system (Mac Key, 1973). When root dry
weight of tall landraces and CIMMYT-derived modern cultivars
were evaluated, landraces were found to possess two- to fourfold
greater root dry weight than modern cultivars (Ehdaie et al., 1991;
Ehdaie and Waines, 1993; Ehdaie, 1995; Waines and Ehdaie,
2007). On the other hand, the semi-dwarf Rht-B1 and Rht-D1
isolines were reported to have greater root dry weight than their
Abbreviations: AM, association mapping; PVC, polyvinyl chloride; RLD,
root length density; TCAP, Triticeae Coordinated Agricultural Project.
A g ro n o myJ o u r n a l • Vo l u m e10 6 ,I s s u e5 • 2 014
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tall counterparts (Lupton et al., 1974; Ehdaie and Waines, 1994;
Miralles et al., 1997). Similarly, Li et al. (2011) suggested that the
dwarfing gene Rht-B1 has a pleiotropic effect of increasing longest
root length and total root length. Taken together, controversial
reports exist on relationship of root traits to plant height.
Despite the importance of root traits in improving crop yields,
limited information is available on the variation of root traits
and their genetic control in wheat. Phenotypic plasticity of root
traits in response to changing soil conditions, lack of cost-effective and high-throughput screening methods, and difficulties
associated with root recovery impose major challenges to root
studies (Poorter and Nagel, 2000; Fitter, 2002; Manschadi et al.,
2008). Exploring genetic variability of root traits helps to identify contrasting genotypes for root traits that can be included in
crop improvement programs. An understanding of the relationship of root traits with shoot traits that are related to grain yield
will further help to improve productivity.
The objectives of this research were to characterize genetic
variability for root traits in the Spring Wheat AM Panel of Triticeae Coordinated Agricultural Project (TCAP) consisting of
250 genotypes and to determine whether root traits are related to
shoot dry weight, tiller number per plant, and plant height.
MATERIALS AND METHODS
Germplasm
The germplasm used in this study was the Spring Wheat AM
Panel of TCAP consisting of 250 genotypes (Supplementary Table
1). The germplasm included 10 genotypes from Alberta, Canada;
13 genotypes from Manitoba, Canada; 28 genotypes from
Saskatchewan, Canada; 25 genotypes from CIMMYT, Mexico;
27 genotypes from Minnesota; 25 genotypes from Montana; 30
genotypes from South Dakota; 34 genotypes from California;
32 genotypes from Idaho; and 26 genotypes from Washington.
Genotypes in the spring wheat AM panel represented three different market classes of wheat: soft white spring (26 genotypes), hard
white spring (42 genotypes), and hard red spring (157 genotypes)
(Supplementary Table 1). Twenty-five out of 250 genotypes in the
AM panel were not confirmed of their market classes.
Genotyping data on Rht-B1, Rht-D1(dwarfing genes), and
Ppd-D1 (photoperiod insensitivity gene) for the AM panel were
provided by L. Talbert and N. Blake (unpublished data, 2014).
The AM panel included 68 Rht-B1and Rht-D1 wild-types (tall
statured), 106 Rht-B1 mutants, 68 Rht-D1 mutants, and 3
Rht-B1and Rht-D1 mutants. Data on Rht mutations were not
available for five genotypes. Similarly, 138 genotypes in the AM
panel were Ppd-D1 mutants (photoperiod insensitive) and 97
genotypes were Ppd-D1 wild-types (photoperiod sensitive). Data
on Ppd mutations were not available for 15 genotypes.
Experimental Details
This research was conducted in controlled environment facilities (greenhouse) at the Department of Agronomy, Kansas State
University, Manhattan, KS. Two independent experiments
were conducted to evaluate the genetic variability of root traits
in the spring wheat AM panel. The greenhouse was equipped
with an automated S vaporizer (Rosemania, Franklin, TN) that
vaporized S for 1 h between 2300 and 2400 h. Sulfur vaporization was done as a preventive measure against powdery mildew.
Plants were grown in polyvinyl chloride (PVC) columns with
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inside diameter of 7.5 cm and height of 150 cm. The bottom of
the columns was sealed with plastic caps with a central hole of
0.5 cm diam. for drainage. Rooting medium was Turface MVP
(PROFILE Products LLC, Buffalo Grove, IL), which had a
bulk density of 576.66 ± 32 kg m–3. Turface is calcined, nonswelling illite and silica clay, and allows easy separation of roots.
The rooting medium was fertilized with Osmocote (Scotts,
Marysville, OH), a controlled-release fertilizer with 19–6–12
(N–P2O5–K 2O), respectively, at 4 g per column before sowing.
A systemic insecticide, Marathon 1% G (a.i.: Imidacloprid: 1–
[(6-Chloro-3-pyridinyl)methyl]-N–nitro-2-imidazolidinimine;
OHP, Inc., Mainland, PA) was applied at 1 g per column before
sowing to control sucking pests. Three seeds of a single genotype
were sown at 4-cm depth in each column on 17 Mar. 2012 (Exp.
1) and 14 Sept. 2012 (Exp. 2). Purity of seeds was not tested.
After emergence, plants were thinned to one plant per column,
which was maintained until harvest. Plants were irrigated daily
(0.9±0.1 L per day) using an automated drip irrigation system
until harvest to avoid water stress. Emissions from drip-tubes
were examined weekly for proper water delivery. Irrigation was
provided three times per day at 0600, 1200, and 1800 h. Plants
were maintained under optimum temperature (24/14°C, daytime maximum/nighttime minimum) conditions from sowing
to harvest at a photoperiod of 16 h. The fungicide, Bumper 41.8
EC (a.i.: Propiconazole: 1-[[2-(2,4 dichlorophenyl)-4-propyl1,3-dioxolan-2-yl]Methyl]-1H-1,2,4-triazole; 1.2 mL L–1;
Makhteshim Agan of North America, Inc., Raleigh, NC) was
also applied at 20 d after sowing to prevent powdery mildew.
The insecticide and fungicide treatments helped to maintain
the plants without any pest or pathogen problems until harvest.
Plants were harvested at 50 d after sowing when 50% of the
population reached flowering stage to avoid roots reaching the
bottom of the columns and coiling, which would have complicated harvest procedure, separation of roots, and measurement
of root traits. Our preliminary observations showed that roots
reach the bottom of the columns in about 55 d.
Data Collection
Shoot Traits
Shoot traits evaluated in this study were plant height, number
of tillers per plant, and shoot dry weight (see the measurement
details below). These traits were measured on all 250 genotypes in
the AM panel. Height, tiller number, and growth stage of all 250
plants were recorded 1 d before harvest. Plant height was determined as the distance between Turface level and the last leaf ligule.
Rooting Depth
At harvest, the shoot of each plant was separated by cutting at
the base of the stem. After removing shoots, roots were laid on a
flat surface and stretched to measure their length as an estimate of
rooting depth. Rooting depth was measured for all 250 genotypes
in the AM panel. The root system was then washed, placed between
moist paper towels, sealed in Ziploc bags (S.C. Johnson & Sons, Inc.
Racine, WI), transported to the laboratory, and stored at 4°C.
Length, Surface Area, Volume, Diameter,
and Length Density of Roots
Fifteen genotypes that were ranked the highest and 15 genotypes that were ranked the lowest for rooting depth were selected
Agronomy Journal • Volume 106, Issue 5 • 2014
for further complete root analyses. Root system of each of these
30 genotypes was stretched and sliced into 30-cm long portions.
Each portion was submerged in a water bath (20 by 15 by 2 cm)
to maximize separation of roots and to minimize their overlap,
and scanned using an Epson photo scanner (Epson Perfection
V700 with 6400 dpi resolution) (Epson, Long Beach, CA).
Images of scanned roots of the 30 genotypes were analyzed using
WinRHIZO Pro image analysis system (Regent Instruments,
Inc., Quebec City, QC, Canada) to estimate total root length
(sum of the lengths of all roots in the root system), total root
surface area, total root volume, average root diameter, surface
area, and RLD of roots in 0- to 30-cm and 30- to 60-cm soil
depths, fine root (roots with diameter <0.50 mm) length, fine
root surface area, and fine root volume (McPhee, 2005; Singh
et al., 2011). Root length density in each 30-cm depth of root
system was calculated as the ratio of root length to the volume of
30-cm section of the PVC column, and it represented RLD in
each 30 cm of soil depth (Kashiwagi et al., 2005).
Root Dry Weight and Root/Shoot Ratio
After scanning, root systems were packed in paper bags for
drying. Roots and shoots of all 250 genotypes were dried to
constant weight at 60°C for determining dry weight. Root/shoot
ratio for each of the 250 genotypes was calculated as the ratio of
root dry weight to shoot dry weight.
Statistical Analyses
The experimental design was a randomized complete block
in both experiments. There were two blocks (replications) in
both experiments. Analysis of variance was performed for root
and shoot traits using a model for a randomized block design
combined over experiments using the GLM procedure in SAS
v9.2 (SAS Institute, 2010). Genotypes and experiments were
considered fixed while replication within experiments was
considered random effects. Separation of means was done using
the least significant difference (LSD) test (P < 0.05). Analysis of
variance was done using the GLM procedure to test the effects
of Rht and Ppd mutations on root traits in the AM panel. The
influence of Rht and Ppd mutations on the relationships among
root and shoot traits was tested by analysis of covariance using
the GLM procedure (model, shoot trait = root trait + mutation
[Rht or Ppd] + root trait by mutation interaction). If the effect
of “root trait × mutation interaction” was significant on a shoot
trait, the relationship between that root trait and shoot trait
was evaluated separately for different Rht or Ppd classes (e.g.,
mutants or wild-types). The CORR procedure in SAS was used
to determine the correlation between different root and shoot
traits. The REG procedure in SAS was used to regress root traits
against shoot traits. Frequency distributions of root traits were
tested for deviation from normality using the Shapiro–Wilk test
of UNIVARIATE procedure in SAS.
Table 1. Analyses of variance results on effects of experiment (E), genotype (G), and E × G interaction, range, mean, standard deviation, and coefficient of variation for root and shoot traits.
E
P values
G
E×G
Range
249
249
249
249
249
249
29
29
0.0027
0.3642
0.0170
0.0237
0.5183
0.1315
0.1282
0.8708
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0002
0.0002
0.4078
0.0776
0.0523
0.3452
0.2543
0.0576
0.1121
0.0060
Total root volume, cm3
29
0.0568
0.0004
0.0421
Average root diameter, cm
29
0.0831
0.0002
0.0135
67–165
8–50
0.89–4.7
0.12–3.5
0.07–0.89
2–10
224–1046
1190–6663 (E 1§)
1679–6270 (E 2¶)
1.5–15 (E 1)
3.2–34 (E 2)
0.39–0.51(E 1)
0.37–0.99 (E 2)
119
29
2.5
1.1
0.40
6
608
3634 (E1)
3725 (E2)
6.8 (E1)
11 (E2)
0.46 (E1)
0.57 (E2)
15
9
0.67
0.62
0.17
2
208
1659 (E1)
1029 (E2)
4.1 (E1)
6.3 (E2)
0.34 (E1)
0.16 (E2)
0.0895
0.5393
<0.0001
0.0473
0.1202
0.2268
188–433
0.986–1.68
336
1.40
68
0.147
20
10
0.0290
0.0007
<0.0001
0.0085
0.2206
0.2250
102–385
0.705–1.55
266
1.22
131
0.235
49
19
0.8898
0.0006
0.0018
911–4826 (E 1)
1083–4580 (E 2)
67–356 (E 1)
72–308 (E 2)
0.48–2.5 (E 1)
0.53–2.1 (E 2)
2680 (E1)
2751 (E2)
189 (E1)
179 (E2)
1.3 (E1)
1.2 (E2)
1158 (E1)
794 (E2)
84 (E1)
52 (E2)
0.58 (E1)
0.35 (E2)
43 (E1)
29 (E2)
44 (E1)
29 (E2)
44 (E1)
28 (E2)
Traits†
Rooting depth, cm
Plant height, cm
Shoot dry weight, g
Root dry weight, g
Root/shoot ratio
Tiller number per plant
Total root surface area, cm2
Total root length, cm
df (G)‡
Root traits in 0- to 30-cm soil depth
29
Surface area, cm2
29
Root length density, cm cm–3
Root traits in 30- to 60-cm soil depth
29
Surface area, cm2
29
Root length density, cm cm–3
Traits of fine roots with diameter <0.5 mm
Length, cm
29
Surface area, cm2
29
0.5688
0.0002
0.0015
Volume, cm3
29
0.5869
0.0002
0.0021
Mean
Standard
deviation
Coefficient of
variation
%
13
31
27
59
43
27
34
46 (E1)
28 (E2)
60 (E1)
56 (E2)
8 (E1)
27 (E2)
† Rooting depth, plant height, shoot dry weight, root dry weight, root/shoot ratio, and tiller number were estimated for all the 250 genotypes of the spring wheat association mapping panel. Other traits were estimated within a subset of 30 genotypes selected based on rooting depth.
‡ Degrees of freedom for genotype.
§ Experiment 1.
¶ Experiment 2.
Agronomy Journal • Volume 106, Issue 5 • 2014
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Fig. 1. Distribution of rooting depth, root dry weight, and root/shoot ratio among 250 spring wheat genotypes.
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RESULTS
Variability of Root and Shoot Traits in the Spring
Wheat Association Mapping Panel
Considerable variability was observed for root and shoot traits
in the spring wheat AM panel and the subset of 30 genotypes
selected for divergent rooting depth (Table 1). Analysis of
variance showed that experiment × genotype interaction was
significant for total root length, total root volume, average root
diameter, fine root length, fine root surface area, and fine root
volume for the selected subset (Table 1). Therefore, data were
analyzed separately for each experiment for these traits. Data
were pooled across experiments for other traits in the AM panel
or its subset because there were no significant experiment ×
genotype interactions for those traits.
To further explore the cause of genetic variability in root
traits, we analyzed whether root traits vary among genotypes
with Rht-B1, Rht-D1 (dwarfing genes), and Ppd-D1 (photoperiod
insensitivity gene) mutations (mean root traits of wild-types and
mutants were compared). Our data showed that these mutations had no effect (P > 0.05) on any of the root traits evaluated
among the 250 genotypes of the AM panel (mean root traits of
wild-types and mutants were not significantly different).
A wide range was observed for all root traits in the AM panel
and the selected subset with more than 100% variation between
minimum and maximum values of most traits (Table 1). Among
the root traits, the widest range on a percentage basis was
observed for root dry weight (Table 1). Frequency distributions
of root traits (Fig. 1 and 2) showed the extent of genetic variability for these traits. Rooting depth followed a normal distribution (P > 0.05, Shapiro–Wilk test) in the AM panel (Fig. 1a).
Most genotypes (85%) were included in the intermediate classes
(100–140 cm) of rooting depth. Root dry weight and root/shoot
ratio did not follow a normal distribution (P < 0.05, Shapiro–
Wilk test) in the AM panel (Fig. 1b and 1c, respectively). Root
dry weight showed a skewed distribution to the right (Fig. 1b).
Root dry weight of majority of the genotypes (90%) ranged
between 0.12 and 1.86 g. Half of the genotypes (58%) possessed
root/shoot ratios between 0.27 and 0.53. Total root surface area
followed a normal distribution (P > 0.05, Shapiro–Wilk test)
in the selected subset (Fig. 2a). Total root surface area of 40%
of the genotypes ranged between 500 and 650 cm2 (Fig. 2a). A
consistent pattern was not visible across experiments for other
root traits in the selected subset (Fig. 2b–2g).
Genotypes that were ranked high or low for root traits within
the AM panel and the selected subset are listed in Tables 2 and
3, respectively. Treasure and IDO686 had similar root characteristics. Both were ranked in the top 4% of genotypes in the
AM panel for rooting depth (Table 2) and were ranked in the
top one-third of genotypes in the selected subset for total root
surface area, total root length, surface area in 0- to 30-cm and
30- to 60-cm soil depths, fine root length, and fine root surface
area (Table 3). Two other genotypes IDO377S and Supurb were
ranked in the top 4% of genotypes in the AM panel for rooting depth (Table 2). In addition, IDO377S was ranked in the
top one-third of genotypes in the selected subset for total root
surface area, average root diameter, and surface area and RLD
in 30- to 60-cm soil depth. Similarly, Superb was in the top
one-third of the selected subset for total root length, fine root
length, fine root surface area, and RLD in 0- to 30- and 30- to
Agronomy Journal • Volume 106, Issue 5 • 2014
Fig. 2. Distribution of major root traits within the subset of 30 spring wheat genotypes. Fifteen genotypes that were ranked the highest and 15
genotypes that were ranked the lowest for rooting depth were used to estimate total root surface area, total root length, average root diameter,
root length density, fine root (diameter < 0.5 mm) length, and fine root surface area. Root length density is the ratio of root length in 0- to 30-cm or
30- to 60-cm depth of root system to the volume of 30-cm section of the PVC column. Data were presented separately for each experiment for total
root length, average root diameter, fine root length, and fine root surface area because analysis of variance showed significant experiment × genotype
interaction (P < 0.05) for these traits.
60-cm soil depths. The experimental line 9223 was in the top 4%
of genotypes in the AM panel for rooting depth, root dry weight,
and root/shoot ratio.
There were genotypes that were not in the top 4% of genotypes
in the AM panel for rooting depth, but still ranked in the top
one-third of genotypes in the subset for some other root traits.
For example, experimental line UC1602 was in the top one-third
of genotypes in the subset for total root surface area, average root
diameter, and surface area and RLD in 0- to 30-cm and 30- to
60-cm soil depths. Lolo was in the top one-third of genotypes in
the subset for total root length, total root surface area, fine root
length, and fine root surface area. Experimental line SD4243
was in the top one-third of genotypes in the subset for total root
length, total root surface area, surface area and RLD in 0- to
30-cm soil depth, fine root length, and fine root surface area.
Experimental lines MN08106-6, MT1016, and SD4215
were ranked in the lowest 4% of genotypes in the AM panel for
rooting depth (Table 2). In addition, these three genotypes were
ranked in the lowest one-third of genotypes in the subset for all
root traits (Table 3).
Relationships among Root and Shoot Traits
Significant correlations were observed among root and shoot
traits within the AM panel of 250 genotypes or its subset of 30
genotypes (Fig. 3 and 4, respectively). Shoot dry weight showed
positive relationships (Pearson correlation coefficient, r ≥ 0.50)
Agronomy Journal • Volume 106, Issue 5 • 2014
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Table 2. Genotypes that were ranked high and low for rooting depth, root dry weight, and root/shoot ratio in the spring wheat association mapping
panel of 250 genotypes.
Rank
Highest 10
Lowest 10
LSD
Rooting depth
cm
9252 (165)†
SD4181 (152)
IDO686 (151)
IDO377S (150)
BW947 (149)
Treasure (149)
9223 (145)
IDO671 (145)
9256 (143)
Superb (143)
9240 (92)
IDO694 (90)
WA8133 (89)
MT1016 (88)
Newana (85)
MN08106-6 (83)
SD4215 (83)
Verde (77)
HR07024-5 (71)
UC1554 (67)
27
Root dry weight
g
IDO629 (3.5)
Thatcher (3.4)
Alturas (3.2)
Peace (2.9)
9223 (2.8)
AC Cadillac (2.7)
GP069 (2.5)
Sabin (2.5)
Penawawa (2.5)
Attila-1RS (2.5)
Jefferson (0.26)
MN05141–2 (0.24)
HWO90071M (0.22)
MN02072–7 (0.22)
SD4181 (0.22)
MN08106–6 (0.21)
MN07199–6 (0.17)
HR07024–5 (0.15)
SD4215 (0.13)
Harvest (0.12)
1.31
Root/shoot ratio
Thatcher (1.0)
MT1002 (0.89)
H0800080 (0.87)
Peace (0.87)
Penawawa (0.83)
AC Cadillac(0.82)
Blanca Fuerte (0.80)
9233 (0.78)
Traverse (0.76)
AC Andrew (0.74)
SD4181 (0.14)
Harvest (0.14)
MN02072–7 (0.13)
IDO377S (0.13)
Glenlea (0.13)
SD4280 (0.13)
Jefferson (0.12)
IDO852 (0.12)
SD4243 (0.12)
HWO90071M (0.07)
0.36
† Values in parentheses are means of the respective traits.
with rooting depth (Fig. 3a) and root dry weight (Fig. 3b) in the
AM panel, and with total root surface area (Fig. 4a), RLD in 30to 60-cm soil depth (Fig. 4b), total root length (Fig. 4c), fine root
length (Fig. 4d), and fine root surface area (Fig. 4e) in the subset
selected for divergent rooting depth. Tiller number showed positive relationships (r ≥ 0.50) with total root surface area (Fig. 4f),
RLD in 30- to 60-cm soil depth (Fig. 4g), total root length (Fig.
4h), fine root length (Fig. 4i), and fine root surface area (Fig. 4j)
in the subset selected for divergent rooting depth. The relationships that tiller number had with rooting depth (Fig. 3d), root
dry weight (Fig. 3e), and root/shoot ratio (Fig. 3f) in the AM
panel were not strong (r < 0.50). Plant height did not show any
correlation with most root traits within the AM panel or in the
selected subset (Fig. 3g and 4k–4o). Plant height had only weak
negative relationships (|r| < 0.50) with root dry weight and root/
shoot ratio in the AM panel (Fig. 3h and 3i).
We also analyzed whether the Rht or Ppd mutations had any
effect on the relationships among shoot and root traits. Our data
showed that the effect of interaction between rooting depth and
Rht mutations was significant on plant height (P = 0.03), shoot
dry weight (P = 0.03), and tiller number (P = 0.03). This indicated
that the slopes of the relationships between (i) plant height and
rooting depth, (ii) shoot dry weight and rooting depth, and (iii)
tiller number and rooting depth differed among Rht mutants and
wild-types. The above relationships for Rht mutants and wildtypes are shown in Fig. 5. Shoot dry weight had strong positive
correlations (r ≥ 0.50) with rooting depth among Rht-B1 mutants
and Rht-B1 and/or Rht-D1 mutants and weak positive correlations
(r < 0.50) with rooting depth among Rht-D1 mutants and Rht-B1
and Rht-D1 wild-types. A strong positive correlation (r ≥ 0.50)
between tiller number and rooting depth was observed only for
Rht-B1 mutants. The correlation between tiller number and rooting depth was not strong (r < 0.50) among Rht-B1 and/or Rht-D1
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mutants. Plant height did not show a correlation (r ≥ 0.50) with
rooting depth in most of the Rht classes. It had a weak correlation
(r = 0.38) with rooting depth among Rht-B1 and Rht-D1wildtypes (tall plants). Effect of interaction between root dry weight
and Ppd-D1 mutation was significant (P = 0.03) on plant height.
This indicated that the slope of the relationship between plant
height and root dry weight differed between Ppd-D1 mutants and
wild-types. A weak negative relationship (r = –0.40) was observed
between plant height and root dry weight among Ppd-D1 mutants
(photoperiod insensitive; Fig. 6a). Taken together, the above
relationships (Fig. 5 and 6) among shoot traits and root traits were
similar to the relationships obtained when data were pooled across
Rht or Ppd mutations (Fig. 3). The effects of Rht or Ppd mutations
were not significant on any other relationships shown in Fig. 3.
DISCUSSION
This study demonstrated that considerable genetic variability
exists for root and shoot traits in the Spring Wheat TCAP AM
Panel. The variability observed for root traits among genotypes is
evident from the wide range observed for these traits. In some cases,
the rooting depth exceeded column height because of the zigzag
pattern of root growth within the columns. We have presented
10 genotypes that were ranked high and 10 genotypes that were
ranked low for major root traits within the AM panel of 250 genotypes and the subset of 30 genotypes selected for divergent rooting
depth in Tables 2 and 3, respectively. Overall, genotypes Treasure
and IDO686 were ranked high and genotypes MN08106-6 and
MT1016 were ranked low for most root traits in the AM panel
and selected subset. The contrasting genotypes identified in this
study for root traits can be exploited to improve drought tolerance
and/or resource capture in wheat. Many of the genotypes identified for improved root traits were hard wheats (e.g., IDO377S,
Superb, UC1602, Lolo, SD4243; Supplementary Table 1). This is
Agronomy Journal • Volume 106, Issue 5 • 2014
Agronomy Journal • Volume 106, Issue 5 • 2014
1599
Treasure
(6270)
IDO686
(5649)
HWO90071M
(4791)
SD4243
(4736)
Jefferson
(4676)
SD4181
(4674)
MN07098–6
(4668)
Lolo
(4488)
Superb
(4145)
Stettler
(3851)
UC1602
(6663)§
Expresso
(6246)
IDO686
(5983)
IDO377S
(5801)
Treasure
(5688)
SD4243
(5404)
Lolo
(4567)
Stettler
(4388)
Superb
(4170)
SD4280
(4139)
Total root length
E 1†
E 2‡
–––––––––– cm ––––––––––
Continued next page.
Highest 10
Rank
PT765
(0.46)
UC1602
(0.47)
Treasure
(0.51)
Expresso
(0.58)
UC1603
(0.68)
IDO377S
(0.70)
Treasure
(0.48)
IDO560
(0.48)
9247
(0.70)
CDC Utmost
(BW883)
(0.71)
CDC Utmost
(BW883)
(0.51)
Expresso
(0.50)
UC1602
(0.72)
IDO560
(0.86)
IDO686
(0.51)
CDC Osler
(0.51)
IDO852
(0.96)
CDC Osler
(0.99)
Park
(0.51)
IDO377S
(0.51)
Average root diameter
E1
E2
–––––––––– mm ––––––––––
MN07098-6
(672)
HWO90071M
(673)
Lolo
(697)
CDC Utmost
(BW883) (774)
IDO377S
(824)
SD4243
(855)
Expresso
(867)
UC1602
(922)
IDO686
(1032)
Treasure
(1046)
IDO686
(1.45)
CDC Osler
(1.47)
CDC Utmost
(BW883) (384)
PT765
(383)
UC1602
(1.49)
Park
(273)
HWO90071M
(294)
IDO686
(298)
IDO560
(310)
Treasure
(312)
RIL203
UC1110x
CIMMYT2
(1.53)
Glenlea
(1.50)
CDC Utmost
(BW883)
(326)
CDC Osler
(334)
UC1602
(336)
IDO377S
(339)
9247
(385)
Surface area
cm2
UC1602
(1.36)
Superb
(1.36)
IDO377S
(1.37)
IDO852
(1.39)
HWO90071M
(1.42)
Treasure
(1.44)
IDO560
(1.45)
9247
(1.52)
CDC Osler
(1.52)
SD4181
(1.55)
Root length
density
cm cm–3
Root traits in 30- to 60-cm
soil depth
Superb
(1.55)
UC1603
(1.55)
Stettler
(1.60)
SD4243
(1.61)
SD4181
(1.68)
Root length
density
cm cm–3
IDO560
(395)
UC1602
(397)
9247
(403)
UC1603
(403)
Treasure
(421)
CDC Osler
(422)
IDO686
(430)
SD4243
(433)
Total root
surface area Surface area
–––––––––– cm2 ––––––––––
Root traits in 0- to 30-cm
soil depth
Table 3. Spring wheat genotypes that were ranked high and low for major root traits within the subset of 30 genotypes.
MN07098-6
(2995)
Superb
(3098)
SD4280
(3155)
Lolo
(3172)
Stettler
(3388)
Treasure
(3872)
SD4243
(4009)
IDO377S
(4138)
IDO686
(4351)
Expresso
(4415)
SD4280
(2954)
Superb
(3103)
SD4243
(3401)
Lolo
(3438)
MN07098–6
(3504)
Jefferson
(3537)
HWO90071M
(3574)
SD4181
(3680)
IDO686
(4042)
Treasure
(4580)
Length
E1
E2
–––––––––––– cm ––––––––––––
HWO90071M
(202)
Park
(214)
Superb
(216)
SD4280
(222)
Stettler
(223)
Lolo
(230)
Treasure
(278)
SD4243
(287)
IDO686
(303)
UC1602
(356)
SD4280
(196)
Superb
(199)
SD4243
(220)
MN07098–6
(222)
Jefferson
(229)
SD4181
(231)
HWO90071M
(233)
Lolo
(234)
IDO686
(271)
Treasure
(308)
Surface area
E1
E2
––––––––––– cm2 –––––––––––
Fine root (diameter < 0.5 mm) traits
1600
Agronomy Journal • Volume 106, Issue 5 • 2014
Verde
(1921)
SD4215
(1190)
2445
MN08106-6
(2139)
MN08106-6
(1260)
2446
Glenlea
(2750)
IDO852
(1780)
332
SD4215
(0.37)
0.27
MT1016
(0.39)
0.06
MN08106-6
(224)
MN05141-2
(0.41)
SD4215
(265)
Verde
(340)
MN05141-2
(377)
RIL203
UC1110x
CIMMYT2
(0.42)
SD4280
(0.42)
Glenlea
(408)
89
SD4215
(188)
MN08106-6
(198)
MT1016
(225)
IDO852
(257)
SD4280
(266)
HWO90071M
(273)
Verde
(281)
RIL203
UC1110x
CIMMYT2 (420)
MT1016
(420)
Lolo
(290)
MN02072-7
(292)
Jefferson
(292)
MN02072-7
(440)
SD4181
(504)
9247
(537)
SD4181
(0.44)
MN08106-6
(0.44)
Jefferson
(0.44)
MN02072-7
(0.45)
Verde
(0.47)
Superb
(0.49)
0.34
MT1016
(0.99)
MN08106-6
(1.07)
SD4215
(1.12)
Lolo
(1.30)
HWO90071M
(1.31)
IDO852
(1.32)
IDO377S
(1.33)
MN02072-7
(1.33)
Park
(1.33)
SD4280
(1.37)
Root length
density
cm cm–3
Root traits in 0- to 30-cm
soil depth
Total root
surface area Surface area
–––––––––– cm2 ––––––––––
MN08106-6
(0.39)
MN02072-7
(0.39)
SD4215
(0.41)
UC1551
(0.42)
CDC Osler
(3091)
MT1016
(3225)
RIL203
UC1110x
CIMMYT2
(2105)
Superb
(0.43)
MN05141-2
(1919)
9247
(3305)
PT765
(2131)
Stettler
(0.43)
SD4243
(0.42)
UC1551
(3367)
9247
(2159)
MN07098-6
(0.43)
SD4215
(3212)
MN05141-2
(3403)
Glenlea
(2222)
RIL203
UC1110x
CIMMYT2
(0.43)
Average root diameter
E1
E2
–––––––––– mm ––––––––––
MT1016
(1957)
Park
(3523)
Verde
(2782)
Total root length
E 1†
E 2‡
–––––––––– cm ––––––––––
† Experiment 1.
‡ Experiment 2.
§ Values in parentheses are means of the respective traits.
LSD
Lowest 10
Rank
Table 3. (continued).
122
MN08106-6
(102)
MN05141-2
(115)
Glenlea
(133)
RIL203
UC1110x
CIMMYT2
(149)
SD4215
(155)
MN02072-7
(168)
MT1016
(170)
Stettler
(216)
Jefferson
(219)
SD4280
(230)
Surface area
cm2
0.36
MN08106-6
(0.70)
MN05141-2
(0.76)
MT1016
(0.77)
Glenlea
(0.84)
MN02072-7
(0.98)
RIL203
UC1110x
CIMMYT2
(0.98)
SD4215
(0.99)
PT765
(1.11)
UC1551
(1.15)
Jefferson
(1.18)
Root length
density
cm cm–3
Root traits in 30- to 60-cm
soil depth
1716
SD4215
(911)
MN08106-6
(977)
IDO852
(1322)
MN05141-2
(1460)
MT1016
(1546)
RIL203
UC1110xCIMMYT2
(1584)
PT765
(1611)
9247
(1634)
Glenlea
(1703)
Verde
(2098)
1822
Verde
(1485)
MN08106-6
(1702)
CDC Osler
(2061)
Glenlea
(2096)
9247
(2227)
Park
(2264)
MT1016
(2277)
UC1551
(2411)
UC1603
(2420)
SD4215
(2669)
Length
E1
E2
–––––––––––– cm ––––––––––––
122
SD4215
(67)
MN08106-6
(69)
IDO852
(93)
MN05141-2
(101)
MT1016
(108)
9247
(109)
RIL203
UC1110xCIMMYT2
(111)
Glenlea
(118)
Verde
(146)
CDC Osler
(175)
119
Verde
(95)
MN08106-6
(105)
Glenlea
(131)
CDC Osler
(137)
9247
(151)
UC1551
(154)
MT1016
(156)
UC1603
(158)
MN05141–2
(170)
IDO852
(180)
Surface area
E1
E2
––––––––––– cm2 –––––––––––
Fine root (diameter < 0.5 mm) traits
Fig. 3. Relationships of shoot traits with root traits of 250 spring wheat genotypes. Pearson correlation coefficients (r) and the associated P values are
shown in each figure. A negative correlation coefficient in Fig. 3h and 3i indicates that plant height showed a negative relationship with root dry weight
and root/shoot ratio.
promising because drought tolerance is more important in hard
wheat than in soft wheat. The soft wheat tends to have higher protein content when it is grown under drought, which is not desirable
for its end-use quality (Gaines, 1985; Weightman et al., 2008).
Ppd mutation had no effect on any of the root traits evaluated
among the 250 genotypes of the AM panel. This implies that our
results were not biased in favor of photoperiod sensitive or insensitive genotypes even though plants were harvested and root traits
were estimated when 50% of the population reached flowering
stage. Rht mutation also had no effect on any of the root traits
evaluated among the 250 genotypes of the AM panel. This implies
that dwarfing genes also did not influence the root traits.
In the present study, root traits showed positive correlations
with shoot traits that are related with productivity. For example,
shoot dry weight had positive relationships (r > 0.50) with rooting depth, root dry weight, total root length, total root surface
area, RLD in 30- to 60-cm soil depth, fine root length, and fine
root surface area (Fig. 3 and 4). Tiller number also had positive relationships (r > 0.50) with all the above root traits except
rooting depth and root dry weight (Fig. 3 and 4). Total root
length and rooting depth affect distribution of roots in the soil
profile and influence the amount of water absorbed (reviewed
by Ludlow and Muchow, 1990 and Boyer, 1996; Manschadi et
al., 2006). Root length density is the primary trait that affects
P uptake from the soil (Manske et al., 2000). Reports on other
cereals suggest that resource uptake increases with root surface
area because water and nutrient absorption from the soil is
directly proportional to contact area between root surface and
soil (Caassen and Barber, 1976; Yoshida and Hasegawa, 1982).
Fine roots improve water and nutrient absorption because they
increase root surface area per unit mass (Eissenstat, 1992). Taken
together, the increased resource uptake through these improved
root traits might have helped the plant to produce more tillers,
add more dry matter, and increase shoot dry weight. However,
further investigations are needed under field conditions to verify
the relationship of shoot dry weight and tiller number with root
traits and also to pinpoint the cause of these relationships.
Plant height showed negative correlations (|r| ≤ 0.36) with root
dry weight and root/shoot ratio in the AM panel of 250 genotypes
(Fig. 3h and 3i). This does not support the view that selecting
for decreased plant height might result in indirectly selecting for
small root system. Our result is supported by previous reports that
semidwarf wheat genotypes had greater root dry weight than tall
genotypes under field (Lupton et al., 1974; Ehdaie and Waines,
1994; Miralles et al., 1997) and controlled environmental conditions (Bush and Evens, 1988). This may be because assimilates
that are not used to increase plant height might have diverted to
root system to increase root dry weight. Root dry weight and plant
height were found to be prevailingly controlled by different sets
of genes under controlled environmental conditions (Sanguineti
et al., 2007). Studies that evaluated root traits in relation to plant
height reported that differences in root dry weight between a tall
Agronomy Journal • Volume 106, Issue 5 • 2014
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Agronomy Journal • Volume 106, Issue 5 • 2014
Fig. 4. Relationships among root and shoot traits of 30 selected spring wheat genotypes. Fifteen genotypes that were ranked the highest (black circles in Fig. 4a–4o) and 15 genotypes that were ranked the lowest (gray
triangles in Fig. 4a–4o) for rooting depth were selected for further complete root analyses to estimate total root surface area, root length density, total root length, fine root (diameter < 0.5 mm) length, and fine root
surface area. Root length density was the ratio of root length in 30- to 60-cm depth of root system to the volume of 30-cm section of the PVC column. Significant effect of experiment × genotype interaction (P < 0.05)
was noticed for total root length, fine root length, and fine root surface area. Therefore, data were not averaged across experiments for these traits (n = 60; Fig. 4c, 4d, 4e, 4h, 4i, 4j, 4m, 4n, and 4o). Pearson correlation
coefficients (r) and the associated P values are shown in each figure.
Fig. 5. Relationships of shoot traits with root traits among Rht mutants (Rht-B1, Fig. 5a, 5e, and 5i; Rht-D1, Fig. 5b, 5f, and 5j; Rht-B1 and/or Rht-D1,
Fig. 5c, 5g, and 5k) and wild-types of Rht-B1 and Rht-D1 (Fig. 5d, 5h, and 5l) in the spring wheat association mapping panel. Pearson correlation
coefficients (r) and the associated P values are shown in each figure.
Fig. 6. Relationship between plant height and root dry weight among Ppd-D1 mutants and wild-types in the spring wheat association mapping panel.
Pearson correlation coefficients (r) and the associated P values are shown in each figure.
wheat landrace with greater root dry weight and a short wheat
cultivar with less root dry weight were largely due to genetic control on chromosome 1A, which is different from the Rht genetic
system on chromosome 4 that controls plant height (Troughton
and Whittington, 1968).
The results presented here are based on experiments conducted
under controlled environment with two replications per each
experiment. Due to the increased level of phenotypic plasticity of
roots in response to changing environmental conditions, further
investigations are needed to evaluate selected genotypes under
field conditions. Our future research will involve evaluating
the identified genotypes with contrasting root traits for water
use efficiency, and yield under irrigated and drought conditions
in the field and controlled environment with greater number
of replications. Future investigations will also be conducted to
identify chromosome regions responsible for differences in root
traits. However, the present research provides valuable baseline
knowledge to wheat improvement programs for selecting for root
traits that are associated with increased productivity.
In summary, considerable genetic variability was observed for
root and shoot traits in the Spring Wheat AM Panel of TCAP.
Genotypes Treasure and IDO686 were ranked high and genotypes MN08106-6 and MT1016 were ranked low for most root
traits in the AM panel of 250 genotypes and for additional traits
measured in a subset of 30 genotypes selected for divergent rooting
depth. In addition, genotypes IDO377S, Superb, 9223, UC1602,
Lolo, and SD4243 also possessed improved root traits within the
AM panel or its subset. Positive relationships were observed among
root traits, shoot dry weight, and tiller number in the AM panel
or its subset. Shoot dry weight had positive relationships (r ≥ 0.50)
with rooting depth and root dry weight in the AM panel and with
total root length, total root surface area, RLD in 30- to 60-cm soil
depth, fine root (diameter <0.5 mm) length, and fine root surface
area in the subset. Tiller number also had positive relationships
Agronomy Journal • Volume 106, Issue 5 • 2014
1603
(r ≥ 0.50) with all the above root traits except rooting depth and
root dry weight. Plant height had no correlations with most root
traits. Plant height had only weak negative relationships (|r| ≤
0.36) with root dry weight and root/shoot ratio in the AM panel.
The genetic variability identified in this research for root traits
offers useful baseline knowledge for wheat improvement programs
and for choosing genotypes with contrasting root characteristics.
ACKNOWLEDGMENTS
Lines for the AM panel were contributed by Jianli Chen (University
of Idaho), Karl Glover (South Dakota State University), Jorge Dubcovsky
(University of California-Davis), Mike Pumphrey (Washington
State University), Jim Anderson (University of Minnesota), Pierre
Hucl (University of Saskatchewan), Curtis Pozniak (University of
Saskatchewan), Luther Talbert (Montana State University), Dean Spaner
(University of Alberta), Gavin Humphries (Agriculture and Agri-Food
Canada), Ron DePauw (Agriculture and Agri-Food Canada), Ron Knox
(Agriculture and Agri-Food Canada), and the International Maize and
Wheat Improvement Center (CIMMYT). Genotyping data for Rht-B1,
Rht-D1, and Ppd-D1 were provided by Nancy Blake and Luther Talbert
(Montana State University). We thank the Kansas Wheat Alliance and
Triticeae Coordinated Agricultural Project Grant no. 2011-6800230029 (Triticeae-CAP) from the USDA National Institute of Food and
Agriculture for financial support. We thank Austin Hughes, Prudhvi Tej
Sri Adhibatla, Sheila Ngao, Prakarsh Tiwari, George Paul, and Predeesh
Chandran for help in data collection. This publication is Kansas
Agriculture Experiment Station Contribution no. 14-242-J.
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Agronomy Journal • Volume 106, Issue 5 • 2014