PRECISION AGRICULTURE IN
PLANT BREEDING
BISHWAJIT PRASAD
SOIL/BAE 4213
WHAT IS PLANT BREEDING
Plant Breeding is the Art and the Science
for Improving the Heredity of Plants for the
Benefit of Humankind
Art: The breeder’s skill in observing plants
with unique economical, environmental,
nutritional, or aesthetical characteristics
Science: The genetic basis behind the
expression of desired characters
Strategy of Plant Breeding
Basic elements:
• Identifying morpho-physiological and pathological
traits in a cultivated plant species : Adaptation,
health, productivity and suitability for food, fiber or
industrial products
• Combining those traits into improved cultivars
• Selecting the improved breeding lines in the local
environment comparing to the existing standard
cultivars
Breeding Approach for selection
• Empirical approach: Evaluating grain yield per se
as the main selection criterion
• Analytical approach: An alternate breeding
approach that requires a better understanding of the
factors responsible for the development, growth
and yield
Genetic gains
• 1% yield gain annually in most cereal grains
• Lower in dry environment compared to the
irrigated environment
• Heterogeneity of breeding nurseries results in
performance based selection untrustworthy in
dry environments
• Analytical approach requires the use of
morpho-physiological selection criteria
• The limited application of this analytical
approach is due to the lack of appropriate
understanding about the physiological
parameters, estimation, and their true
association with grain yield
• Yield in a given situation is the most integrative trait :
morphological, physiological & environmental factors
• Yield of a certain crop is a function of the interception of
solar energy by the crop canopy, conversion of the energy
into dry matter and partitioning of the dry matter into
harvestable yield
• Identifying promising genotypes in a breeding program will
be very much helpful if one can predict yield before the crop
is harvested.
• This prediction will also be very helpful if the top performing
families can be detected from a group of thousands within
segregating generations in a breeding program
• Selection of breeding materials often needs repetition to
end up with a decision in a breeding nursery
• Commonly used procedures sometimes fail to
discriminate the performance of the advanced genotypes
in a given environment
• Morphological characters like number of grains, harvest
index etc. can be used in the visual selection of breeding
lines, but those traits aren't truthfully expressed in small
plots or at low densities in early generations (Reynolds et
al., 1999)
HOW PRECISION AGRICULTURE CAN
PLAY ROLE
• Spectral properties of the plant came into focus as a
selection tool for improved yield and biomass especially
in wheat in recent times
• Spectral reflectance is a powerful tool that can estimate a
wide range of physiological traits of a plant.
• When electromagnetic wavelengths hit the plant surface,
a part of the spectrum is absorbed by the plant, some are
transmitted through the plant and the rest are reflected
from the plant.
• The basic principle that governs the canopy spectral
reflectance is that, specific plant traits are associated with
the absorption of the specific wavelengths of the spectrum
Spectral reflectance from a crop surface
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reflectance
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Wavelength (nm)
Typical reflectance pattern of a crop canopy
HOW PRECISION AGRICULTURE CAN
PLAY ROLE
• Plant water status, leaf area index (LAI), chlorophyll and
other pigments concentration and photosynthetic radiation
use efficiency (PRUE) can be determined by the canopy
spectral reflectance
• The most common uses of spectral reflectance are the
remote estimation of the parameters involved in the
canopy greenness: Related to the photosynthetic size of
the canopy, green biomass and LAI (Araus et al., 2002)
HOW PRECISION AGRICULTURE CAN
PLAY ROLE
• Reflectance indices are made as formulations based on
typically a sum, difference or ratio of two or more spectral
wavelengths which are indicative of important function of the
crop
• The most commonly used spectral vegetation indices (VI) are
simple ratio (SR = RNIR / RR) and normalized difference
vegetative index (NDVI= RNIR-RR / RNIR+RR)
• Green biomass, LAI, green area index (GAI), green leaf area
index (GLAI), fraction of photosynthetically active radiation
(fPAR) were found positively correlated with VI’s
• Measuring vegetation indices periodically during the crop
growing cycle allow the estimation of leaf area duration (LAD) :
Indicator of stress tolerance and the total PAR absorbed by the
canopy, the most considerable factors for predicting yield
HOW PRECISION AGRICULTURE CAN
PLAY ROLE
• Photochemical reflectance index (PRI) can determine
the PRUE and this PRUE is induced by factors like
nutritional status and drought stress
• The usefulness of pigment remote sensing includes the
assessment of the phenological stages of the crop and
the occurrence of several stress factors.
• PRI has been demonstrated as a good index to
discriminate crops in different water regimes and can be
considered as a good water stress index
HOW PRECISION AGRICULTURE CAN
PLAY ROLE
• Several indices like RARSa, RARSb, RARSc are related
to the changes in pigment compositions and can be used
for the remote detection of nutrient deficiencies,
environmental stresses and pest attacks
• Stress assessment in plants is one of the important
physiological tool that has been demonstrated to be
associated with certain spectral indices.
• Water index (WI) has been demonstrated to assess
relative water content, leaf water potential, stomatal
conductance and canopy temperature
HOW PRECISION AGRICULTURE CAN
PLAY ROLE
• Yield prediction using vegetation indices is one of the
most important uses of spectral properties
• Adequate discrimination can be established between
high and low yielding genotypes of soybeans by using
NDVI as a spectral reflectance index (Ma et al., 2001)
• SR can provide reliable information for yield monitoring
in winter wheat under different nitrogen stresses
(Serrano et al., 2000)
HOW PRECISION AGRICULTURE CAN
PLAY ROLE
• NDVI calculated from late tillering stage to the beginning of
flowering growth stage is useful in predicting total dry matter in
winter wheat (Aase and Siddoway,1981)
• 50% in the yield variability can be explained by NDVI as a
vegetation index while conducting experiments with winter
wheat in nine locations for two successive years (Raun et al.,
2001)
• NDVI, SR and PRI can explain 52, 59 and 39 % yield
variability respectively in durum wheat( Aparicio et al., 2000)
• Green NDVI calculated at mid grain filling stage in corn was
found highly correlated (r = 0.72 to 0.92) with grain yield
variations (Shanahan et al., 2001)
Challenges
• The routinely used VI’s saturate at a level of plant
growth (LAI=3), which is not desirable as a selection
strategy for yield and biomass in a breeding program
especially in wheat
• So far, few wavelengths of the spectrum are used to
calculate spectral indices that restricts the use of this
technique to be useful in a breeding program as indirect
selection criteria
Solution
• The practical use of spectral indices as indirect tool for
selection in a breeding program needs to identify the
appropriate growth stage/s and spectral vegetation
indices that can be used to maximize genotypic
difference in a much diverse growing condition and
growth stages of the crop
conclusion
• Every genotype can produce a unique spectral reflectance
pattern and by utilizing this, there is a very good possibility
to look for the characteristics reflectance patterns
associated with the performance of the specific genotype
• This strategy will be supplemental in achieving desired
genotypes from a breeding program
References
• Aase, J.K., and F. H. Siddoway. 1981. Assessing winter wheat dry matter
production via spectral reflectance measurements. Remote Sens. Environ.
11: 267-277.
• Aparicio, N., D. Villegas, J. L. Araus, J. Casadesus, and C. Royo. 2002.
Relationship between growth traits and spectral vegetation indices in durum
wheat. Crop Sci. 42: 1547-1555.
• Aparicio, N., D. Villegas, J. Casadesus, J. L. Araus, and C. Royo. 2000.
Spectral vegetation indices as nondestructive tools for determining durum
wheat yield. Agron. J. 92:83-91.
• Araus, J.L., G.A. Slafer, M.P. Reynolds, and C. Royo. 2002. Plant breeding
and drought in C3 cereals: What should we breed for. Annals of Botany. 89:
925-940
• Baret, F., and G. Guyot.1991. Potentials and limits of vegetation indices for
LAI and APAR estimation. Remote Sens. Environ. 35:161-173.
• Jackson, P., M. Robertson, M. Copper, and G. Hammer. 1996. The role of
physiological understanding in plant breeding; from a breeding perspective.
Field Crop Res. 49: 1-37.
• Ma, B.L., L. M. Dwyer, C. Costa, E. L. Cober, and M. J. Morrision. 2001. Early
prediction of soybean yield from canopy reflectance measurements. Agron. J.
93: 1227-1234.
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