PRECISION AGRICULTURE IN PLANT BREEDING BISHWAJIT PRASAD SOIL/BAE 4213.

PRECISION AGRICULTURE IN PRECISION AGRICULTURE IN PLANT BREEDINGPLANT BREEDING

BISHWAJIT PRASADBISHWAJIT PRASAD

SOIL/BAE 4213SOIL/BAE 4213

WHAT IS PLANT BREEDINGWHAT IS PLANT BREEDING

Plant Breeding is the Plant Breeding is the ArtArt and the and the ScienceScience for Improving the Heredity of Plants for for Improving the Heredity of Plants for the Benefit of Humankindthe Benefit of Humankind

Art: Art: The breeder’s skill in observing plants The breeder’s skill in observing plants with unique economical, environmental, with unique economical, environmental, nutritional, or aesthetical characteristicsnutritional, or aesthetical characteristics

Science:Science: The genetic basis behind the The genetic basis behind the expression of desired charactersexpression of desired characters

Strategy of Plant BreedingStrategy of Plant Breeding

Basic elements:Basic elements:

• IdentifyingIdentifying morpho-physiological and pathological morpho-physiological and pathological traits in a cultivated plant species : Adaptation, traits in a cultivated plant species : Adaptation, health, productivity and suitability for food, fiber or health, productivity and suitability for food, fiber or industrial productsindustrial products

• CombiningCombining those traits into improved cultivars those traits into improved cultivars

• SelectingSelecting the improved breeding lines in the local the improved breeding lines in the local environment comparing to the existing standard environment comparing to the existing standard cultivarscultivars

• Empirical approachEmpirical approach: : Evaluating grain yield Evaluating grain yield per seper se as the main selection criterion as the main selection criterion

• Analytical approach: Analytical approach: An alternate breeding An alternate breeding approach that requires a better understanding of the approach that requires a better understanding of the factors responsible for the development, growth factors responsible for the development, growth and yield and yield

Breeding Approach for selection

GeneticGenetic gainsgains

• 1% yield gain annually in most cereal grains1% yield gain annually in most cereal grains

• Lower in dry environment compared to the Lower in dry environment compared to the irrigated environmentirrigated environment

• HeterogeneityHeterogeneity of breeding nurseries results in of breeding nurseries results in performance based selection untrustworthy in performance based selection untrustworthy in dry environmentsdry environments

• Analytical approach requiresAnalytical approach requires the use of the use of morpho-physiological selection criteriamorpho-physiological selection criteria

• The The limited applicationlimited application of this analytical of this analytical approach is due to the lack of appropriate approach is due to the lack of appropriate understanding about the physiological understanding about the physiological parameters, estimation, and their true parameters, estimation, and their true association with grain yield association with grain yield

• YieldYield in a given situation is the most integrative trait : in a given situation is the most integrative trait : morphological, physiological & environmental factorsmorphological, physiological & environmental factors

• Yield of a certain crop is a function of the interception of Yield of a certain crop is a function of the interception of solar energy by the crop canopy, conversion of the energy solar energy by the crop canopy, conversion of the energy into dry matter and partitioning of the dry matter into into dry matter and partitioning of the dry matter into harvestable yieldharvestable yield

• IdentifyingIdentifying promising genotypes in a breeding program will promising genotypes in a breeding program will be very much helpful if one can predict yield before the crop be very much helpful if one can predict yield before the crop is harvested. is harvested.

• This This predictionprediction will also be very helpful if the top performing will also be very helpful if the top performing families can be detected from a group of thousands within families can be detected from a group of thousands within segregating generations in a breeding program segregating generations in a breeding program

• Selection of breeding materials often Selection of breeding materials often needs repetitionneeds repetition to to end up with a decision in a breeding nurseryend up with a decision in a breeding nursery

• Commonly used procedures sometimes Commonly used procedures sometimes fail to fail to discriminatediscriminate the performance of the advanced genotypes the performance of the advanced genotypes in a given environmentin a given environment

• Morphological characters like number of grains, harvest Morphological characters like number of grains, harvest index etc. can be used in the visual selection of breeding index etc. can be used in the visual selection of breeding lines, but those traits aren't lines, but those traits aren't truthfully expressedtruthfully expressed in small in small plots or at low densities in early generations (Reynolds plots or at low densities in early generations (Reynolds et et alal., 1999)., 1999)

• Spectral properties of the plant came into focus as a Spectral properties of the plant came into focus as a selection toolselection tool for improved yield and biomass especially for improved yield and biomass especially in wheat in recent times in wheat in recent times

• Spectral reflectance is a powerful tool that can estimate a Spectral reflectance is a powerful tool that can estimate a wide range of physiological traits of a plant. wide range of physiological traits of a plant.

• When electromagnetic wavelengths hit the plant surface, When electromagnetic wavelengths hit the plant surface, a part of the spectrum is absorbed by the plant, some are a part of the spectrum is absorbed by the plant, some are transmitted through the plant and the rest are reflected transmitted through the plant and the rest are reflected from the plant. from the plant.

• The basic principle that governs the canopy spectral The basic principle that governs the canopy spectral reflectance is that, specific plant traits are associated with reflectance is that, specific plant traits are associated with the absorption of the specific wavelengths of the spectrumthe absorption of the specific wavelengths of the spectrum

HOW PRECISION AGRICULTURE CAN HOW PRECISION AGRICULTURE CAN

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Spectral reflectance from a crop surface

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Typical reflectance pattern of a crop canopyTypical reflectance pattern of a crop canopy

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HOW PRECISION AGRICULTURE CAN HOW PRECISION AGRICULTURE CAN PLAY ROLEPLAY ROLE

• Plant water status, leaf area index (LAI), chlorophyll and Plant water status, leaf area index (LAI), chlorophyll and other pigments concentration and photosynthetic radiation other pigments concentration and photosynthetic radiation use efficiency (PRUE) can be determined by the canopy use efficiency (PRUE) can be determined by the canopy spectral reflectance spectral reflectance

• The most common uses of spectral reflectance are the The most common uses of spectral reflectance are the remote estimation of the parameters involved in the remote estimation of the parameters involved in the canopy greenness: Related to the photosynthetic size of canopy greenness: Related to the photosynthetic size of the canopy, green biomass and LAI (Arausthe canopy, green biomass and LAI (Araus et al et al., 2002)., 2002)


• Reflectance indices are made as formulations based on Reflectance indices are made as formulations based on typically a sum, difference or ratio of two or more spectral typically a sum, difference or ratio of two or more spectral wavelengths which are indicative of important function of the wavelengths which are indicative of important function of the cropcrop

• The most commonly used spectral vegetation indices (VI) are The most commonly used spectral vegetation indices (VI) are simple ratio (SR = simple ratio (SR = RRNIR NIR / R/ RRR) and normalized difference ) and normalized difference vegetative index (NDVI= vegetative index (NDVI= RRNIRNIR-R-RRR / R / RNIRNIR+R+RRR) )

• Green biomass, LAI, green area index (GAI), green leaf area Green biomass, LAI, green area index (GAI), green leaf area index (GLAI), fraction of photosynthetically active radiation index (GLAI), fraction of photosynthetically active radiation (fPAR) were found positively correlated with VI’s (fPAR) were found positively correlated with VI’s

• Measuring vegetation indices periodically during the crop Measuring vegetation indices periodically during the crop growing cycle allow the estimation of leaf area duration (LAD) : growing cycle allow the estimation of leaf area duration (LAD) : Indicator of stress tolerance and the total PAR absorbed by the Indicator of stress tolerance and the total PAR absorbed by the canopy, the most considerable factors for predicting yield canopy, the most considerable factors for predicting yield


• Photochemical reflectance index (PRI) can determine Photochemical reflectance index (PRI) can determine the PRUE and this PRUE is induced by factors like the PRUE and this PRUE is induced by factors like nutritional status and drought stress nutritional status and drought stress

• The usefulness of pigment remote sensing includes the The usefulness of pigment remote sensing includes the assessment of the phenological stages of the crop and assessment of the phenological stages of the crop and the occurrence of several stress factors.the occurrence of several stress factors.

• PRI has been demonstrated as a good index to PRI has been demonstrated as a good index to discriminate crops in different water regimes and can be discriminate crops in different water regimes and can be considered as a good water stress indexconsidered as a good water stress index


• Several indices like RARSa, RARSb, RARSc are related Several indices like RARSa, RARSb, RARSc are related to the changes in pigment compositions and can be used to the changes in pigment compositions and can be used for the remote detection of nutrient deficiencies, for the remote detection of nutrient deficiencies, environmental stresses and pest attacksenvironmental stresses and pest attacks

• Stress assessment in plants is one of the important Stress assessment in plants is one of the important physiological tool that has been demonstrated to be physiological tool that has been demonstrated to be associated with certain spectral indices. associated with certain spectral indices.

• Water index (WI) has been demonstrated to assess Water index (WI) has been demonstrated to assess relative water content, leaf water potential, stomatal relative water content, leaf water potential, stomatal conductance and canopy temperature conductance and canopy temperature


• Yield prediction using vegetation indices is one of the Yield prediction using vegetation indices is one of the most important uses of spectral properties most important uses of spectral properties

• Adequate discrimination can be established between Adequate discrimination can be established between high and low yielding genotypes of soybeans by using high and low yielding genotypes of soybeans by using NDVI as a spectral reflectance index (Ma NDVI as a spectral reflectance index (Ma et alet al., 2001) ., 2001)

• SR can provide reliable information for yield monitoring SR can provide reliable information for yield monitoring in winter wheat under different nitrogen stresses in winter wheat under different nitrogen stresses (Serrano (Serrano et alet al., 2000) ., 2000)


• NDVI calculated from late tillering stage to the beginning of NDVI calculated from late tillering stage to the beginning of flowering growth stage is useful in predicting total dry matter in flowering growth stage is useful in predicting total dry matter in winter wheat (Aase and Siddoway,1981) winter wheat (Aase and Siddoway,1981)

• 50% in the yield variability can be explained by NDVI as a 50% in the yield variability can be explained by NDVI as a vegetation index while conducting experiments with winter vegetation index while conducting experiments with winter wheat in nine locations for two successive years (Raun wheat in nine locations for two successive years (Raun et alet al., ., 2001) 2001)

• NDVI, SR and PRI can explain 52, 59 and 39 % yield NDVI, SR and PRI can explain 52, 59 and 39 % yield variability respectively in durum wheat( Aparicio variability respectively in durum wheat( Aparicio et alet al., 2000) ., 2000)

• Green NDVI calculated at mid grain filling stage in corn was Green NDVI calculated at mid grain filling stage in corn was found highly correlated (r = 0.72 to 0.92) with grain yield found highly correlated (r = 0.72 to 0.92) with grain yield variations (Shanahan variations (Shanahan et alet al., 2001) ., 2001)

ChallengesChallenges • The routinely used VI’s The routinely used VI’s saturatesaturate at a level of plant at a level of plant

growth (LAI=3), which is not desirable as a selection growth (LAI=3), which is not desirable as a selection strategy for yield and biomass in a breeding program strategy for yield and biomass in a breeding program especially in wheatespecially in wheat

• So far, few wavelengths of the spectrum are used to So far, few wavelengths of the spectrum are used to calculate spectral indices that restricts the use of this calculate spectral indices that restricts the use of this technique to be useful in a breeding program as indirect technique to be useful in a breeding program as indirect selection criteria selection criteria

SolutionSolution

• The practical use of spectral indices as indirect tool for The practical use of spectral indices as indirect tool for selection in a breeding program needs to identify the selection in a breeding program needs to identify the appropriate growth stage/s and spectral vegetation appropriate growth stage/s and spectral vegetation indices that can be used to maximize genotypic indices that can be used to maximize genotypic difference in a much diverse growing condition and difference in a much diverse growing condition and growth stages of the crop growth stages of the crop

• Every genotype can produce a unique spectral reflectance Every genotype can produce a unique spectral reflectance pattern and by utilizing this, there is a very good possibility pattern and by utilizing this, there is a very good possibility to look for the characteristics reflectance patterns to look for the characteristics reflectance patterns associated with the performance of the specific genotypeassociated with the performance of the specific genotype

• This strategy will be supplemental in achieving desired This strategy will be supplemental in achieving desired genotypes from a breeding program genotypes from a breeding program

conclusionconclusion

ReferencesReferences

• Aase, J.K., and F. H. Siddoway. 1981. Assessing winter wheat dry matter Aase, J.K., and F. H. Siddoway. 1981. Assessing winter wheat dry matter production via spectral reflectance measurements. Remote Sens. Environ. production via spectral reflectance measurements. Remote Sens. Environ. 11: 267-277.11: 267-277.

• Aparicio, N., D. Villegas, J. L. Araus, J. Casadesus, and C. Royo. 2002. Aparicio, N., D. Villegas, J. L. Araus, J. Casadesus, and C. Royo. 2002. Relationship between growth traits and spectral vegetation indices in durum Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Sci. 42: 1547-1555.wheat. Crop Sci. 42: 1547-1555.

• Aparicio, N., D. Villegas, J. Casadesus, J. L. Araus, and C. Royo. 2000. Aparicio, N., D. Villegas, J. Casadesus, J. L. Araus, and C. Royo. 2000. Spectral vegetation indices as nondestructive tools for determining durum Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J. 92:83-91.wheat yield. Agron. J. 92:83-91.

• Araus, J.L., G.A. Slafer, M.P. Reynolds, and C. Royo. 2002. Plant breeding 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: and drought in C3 cereals: What should we breed for. Annals of Botany. 89:

925-940925-940 • Baret, F., and G. Guyot.1991. Potentials and limits of vegetation indices for Baret, F., and G. Guyot.1991. Potentials and limits of vegetation indices for

LAI and APAR estimation. Remote Sens. Environ. 35:161-173. LAI and APAR estimation. Remote Sens. Environ. 35:161-173. • Jackson, P., M. Robertson, M. Copper, and G. Hammer. 1996. The role of Jackson, P., M. Robertson, M. Copper, and G. Hammer. 1996. The role of

physiological understanding in plant breeding; from a breeding perspective. physiological understanding in plant breeding; from a breeding perspective. Field Crop Res. 49: 1-37.Field Crop Res. 49: 1-37.

• Ma, B.L., L. M. Dwyer, C. Costa, E. L. Cober, and M. J. Morrision. 2001. 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. Early prediction of soybean yield from canopy reflectance measurements. Agron. J. 93: 1227-1234. Agron. J. 93: 1227-1234.

ReferencesReferences

• Peñuelas, J., I.Filella, C. Biel, L. Serrano, and R. Savé. 1993. Peñuelas, J., I.Filella, C. Biel, L. Serrano, and R. Savé. 1993. The reflectance The reflectance at the 950- 970 nm region as an indicator of plant water status. Int. J. of at the 950- 970 nm region as an indicator of plant water status. Int. J. of Remote Sensing. 14:1887-1905.Remote Sensing. 14:1887-1905.

• Peñuelas, J., R. Isla, I. Filella, and J. L. Araus, 1997. Visible and near-infrared Peñuelas, J., R. Isla, I. Filella, and J. L. Araus, 1997. Visible and near-infrared reflectance assessment of salinity effects on barley. Crop Sci. 37:198-202.reflectance assessment of salinity effects on barley. Crop Sci. 37:198-202.

• Raun, W.R., J. B. Solie, G.V. Johnson, M.L. Stone, E. V. Lukina, W.E. Raun, W.R., J. B. Solie, G.V. Johnson, M.L. Stone, E. V. Lukina, W.E. Thomson, and J.S. Schepers. 2001. In-season prediction of potential grain Thomson, and J.S. Schepers. 2001. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93: 131-138. yield in winter wheat using canopy reflectance. Agron. J. 93: 131-138.

• Reynolds, M.P., S. Rajaram, and K.D. Sayre. 1999. Physiological and genetic Reynolds, M.P., S. Rajaram, and K.D. Sayre. 1999. Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Sci. 39: 1611-1621.for meeting projected global demand. Crop Sci. 39: 1611-1621.

• Reynolds, M.P., R. M. Trethowan, M. van Ginkel, and S. Rajaram. 2001. Reynolds, M.P., R. M. Trethowan, M. van Ginkel, and S. Rajaram. 2001. Application of physiology in wheat breeding. In : Application of physiology in Application of physiology in wheat breeding. In : Application of physiology in wheat breeding. Reynolds, M. P., J.I. Ortiz-Monasterio, and A. McNab. (eds.). wheat breeding. Reynolds, M. P., J.I. Ortiz-Monasterio, and A. McNab. (eds.). Mexico D. F. CIMMYT. pp. 2-10.Mexico D. F. CIMMYT. pp. 2-10.

• Richards, R.A. 1996. Defining selection criteria to improve yield under drought. Richards, R.A. 1996. Defining selection criteria to improve yield under drought. Plant Growth Regul. 20: 157-166.Plant Growth Regul. 20: 157-166.

• Serrano, L., I. Filella, and J. Peñuelas. 2000. Serrano, L., I. Filella, and J. Peñuelas. 2000. Remote sensing of biomass and Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Sci. 40: 723-731.yield of winter wheat under different nitrogen supplies. Crop Sci. 40: 723-731.

• Shanahan, J.F., J. S. Schepers, D. D. Francis, G. E. Varvel, W. W. Wilhelm, J. Shanahan, J.F., J. S. Schepers, D. D. Francis, G. E. Varvel, W. W. Wilhelm, J. M. Tringe, M. R. Schlemmer, and D. J. Major. 2001. Use of remote-sensing M. Tringe, M. R. Schlemmer, and D. J. Major. 2001. Use of remote-sensing imagery to estimate corn grain yield. Agron. J. 93: 583-589.imagery to estimate corn grain yield. Agron. J. 93: 583-589.

PRECISION AGRICULTURE IN PLANT BREEDING BISHWAJIT PRASAD SOIL/BAE 4213.

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