PRECISION AGRICULTURE IN PLANT BREEDING

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

2. WHAT IS PLANT BREEDING Plant Breeding is the Artand 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

3. 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 • Combiningthose traits into improved cultivars • Selecting the improved breeding lines in the local environment comparing to the existing standard cultivars

4. 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

5. Geneticgains • 1% yield gain annually in most cereal grains • Lower in dry environment compared to the irrigated environment • Heterogeneityof breeding nurseries results in performance based selection untrustworthy in dry environments

6. 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

7. 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

8. 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)

9. 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

10. Spectral reflectance from a crop surface

11. Typical reflectance pattern of a crop canopy reflectance Wavelength (nm)

12. 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)

13. 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

14. 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

15. 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

16. 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)

17. 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)

18. 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

19. 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

20. 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

21. 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.

22. References • Peñuelas, J., I.Filella, C. Biel, L. Serrano, and R. Savé. 1993. The reflectance at the 950- 970 nm region as an indicator of plant water status. Int. J. of Remote Sensing. 14:1887-1905. • 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. • 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 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 changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Sci. 39: 1611-1621. • Reynolds, M.P., R. M. Trethowan, M. van Ginkel, and S. Rajaram. 2001. Application of physiology in wheat breeding. In : Application of physiology in wheat breeding. Reynolds, M. P., J.I. Ortiz-Monasterio, and A. McNab. (eds.). Mexico D. F. CIMMYT. pp. 2-10. • Richards, R.A. 1996. Defining selection criteria to improve yield under drought. Plant Growth Regul. 20: 157-166. • Serrano, L., I. Filella, and J. Peñuelas. 2000. Remote sensing of biomass and 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. 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.