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Research Article

Estimating carbon isotope discrimination and grain yield of bread wheat grown under water-limited and full irrigation conditions by hyperspectral canopy reflectance and multilinear regression analysis

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Pages 2848-2871 | Received 19 Dec 2019, Accepted 07 Sep 2020, Published online: 10 Jan 2021

References

  • Abdolshahi, R., M. Nazari, A. Safarian, T. S. Sadathossini, M. Salarpour, and H. Amiri. 2015. “Integrated Selection Criteria for Drought Tolerance in Wheat (Triticum Aestivum L.) Breeding Programs Using Discriminant Analysis.” Field Crop Research 174: 20–29. doi:10.1016/j.fcr.2015.01.009.
  • Akhter, J., P. Monneveux, S. A. Sabir, M. Y. Ashraf, Z. Lateef, and R. Serraj. 2010. “Selection of Drought Tolerant and High Water Use Efficient Rice Cultivars through 13C Isotope Discrimination Techniques.” Pakistan Journal of Botany 42 (6): 3887–3897.
  • Aparicio, N., D. Villegas, J. L. Araus, J. Casadesús, and C. Royo. 2002. “Relationship between Growth Traits and Spectral Vegetation Indices in Durum Wheat.” Crop Science 42 (5): 1547–1555. doi:10.2135/cropsci2002.1547.
  • Arakawa, M., Y. Yamashita, and K. Funatsu. 2011. “Genetic Algorithm-based Wavelength Selection Method for Spectral Calibration.” Journal of Chemometrics 25 (1): 10–19. doi:10.1002/cem.1339.
  • Araus, J. L., D. Villegas, N. Aparicio, L. F. G. Del Moral, S. El Hani, Y. Rharrabti, J. P. Ferrio, and C. Royo. 2003. “Environmental Factors Determining Carbon Isotope Discrimination and Yield in Durum Wheat under Mediterranean Conditions.” Crop Science 43 (1): 170–180. doi:10.2135/cropsci2003.1700.
  • Araus, J. L., G. A. Slafer, C. Royo, and M. D. Serret. 2008. “Breeding for Yield Potential and Stress Adaptation in Cereals.” Critical Reviews in Plant Science 27 (6): 377–412. doi:10.1080/07352680802467736.
  • Araus, J. L., and J. E. Cairns. 2014. “Field High-throughput Phenotyping: The New Crop Breeding Frontier.” Trends in Plant Science 19 (1): 52–61. doi:10.1016/j.tplants.2013.09.008.
  • Araus, J. L., L. Cabrera-Bosquet, M. D. Serret, J. Bort, and M. T. Nieto-Taladriz. 2013. “Comparative Performance of δ13C, δ18O and δ15N for Phenotyping Durum Wheat Adaptation to a Dryland Environment.” Functional Plant Biology 40 (6): 595–608. doi:10.1071/fp12254.
  • Arshad, M., S. Ullah, K. Khurshid, and A. Ali. 2018. “Estimation of Leaf Water Content from Mid-and Thermal-infrared Spectra by Coupling Genetic Algorithm and Partial Least Squares Regression.” Journal of Applied Remote Sensing 12 (2): 022203. doi:10.1117/1.JRS.12.022203.
  • Asseng, S., F. Ewert, P. Martre, R. P. Rötter, D. B. Lobell, D. Cammarano, and M. P. Reynolds. 2015. “Rising Temperatures Reduce Global Wheat Production.” Nature Climate Change 5 (2): 143–147. doi:10.1038/nclimate2470.
  • Babar, M. A., M. Van Ginkel, A. R. Klatt, B. Prasad, and M. P. Reynolds. 2006b. “The Potential of Using Spectral Reflectance Indices to Estimate Yield in Wheat Grown under Reduced Irrigation.” Euphytica 150 (1–2): 155–172. doi:10.1007/s10681-006-9104-9.
  • Babar, M. A., M. P. Reynolds, M. van Ginkel, A. R. Klatt, W. R. Raun, and M. L. Stone. 2006a. “Spectral Reflectance Indices as a Potential Indirect Selection Criteria for Wheat Yield under Irrigation.” Crop Science 46 (2): 578–588. doi:10.2135/cropsci2005.0059.
  • Basheer, I. A., and M. Hajmeer. 2000. “Artificial Neural Networks: Fundamentals, Computing, Design, and Application.” Journal of Microbiological Methods 43 (1): 3–31. doi:10.1016/S0167-7012(00)00201-3.
  • Braun, H. J., G. Atlin, and T. Payne. 2010. “Multi-location Testing as a Tool to Identify Plant Response to Global Climate Change.” In Climate Change and Crop Production, edited by M. P. Reynolds, 115–138. Wallingford, U.K: CABI Publishers.
  • Braun, H. J., and T. Payne. 2013. “Fitomejoramiento en mega-ambientes.” In Fitomejoramiento fisiológico I: Enfoques interdisciplinarios para mejorar la adaptación del cultivo, edited by M. P. Reynolds, A. J. D. Pask, D. M. Mullan, and P. N. Chavez-Dulanto, 6–17. México: CIMMYT.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
  • Cabrera-Bosquet, L., J. Crossa, J. von Zitzewitz, M. D. Serret, and J. L. Araus. 2012. “High-throughput Phenotyping and Genomic Selection: The Frontiers of Crop Breeding Converge.” Journal of Integrative Plant Biology 54 (5): 312–320. doi:10.1111/j.1744-7909.2012.01116.x.
  • Cattivelli, L., F. Rizza, F. W. Badeck, E. Mazzucotelli, A. M. Mastrangelo, E. Francia, C. Marè, A. Tondelli, and A. M. Stanca. 2008. “Drought Tolerance Improvement in Crop Plants: An Integrated View from Breeding to Genomics.” Field Crop Research 105 (1–2): 1–14. doi:10.1016/j.fcr.2007.07.004.
  • Ceccato, P., S. Flasse, S. Tarantola, S. Jacquemoud, and J. M. Gregoire. 2001. “Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain.” Remote Sensing of Environment 77 (1): 22–33. doi:10.1016/s0034-4257(01)00191-2.
  • Coast, O., S. Shah, A. Ivakov, O. Gaju, P. B. Wilson, B. C. Posch, J. B. Callum, et al. 2019. “Predicting Dark Respiration Rates of Wheat Leaves from Hyperspectral Reflectance.” Plant, Cell & Environment 42 (7): 2133–2150. doi:10.1111/pce.13544.
  • Condon, A. G., R. A. Richards, G. J. Rebetzke, and G. D. Farquhar. 2002. “Improving Intrinsic Water-use Efficiency and Crop Yield.” Crop Science 42 (1): 122–132. doi:10.2135/cropsci2002.1220.
  • de Oliveira, D. B., and A. C. Gaudio. 2000. “BuildQSAR: A New Computer Program for QSAR Analysis.” Molecular Informatics 19 (6): 599–601. doi:10.1002/1521-3838(200012)19:6<599::aid-qsar599>3.0.co;2-b.
  • Del Pozo, A., A. Yáñez, I. A. Matus, G. Tapia, D. Castillo, L. Sanchez-Jardón, and J. L. Araus. 2016. “Physiological Traits Associated with Wheat Yield Potential and Performance under Water-stress in a Mediterranean Environment.” Frontiers in Plant Science 7 (987). doi:10.3389/fpls.2016.00987.
  • Del Pozo, A., N. Brunel-Saldias, A. Engler, S. Ortega-Farias, C. Acevedo-Opazo, G. A. Lobos, R. Jara-Rojas, and M. A. Molina-Montenegro. 2019. “Climate Change Impacts and Adaptation Strategies of Agriculture in Mediterranean-Climate Regions (Mcrs).” Sustainability 11 (10): 2769. doi:10.3390/su11102769.
  • Dixon, J., H. J. Braun, and J. Crouch. 2009. “Overview: Transitioning Wheat Research to Serve the Future Needs of the Developing World.” In Wheat Facts and Futures, edited by J. Dixon, H. J. Braun, P. Kosina, and J. Crouch, 1–19. Mexico: CIMMYT.
  • Dolferus, R., N. Powell, X. Ji, R. Ravash, J. Edlington, S. Oliver, J. van Dongen, and B. Shiran. 2013. “The Physiology of Reproductive-stage Abiotic Stress Tolerance in Cereals.” In Molecular Stress Physiology of Plants, edited by G. R. Rout and A. B. Das, 193–218, Springer, Switzerland.
  • Dreccer, M. F., L. R. Barnes, and R. Meder. 2014. “Quantitative Dynamics of Stem Water Soluble Carbohydrates in Wheat Can Be Monitored in the Field Using Hyperspectral Reflectance.” Field Crops Research 159: 70–80. doi:10.1016/j.fcr.2014.01.001.
  • Elazab, A., J. Bort, B. Zhou, M. D. Serret, M. T. Nieto-Taladriz, and J. L. Araus. 2015. “The Combined Use of Vegetation Indices and Stable Isotopes to Predict Durum Wheat Grain Yield under Contrasting Water Conditions.” Agricultural Water Management 158: 196–208. doi:10.1016/j.agwat.2015.05.003.
  • El-Hendawy, S. E., N. A. Al-Suhaibani, S. Elsayed, W. M. Hassan, Y. H. Dewir, Y. Refay, and K. A. Abdella. 2019a. “Potential of the Existing and Novel Spectral Reflectance Indices for Estimating the Leaf Water Status and Grain Yield of Spring Wheat Exposed to Different Irrigation Rates.” Agricultural Water Management 217: 356–373. doi:10.1016/j.agwat.2019.03.006.
  • El-Hendawy, S. E. S., M. Alotaibi, N. A. Al-Suhaibani, K. Al-Gaadi, W. M. Hassan, Y. H. Dewir, M. A. El-Gawad, et al. 2019b. “Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines under Two Contrasting Irrigation Regimes.” Frontiers in Plant Science 10 (1537). doi:10.3389/fpls.2019.01537.
  • Elsayed, S., M. Elhoweity, H. H. Ibrahim, Y. H. Dewir, H. M. Migdadi, and U. Schmidhalter. 2017. “Thermal Imaging and Passive Reflectance Sensing to Estimate the Water Status and Grain Yield of Wheat under Different Irrigation Regimes.” Agricultural Water Management 189: 98–110. doi:10.1016/j.agwat.2017.05.001.FAOSTAT.
  • Farquhar, G. D., J. R. Ehleringer, and K. T. Hubick. 1989. “Carbon Isotope Discrimination and Photosynthesis.” Annual Review of Plant Biology 40 (1): 503–537. doi:10.1146/annurev.arplant.40.1.503.
  • Farquhar, G. D., M. H. O’Leary, and J. A. Berry. 1982. “On the Relationship between Carbon Isotope Discrimination and the Intercellular Carbon Dioxide Concentration in Leaves.” Australian Journal of Plant Physiology 9 (2): 121–137. doi:10.1071/pp9820121.
  • Farquhar, G. D., and R. A. Richards. 1984. “Isotopic Composition of Plant Carbon Correlates with Water Use Efficiency of Wheat Genotypes.” Australian Journal of Plant Physiology 11 (6): 539–552. doi:10.1071/pp9840539.
  • Feng, P. B., W. D. Li Liu, H. Xing, F. Ji, I. Macadam, H. Ruan, and Q. Yu. 2018. “Impacts of Rainfall Extremes on Wheat Yield in Semi-arid Cropping Systems in Eastern Australia.” Climatic Change 147 (3–4): 555–569. doi:10.1007/s10584-018-2170-x.
  • Ferrio, J. P., M. A. Mateo, J. Bort, O. Abdalla, J. Voltas, and J. L. Araus. 2007. “Relationships of Grain δ13C and δ 18O with Wheat Phenology and Yield under Water-limited Conditions.” Annals of Applied Biology 150 (2): 207–215. doi:10.1111/j.1744-7348.2007.00115.x.
  • Filella, I., and J. Peñuelas. 1998. “Visible and Near-infrared Reflectance Techniques for Diagnosing Plant Physiological Status.” Trends in Plant Science 3 (4): 151–156. doi:10.1016/s1360-1385(98)01213-8.
  • Flexas, J. 2016. “Genetic Improvement of Leaf Photosynthesis and Intrinsic Water Use Efficiency in C3 Plants: Why so Much Little Success?” Plant Science 251: 155–161. doi:10.1016/J.PLANTSCI.2016.05.002.
  • Flexas, J., Ü. Niinemets, A. Gallé, M. M. Barbour, M. Centritto, A. Diaz-Espejo, and F. Rosselló. 2013. “Diffusional Conductances to CO2 as a Target for Increasing Photosynthesis and Photosynthetic Water-use Efficiency.” Photosynthesis Research 117 (1–3): 45–59. doi:10.1007/s11120-013-9844-z.
  • Fu, Y., G. Yang, J. Wang, X. Song, and H. Feng. 2014. “Winter Wheat Biomass Estimation Based on Spectral Indices, Band Depth Analysis and Partial Least Squares Regression Using Hyperspectral Measurements.” Computers and Electronics in Agriculture 100: 51–59. doi:10.1016/j.compag.2013.10.010.
  • Fu, Y. Y., J. H. Wang, G. J. Yang, and H. K. Feng. 2013. “Comparison of Three Regression Methods for the Winter Wheat Biomass Estimation Using Hyperspectral Data.” Applied Mechanics and Materials 380: 1843–1846. doi:10.4028/www.scientific.net/amm.380-384.1843.
  • Garreaud, R. D., C. Alvarez-Garreton, J. Barichivich, J. P. Boisier, C. Duncan, M. Galleguillos, and M. Zambrano-Bigiarini. 2017. “The 2010–2015 Megadrought in Central Chile: Impacts on Regional Hydroclimate and Vegetation.” Hydrology & Earth System Sciences 21 (12): 6307–6327. doi:10.5194/hess-21-6307-2017.
  • Garriga, M., J. B. Retamales, S. Romero-Bravo, P. D. S. Caligari, and G. A. Lobos. 2014. “Chlorophyll, Anthocyanin, and Gas Exchange Changes Assessed by Spectroradiometry in Fragaria Chiloensis under Salt Stress.” Journal of Integrative Plant Biology 56 (5): 505–515. doi:10.1111/jipb.12193.
  • Garriga, M., S. Romero-Bravo, F. Estrada, A. Escobar, I. A. Matus, A. Del Pozo, C. Astudillo, and G. A. Lobos. 2017. “Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-values or Directly Identify the Elite Genotypes Group?” Frontiers in Plant Science 8: 280. doi:10.3389/fpls.2017.00280.
  • Gausman, H. W. 1985. Plant Leaf Optical Properties in Visible and Near-infrared Light. Lubbock, Texas: Texas Tech University Press.
  • Gitelson, A. A., G. P. Keydan, and M. N. Merzlyak. 2006. “Three-band Model for Noninvasive Estimation of Chlorophyll, Carotenoids, and Anthocyanin Contents in Higher Plant Leaves.” Geophysical Research Letters 33 (11): 1402. doi:10.1029/2006gl026457.
  • Gitelson, A. A., M. N. Merzlyak, and O. B. Chivkunova. 2001. “Optical Properties and Non-destructive Estimation of Anthocyanin Content in Plant Leaves.” Photochemistry and Photobiology 74 (1): 38–45. doi:10.1562/0031-8655(2001)0740038opaneo2.0.co2.
  • Gizaw, S. A., K. Garland-Campbell, and A. H. Carter. 2016. “Use of Spectral Reflectance for Indirect Selection of Yield Potential and Stability in Pacific Northwest Winter Wheat.” Field Crops Research 196: 199–206. doi:10.1016/j.fcr.2016.06.022.
  • Goicoechea, H. C., and A. C. Olivieri. 2002. “Wavelength Selection for Multivariate Calibration Using A Genetic Algorithm: A Novel Initialization Strategy.” Journal of Chemical Information and Computer Sciences 42 (5): 1146–1153. doi:10.1021/ci0255228..
  • Gutierrez, M., M. P. Reynolds, and A. R. Klatt. 2010. “Association of Water Spectral Indices with Plant and Soil Water Relations in Contrasting Wheat Genotypes.” Journal of Experimental Botany 61 (12): 3291–3303. doi:10.1093/jxb/erq156.
  • Gutierrez, M., M. P. Reynolds, W. R. Raun, M. L. Stone, and A. R. Klatt. 2010. “Spectral Water Indices for Assessing Yield in Elite Bread Wheat Genotypes under Well-irrigated, Water‐stressed, and High-temperature Conditions.” Crop Science 50 (1): 197–214. doi:10.2135/cropsci2009.07.0381.
  • Hastie, T., R. Tibshirani, and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer.
  • Hawkins, D. M. 2004. “The Problem of Overfitting.” Journal of Chemical Information and Computer Sciences 44 (1): 1–12. doi:10.1002/chin.200419274.
  • Hernández, J., G. A. Lobos, I. Matus, A. Del Pozo, P. Silva, and M. Galleguillos. 2015. “Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes.” Remote Sensing 7 (2): 2109–2126. doi:10.3390/rs70202109.
  • Hernández-Barrera, S., C. Rodríguez-Puebla, and A. J. Challinor. 2017. “Effects of Diurnal Temperature Range and Drought on Wheat Yield in Spain.” Theoretical and Applied Climatology 129 (1–2): 503–519. doi:10.1007/s00704-016-1779-9.
  • Horler, D. N. H., M. Dockray, and J. Barber. 1983. “The Red-edge of Plant Leaf Reflectance.” International Journal of Remote Sensing 4 (2): 273–288. doi:10.1080/01431168308948546.
  • Huete, A.R. 1988. „A Soil-Adjusted Index (SAVI). Remote Sensing of Environment 25: 295-309.
  • Huseynova, I. M., S. M. Rustamova, S. Y. Suleymanov, D. R. Aliyeva, A. C. Mammadov, and J. A. Aliyev. 2016. “Drought-induced Changes in Photosynthetic Apparatus and Antioxidant Components of Wheat (Triticum Durum Desf.) Varieties.” Photosynthesis Research 130 (1–3): 215–223. doi:10.1007/s11120-016-0244-z.
  • Jackson, P., M. Robertson, M. Cooper, and G. Hammer. 1996. “The Role of Physiological Understanding in Plant Breeding from a Breeding Perspective.” Field Crops Research 49 (1): 11–37. doi:10.1016/s0378-4290(96)01012-x.
  • Jain, A. K., J. Mao, and K. M. Mohiuddin. 1996. “Artificial Neural Networks: A Tutorial.” Computer 29 (3): 31–44. doi:10.1109/2.485891.
  • Kursa, M. B., A. Jankowski, and W. R. Rudnicki. 2010. “Boruta–a System for Feature Selection.” Fundamenta Informaticae 101 (4): 271–285. doi:10.3233/FI-2010-288.
  • Le Maire,G., François, C., Soudani, K., Berveiller, D., Pontailler,D., Bréda,N., Genet, H., Davi, H., and Dufrêne, E. 2008. “Calibration and Validation of Hyperspectral Indices for the Estimation of Broadleaved Forest Leaf Chlorophyll Content, Leaf Mass per Area, Leaf Area Index and Leaf Canopy Biomass.” Remote Sensing of Environment, 112: 3846–3864.
  • Li, F., B. Mistele, Y. Hu, X. Chen, and U. Schmidhalter. 2014a. “Reflectance Estimation of Canopy Nitrogen Content in Winter Wheat Using Optimized Hyperspectral Spectral Indices and Partial Least Squares Regression.” European Journal of Agronomy 52: 198–209. doi:10.1016/j.eja.2013.09.006.
  • Li, X., Y. Zhang, Y. Bao, J. Luo, X. Jin, X. Xu, X. Song, and G. Yang. 2014b. “Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression.” Remote Sensing 6 (7): 6221–6241. doi:10.3390/rs6076221.
  • Li, Y., H. Li, Y. Li, and S. Zhang. 2017. “Improving Water-use Efficiency by Decreasing Stomatal Conductance and Transpiration Rate to Maintain Higher Ear Photosynthetic Rate in Drought-resistant Wheat.” The Crop Journal 5 (3): 231–239. doi:10.1016/j.cj.2017.01.001.
  • Lobos, G. A., A. Escobar-Opazo, F. Estrada, S. Romero-Bravo, M. Garriga, A. Del Pozo, C. Poblete-Ecghevarría, J. González-Talice, L. González-Martinez, and P. D. S. Caligari. 2019. “Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions.” Remote Sensing 11 (3): 329. doi:10.3390/rs11030329.
  • Lobos, G. A., and C. Poblete-Echeverría. 2017. “Spectral Knowledge (SK-UTALCA): Software for Exploratory Analysis of High-resolution Spectral Reflectance Data.” Frontiers in Plant Science 7 (1996). doi:10.3389/fpls.2016.01996.
  • Lobos, G. A., I. Matus, A. Rodriguez, S. Romero-Bravo, J. L. Araus, and A. Del Pozo. 2014. “Wheat Genotypic Variability in Grain Yield and Carbon Isotope Discrimination under Mediterranean Conditions Assessed by Spectral Reflectance.” Journal of Integrative Plant Biology 56 (5): 470–479. doi:10.1111/jipb.12114.
  • Misra, S. C., S. Shinde, S. Geerts, V. S. Rao, and P. Monneveux. 2010. “Can Carbon Isotope Discrimination and Ash Content Predict Grain Yield and Water Use Efficiency in Wheat?” Agricultural Water Management 97 (1): 57–65. doi:10.1016/j.agwat.2009.08.014.
  • Mohankumar, M. V., M. S. Sheshshayee, M. P. Rajanna, and M. Udayakumar. 2011. “Correlation and Path Analysis of Drought Tolerance Traits on Grain Yield in Rice Germplasm Accessions.” ARPN Journal of Agricultural and Biological Science 6 (7): 70–77.
  • Møller, M. F. 1993. “A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning.” Neural Networks 6 (4): 525–533. doi:10.1016/S0893-6080(05)80056-5.
  • Montesinos-López, O. A., A. Montesinos-López, J. Crossa, G. de Los Campos, G. Alvarado, M. Suchismita, J. Rutkoski, L. González-Pérez, and J. Burgueño. 2017. “Predicting Grain Yield Using Canopy Hyperspectral Reflectance in Wheat Breeding Data.” Plant Methods 13 (1): 4. doi:10.1186/s13007-016-0154-2.
  • Nagasubramanian, K., S. Jones, S. Sarkar, A. K. Singh, A. Singh, and B. Ganapathysubramanian. 2018. “Hyperspectral Band Selection Using Genetic Algorithm and Support Vector Machines for Early Identification of Charcoal Rot Disease in Soybean Stems.” Plant Methods 14 (1): 86. doi:10.1186/s13007-018-0349-9.
  • Øvergaard, S. I., T. Isaksson, K. Kvaal, and A. Korsaeth. 2010. “Comparisons of Two Hand-held, Multispectral Field Radiometers and a Hyperspectral Airborne Imager in Terms of Predicting Spring Wheat Grain Yield and Quality by Means of Powered Partial Least Square Regression.” Journal of near Infrared Spectroscopy 18 (4): 247–261. doi:10.1255/jnirs.892.
  • Passioura, J. B. 1977. “Grain Yield, Harvest Index, and Water Use of Wheat.” Journal of the Australian Institute of Agricultural Science 43: 117–120.
  • Pavuluri, K., B. K. Chim, C. A. Griffey, M. S. Reiter, M. Balota, and W. E. Thomason. 2015. “Canopy Spectral Reflectance Can Predict Grain Nitrogen Use Efficiency in Soft Red Winter Wheat.” Precision Agriculture 16 (4): 405–424. doi:10.1007/s11119-014-9385-2.
  • Peñuelas, J., P. R. Ogaya, and I. Filella. 1997. “Estimation of Plant Water Content by the Reflectance Water Index WI (R900/R970).” International Journal of Remote Sensing 18 (13): 2869–2875. doi:10.1080/014311697217396.
  • Pimstein, A., A. Karnieli, S. K. Bansal, and D. J. Bonfil. 2011. “Exploring Remotely Sensed Technologies for Monitoring Wheat Potassium and Phosphorus Using Field Spectroscopy.” Field Crops Research 121 (1): 125–135. doi:10.1016/j.fcr.2010.12.001.
  • Prasad, B., B. F. Carver, M. L. Stone, M. A. Babar, W. R. Raun, and A. R. Klatt. 2007. “Potential Use of Spectral Reflectance Indices as a Selection Tool for Grain Yield in Winter Wheat under Great Plains Conditions.” Crop Science 47 (4): 1426–1440. doi:10.2135/cropsci2006.07.0492.
  • Prey, L., Y. Hu, and U. Schmidhalter. 2020. “High-throughput Field Phenotyping Traits of Grain Yield Formation and Nitrogen Use Efficiency: Optimizing the Selection of Vegetation Indices and Growth Stages.” Frontiers in Plant Science 10: 1672. doi:10.3389/fpls.2019.01672.
  • R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
  • Rebetzke, G. J., A. G. Condon, R. A. Richards, and G. D. Farquhar. 2002. “Selection for Reduced Carbon Isotope Discrimination Increases Aerial Biomass and Grain Yield of Rainfed Bread Wheat.” Crop Science 42 (3): 739–745. doi:10.2135/cropsci2002.7390.
  • Rebetzke, G. J., R. A. Richards, A. G. Condon, and G. D. Farquhar. 2006. “Inheritance of Carbon Isotope Discrimination in Bread Wheat (Triticum Aestivum L.” Euphytica 150 (1–2): 97–106. doi:10.1007/s10681-006-9097-4.
  • Reynolds, M. P., C. S. Pierre, A. S. Saad, M. Vargas, and A. G. Condon. 2007. “Evaluating Potential Genetic Gains in Wheat Associated with Stress-adaptive Trait Expression in Elite Genetic Resources under Drought and Heat Stress.” Crop Science 47 (Supplement 3): 172–189. doi:10.2135/cropsci2007.10.0022ipbs.
  • Rischbeck, P., S. Elsayed, B. Mistele, G. Barmeier, K. Heil, and U. Schmidhalter. 2016. “Data Fusion of Spectral, Thermal and Canopy Height Parameters for Improved Yield Prediction of Drought Stressed Spring Barley.” European Journal of Agronomy 78: 44–59. doi:10.1016/j.eja.2016.04.013..
  • Royo, C., N. Aparicio, D. Villegas, J. Casadesus, P. Monneveux, and J. L. Araus. 2003. “Usefulness of Spectral Reflectance Indices as Durum Wheat Yield Predictors under Contrasting Mediterranean Conditions.” International Journal of Remote Sensing 24 (22): 4403–4419. doi:10.1080/0143116031000150059.
  • Royo, C., V. Martos, A. Ramdani, D. Villegas, Y. Rharrabti, and L. F. García Del Moral. 2008. “Changes in Yield and Carbon Isotope Discrimination of Italian and Spanish Durum Wheat during the 20th Century.” Agronomy Journal 100 (2): 352–360. doi:10.2134/agrojnl2007.0060.
  • Sánchez-Bragado, R., A. Elazab, B. Zhou, M. D. Serret, J. Bort, M. T. Nieto-Taladriz, and J. L. Araus. 2014. “Contribution of the Ear and the Flag Leaf to Grain Filling in Durum Wheat Inferred from the Carbon Isotope Signature: Genotypic and Growing Conditions Effects.” Journal of Integrative Plant Biology 56 (5): 444–454. doi:10.1111/jipb.12106.
  • Sharabian, V. R., N. Noguchi, and K. Ishi. 2014. “Significant Wavelenghts for Prediction of Winter Wheat Growth Status and Grain Yield Using Multivariate Analysis.” Engenieering in Agriculture, Environment and Food 7 (1): 14–21. doi:10.1016/j.eaef.2013.12.003.
  • Siegmann, B., and T. Jarmer. 2015. “Comparison of Different Regression Models and Validation Techniques for the Assessment of Wheat Leaf Area Index from Hyperspectral Data.” International Journal of Remote Sensing 36 (18): 4519–4534. doi:10.1080/01431161.2015.1084438.
  • Silva-Perez, V., G. Molero, S. P. Serbin, A. G. Condon, M. P. Reynolds, R. T. Furbank, and J. R. Evans. 2018. “Hyperspectral Reflectance as a Tool to Measure Biochemical and Physiological Traits in Wheat.” Journal of Experimental Botany 69 (3): 483–496. doi:10.1093/jxb/erx421.
  • Tcherkez, G., A. Mahé, and M. Hodges. 2011. “12C⁄ 13C Fractionations in Plant Primary Metabolism.” Trends in Plant Science 16 (9): 499–506. doi:10.1016/j.tplants.2011.05.010.
  • Trethowan, R. M., M. van Ginkel, K. Ammar, J. Crossa, T. S. Payne, B. Cukadar, S. Rajaram, and E. Hernandez. 2003. “Associations among Twenty Years of International Bread Wheat Yield Evaluation Environments.” Crop Science 43 (5): 1698–1711. doi:10.2135/cropsci2003.1698.
  • Tucker, C. J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8 (2): 127–150. doi:10.1016/0034-4257(79)90013-0.
  • Vapnik, V. 1998. Statistical Learning Theory. New York: John Wiley & Sons. .
  • Varshney, R. K., V. K. Singh, A. Kumar, W. Powell, and M. E. Sorrells. 2018. “Can Genomics Deliver Climate-change Ready Crops?” Current Opinion in Plant Biology 45: 205–211. doi:10.1016/J.PBI.2018.03.007.
  • Wahbi, A., and A. S. A. Shaaban. 2011. “Relationship between Carbon Isotope Discrimination (Δ), Yield and Water Use Efficiency of Durum Wheat in Northern Syria.” Agricultural Water Management 98 (12): 1856–1866. doi:10.1016/j.agwat.2011.06.012.
  • Wang, B., D. L. Liu, S. Asseng, I. Macadam, and Q. Yu. 2015. “Impact of Climate Change on Wheat Flowering Time in Eastern Australia.” Agricultural and Forest Meteorology 209-210: 11–21. doi:10.1016/j.agrformet.2015.04.028.
  • Wang, F. M., J. F. Huang, and Z. H. Lou. 2011. “A Comparison of Three Methods for Estimating Leaf Area Index of Paddy Rice from Optimal Hyperspectral Bands.” Precision Agriculture 12 (3): 439–447. doi:10.1007/s11119-010-9185-2.
  • Wang, Y., X. Zhang, X. Zhang, L. Shao, S. Chen, and X. Liu. 2016. “Soil Water Regime Affecting Correlation of Carbon Isotope Discrimination with Yield and Water-use Efficiency of Winter Wheat.” Crop Science 56 (2): 760–772. doi:10.2135/cropsci2014.11.0793.
  • White, J. W., P. Andrade-Sanchez, M. A. Gore, K. F. Bronson, T. A. Coffelt, M. M. Conley, K. A. Feldmann, et al. 2012. “Field-based Phenomics for Plant Genetics Research.” Field Crops Research 133:101–112. doi:10.1016/j.fcr.2012.04.003.
  • Whitley, D. 1994. “A Genetic Algorithm Tutorial.” Statistics and Computing 4 (2): 65–85. doi:10.1007/bf00175354.
  • Wold, H. 1975. “Soft Modelling by Latent Variables: The Non-linear Iterative Partial Least Squares (NIPALS) Approach.” Journal of Applied Probability 12 (S1): 117–142. doi:10.1017/S0021900200047604.
  • Yao, X., Y. Huang, G. Shang, C. Zhou, T. Cheng, Y. Tian, W. Cao, and Y. Zhu. 2015. “Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration.” Remote Sensing 7 (11): 14939–14966. doi:10.3390/rs71114939.
  • Yasir, T. A., D. Min, X. Chen, A. G. Condon, and Y. G. Hu. 2013. “The Association of Carbon Isotope Discrimination (Δ) with Gas Exchange Parameters and Yield Traits in Chinese Bread Wheat Cultivars under Two Water Regimes.” Agricultural Water Management 119: 111–120. doi:10.1016/j.agwat.2012.11.020.
  • Yousfi, S., N. Kellas, L. Saidi, Z. Benlakehal, L. Chaou, D. Said, F. Herda, et al. 2016. “Comparative Performance of Remote Sensing Methods in Assessing Wheat Performance under Mediterranean Conditions.” Agricultural Water Management 164 :137–147. doi:10.1016/j.agwat.2015.09.016.
  • Zadoks, J. C., T. T. Chang, and C. F. Konzak. 1974. “A Decimal Code for the Growth Stages of Cereals.” Weed Research 14 (6): 415–421. doi:10.1111/j.1365-3180.1974.tb01084.x.
  • Zarco-Tejada, P, Rueda, C.A., and Ustin, S.L. 2003. „Water Content Estimation in Vegetation with MODIS Reflectance Data and Model Inversion Methods. Remote Sensing of Environment 85: 109–124 1 doi:10.1016/S0034-4257(02)00197-9
  • Zhai, Y., L. Cui, X. Zhou, Y. Gao, T. Fei, and W. Gao. 2013. “Estimation of Nitrogen, Phosphorus, and Potassium Contents in the Leaves of Different Plants Using Laboratory-based Visible and Near-infrared Reflectance Spectroscopy: Comparison of Partial Least-square Regression and Support Vector Machine Regression Methods.” International Journal of Remote Sensing 34 (7): 2502–2518. doi:10.1080/01431161.2012.746484.
  • Zheng, H., W. Li, J. Jiang, Y. Liu, T. Cheng, Y. Tian, Y. Zhu, et al. 2018. “A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle.” Remote Sensing 10 (12): 2026. doi:10.3390/rs10122026.
  • Zhu, L., Z. S. Liang, X. Xu, and S. H. Li. 2008. “Relationship between Carbon Isotope Discrimination and Mineral Content in Wheat Grown under Three Different Water Regimes.” Journal of Agronomy and Crop Science 194 (6): 421–428. doi:10.1111/j.1439-037x.2008.00333.x.

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