Spectral Reflectance Indices as a Potential Indirect Selection Criteria for Wheat Yield under Irrigation

The objectives of this study were to assess the potential of using spectral reflectance indices (SRI) as an indirect selection tool to differentiate spring wheat (Triticum aestivum L.) genotypes for grain yield under irrigated conditions. This paper demonstrates only the first step in using the SRI as indirect selection criteria by reporting genetic variation for SRI among genotypes, the effect of phenology and year on SRI and their interaction with genotypes, and the correlations between SRI and grain yield and yield components of wheat. Three field experiments—15 CIMMYT globally adapted genotypes (GHIST), 25 random F3–derived lines (RLs1), and 36 random F3–derived lines (RLs2)—were conducted under irrigated conditions at the CIMMYT research station in northwest Mexico in three different years. Five previously developed SRI (photochemical reflectance index [PRI], water index [WI], red normalized difference vegetation index [RNDVI], green normalized difference vegetation index [GNDVI], simple ratio [SR]) and two newly calculated SRI (normalized water index-1 [NWI-1] and normalized water index-2 [NWI-2]) were evaluated in the experiments. In general, genotypic variation for all the indices was significant. Near infrared radiation (NIR)–based indices (WI, NWI-1, NWI-2) gave the highest levels of association with grain yield during the 3 yr of the study. A clear trend for higher association between grain yield and the NIR-based indices was observed at heading and grainfilling than at booting. Overall, NIR-based indices were more consistent and differentiated grain yield more effectively compared to the other indices. The results demonstrated the potential of using SRI as a tool in breeding programs for selecting for increased genetic gains for yield.

[1]  Matthew P. Reynolds,et al.  Application of physiology in wheat breeding , 2001 .

[2]  E. B. Knipling.  Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation , 1970 .

[3]  H. Grüneberg,et al.  Introduction to quantitative genetics , 1960 .

[4]  M. E. Bauer,et al.  Relation of agronomic and multispectral reflectance characteristics of spring wheat canopies , 1983 .

[5]  Sean M. Bellairs,et al.  Plant and soil influences on estimating biomass of wheat in plant breeding plots using field spectral radiometers , 1996 .

[6]  R. A. Fischer,et al.  Yield potential progress in short bread wheats in Northwest Mexico , 1997 .

[7]  R. A. Fischer,et al.  Physiological and Morphological Traits Associated with Spring Wheat Yield Under Hot, Irrigated Conditions , 1994 .

[8]  J. L. Araus,et al.  Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions , 2003 .

[9]  Identifying and removing spatial correlation from yield experiments , 1991 .

[10]  Gustavo A. Slafer,et al.  Consequences of breeding on biomass, radiation interception and radiation-use efficiency in wheat , 1997 .

[11]  J. Araus,et al.  Spectral vegetation indices as nondestructive tools for determining durum wheat yield. , 2000 .

[12]  J.,et al.  A decimal code for the growth stages of cereals , 2022 .

[13]  P. Sellers.  Canopy reflectance, photosynthesis and transpiration , 1985 .

[14]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[15]  Josep Peñuelas,et al.  Visible and Near‐Infrared Reflectance Assessment of Salinity Effects on Barley , 1997 .

[16]  A. J. Richardson,et al.  Vegetation indices in crop assessments , 1991 .

[17]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .

[18]  P. Sellers.  Canopy reflectance, photosynthesis, and transpiration. II. the role of biophysics in the linearity of their interdependence , 1987 .

[19]  R. Richards,et al.  Defining selection criteria to improve yield under drought , 1996, Plant Growth Regulation.

[20]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[21]  Matthew P. Reynolds,et al.  Physiological and Genetic Changes of Irrigated Wheat in the Post–Green Revolution Period and Approaches for Meeting Projected Global Demand , 1999 .

[22]  EVALUATING PHYSIOLOGICAL TRAITS TO COMPLEMENT EMPIRICAL SELECTION FOR WHEAT IN WARM ENVIRONMENTS , 1997 .

[23]  K. Siddique,et al.  Morphological and physiological traits associated with wheat yield increases in Mediterranean environments , 1994 .

[24]  J. Peñuelas,et al.  The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .

[25]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[26]  M. P. Reynolds,et al.  Increasing yield potential in wheat : breaking the barriers. Proceedings of a workshop held in Ciudad Obregon, Sonora, Mexico , 1996 .

[27]  J. Schepers,et al.  Use of Spectral Radiance to Estimate In-Season Biomass and Grain Yield in Nitrogen- and Water-Stressed Corn. , 2002, Crop science.

[28]  G. Asrar,et al.  Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1 , 1984 .

[29]  John B. Solie,et al.  In‐Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance , 2001 .

[30]  M. P. Reynolds,et al.  Evaluating physiological traits to complement empirical selection for wheat in warm environments , 2004, Euphytica.

[31]  Kl Regan,et al.  Use of reflectance measurements to estimate early cereal biomass production on sandplain soils , 1993 .

[32]  José Luis Araus,et al.  Relationship between Growth Traits and Spectral Vegetation Indices in Durum Wheat , 2002 .

[33]  Graeme L. Hammer,et al.  The role of physiological understanding in plant breeding; From a breeding perspective , 1996 .

[34]  J. Peñuelas,et al.  Assessment of photosynthetic radiation‐use efficiency with spectral reflectance , 1995 .

[35]  Moon S. Kim,et al.  Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves , 1992 .

[36]  B. Ma,et al.  Early prediction of soybean yield from canopy reflectance measurements , 2001 .

[37]  C. Wiegand,et al.  Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield. I. Rationale. , 1990 .

[38]  R. A. Fischer,et al.  Wheat Yield Progress Associated with Higher Stomatal Conductance and Photosynthetic Rate, and Cooler Canopies , 1998 .

[39]  J. Ransom,et al.  Improvement in the Yield Potential of Bread Wheat Adapted to Northwest Mexico 1 , 1986 .

[40]  R. K. Singh,et al.  Biometrical methods in quantitative genetic analysis , 1978 .

[41]  J. Hatfield.  Spectral behavior of wheat yield variety trials , 1981 .

[42]  C. Konzak,et al.  Relationship between Grain Yield and Remotely‐Sensed Data in Wheat Breeding Experiments , 1993 .