Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance.

A collection of 368 advanced lines and cultivars of spring wheat (Triticum aestivum L.) from Chile, Uruguay, and CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo), with good agronomic characteristics were evaluated under the Mediterranean conditions of central Chile. Three different water regimes were assayed: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation around booting), and full irrigation (FI; four irrigations: at tillering, flag leaf appearance, heading, and middle grain filling). Traits evaluated were grain yield (GY), agronomical yield components, days from sowing to heading, carbon isotope discrimination (Δ(13) C) in kernels, and canopy spectral reflectance. Correlation analyses were performed for 70 spectral reflectance indices (SRI) and the other traits evaluated in the three trials. GY and Δ(13) C were the traits best correlated with SRI, particularly when these indices were measured during grain filling. However, only GY could be predicted using a single regression, with Normalized Difference Moisture Index (NDMI2: 2,200; 1,100) having the best fit to the data for the three trials. For Δ(13) C, only individual regressions could be forecast under FI (r(2): 0.25-0.37) and MWS (r(2): 0.45-0.59) but not under SWS (r(2): 0.03-0.09). NIR-based SRI proved to be better predictors than those that combine visible and NIR wavelengths.

[1]  C. Tebaldi,et al.  Prioritizing Climate Change Adaptation Needs for Food Security in 2030 , 2008, Science.

[2]  G. Slafer,et al.  CHANGES IN YIELD AND YIELD STABILITY IN WHEAT DURING THE 20TH CENTURY , 1998 .

[3]  W. Raun,et al.  Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices , 2007 .

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

[5]  J. Araus,et al.  The Photosynthetic Role of Ears in C3 Cereals: Metabolism, Water Use Efficiency and Contribution to Grain Yield , 2007 .

[6]  José Luis Araus,et al.  Environmental Factors Determining Carbon Isotope Discrimination and Yield in Durum Wheat under Mediterranean Conditions , 2003 .

[7]  A. Condon,et al.  Improving Intrinsic Water-Use Efficiency and Crop Yield. , 2002, Crop science.

[8]  J. Peñuelas,et al.  Relationship between photosynthetic radiation-use efficiency of barley canopies and the photochemical reflectance index (PRI) , 1996 .

[9]  Armando Apan,et al.  Predicting grain protein content in wheat using hyperspectral sensing of in-season crop canopies and partial least squares regression , 2006 .

[10]  Graham D. Farquhar,et al.  Carbon isotope discrimination is positively correlated with grain yield and dry matter production in field-grown wheat , 1987 .

[11]  J. Eitel,et al.  Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. , 2006 .

[12]  W. Raun,et al.  Potential Use of Spectral Reflectance Indices as a Selection Tool for Grain Yield in Winter Wheat under Great Plains Conditions , 2007 .

[13]  Suming Jin,et al.  Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances , 2005 .

[14]  P. Land,et al.  Aerosol optical depth over the Baltic Sea derived from AERONET and SeaWiFS measurements , 2005 .

[15]  J. Peñuelas,et al.  Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice , 2008 .

[16]  Matthew P. Reynolds,et al.  Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes , 2010, Journal of experimental botany.

[17]  Abraham Blum,et al.  Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress , 2009 .

[18]  A. Condon,et al.  Breeding for high water-use efficiency. , 2004, Journal of experimental botany.

[19]  Iván Matus,et al.  Physiological and yield responses of recombinant chromosome substitution lines of barley to terminal drought in a Mediterranean-type environment , 2012 .

[20]  A. Condon,et al.  Selection for reduced carbon isotope discrimination increases aerial biomass and grain yield of rainfed bread wheat , 2002 .

[21]  A. Klatt,et al.  The Potential of Using Spectral Reflectance Indices to Estimate Yield in Wheat Grown Under Reduced Irrigation , 2006, Euphytica.

[22]  W. Raun,et al.  Relationship Between Coefficient of Variation Measured by Spectral Reflectance and Plant Density at Early Growth Stages in Winter Wheat , 2006 .

[23]  R. A. Fischer,et al.  PAPER PRESENTED AT INTERNATIONAL WORKSHOP ON INCREASING WHEAT YIELD POTENTIAL, CIMMYT, OBREGON, MEXICO, 20–24 MARCH 2006 Understanding the physiological basis of yield potential in wheat , 2007, The Journal of Agricultural Science.

[24]  J. Ehleringer,et al.  Carbon Isotope Discrimination and Photosynthesis , 1989 .

[25]  W. Vargas,et al.  Non-linear trends and low frequency oscillations in annual precipitation over Argentina and Chile, 1931-1999 , 2003 .

[26]  J. Araus,et al.  Factors affecting the grain yield predicting attributes of spectral reflectance indices in durum wheat: growing conditions, genotype variability and date of measurement , 2005 .

[27]  Other,et al.  Mediterranean climate variability , 2006 .

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

[29]  J. Araus,et al.  Plant breeding and drought in C3 cereals: what should we breed for? , 2002, Annals of botany.

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

[31]  Aldo Montecinos,et al.  Seasonality of the ENSO-Related Rainfall Variability in Central Chile and Associated Circulation Anomalies , 2003 .

[32]  Gustavo A. Slafer,et al.  Breeding for Yield Potential and Stress Adaptation in Cereals , 2008 .

[33]  D. Villegas,et al.  Field Measurements of Canopy Spectra for Biomass Assessment of Small-Grain Cereals , 2011 .

[34]  William R. Raun,et al.  Spectral Reflectance to Estimate Genetic Variation for In-Season Biomass, Leaf Chlorophyll, and Canopy Temperature in Wheat , 2006 .

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

[36]  W. Raun,et al.  Spectral water indices for assessing yield in elite bread wheat genotypes under well-irrigated, water-stressed, and high-temperature conditions , 2010 .

[37]  R. Richards,et al.  Breeding Opportunities for Increasing the Efficiency of Water Use and Crop Yield in Temperate Cereals. , 2002, Crop science.

[38]  J. L. Araus,et al.  Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions , 2004 .

[39]  F. J. Lozano,et al.  Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modelling , 2007 .

[40]  Philip Lewis,et al.  Investigation of the Utility of Spectral Vegetation Indices for Determining Information on Coniferous Forests , 1998 .

[41]  A. Engler,et al.  Assessing long- and short-term trends in cereal yields: the case of Chile between 1929 and 2009 , 2013 .

[42]  William R. Raun,et al.  Spectral Reflectance Indices as a Potential Indirect Selection Criteria for Wheat Yield under Irrigation , 2006 .

[43]  Matthew P. Reynolds,et al.  Stay-green in spri g wheat can be det rmined by spectral reflect nce measurements ( normalized difference vegetation index ) independently from phenology , 2012 .