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

Javier Hernandez²  Gustavo A. Lobos⁵  Iván Matus⁴  Alejandro del Pozo⁵  Paola Silva²  Mauricio Galleguillos³  Clement Atzberger¹ 

[1]Department of Agricultural Production, Faculty of Agronomics Sciences, University of Chile, Santiago, Casilla 1004, Chile

[2] E-Mail

[3]Department of Agricultural Production, Faculty of Agronomics Sciences, University of Chile, Santiago, Casilla 1004, Chile

[4] E-Mail:

[5]Department of Environmental Sciences, Faculty of Agronomic Sciences, University of Chile, Santa Rosa 11315, La Pintana, Santiago, Chile

[6]Research Regional Center-Quilamapu, Agricultural Research Institute, Chillán, Casilla 426, Chile

[7] E-Mail:

[8]Plant Breeding and Phenomic Center, Faculty of Agricultural Sciences, University of Talca, Talca, Casilla 747–721, Chile

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关键词: drought;    high-throughput;    plant selection;    reflectance;    spectroradiometer;    water stress;   

DOI  :  10.3390/rs70202109

来源: mdpi

【 摘 要 】

Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg∙ha⁻¹, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R²) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.