Modelling crop traits and fluxes under multiple abiotic stressors

Abstract

Agriculture is the largest consumer of freshwater, accounting for approximately 70% of the total global usage. As the human population continues to grow, demand for water will be exacerbated by a changing climate and shifting temperature and precipitation regimes. Dynamically modelling crop physiological function will be crucial to optimising crop management strategies. In this study we synergise hyperspectral and thermal remotely-sensed data to model plant traits and water fluxes in spring wheat (Triticum aestivum) in growth chambers within a controlled environment experiment under water and/or nitrogen stress conditions. Results showed that plants which had first received nitrogen fertiliser and were subsequently droughted presented the lowest water fluxes, and the lowest leaf chlorophyll content and photosynthetic capacity (Vcmax) values. Partial least squares regression (PLSR) analysis of hyperspectral reflectance data revealed key wavelengths sensitive to six different plant traits and fluxes (including relative water content, leaf nitrogen, stomatal conductance), with strong correlations between measured and modelled values (R2 = 0.84; p

Plant phenotyping relevance

ハイパースペクトル・熱画像データとPLSRを用いて、複数の植物形質および水フラックスを推定する手法が研究の中心であり、測定値とモデル値の相関も評価しているため。

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