Abstract

Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modeling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement Project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle (θ) or that θ distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed θ distribution in the canopies from three-dimensional canopy reconstructions. In all the canopies, θ distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP-Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping (KellC). Results showed that the KellC model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.

Highlights

  • Crop growth models (CGMs) are popular tools used to optimize crop management, assess the impact of climate change on crop production (Liu et al, 2016), or assist plant breeders (e.g. Martre et al, 2015a, 2015b, 2015c; Chenu et al, 2017)

  • We evaluated the accuracy of the FIPAR models in the AgMIP–wheat CGM ensemble at five locations spanning the range of latitudes at which wheat is grown globally

  • Sensitivity of FIPAR to canopy structure and illumination conditions Since we found that KeCll was the best model for FIPAR, we used it to explore the sensitivity of FIPAR with respect to changes in canopy structure, green area index (GAI) and h (Figure 4)

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Summary

Introduction

Crop growth models (CGMs) are popular tools used to optimize crop management, assess the impact of climate change on crop production (Liu et al, 2016), or assist plant breeders (e.g. Martre et al, 2015a, 2015b, 2015c; Chenu et al, 2017). To meet this need, CGMs should be capable of predicting the response of genotypes to various environments (Yin et al, 2003; Parent and Tardieu, 2014; Messina et al, 2018).

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