Downscaling the APSIM Crop Model for Simulation at the Within-Field Scale

Pasquel, Daniel; Cammarano, Davide; Roux, Sébastiên; Castrignanò, A.; Tisseyre, Bruno; Rinaldi, Michele; Troccoli, A.; Taylor, James A.

doi:10.2139/ssrn.4394507

Cited by 1 publication

(3 citation statements)

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“…One of the agricultural system models that has been developed through many years of research and has been used to comprehend G × E × M effects on yield under present changing climatic scenarios is the Agricultural Production System Simulation (APSIM) model (Asseng et al, 2002;D. P. Holzworth et al, 2014;Keating et al, 2003;Pasquel et al, 2023). The APSIM initiative recently upgraded APSIM to APSIM Next Generation, which can be run on different operating systems.…”

Section: Introductionmentioning

confidence: 99%

“…Spatial simulation can also help in determining the yield heterogeneity at regional scale and contribute to understanding the nature of recent yield progress (Lobell & Azzari, 2017; van Ittersum et al., 2013; van Wart et al., 2013). One of the agricultural system models that has been developed through many years of research and has been used to comprehend G × E × M effects on yield under present changing climatic scenarios is the Agricultural Production System Simulation (APSIM) model (Asseng et al., 2002; D. P. Holzworth et al., 2014; Keating et al., 2003; Pasquel et al., 2023). The APSIM initiative recently upgraded APSIM to APSIM Next Generation, which can be run on different operating systems.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of crop models now in use are created for field-scale modeling and simulate agronomic factors using homogenous (average) field conditions (You et al, 2022). However, simulation at the field scale is no longer sufficient to address precision agriculture concerns (Pasquel et al, 2023), confirming the necessity of spatial simulation, which shifts crop model simulation scale from field scale to finer scale (Pasquel et al, 2022). In addition, if models are parameterized with coarse-scale information, such as an entire country, and then used to address consequences at a finer geographical scale, there is a greater possibility of generating incorrect inferences due to substantial biases (Ogle et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Integrating APSIM model with machine learning to predict wheat yield spatial distribution

Kheir,

Mkuhlani,

Mugo

et al. 2023

Agronomy Journal

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Traditional simulation models are often point based, thus more research is needed to emphasize the spatial simulation, providing decision makers with fast recommendations. Combining machine learning algorithms with spatial process‐based models could be considered an appropriate solution. We created a spatial model in R (APSIMx_R) to generate fine resolution data from coarse resolution data, which is typically available at the regional level. The APSIM crop model outputs were then deployed to train and test the Artificial Neural Network (ANN), creating a hybrid modelling approach for robust spatial simulations. The APSIMx_R package facilitates preparing the required model inputs, executes the prediction, processes, and analyzes the APSIM crop model outputs. This note demonstrates the use of a new approach for creating reproducible crop modelling workflows with the spatial APSIM next generation model and machine learning algorithms. The tool was deployed for spatial and temporal simulation of potential wheat yield under different nitrogen rates, and various wheat cultivars. The spatial APSIMx_R was validated by comparing the simulated yield at 100 kg Nha−1 to the analogues' actual yield at the same grid points, which showed good agreement (d = 0.89), between spatially predicted and actual yield. The hybrid approach increased such precision, resulting in higher agreement (d = 0.95) with actual yield. When the interaction between cultivars and nitrogen levels was considered, it was found that the novel cultivar Sakha95 is nitrogen voracious, exhibiting a larger drop in yield (65%) under minimal nitrogen treatment (0 kg N ha−1) relative to the potential yield.This article is protected by copyright. All rights reserved

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Section: Introductionmentioning

confidence: 99%