Integrating APSIM model with machine learning to predict wheat yield spatial distribution - NOTES AND INSIGHTS

Aug 4, 2026

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Traditional simulation models are often point-based, limiting their usefulness for spatial decision support. This work integrates machine learning with the APSIM crop model to build reproducible spatial workflows that generate fine-resolution wheat yield estimates from coarser regional inputs. A spatial model in R (APSIMx_R) prepares inputs, runs APSIMx predictions, and trains/tests an artificial neural network to form a hybrid approach. Validation compares simulated yield at 100 kg N ha−1 against actual yield at matching grid points, showing strong agreement (d=0.89 with the spatial prediction; d=0.95 with the hybrid model). Considering cultivar–nitrogen interactions, cultivar Sakha95 shows nitrogen responsiveness, dropping yield by 65% under 0 kg N ha−1 relative to potential yield.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

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Abstract

Hybrid APSIMx_R and ANN modeling

Validation against observed yield

Cultivar–nitrogen interaction insights

Introduction

DOI: 10.1002/agj2.21470 NOTES AND INSIGHTS Integrating APSIM model with machine learning to predict wheat yield spatial distribution Ahmed M. S. Kheir1,2,3 Abdelrazek Elnashar6,7 Siyabusa Mkuhlani4 Jane W. Mugo4,5 Vinay Nangia8 Medha Devare4 Ajit Govind1 1International Center for Agricultural Research in the Dry Areas (ICARDA), Maadi, Egypt 2Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Strategies and Technology Assessment, Kleinmachnow, Germany 3 Soils, Water and Environment Research Institute, Agricultural Research Center, Giza, Egypt 4International Institute for Tropical Agriculture (IITA), c/o ICIPE, Nairobi, Kenya 5Department of Earth and Climate Science, University of Nairobi, Nairobi, Kenya 6 Section of Soil Science, Faculty of Organic Agricultural Sciences, University of Kassel, Witzenhausen, Germany 7Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza, Egypt 8International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco Correspondence Ahmed M. S. Kheir, International Center for Agricultural Research in the Dry Areas (ICARDA), Maadi, 11728, Egypt. Email: [drahmedkheir2015@gmail.com](drahmedkheir2015@gmail.com); [a.kheir@cgiar.org](a.kheir@cgiar.org) Assigned to Associate Editor Yao Zhang Abstract Traditional simulation models are often point based; thus, more research is needed to emphasize 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, creating a hybrid modeling 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 modeling 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 N ha−1 to the analogues’ actual yield at the same grid points, which showed good agreement (d = 0.89) between the 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 Nha−1) relative to the potential yield. Abbreviations: ANN, artificial neural network; APSIM, agricultural production system simulation; GEM, genotype × environment × management; ML, machine learning; RB, relative bias. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2023 The Authors. Agronomy Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy. 1 INTRODUCTION New technology will transform farming and agribusiness, making it more profitable (Popescu et al., 2022) . Crop modelshave been widely used in cropping system simulations for a variety of purposes, including agricultural water management (Kheir et al., 2021), food security and nutrition, and genotype × environment × management (G × E

FAQs

What problem does the document address in crop modeling?

Traditional crop simulation is often point-based, so it cannot provide fast, reliable spatial recommendations for precision agriculture and regional decision-making. The work motivates spatial simulation and highlights biases when coarse parameters are applied to finer locations.

How does the proposed APSIMx_R approach combine APSIM and machine learning?

APSIMx_R generates fine-resolution data from coarse-resolution inputs in R, then uses APSIM crop model outputs to train and test an artificial neural network. The result is a hybrid modeling workflow for spatial wheat yield simulation.

How is the spatial model validated in the document?

Validation compares simulated yield at 100 kg N ha−1 with the analogues’ actual yield at the same grid points. The spatial prediction shows good agreement (d=0.89), and the hybrid approach improves agreement (d=0.95).

What key insight is reported about wheat cultivar and nitrogen interactions?

When cultivar and nitrogen levels are considered, Sakha95 is identified as nitrogen voracious, with yield dropping by 65% under minimal nitrogen (0 kg N ha−1) compared with potential yield.

Aug 4, 2026

Upload by Liam