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

Author:

Kheir Ahmed M. S.123ORCID,Mkuhlani Siyabusa4,Mugo Jane W.45ORCID,Elnashar Abdelrazek67,Nangia Vinay8ORCID,Devare Medha4ORCID,Govind Ajit1ORCID

Affiliation:

1. International Center for Agricultural Research in the Dry Areas (ICARDA) Maadi Egypt

2. Julius 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

4. International Institute for Tropical Agriculture (IITA), c/o ICIPE Nairobi Kenya

5. Department 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

7. Department of Natural Resources, Faculty of African Postgraduate Studies Cairo University Giza Egypt

8. International Center for Agricultural Research in the Dry Areas (ICARDA) Rabat Morocco

Abstract

AbstractTraditional 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 N ha−1) relative to the potential yield.

Publisher

Wiley

Subject

Agronomy and Crop Science

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3