| dc.contributor.author | Kheir, A.M.S. |
| dc.contributor.author | Mkuhlani, S. |
| dc.contributor.author | Mugo, J.W. |
| dc.contributor.author | Elnashar, A. |
| dc.contributor.author | Nangia, V. |
| dc.contributor.author | Devare, M. |
| dc.contributor.author | Govind, A. |
| dc.date.accessioned | 2023-11-30T12:59:51Z |
| dc.date.available | 2023-11-30T12:59:51Z |
| dc.date.issued | 2023-09 |
| dc.identifier.citation | Kheir, A.M.S., Mkuhlani, S., Mugo, J.W., Elnashar, A., Nangia, V., Devare, M. & Govind, A. (2023). Integrating APSIM model with machine learning to predict wheat yield spatial distribution. Agronomy Journal, 1-9. |
| dc.identifier.issn | 0002-1962 |
| dc.identifier.uri | https://hdl.handle.net/20.500.12478/8345 |
| dc.description.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 N ha−1) relative to the potential yield. |
| dc.format.extent | 1-9 |
| dc.language.iso | en |
| dc.subject | Machine Learning |
| dc.subject | Wheat |
| dc.subject | Yields |
| dc.subject | Varieties |
| dc.subject | Models |
| dc.title | Integrating APSIM model with machine learning to predict wheat yield spatial distribution |
| dc.type | Journal Article |
| cg.contributor.affiliation | International Center for Agricultural Research in the Dry Areas |
| cg.contributor.affiliation | International Institute of Tropical Agriculture |
| cg.contributor.affiliation | University of Kassel |
| cg.coverage.region | Southern Africa |
| cg.coverage.country | Egypt |
| cg.coverage.hub | Eastern Africa Hub |
| cg.coverage.hub | Headquarters and Western Africa Hub |
| cg.identifier.bibtexciteid | KHEIR:2023 |
| cg.isijournal | ISI Journal |
| cg.authorship.types | CGIAR and advanced research institute |
| cg.iitasubject | Agronomy |
| cg.iitasubject | Food Security |
| cg.iitasubject | Post-Harvesting Technology |
| cg.journal | Agronomy Journal |
| cg.notes | Open Access Article |
| cg.accessibilitystatus | Open Access |
| cg.reviewstatus | Peer Review |
| cg.usagerightslicense | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0) |
| cg.targetaudience | Scientists |
| cg.identifier.doi | https://doi.org/10.1002/agj2.21470 |
| cg.iitaauthor.identifier | Jane Mugo: 0000-0003-3836-9113 |
| cg.iitaauthor.identifier | Medha Devare: 0000-0003-0041-4812 |
| cg.futureupdate.required | No |
