A data-driven crop model for maize yield prediction
Abstract Accurate estimation of crop yield predictions is of great importance for food security under the impact of climate change. We propose a data-driven crop model that combines the knowledge advantage of process-based modeling and the computational advantage of data-driven modeling. The propose...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-023-04833-y |
| _version_ | 1797840845052837888 |
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| author | Yanbin Chang Jeremy Latham Mark Licht Lizhi Wang |
| author_facet | Yanbin Chang Jeremy Latham Mark Licht Lizhi Wang |
| author_sort | Yanbin Chang |
| collection | DOAJ |
| description | Abstract Accurate estimation of crop yield predictions is of great importance for food security under the impact of climate change. We propose a data-driven crop model that combines the knowledge advantage of process-based modeling and the computational advantage of data-driven modeling. The proposed model tracks the daily biomass accumulation process during the maize growing season and uses daily produced biomass to estimate the final grain yield. Computational studies using crop yield, field location, genotype and corresponding environmental data were conducted in the US Corn Belt region from 1981 to 2020. The results suggest that the proposed model can achieve an accurate prediction performance with a 7.16% relative root-mean-square-error of average yield in 2020 and provide scientifically explainable results. The model also demonstrates its ability to detect and separate interactions between genotypic parameters and environmental variables. Additionally, this study demonstrates the potential value of the proposed model in helping farmers achieve higher yields by optimizing seed selection. |
| first_indexed | 2024-04-09T16:21:23Z |
| format | Article |
| id | doaj.art-40efa7e64bae4a5291e3b6c41fb7cab3 |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-04-09T16:21:23Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-40efa7e64bae4a5291e3b6c41fb7cab32023-04-23T11:25:44ZengNature PortfolioCommunications Biology2399-36422023-04-01611910.1038/s42003-023-04833-yA data-driven crop model for maize yield predictionYanbin Chang0Jeremy Latham1Mark Licht2Lizhi Wang3Department of Industrial and Manufacturing Systems Engineering, Iowa State UniversityDepartment of Industrial and Manufacturing Systems Engineering, Iowa State UniversityDepartment of Agronomy, Iowa State UniversityDepartment of Industrial and Manufacturing Systems Engineering, Iowa State UniversityAbstract Accurate estimation of crop yield predictions is of great importance for food security under the impact of climate change. We propose a data-driven crop model that combines the knowledge advantage of process-based modeling and the computational advantage of data-driven modeling. The proposed model tracks the daily biomass accumulation process during the maize growing season and uses daily produced biomass to estimate the final grain yield. Computational studies using crop yield, field location, genotype and corresponding environmental data were conducted in the US Corn Belt region from 1981 to 2020. The results suggest that the proposed model can achieve an accurate prediction performance with a 7.16% relative root-mean-square-error of average yield in 2020 and provide scientifically explainable results. The model also demonstrates its ability to detect and separate interactions between genotypic parameters and environmental variables. Additionally, this study demonstrates the potential value of the proposed model in helping farmers achieve higher yields by optimizing seed selection.https://doi.org/10.1038/s42003-023-04833-y |
| spellingShingle | Yanbin Chang Jeremy Latham Mark Licht Lizhi Wang A data-driven crop model for maize yield prediction Communications Biology |
| title | A data-driven crop model for maize yield prediction |
| title_full | A data-driven crop model for maize yield prediction |
| title_fullStr | A data-driven crop model for maize yield prediction |
| title_full_unstemmed | A data-driven crop model for maize yield prediction |
| title_short | A data-driven crop model for maize yield prediction |
| title_sort | data driven crop model for maize yield prediction |
| url | https://doi.org/10.1038/s42003-023-04833-y |
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