Low-input interpretable models to forecast maize yield at multiple scales based on absorbed radiation
Most crop yield forecast models operate at coarse scales (e.g., county or region) or need extensive input data for finer resolutions. Here, we present maize (Zea mays L.) yield forecast models that require minimal user data and operate at field and regional scales throughout the growing season. Usin...
| Autores principales: | , , , , , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2025
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/22797 https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.70089 https://doi.org/10.1002/agj2.70089 |
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| author | Menendez-Coccoz, Martin Rotili, Diego Hernán Otegui, María Elena Martini, Gustavo Paolini, María Di Bella, Carlos Piñeiro, Gervasio Oesterheld, Martín |
| author_browse | Di Bella, Carlos Martini, Gustavo Menendez-Coccoz, Martin Oesterheld, Martín Otegui, María Elena Paolini, María Piñeiro, Gervasio Rotili, Diego Hernán |
| author_facet | Menendez-Coccoz, Martin Rotili, Diego Hernán Otegui, María Elena Martini, Gustavo Paolini, María Di Bella, Carlos Piñeiro, Gervasio Oesterheld, Martín |
| author_sort | Menendez-Coccoz, Martin |
| collection | INTA Digital |
| description | Most crop yield forecast models operate at coarse scales (e.g., county or region) or need extensive input data for finer resolutions. Here, we present maize (Zea mays L.) yield forecast models that require minimal user data and operate at field and regional scales throughout the growing season. Using 1853 maize field-years in Argentina, with known location, sowing date, and yield, our models leveraged absorbed radiation (from satellite imagery), temperature-based phenology, regional site-year properties, El Niño-Southern Oscillation (ENSO) phase predictions, and sowing period. At the field scale, our models achieved high accuracy at physiological maturity, with a mean error of 1 t ha−1 (16%). Yield forecasts were mainly driven by absorbed radiation during the reproductive phase and a regional factor. Early-season forecasts incorporated ENSO and sowing period, but with reduced accuracy. When scaled to regional forecasts, the models performed even better, with a mean error of 0.3 t ha−1 (4%). These results combine a novel case of yield forecast because of the low data requirements from users, high anticipation (30–90 days before harvest), and good levels of accuracy at both field and regional scales. Additionally, the models’ interpretability makes them valuable diagnostic tools for post-season analysis. |
| format | info:ar-repo/semantics/artículo |
| id | INTA22797 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | INTA227972025-06-26T11:47:01Z Low-input interpretable models to forecast maize yield at multiple scales based on absorbed radiation Menendez-Coccoz, Martin Rotili, Diego Hernán Otegui, María Elena Martini, Gustavo Paolini, María Di Bella, Carlos Piñeiro, Gervasio Oesterheld, Martín Maíz Maize Rendimiento de cultivos Crop yield Manejo del cultivo Crop management Radiación fotosintéticamente activa Photosynthetically active radiation El Niño Índice de cosecha Harvest index Most crop yield forecast models operate at coarse scales (e.g., county or region) or need extensive input data for finer resolutions. Here, we present maize (Zea mays L.) yield forecast models that require minimal user data and operate at field and regional scales throughout the growing season. Using 1853 maize field-years in Argentina, with known location, sowing date, and yield, our models leveraged absorbed radiation (from satellite imagery), temperature-based phenology, regional site-year properties, El Niño-Southern Oscillation (ENSO) phase predictions, and sowing period. At the field scale, our models achieved high accuracy at physiological maturity, with a mean error of 1 t ha−1 (16%). Yield forecasts were mainly driven by absorbed radiation during the reproductive phase and a regional factor. Early-season forecasts incorporated ENSO and sowing period, but with reduced accuracy. When scaled to regional forecasts, the models performed even better, with a mean error of 0.3 t ha−1 (4%). These results combine a novel case of yield forecast because of the low data requirements from users, high anticipation (30–90 days before harvest), and good levels of accuracy at both field and regional scales. Additionally, the models’ interpretability makes them valuable diagnostic tools for post-season analysis. EEA Pergamino Fil: Menendez-Coccoz, Martin. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina Fil: Rotili, Diego H. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina Fil: Rotili, Diego H. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina Fil: Otegui, María Elena. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina Fil: Otegui, María E. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Otegui, María E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; Argentina Fil: Martini, Gustavo. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola. Unidad de Investigación y Desarrollo, Área de Agricultura; Argentina Fil: Paolini, María. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola. Unidad de Investigación y Desarrollo, Área de Agricultura; Argentina Fil: Di Bella, Carlos. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina Fil: Piñeiro, Gervasio. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina Fil: Oesterheld, Martín. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; Argentina 2025-06-26T11:38:11Z 2025-06-26T11:38:11Z 2025-05 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/22797 https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.70089 0002-1962 1435-0645 (online) https://doi.org/10.1002/agj2.70089 eng info:eu-repo/semantics/restrictedAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf Wiley Agronomy Journal 117 (3) : e70089. (May/June 2025) |
| spellingShingle | Maíz Maize Rendimiento de cultivos Crop yield Manejo del cultivo Crop management Radiación fotosintéticamente activa Photosynthetically active radiation El Niño Índice de cosecha Harvest index Menendez-Coccoz, Martin Rotili, Diego Hernán Otegui, María Elena Martini, Gustavo Paolini, María Di Bella, Carlos Piñeiro, Gervasio Oesterheld, Martín Low-input interpretable models to forecast maize yield at multiple scales based on absorbed radiation |
| title | Low-input interpretable models to forecast maize yield at multiple scales based on absorbed radiation |
| title_full | Low-input interpretable models to forecast maize yield at multiple scales based on absorbed radiation |
| title_fullStr | Low-input interpretable models to forecast maize yield at multiple scales based on absorbed radiation |
| title_full_unstemmed | Low-input interpretable models to forecast maize yield at multiple scales based on absorbed radiation |
| title_short | Low-input interpretable models to forecast maize yield at multiple scales based on absorbed radiation |
| title_sort | low input interpretable models to forecast maize yield at multiple scales based on absorbed radiation |
| topic | Maíz Maize Rendimiento de cultivos Crop yield Manejo del cultivo Crop management Radiación fotosintéticamente activa Photosynthetically active radiation El Niño Índice de cosecha Harvest index |
| url | http://hdl.handle.net/20.500.12123/22797 https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.70089 https://doi.org/10.1002/agj2.70089 |
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