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| 6. |  | ROEL, A. Análisis de Cluster y Cart. Capítulo 8. Análisis de Datos. ln: Bongiovanni, R.; Chartuni Mantovani, E.; Best, S.; Roel, A., ed. Agricultura de precisión: integrando conocimientos para una agricultura moderna y sustentable. Montevideo (Uruguay): IICA/PROCISUR, 2006. p. 167-170| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó; INIA Treinta y Tres. |
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| 18. | | ROEL, A. Variabilidad espacial del rendimiento. Manejo de suelos y nutrición vegetal. II. Agricultura de precisión. ln: INIA TREINTA Y TRES. PROGRAMA NACIONAL DE ARROZ. Investigaciones agronómicas: reporte técnico anual 2003.Área cultivos. Treinta y Tres (Uruguay): INIA, 2003. cap. 3, p. 3-10. (INIA Reporte Técnico Anual; 01)| Biblioteca(s): INIA Treinta y Tres. |
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| 19. | | ROEL, A. Variabilidad espacial del rendimiento. Agricultura de precisión. ln: INIA TREINTA Y TRES. Arroz. Resultados Experimentales 2004-2005. Treinta y Tres (Uruguay): INIA, 2005. Cap. 5, p. 1-8. (INIA Serie Actividades de Difusión; 418)| Biblioteca(s): INIA Treinta y Tres. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
16/04/2024 |
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Actualizado : |
18/04/2024 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
MACEDO, I.; PITTELKOW, C.M.; TERRA, J.A.; CASTILLO, J.; ROEL, A. |
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Afiliación : |
IGNACIO MACEDO YAPOR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Plant Sciences, Univ. of California, Davis, CA, USA; CAMERON M. PITTELKOW, Department of Plant Sciences, Univ. of California, Davis, CA, USA; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; EMILSE JESUS CASTILLO VELAZQUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALVARO ROEL DELLAZOPPA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
The power of on-farm data for improved agronomy. |
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Fecha de publicación : |
2024 |
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Fuente / Imprenta : |
Global Food Security. 2024, Volume 40, 100752. https://doi.org/10.1016/j.gfs.2024.100752 -- OPEN ACCESS. |
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ISSN : |
2211-9124 |
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DOI : |
10.1016/j.gfs.2024.100752 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 24 November 2023, Revised 27 February 2024, Accepted 3 March 2024, Available online 16 March 2024, Version of Record 16 March 2024. -- Correspondence: Macedo, I.; Department of Plant Sciences, Univ. of California, Davis, CA, United States; email:imacedo@inia.org.uy -- Document type: Article Hybrid Gold Open Access. -- Incluye Appendix A. Supplementary data -- Data availability:
Data will be made available on request. -- License: Under Creative Commons license http://creativecommons.org/licenses/by-nc-nd/4.0/ -- |
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Contenido : |
ABSTRACT.- Advances in technology and analytics to support data-driven agriculture has important implications for global food security and environmental sustainability. However, relatively few studies have investigated the potential to leverage the power of on-farm data for improved agronomy at scale using geospatial machine learning methods. Working in high-yielding rice systems of Uruguay, we developed a geospatial framework to identify yield-limiting factors across 55,000 ha annually of cropland over four seasons (2018?2021 harvest years), while also testing for tradeoffs in the environmental footprint related to nitrogen (N) fertilizer use. Our application of geographically-weighted random forest models showed that crop management decisions influenced rice yield more than variation in soil properties, highlighting the potential for improved agronomy to boost crop production by 1.4-1.8 Mg ha-1 across regions. Seeding date, variety, P rate, and K rate were the most important variables controlling yield, but with significant variation across fields. When these factors were optimized by farmers, the risk of environmental N losses or soil N mining did not increase, highlighting the potential for sustainable intensification by improving N use efficiency. These findings present a pathway for harnessing the benefits of increasingly available on-farm data to identify yield-limiting factors while minimizing negative environmental externalities at the field-level. To enable the development of such geospatial frameworks in other regions, new partnerships are required to engage stakeholders and promote data sharing and collaboration among farmers, researchers, and industry, helping guide regional extension programs and orient future investments in agricultural research. © 2024 The Authors MenosABSTRACT.- Advances in technology and analytics to support data-driven agriculture has important implications for global food security and environmental sustainability. However, relatively few studies have investigated the potential to leverage the power of on-farm data for improved agronomy at scale using geospatial machine learning methods. Working in high-yielding rice systems of Uruguay, we developed a geospatial framework to identify yield-limiting factors across 55,000 ha annually of cropland over four seasons (2018?2021 harvest years), while also testing for tradeoffs in the environmental footprint related to nitrogen (N) fertilizer use. Our application of geographically-weighted random forest models showed that crop management decisions influenced rice yield more than variation in soil properties, highlighting the potential for improved agronomy to boost crop production by 1.4-1.8 Mg ha-1 across regions. Seeding date, variety, P rate, and K rate were the most important variables controlling yield, but with significant variation across fields. When these factors were optimized by farmers, the risk of environmental N losses or soil N mining did not increase, highlighting the potential for sustainable intensification by improving N use efficiency. These findings present a pathway for harnessing the benefits of increasingly available on-farm data to identify yield-limiting factors while minimizing negative environmental externalities at the field-level. To enable the dev... Presentar Todo |
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Palabras claves : |
Data-driven research; Decent work and economic growth - Goal 8; Geospatial data; Industry, innovation and infrastructure - Goal 9; Life on land - Goal 15; Nitrogen balance; Partnership for the goals - Goal 17; Responsible consumption and production - Goal 12; Rice; SISTEMA ARROZ-GANADERÍA - INIA; Sustainability; Sustainable Development Goals (SDGs); Zero hunger - Goal 2. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
https://www.sciencedirect.com/science/article/pii/S2211912424000142/pdf
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Marc : |
LEADER 03526naa a2200361 a 4500
001 1064590
005 2024-04-18
008 2024 bl uuuu u00u1 u #d
022 $a2211-9124
024 7 $a10.1016/j.gfs.2024.100752$2DOI
100 1 $aMACEDO, I.
245 $aThe power of on-farm data for improved agronomy.$h[electronic resource]
260 $c2024
500 $aArticle history: Received 24 November 2023, Revised 27 February 2024, Accepted 3 March 2024, Available online 16 March 2024, Version of Record 16 March 2024. -- Correspondence: Macedo, I.; Department of Plant Sciences, Univ. of California, Davis, CA, United States; email:imacedo@inia.org.uy -- Document type: Article Hybrid Gold Open Access. -- Incluye Appendix A. Supplementary data -- Data availability: Data will be made available on request. -- License: Under Creative Commons license http://creativecommons.org/licenses/by-nc-nd/4.0/ --
520 $aABSTRACT.- Advances in technology and analytics to support data-driven agriculture has important implications for global food security and environmental sustainability. However, relatively few studies have investigated the potential to leverage the power of on-farm data for improved agronomy at scale using geospatial machine learning methods. Working in high-yielding rice systems of Uruguay, we developed a geospatial framework to identify yield-limiting factors across 55,000 ha annually of cropland over four seasons (2018?2021 harvest years), while also testing for tradeoffs in the environmental footprint related to nitrogen (N) fertilizer use. Our application of geographically-weighted random forest models showed that crop management decisions influenced rice yield more than variation in soil properties, highlighting the potential for improved agronomy to boost crop production by 1.4-1.8 Mg ha-1 across regions. Seeding date, variety, P rate, and K rate were the most important variables controlling yield, but with significant variation across fields. When these factors were optimized by farmers, the risk of environmental N losses or soil N mining did not increase, highlighting the potential for sustainable intensification by improving N use efficiency. These findings present a pathway for harnessing the benefits of increasingly available on-farm data to identify yield-limiting factors while minimizing negative environmental externalities at the field-level. To enable the development of such geospatial frameworks in other regions, new partnerships are required to engage stakeholders and promote data sharing and collaboration among farmers, researchers, and industry, helping guide regional extension programs and orient future investments in agricultural research. © 2024 The Authors
653 $aData-driven research
653 $aDecent work and economic growth - Goal 8
653 $aGeospatial data
653 $aIndustry, innovation and infrastructure - Goal 9
653 $aLife on land - Goal 15
653 $aNitrogen balance
653 $aPartnership for the goals - Goal 17
653 $aResponsible consumption and production - Goal 12
653 $aRice
653 $aSISTEMA ARROZ-GANADERÍA - INIA
653 $aSustainability
653 $aSustainable Development Goals (SDGs)
653 $aZero hunger - Goal 2
700 1 $aPITTELKOW, C.M.
700 1 $aTERRA, J.A.
700 1 $aCASTILLO, J.
700 1 $aROEL, A.
773 $tGlobal Food Security. 2024, Volume 40, 100752. https://doi.org/10.1016/j.gfs.2024.100752 -- OPEN ACCESS.
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