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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
16/04/2024 |
Actualizado : |
18/04/2024 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
MACEDO, I.; PITTELKOW, C.M.; TERRA, J.A.; CASTILLO, J.; ROEL, A. |
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. |
Título : |
The power of on-farm data for improved agronomy. |
Fecha de publicación : |
2024 |
Fuente / Imprenta : |
Global Food Security. 2024, Volume 40, 100752. https://doi.org/10.1016/j.gfs.2024.100752 -- OPEN ACCESS. |
ISSN : |
2211-9124 |
DOI : |
10.1016/j.gfs.2024.100752 |
Idioma : |
Inglés |
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/ -- |
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 |
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. |
Asunto categoría : |
F01 Cultivo |
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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Registro original : |
INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
14/09/2017 |
Actualizado : |
06/03/2020 |
Tipo de producción científica : |
Abstracts/Resúmenes |
Autor : |
MACEDO, I.; TERRA, J.A. |
Afiliación : |
IGNACIO MACEDO YAPOR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Rice-pastures rotations conversion to more intensive soil use systems: soil organic carbon dynamics impacts.[Abstract]. |
Fecha de publicación : |
2017 |
Fuente / Imprenta : |
In: INTERNATIONAL SYMPOSIUM ON SOIL ORGANIC MATTER (6., 3-7 Sep. 2017, HARPENDER, UK9. Proceedings. Harpender, UK: BSSS, 2017. |
Páginas : |
p. 410. |
Idioma : |
Inglés |
Notas : |
Sessin 6c: SOM in rice paddy systems |
Palabras claves : |
CARBONO ORGÁNICO DEL SUELO; EXPERIMENTOS DE LARGO PLAZO; INTENSIFICACIÓN SOSTENIBLE; LONG TERM EXPERIMENT; RICE ROTATIONS; ROTACIONES ARROCERAS; SISTEMA ARROZ-PASTURAS; SISTEMA ARROZ-SOJA; SOIL ORGANIC CARBON; SUSTAINABLE INTENSIFICATION. |
Thesagro : |
Arroz; SISTEMAS DE PRODUCCIÓN. |
Asunto categoría : |
P36 Erosión conservación y recuperación del suelo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/7263/1/Congreso-2017-Macedo-1.pdf
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Marc : |
LEADER 01008nam a2200277 a 4500 001 1057570 005 2020-03-06 008 2017 bl uuuu u01u1 u #d 100 1 $aMACEDO, I. 245 $aRice-pastures rotations conversion to more intensive soil use systems$bsoil organic carbon dynamics impacts.[Abstract].$h[electronic resource] 260 $aIn: INTERNATIONAL SYMPOSIUM ON SOIL ORGANIC MATTER (6., 3-7 Sep. 2017, HARPENDER, UK9. Proceedings. Harpender, UK: BSSS$c2017 300 $ap. 410. 500 $aSessin 6c: SOM in rice paddy systems 650 $aArroz 650 $aSISTEMAS DE PRODUCCIÓN 653 $aCARBONO ORGÁNICO DEL SUELO 653 $aEXPERIMENTOS DE LARGO PLAZO 653 $aINTENSIFICACIÓN SOSTENIBLE 653 $aLONG TERM EXPERIMENT 653 $aRICE ROTATIONS 653 $aROTACIONES ARROCERAS 653 $aSISTEMA ARROZ-PASTURAS 653 $aSISTEMA ARROZ-SOJA 653 $aSOIL ORGANIC CARBON 653 $aSUSTAINABLE INTENSIFICATION 700 1 $aTERRA, J.A.
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