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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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INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha actual : |
21/02/2014 |
Actualizado : |
22/03/2019 |
Tipo de producción científica : |
Documentos |
Autor : |
LUZARDO, S.; CUADRO, R.; BOVE, P.; BOCKING, B.; MONTOSSI, F.; LAVECCHIA, A. |
Afiliación : |
SANTIAGO FELIPE LUZARDO VILLAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WASHINGTON ROBIN CUADRO LOPEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRES PASCUAL LAVECCHIA GONZALEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
2007. I. Experiencia de campo en la recría de terneros Hereford sobre un verdeo de raigrás (CV. LE 284) implantado sobre un rastrojo de arroz. II. Efecto de la utilización de dos cargas en la recría de terneros Hereford sobre un verdeo de raigrás (CV. LE 284) implantado sobre un rastrojo de arroz. |
Fecha de publicación : |
2008 |
Fuente / Imprenta : |
In: INIA TACUAREMBÓ. Presentación resultados experimentales arroz Zafra 2007-2008. Tacuarembó (Uruguay): INIA, 2008. |
Páginas : |
p. 61-69 |
Serie : |
(INIA Serie Actividades de Difusión ; 543) |
Idioma : |
Español |
Contenido : |
I)Objetivo General: Evaluar la productividad del uso de un verdeo de raigrás sembrado sobre rastrojo de arroz, para mejorar el proceso de recría de terneros/as Hereford, en el marco de la intensificación productiva de un sistema de rotación arroz ? pasturas de la región norte del Uruguay. II) ?Objetivo General: Evaluar la productividad de un verdeo de raigrás, utilizado para el proceso de recría de terneros Hereford, bajo el manejo comparativo de dos cargas animales, en el marco de la intensificación de un sistema de rotación arroz ? pasturas de la región Norte del Uruguay. |
Palabras claves : |
RICE (ORYZA SATIVA L.). |
Thesagro : |
ARROZ; PRODUCCION ANIMAL. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/9205/1/SAD-543p61-69.pdf
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Marc : |
LEADER 01553naa a2200241 a 4500 001 1021034 005 2019-03-22 008 2008 bl uuuu u00u1 u #d 100 1 $aLUZARDO, S. 245 $a2007. I. Experiencia de campo en la recría de terneros Hereford sobre un verdeo de raigrás (CV. LE 284) implantado sobre un rastrojo de arroz. II. Efecto de la utilización de dos cargas en la recría de terneros Hereford sobre un verdeo de raigrás (CV. LE 284) implantado sobre un rastrojo de arroz. 260 $c2008 300 $ap. 61-69 490 $a(INIA Serie Actividades de Difusión ; 543) 520 $aI)Objetivo General: Evaluar la productividad del uso de un verdeo de raigrás sembrado sobre rastrojo de arroz, para mejorar el proceso de recría de terneros/as Hereford, en el marco de la intensificación productiva de un sistema de rotación arroz ? pasturas de la región norte del Uruguay. II) ?Objetivo General: Evaluar la productividad de un verdeo de raigrás, utilizado para el proceso de recría de terneros Hereford, bajo el manejo comparativo de dos cargas animales, en el marco de la intensificación de un sistema de rotación arroz ? pasturas de la región Norte del Uruguay. 650 $aARROZ 650 $aPRODUCCION ANIMAL 653 $aRICE (ORYZA SATIVA L.) 700 1 $aCUADRO, R. 700 1 $aBOVE, P. 700 1 $aBOCKING, B. 700 1 $aMONTOSSI, F. 700 1 $aLAVECCHIA, A. 773 $tIn: INIA TACUAREMBÓ. Presentación resultados experimentales arroz Zafra 2007-2008. Tacuarembó (Uruguay): INIA, 2008.
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