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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha : |
21/02/2014 |
Actualizado : |
24/01/2020 |
Autor : |
BOCKING GUTIÉRREZ, N.B.; CASTRO SOTO, G. DE; GRONDONA JUNG, M.; RODRÍGUEZ FERREIRA, G.A. |
Afiliación : |
NICOLÁS BERNARDO BOCKING GUTIÉRREZ; GONZALO DE CASTRO SOTO; MARTÍN GRONDONA JUNG; GABRIEL ANTONIO RODRÍGUEZ FERREIRA. |
Título : |
I. Ensayos regionales para la evaluación de la respuesta a la fertilización de la soja (Glycine max L) sobre la Unidad de suelo Alférez. II. Evaluación de la respuesta a fuente, tiempo y métodos de aplicación de fertilizante del cultivo de soja (Glicine max L): tesis Ing. Agr. |
Fecha de publicación : |
1987 |
Fuente / Imprenta : |
Montevideo (Uruguay): Facultad de Agronomía, 1987. |
Páginas : |
2 v. |
Idioma : |
Español |
Thesagro : |
ABONOS FOSFATADOS; ABONOS NITROGENADOS; ABONOS POTASICOS; ABSORCION DE SUSTANCIAS NUTRITIVAS; APLICACION DE ABONOS; FACTORES DE RENDIMIENTO; FIJACION DEL NITROGENO; GLYCINE MAX; NUTRICION DE LAS PLANTAS; RENDIMIENTO DE CULTIVOS; RESPUESTA DE LA PLANTA. |
Asunto categoría : |
A50 Investigación agraria |
Marc : |
LEADER 01080nam a2200277 a 4500 001 1017354 005 2020-01-24 008 1987 bl uuuu m 00u1 u #d 100 1 $aBOCKING GUTIÉRREZ, N.B. 245 $aI. Ensayos regionales para la evaluación de la respuesta a la fertilización de la soja (Glycine max L) sobre la Unidad de suelo Alférez. II. Evaluación de la respuesta a fuente, tiempo y métodos de aplicación de fertilizante del cultivo de soja (Glicine max L)$btesis Ing. Agr. 260 $aMontevideo (Uruguay): Facultad de Agronomía$c1987 300 $a2 v. 650 $aABONOS FOSFATADOS 650 $aABONOS NITROGENADOS 650 $aABONOS POTASICOS 650 $aABSORCION DE SUSTANCIAS NUTRITIVAS 650 $aAPLICACION DE ABONOS 650 $aFACTORES DE RENDIMIENTO 650 $aFIJACION DEL NITROGENO 650 $aGLYCINE MAX 650 $aNUTRICION DE LAS PLANTAS 650 $aRENDIMIENTO DE CULTIVOS 650 $aRESPUESTA DE LA PLANTA 700 1 $aCASTRO SOTO, G. DE 700 1 $aGRONDONA JUNG, M. 700 1 $aRODRÍGUEZ FERREIRA, G.A.
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Registro original : |
INIA Tacuarembó (TBO) |
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
16/04/2024 |
Actualizado : |
18/04/2024 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
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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