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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
18/06/2015 |
Actualizado : |
20/06/2015 |
Tipo de producción científica : |
Informes Agroclimáticos |
Autor : |
CASTAÑO, J.; GIMENEZ, A.; FUREST, J.; AUNCHAYNA, R. |
Afiliación : |
JOSE PEDRO CASTAÑO SANCHEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; AGUSTIN EDUARDO GIMENEZ FUREST, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSE MARIA FUREST CROCCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ROSSINA MARIANA AUNCHAYNA REILLY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Informe Agroclimático 2009 - Situación a Mayo. |
Fecha de publicación : |
2009 |
Fuente / Imprenta : |
Montevideo (Uruguay): INIA, 2009. |
Páginas : |
4 p. |
Idioma : |
Español |
Palabras claves : |
AGROCLIMA; AGROCLIMATOLOGÍA; BOLETIN AGROCLIMÁTICO; CARACTERIZACIÓN AGROCLIMÁTICA; DIRECCION VIENTO; ESTACIONES AGROMETEOROLOGICAS; ESTACIONES AUTOMATICAS; ESTACIONES INIA; ESTADO DEL TIEMPO; ESTRÉS HÍDRICO; GRAFICAS AGROCLIMATICOS; GRAS; HELIOFANOGRAFO; INFORMACION SATELITAL; INUNDACIONES; LLUVIAS DIARIAS; MAXIMA; MEDIA; MINIMA; PANEL SOLAR; PERSPECTIVAS CLIMATICAS; PLUVIOMETRO; PRECIPITACION NACIONAL; PREVENCION HELADAS; PRONOSTICO; SENSOR; SIMETRICO; TANQUE A; TERMOCUPLAS; TERMOHIDROGRAFO; VARIABLES AGROCLIMATICAS; VELETA. |
Thesagro : |
AGROCLIMATOLOGIA; CAMBIO CLIMATICO; CLIMA; CLIMATOLOGIA; ESTACIONES METEOROLOGICAS; ESTRES HIDRICO; EVAPORACION; EVAPOTRANSPIRACION; HUMEDAD; HUMEDAD RELATIVA; LLUVIA; METEOROLOGIA; PERSPECTIVAS; PLUVIOMETROS; PRONOSTICO DEL TIEMPO; SENSORES; SISTEMAS; SISTEMAS DE INFORMACION; SUELO; TEMPERATURA; TERMOMETROS. |
Asunto categoría : |
P40 Meteorología y climatología |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/4663/1/Inf.Agr.-mayo-2009.pdf
http://www.inia.uy/Publicaciones/Paginas/publicacion-1900.aspx
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Marc : |
LEADER 02038nam a2200781 a 4500 001 1052819 005 2015-06-20 008 2009 bl uuuu u0uu1 u #d 100 1 $aCASTAÑO, J. 245 $aInforme Agroclimático 2009 - Situación a Mayo.$h[electronic resource] 260 $aMontevideo (Uruguay): INIA$c2009 300 $a4 p. 650 $aAGROCLIMATOLOGIA 650 $aCAMBIO CLIMATICO 650 $aCLIMA 650 $aCLIMATOLOGIA 650 $aESTACIONES METEOROLOGICAS 650 $aESTRES HIDRICO 650 $aEVAPORACION 650 $aEVAPOTRANSPIRACION 650 $aHUMEDAD 650 $aHUMEDAD RELATIVA 650 $aLLUVIA 650 $aMETEOROLOGIA 650 $aPERSPECTIVAS 650 $aPLUVIOMETROS 650 $aPRONOSTICO DEL TIEMPO 650 $aSENSORES 650 $aSISTEMAS 650 $aSISTEMAS DE INFORMACION 650 $aSUELO 650 $aTEMPERATURA 650 $aTERMOMETROS 653 $aAGROCLIMA 653 $aAGROCLIMATOLOGÍA 653 $aBOLETIN AGROCLIMÁTICO 653 $aCARACTERIZACIÓN AGROCLIMÁTICA 653 $aDIRECCION VIENTO 653 $aESTACIONES AGROMETEOROLOGICAS 653 $aESTACIONES AUTOMATICAS 653 $aESTACIONES INIA 653 $aESTADO DEL TIEMPO 653 $aESTRÉS HÍDRICO 653 $aGRAFICAS AGROCLIMATICOS 653 $aGRAS 653 $aHELIOFANOGRAFO 653 $aINFORMACION SATELITAL 653 $aINUNDACIONES 653 $aLLUVIAS DIARIAS 653 $aMAXIMA 653 $aMEDIA 653 $aMINIMA 653 $aPANEL SOLAR 653 $aPERSPECTIVAS CLIMATICAS 653 $aPLUVIOMETRO 653 $aPRECIPITACION NACIONAL 653 $aPREVENCION HELADAS 653 $aPRONOSTICO 653 $aSENSOR 653 $aSIMETRICO 653 $aTANQUE A 653 $aTERMOCUPLAS 653 $aTERMOHIDROGRAFO 653 $aVARIABLES AGROCLIMATICAS 653 $aVELETA 700 1 $aGIMENEZ, A. 700 1 $aFUREST, J. 700 1 $aAUNCHAYNA, R.
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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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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