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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
30/06/2023 |
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Actualizado : |
30/06/2023 |
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Tipo de producción científica : |
Documentos |
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Autor : |
DE BARBIERI, I.; ROVIRA, F.; RODRIGUEZ, B.; LORENZE, P.; MARCHEZAN, F.; MONZALVO, C.; ZAMIT, W.; RODRIGUEZ, A.; NAVAJAS, E.; CARRACELAS, B.; CIAPPESONI, G. |
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Afiliación : |
LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO ROVIRA GALARRAGA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; BRIAM ADRIAN RODRIGUEZ PIREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO ENRIQUE LORENZE MEDEROS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDA MARCHEZAN BARCHET, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS ENRIQUE MONZALVO CAMPAÑA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WILFREDO SHAMIL ZAMIT DUARTE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANGEL ANDRES RODRIGUEZ MARTINOTTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELLY ANA NAVAJAS VALENTINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; EMERITA BEATRIZ CARRACELAS MARQUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS GABRIEL CIAPPESONI SCARONE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Corriedale apuesta fuerte por el ambiente. Primera prueba de eficiencia y emisiones de metano de carneros comerciales. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Anuario Corriedale 2022, p.14-22. |
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Idioma : |
Español |
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Contenido : |
Este 2022 quedará como un hito para la mejora genética de la raza Corriedale en Uruguay. Por primera vez se comenzó a registrar en carneros de cabañas comerciales características de gran importancia económica y de impacto ambiental como lo son la eficiencia de conversión de alimento y las emisiones de metano. Estas características
son de las más estudiadas últimamente a nivel internacional encontrándose nuestro país en la vanguardia tanto en los protocolos utilizados como en el número de animales registrados. A fines de setiembre del 2022 ingresaron 80 carneros de 18 cabañas de diferentes puntos del País a la plataforma de fenotipado intensivo de INIA La
Magnolia en Tacuarembó, demostrando el compromiso de la raza con estos temas emergentes. Estos registros se suman a los 400 animales que ya se han registrado desde 2018 provenientes de los núcleos Corriedale de SUL e INIA. |
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Palabras claves : |
EMISIONES DE METANO; INIA La Magnolia - Tacuarembó; SISTEMA GANADERO EXTENSIVO - INIA. |
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Thesagro : |
CORRIEDALE; MEJORA GENETICA; MEJORAMIENTO GENETICO ANIMAL; OVINOS. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17246/1/Prueba-Eficiencia-Anuario-Corriedale-2022.pdf
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Marc : |
LEADER 01837nam a2200313 a 4500
001 1064213
005 2023-06-30
008 2022 bl uuuu u0uu1 u #d
100 1 $aDE BARBIERI, I.
245 $aCorriedale apuesta fuerte por el ambiente. Primera prueba de eficiencia y emisiones de metano de carneros comerciales.$h[electronic resource]
260 $aAnuario Corriedale 2022, p.14-22.$c2022
520 $aEste 2022 quedará como un hito para la mejora genética de la raza Corriedale en Uruguay. Por primera vez se comenzó a registrar en carneros de cabañas comerciales características de gran importancia económica y de impacto ambiental como lo son la eficiencia de conversión de alimento y las emisiones de metano. Estas características son de las más estudiadas últimamente a nivel internacional encontrándose nuestro país en la vanguardia tanto en los protocolos utilizados como en el número de animales registrados. A fines de setiembre del 2022 ingresaron 80 carneros de 18 cabañas de diferentes puntos del País a la plataforma de fenotipado intensivo de INIA La Magnolia en Tacuarembó, demostrando el compromiso de la raza con estos temas emergentes. Estos registros se suman a los 400 animales que ya se han registrado desde 2018 provenientes de los núcleos Corriedale de SUL e INIA.
650 $aCORRIEDALE
650 $aMEJORA GENETICA
650 $aMEJORAMIENTO GENETICO ANIMAL
650 $aOVINOS
653 $aEMISIONES DE METANO
653 $aINIA La Magnolia - Tacuarembó
653 $aSISTEMA GANADERO EXTENSIVO - INIA
700 1 $aROVIRA, F.
700 1 $aRODRIGUEZ, B.
700 1 $aLORENZE, P.
700 1 $aMARCHEZAN, F.
700 1 $aMONZALVO, C.
700 1 $aZAMIT, W.
700 1 $aRODRIGUEZ, A.
700 1 $aNAVAJAS, E.
700 1 $aCARRACELAS, B.
700 1 $aCIAPPESONI, G.
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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. |
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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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