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| 203. |  | DEAMBROSI, E.; ZORRILLA DE SAN MARTÍN, G.; LAUZ, M.; BLANCO, P.H.; TERRA, J.A. Rompiendo el techo de rendimiento del cultivo de arroz. Proyecto ANII ALI_1_2012_1_3507 (INIA, GMA-COOPAR, ACA) Zafra 2015-2016. ln: JORNADA ANUAL ARROZ, 2016, INIA TREINTA Y TRES, TREINTA Y TRES, UY. Arroz: resultados experimentales 2015-2016. Treinta y Tres, (Uruguay): INIA, 2016. Cap. 2, p. 1-4. (INIA, Serie Actividades de Difusión; 765) Acceso a publicación y presentación oral.| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 204. | | DEAMBROSI, E.; ZORRILLA DE SAN MARTÍN, G.; LAUZ, M.; BLANCO, P.H.; TERRA, J.A. Rompiendo el techo de rendimiento del cultivo de arroz. Proyecto ANII ALI_1_2012_1_3507 (INIA, GMA-COOPAR, ACA) Zafra 2016-2017 - Trabajos de validación. In: Zorrilla, G.; Martínez, S.; Saravia, H. (Eds.) Arroz 2017. Montevideo (UY): INIA, 2017. p. 89-93. (INIA Serie Técnica; 233)| Biblioteca(s): INIA Treinta y Tres. |
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| 211. | | BALDASSINI, P.; BAETHGEN, W.; CAMBA SANS, G.; QUINCKE, A.; PRAVIA, V.; TERRA, J.A.; MACEDO, F.; PIÑEIRO, G.; PARUELO, J. Carbon stocks and potential sequestration of Uruguayan soils. A road map to a comprehensive characterization of temporal and spatial changes to assess Carbon footprint. Original research. Frontiers in Sustainable Food Systems. 2023, Volume 7. https://doi.org/10.3389/fsufs.2023.1045734 Article history: Received 16 Sep 2022; Accepted 25 May 2023; Published 20 July 2023. -- Correspondence: Dr. Pablo Baldassini, Instituto Nacional de Investigación Agropecuaria, INIA La Estanzuela, Colonia, Uruguay. -- Edited by: Bruno José...| Biblioteca(s): INIA Las Brujas. |
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| 212. | | TSENG, M.C.; ROEL, A.; MARELLA, M.; ZORRILLA DE SAN MARTÍN, G.; TERRA, J.A.; PITTELKOW, C. Assessment of yield gaps in high-yielding rice systems in Uruguay using field-level data. [Poster]. In: International Temperate Rice Conference (7., 2020, Pelotas, RS), Science & Innovation: feeding a world of 10 billion people: proceedings. Pelotas RS, Brasil, February 9-12, 2020. Brasília, DF : Embrapa, 2020.| Biblioteca(s): INIA Treinta y Tres. |
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| 213. | | TSENG, M.C.; ROEL, A.; MARELLA, M.; ZORRILLA DE SAN MARTÍN, G.; TERRA, J.A.; PITTELKOW, C.M. Assessment of yield gaps using field-level data in Uruguay. [Abstract]. In: International Temperate Rice Conference (7., 2020, Pelotas, RS), Science & Innovation: feeding a world of 10 billion people: proceedings. Pelotas RS, Brasil, February 9-12, 2020. Brasília, DF : Embrapa, 2020.| Biblioteca(s): INIA Treinta y Tres. |
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| 214. | | HERNÁNDEZ, A.; ROVIRA, G.; BORDAGORRI, P.; ESCALANTE, F.; CASTILLO, J.; MARTÍNEZ, S.; MACEDO, I.; TERRA, J.A. Brechas de rendimiento del cultivo de arroz sobre distinto antecesor de verano para dos variedades de alto potencial. In: Zorrilla, G.; Martínez, S.; Terra, J. A. Saravia, H. (Eds.) Arroz 2018. Montevideo (UY): INIA, 2018. p. 81-83. (INIA Serie Técnica; 246)| Biblioteca(s): INIA Treinta y Tres. |
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| 216. | | OTERO, A.; GASO, D.; CAPURRO, M.C.; BERGER, A.; PÉREZ, O.; GARCÍA, C.; TERRA, J.A.; SAWCHIK, J. Construyendo los componentes de un sistema productivo con la inclusión del riego. En: GIMÉNEZ, L.; PUPPO, L.; BENTANCOR, L.; HAYASHI, R.; SAWCHIK, J.; GARCIA, C. (Eds.). Riego en cultivos y pasturas. 3er. Seminario Internacional, 2014, Paysandú. (Uruguay). Montevideo : INIA, 2014. p. 43-52 Instituciones: Grupo de Desarrollo de Riego (GDR), Facultad de Agronomía (FAGRO), Instituto Nacional de Investigación Agropecuaria (INIA). - Editores: Luis Giménez, Lucía Puppo, Lisette Bentancor, Raquel Hayashi, Jorge Sawchik, Claudio...| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas; INIA Treinta y Tres. |
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| 217. | | TERRA, J.A.; MOLINA, F.; DEAMBROSI, E.; PRAVIA, V.; ROEL, A.; CASALES, L.; FERREIRA, D.; FERREIRA, R. Densidad de siembra y nitrógeno en EP144 a escala de chacra. Manejo de suelos y nutrición vegetal. ln: INIA TREINTA Y TRES. Arroz. Resultados Experimentales 2006-2007. Treinta y Tres (Uruguay): INIA, 2007. Cap. 7., p. 1-8. (INIA Serie Actividades de Difusión; 502)| Biblioteca(s): INIA Treinta y Tres. |
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| 218. | | MARTÍNEZ, A.; OREGGIONI, D.; FERRANDO, L.; FERNÁNDEZ, A.; ILLARZE, G.; IRISARRI, P.; SALVO, L.; TERRA, J.A.; TARLERA, S. Emisión de metano en distintos sistemas arroceros del este uruguayo. [Resumen]. In: Libro de resúmenes de las TERCERAS JORNADAS INTERDISCIPLINARIAS EN BIODIVERSIDAD Y ECOLOGIA. "Desafíos socio-ambientales para el Uruguay del futuro" 28 de Noviembre a 2 de Diciembre 2016, Centro Universitario Regional del Este Rocha, Uruguay. p.113.| Biblioteca(s): INIA Las Brujas. |
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| 219. | | TSENG, M.C.; ROEL, A.; MACEDO, I.; MARELLA, M.; TERRA, J.A.; ZORRILLA DE SAN MARTÍN, G.; PITTELKOW, C. M. Field-level factors for closing yield gaps in high-yielding rice systems of Uruguay. Field Crops Research, February 2021, vol. 264, no. 108097. Doi: https://doi.org/10.1016/j.fcr.2021.108097 12 p. Article history: Received 9 April 2020 / Received in revised form 12 January 2021 / Accepted 5 February 2021 / Available online 24 February 2021.| Biblioteca(s): INIA Treinta y Tres. |
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| 220. | | PINTO, P.; SAWCHIK, J.; TERRA, J.A.; AYALA, W.; SILVA, L.C.DA; BARRIOS, E.; PIÑEIRO, G. Formación y descomposición de la materia orgánica del suelo bajo distintos cultivos de cobertura. [Poster]. In: Congreso Argentino de la Ciencia del Suelo. (15o. 2016, Río Cuarto , Córdoba).Ordenamiento Territorial: un desafío para la Ciencia del Suelo. Río Cuarto: AACS, Universidad Nacional de Río Cuarto, 2016.| Biblioteca(s): INIA La Estanzuela. |
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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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