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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
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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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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Registro original : |
INIA Las Brujas (LB) |
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| Registros recuperados : 13 | |
| 2. |  | MACEDO, I.; ROEL, A.; AYALA, W.; PRAVIA, V.; TERRA, J.A.; PITTELKOW, C.M. 207-4. Rice rotations affect soil organic carbon sequestration and rice yield in a temperate region of South America. [Abstract] Soil Carbon and Greenhouse Gas Emissions Community. ASA Section: Environmental Quality. In: ASA, CSSA, SSSA International Annual Meeting, Salt Lake City, UT. 2021. https://scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/134305 Abstract citation: Macedo, I., Roel, A., Ayala, W., Pravia, M. V., Terra, J. A., & Pittelkow, C. M. (2021). Rice Rotations Affect Soil Organic Carbon Sequestration and Rice Yield in a Temperate Region of South America [Abstract]....| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Las Brujas. |
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| 4. | | ZHANG, Z.; MACEDO, I.; LINQUIST, B.A.; SANDER, B. O.; PITTELKOW, C.M. Opportunities for mitigating net system greenhouse gas emissions in Southeast Asian rice production: A systematic review. Agriculture, Ecosystems and Environment, 2024, Volume 361, article 108812. https://doi.org/10.1016/j.agee.2023.108812 Article history: Received 28 June 2023; Received in revised form 13 September 2023; Accepted 8 November 2023; Available online 21 November 2023. -- Correspondence: Z. Zhang, E-mail address: hcizhang@ucdavis.edu --| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 5. | | MACEDO, I.; ROEL, A.; VELAZCO, J.I.; BORDAGORRI, A.; TERRA, J.A.; PITTELKOW, C.M. Intensification of rice-pasture rotations with annual crops reduces the stability of sustainability across productivity, economic, and environmental indicators. Agricultural Systems, October 2022, volume 202, Article Number 103488. OPEN ACCESS. doi: https://doi.org/10.1016/j.agsy.2022.103488 Article history: Received 6 May 2022, Revised 17 August 2022, Accepted 19 August 2022, Available online 30 August 2022, Version of Record 30 August 2022.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Treinta y Tres. |
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| 6. | | MACEDO, I.; PITTELKOW, C.M.; TERRA, J.A.; CASTILLO, J.; ROEL, A. The power of on-farm data for improved agronomy. Global Food Security. 2024, Volume 40, 100752. https://doi.org/10.1016/j.gfs.2024.100752 -- OPEN ACCESS. 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...| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - -- |
| Biblioteca(s): INIA Las Brujas. |
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| 7. | | TSENG, C-M.; ROEL, A.; MACEDO, I.; MARELLA, M.; TERRA, J.A.; PITTELKOW, C. M. Synergies and tradeoffs among yield, resource use efficiency, and environmental footprint indicators in rice systems. Current Research in Environmental Sustainability, 2021, volume 3, 100070. OPEN ACCESS. DOI: https://doi.org/10.1016/j.crsust.2021.100070 Article history: Received 30 April 2021 / / Revised 12 July 2021 // Accepted 13 July 2021 // Available online 24 July 2021.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : -- - -- |
| Biblioteca(s): INIA Treinta y Tres. |
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| 8. | | 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.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Treinta y Tres. |
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| 9. | | 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.| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : -- - -- |
| Biblioteca(s): INIA Treinta y Tres. |
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| 10. | | ROEL, A.; TERRA, J.A.; ZORRILLA DE SAN MARTÍN, G.; MARELLA, M.; TSENG, M.C.; PITTELKOW, C.M. Rice productivity and resource use efficiencies 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.| Tipo: Abstracts/Resúmenes |
| Biblioteca(s): INIA Treinta y Tres. |
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| 11. | | PITTELKOW, C.M.; ZORRILLA DE SAN MARTÍN, G.; TERRA, J.A.; RICCETTO, S.; MACEDO, I.; BONILLA, C.; ROEL, A. Sostenibilidad de la intensificación arrocera en el Uruguay desde 1993 al 2013. 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. 4, p. 7-10. (Serie Actividades de Difusión; 765) Acceso a la presentación oral del trabajo A. Roel.| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 12. | | PITTELKOW, C.M.; ZORRILLA DE SAN MARTÍN, G.; TERRA, J.A.; RICCETTO, S.; MACEDO, I.; BONILLA, C.; ROEL, A. Sustainability of rice intensification in Uruguay from 1993 to 2013. Global Food Security, 2016, v. 9, p. 10-18. Article history: Received 2 February 2016, Received in revised form 4 May 2016, Accepted 6 May 2016.
Have a Supplementary material| Tipo: Artículos en Revistas Indexadas Internacionales | Circulación / Nivel : Internacional - B |
| Biblioteca(s): INIA Treinta y Tres. |
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| Registros recuperados : 13 | |
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