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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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 | Acceso al texto completo restringido a Biblioteca INIA La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela; INIA Treinta y Tres. |
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Fecha actual : |
14/09/2020 |
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Actualizado : |
15/09/2020 |
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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.; TERRA, J.A.; SIRI-PRIETO, G.; VELAZCO, J.I.; CARRASCO-LETELIER, L. |
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Afiliación : |
IGNACIO MACEDO YAPOR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUILLERMO SIRI-PRIETO, Estación Experimental Mario Cassinoni (EEMAC), Facultad de Agronomía, Universidad de La República, Paysandú, Uruguay.; JOSÉ IGNACIO VELAZCO DE LOS REYES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LEONIDAS CARRASCO-LETELIER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Rice-pasture agroecosystem intensification affects energy use efficiency. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Journal of Cleaner Production, Volume 278, 1 January 2021, 123771. Doi: https://doi.org/10.1016/j.jclepro.2020.123771 |
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Páginas : |
10 p. |
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DOI : |
10.1016/j.jclepro.2020.123771 |
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Idioma : |
Inglés |
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Notas : |
Article history:Received 11 October 2019/Received in revised form 23 July 2020/Accepted 15 August 2020/Available online 29 August 2020. Corresponding author: E-mail addresses: imacedo@inia.org.uy, macedoyapor@gmail.com (I. Macedo),lcarrasco@inia.org.uy (L. Carrasco-Letelier). |
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Contenido : |
Abstract:
Sustainable rice production systems are key to food security. Diversified farming systems are essential for ecological intensification and environmental enhancement. Energy use efficiency is one of the main sustainability indicators in agroecosystems. Thus, an assessment of consumption and efficiency of energy in contrasting cropping systems can discriminate their management practices and components sustainability. The goal of this study was to evaluate the energy performance through energy return on investment (EROI) in four rice-based rotation systems that belong to a long-term experiment located in the Temperate Grassland Terrestrial Ecoregion, at the Atlantic side of South America. Rotations analyzed consisted in: a) continuous rice (Rc); b) rice-soybean (R - S); c) rice-pasture for 1.5 years (R - PS); and, d) rice-pasture for 3.5 years (R - PL). The EROI estimations considered all the inputs and outputs of energy from cradle to farm gate. The greatest EROI was observed in ReS (7.2 MJ MJ-1) and the lowest energy consumption in R - PL (10,607 MJ ðha yrÞ-1). The R- PL?s EROI (6.7 MJ MJ-1) was 6.5% and 8% higher than Rc and R - PS EROI, respectively. Rotations without pastures produced 79% more energy compared with rotations including pastures. However, energy inputs of rice-pasture rotations were 40% lower than either R - S or Rc. The EROI (without animal production) of R- PS, ReS and Rc was 25%, 28% and 43% lower than the EROI of R - PL (10 MJ MJ-1), respectively. For the analyzed South American ecoregion, EROI assessments of four business as usual rice production systems allowed to discriminate and hierarchize their sustainability and diversity. MenosAbstract:
Sustainable rice production systems are key to food security. Diversified farming systems are essential for ecological intensification and environmental enhancement. Energy use efficiency is one of the main sustainability indicators in agroecosystems. Thus, an assessment of consumption and efficiency of energy in contrasting cropping systems can discriminate their management practices and components sustainability. The goal of this study was to evaluate the energy performance through energy return on investment (EROI) in four rice-based rotation systems that belong to a long-term experiment located in the Temperate Grassland Terrestrial Ecoregion, at the Atlantic side of South America. Rotations analyzed consisted in: a) continuous rice (Rc); b) rice-soybean (R - S); c) rice-pasture for 1.5 years (R - PS); and, d) rice-pasture for 3.5 years (R - PL). The EROI estimations considered all the inputs and outputs of energy from cradle to farm gate. The greatest EROI was observed in ReS (7.2 MJ MJ-1) and the lowest energy consumption in R - PL (10,607 MJ ðha yrÞ-1). The R- PL?s EROI (6.7 MJ MJ-1) was 6.5% and 8% higher than Rc and R - PS EROI, respectively. Rotations without pastures produced 79% more energy compared with rotations including pastures. However, energy inputs of rice-pasture rotations were 40% lower than either R - S or Rc. The EROI (without animal production) of R- PS, ReS and Rc was 25%, 28% and 43% lower than the EROI of R - PL (10 MJ MJ-1), respectivel... Presentar Todo |
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Palabras claves : |
AGROSISTEMAS INTEGRADOS; COVER CROPS; CROP ROTATION; CULTIVOS DE COBERTURA; INTEGRATED AGROECOSYSTEMS; LIFE CYCLE ASSESSMENT; PASTOS PERENNES; PERENNIAL PASTURE; ROTACION DE CULTIVOS. |
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Thesagro : |
ARROZ; RICE; SISTEMAS AGRICOLAS. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 03014naa a2200349 a 4500
001 1061311
005 2020-09-15
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1016/j.jclepro.2020.123771$2DOI
100 1 $aMACEDO, I.
245 $aRice-pasture agroecosystem intensification affects energy use efficiency.$h[electronic resource]
260 $c2020
300 $a10 p.
500 $aArticle history:Received 11 October 2019/Received in revised form 23 July 2020/Accepted 15 August 2020/Available online 29 August 2020. Corresponding author: E-mail addresses: imacedo@inia.org.uy, macedoyapor@gmail.com (I. Macedo),lcarrasco@inia.org.uy (L. Carrasco-Letelier).
520 $aAbstract: Sustainable rice production systems are key to food security. Diversified farming systems are essential for ecological intensification and environmental enhancement. Energy use efficiency is one of the main sustainability indicators in agroecosystems. Thus, an assessment of consumption and efficiency of energy in contrasting cropping systems can discriminate their management practices and components sustainability. The goal of this study was to evaluate the energy performance through energy return on investment (EROI) in four rice-based rotation systems that belong to a long-term experiment located in the Temperate Grassland Terrestrial Ecoregion, at the Atlantic side of South America. Rotations analyzed consisted in: a) continuous rice (Rc); b) rice-soybean (R - S); c) rice-pasture for 1.5 years (R - PS); and, d) rice-pasture for 3.5 years (R - PL). The EROI estimations considered all the inputs and outputs of energy from cradle to farm gate. The greatest EROI was observed in ReS (7.2 MJ MJ-1) and the lowest energy consumption in R - PL (10,607 MJ ðha yrÞ-1). The R- PL?s EROI (6.7 MJ MJ-1) was 6.5% and 8% higher than Rc and R - PS EROI, respectively. Rotations without pastures produced 79% more energy compared with rotations including pastures. However, energy inputs of rice-pasture rotations were 40% lower than either R - S or Rc. The EROI (without animal production) of R- PS, ReS and Rc was 25%, 28% and 43% lower than the EROI of R - PL (10 MJ MJ-1), respectively. For the analyzed South American ecoregion, EROI assessments of four business as usual rice production systems allowed to discriminate and hierarchize their sustainability and diversity.
650 $aARROZ
650 $aRICE
650 $aSISTEMAS AGRICOLAS
653 $aAGROSISTEMAS INTEGRADOS
653 $aCOVER CROPS
653 $aCROP ROTATION
653 $aCULTIVOS DE COBERTURA
653 $aINTEGRATED AGROECOSYSTEMS
653 $aLIFE CYCLE ASSESSMENT
653 $aPASTOS PERENNES
653 $aPERENNIAL PASTURE
653 $aROTACION DE CULTIVOS
700 1 $aTERRA, J.A.
700 1 $aSIRI-PRIETO, G.
700 1 $aVELAZCO, J.I.
700 1 $aCARRASCO-LETELIER, L.
773 $tJournal of Cleaner Production, Volume 278, 1 January 2021, 123771. Doi: https://doi.org/10.1016/j.jclepro.2020.123771
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