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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
14/06/2018 |
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Actualizado : |
12/03/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Nacionales |
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Autor : |
HIRIGOYEN, A.; FRANCO, J.; DIÉGUEZ, U. |
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Afiliación : |
ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JORGE FRANCO, Universidad de la República (UdelaR)/ Facultad de Agronomía.; ULISES DIÉGUEZ, Universidad de Santiago de Compostela, Departamento de Ingeniería Agroforestal, Lugo, España. |
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Título : |
Modelo dinámico de rodal para Eucalyptus globulus (L.) en Uruguay. (Dynamic Stand Model for Eucalyptus globulus (L.) in Uruguay). |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Agrociencia Uruguay, v. 22(1): p. 63-80, 2018. |
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Idioma : |
Español |
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Notas : |
Article history: Recibido: 2016-11-17 // Aceptado: 2017-20-12 |
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Contenido : |
RESUMEN. Los modelos dinámicos a nivel de rodal son ampliamente usados en el ámbito forestal. Para su ajuste se emplean datos empíricos que se integran en un conjunto de ecuaciones que describen las relaciones entre diferentes variables. Las metodologías clásicas para desarrollar ecuaciones de transición invariantes con respecto al intervalo de simulación (path invariance) e invariantes respecto a la edad de referencia empleadas son algebraic difference approach (ADA) y generalized
algebraic difference approach (GADA). El objetivo de este trabajo fue desarrollar un modelo dinámico de rodal para Eucalyptus globulus, empleando ecuaciones de transición para área basal, altura media dominante y mortalidad, utilizando enfoque de variables dummy. Los datos utilizados provienen de 168 parcelas permanentes. Las ecuaciones evaluadas individualmente fueron luego ajustadas simultáneamente mediante seemingly unrelated regression (SUR). En base al análisis de bondad de
ajuste y de la capacidad predictiva, se seleccionó el modelo propuesto por Korf, modificado por Cieszewski (2004), para la altura media dominante; el modelo de Levakovic (Zeide, 1993), para el área basal y el modelo de Pienaar y Shiver (1981) para la mortalidad. El modelo de simulación desarrollado es más flexible y permite levantar algunas de las limitantes del modelo utilizado anteriormente. Su integración a sistemas de apoyo a la toma de decisiones constituirá una herramienta de
gran utilidad para la planificación y toma de decisiones en el sector forestal. SUMMARY. Stand level dynamic models are widely used in forestry. Fitting empirical data that is integrated into a set of equations is used to describe the relationships between different variables The classic methodologies to develop equations invariant with respect to transition simulation interval (path invariance) and invariant respect to the reference age are employed algebraic difference approach (ADA) and generalized algebraic difference approach (GADA). The aim of this study was to develop a dynamic model for Eucalyptus globulus stand using transition equations for basal area, dominant average height and mortality, using tle dummy variables approach. The data used are from 168 permanent plots. The equations evaluated individually were then adjusted simultaneously by seemingly unrelated regression (SUR). Based on the analysis of goodness of fit and predictive ability, the model proposed by Korf, as amended by Cieszewski (2004), for the dominant average height was selected; Levakovic model (Zeide, 1993) for the basal area and the model Pienaar and Shiver (1981) for mortality. The simulation model developed is more flexible and can lift some of the limitations of the model used previously. Its integration into a decision support system (DSS), constitute a useful tool for planning and decision making in the forestry sector. MenosRESUMEN. Los modelos dinámicos a nivel de rodal son ampliamente usados en el ámbito forestal. Para su ajuste se emplean datos empíricos que se integran en un conjunto de ecuaciones que describen las relaciones entre diferentes variables. Las metodologías clásicas para desarrollar ecuaciones de transición invariantes con respecto al intervalo de simulación (path invariance) e invariantes respecto a la edad de referencia empleadas son algebraic difference approach (ADA) y generalized
algebraic difference approach (GADA). El objetivo de este trabajo fue desarrollar un modelo dinámico de rodal para Eucalyptus globulus, empleando ecuaciones de transición para área basal, altura media dominante y mortalidad, utilizando enfoque de variables dummy. Los datos utilizados provienen de 168 parcelas permanentes. Las ecuaciones evaluadas individualmente fueron luego ajustadas simultáneamente mediante seemingly unrelated regression (SUR). En base al análisis de bondad de
ajuste y de la capacidad predictiva, se seleccionó el modelo propuesto por Korf, modificado por Cieszewski (2004), para la altura media dominante; el modelo de Levakovic (Zeide, 1993), para el área basal y el modelo de Pienaar y Shiver (1981) para la mortalidad. El modelo de simulación desarrollado es más flexible y permite levantar algunas de las limitantes del modelo utilizado anteriormente. Su integración a sistemas de apoyo a la toma de decisiones constituirá una herramienta de
gran utilidad para la planificación y tom... Presentar Todo |
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Palabras claves : |
ADA; DYNAMIC EQUATIONS; ECUACIONES DINÁMICAS; EUCALYPTUS GLOBULUS; GADA; SUR. |
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Thesagro : |
FORESTACIÓN. |
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Asunto categoría : |
K10 Producción forestal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/10262/1/Agrociencia-2018-Hirigoyen.pdf
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Marc : |
LEADER 03638naa a2200241 a 4500
001 1058711
005 2021-03-12
008 2018 bl uuuu u00u1 u #d
100 1 $aHIRIGOYEN, A.
245 $aModelo dinámico de rodal para Eucalyptus globulus (L.) en Uruguay. (Dynamic Stand Model for Eucalyptus globulus (L.) in Uruguay).
260 $c2018
500 $aArticle history: Recibido: 2016-11-17 // Aceptado: 2017-20-12
520 $aRESUMEN. Los modelos dinámicos a nivel de rodal son ampliamente usados en el ámbito forestal. Para su ajuste se emplean datos empíricos que se integran en un conjunto de ecuaciones que describen las relaciones entre diferentes variables. Las metodologías clásicas para desarrollar ecuaciones de transición invariantes con respecto al intervalo de simulación (path invariance) e invariantes respecto a la edad de referencia empleadas son algebraic difference approach (ADA) y generalized algebraic difference approach (GADA). El objetivo de este trabajo fue desarrollar un modelo dinámico de rodal para Eucalyptus globulus, empleando ecuaciones de transición para área basal, altura media dominante y mortalidad, utilizando enfoque de variables dummy. Los datos utilizados provienen de 168 parcelas permanentes. Las ecuaciones evaluadas individualmente fueron luego ajustadas simultáneamente mediante seemingly unrelated regression (SUR). En base al análisis de bondad de ajuste y de la capacidad predictiva, se seleccionó el modelo propuesto por Korf, modificado por Cieszewski (2004), para la altura media dominante; el modelo de Levakovic (Zeide, 1993), para el área basal y el modelo de Pienaar y Shiver (1981) para la mortalidad. El modelo de simulación desarrollado es más flexible y permite levantar algunas de las limitantes del modelo utilizado anteriormente. Su integración a sistemas de apoyo a la toma de decisiones constituirá una herramienta de gran utilidad para la planificación y toma de decisiones en el sector forestal. SUMMARY. Stand level dynamic models are widely used in forestry. Fitting empirical data that is integrated into a set of equations is used to describe the relationships between different variables The classic methodologies to develop equations invariant with respect to transition simulation interval (path invariance) and invariant respect to the reference age are employed algebraic difference approach (ADA) and generalized algebraic difference approach (GADA). The aim of this study was to develop a dynamic model for Eucalyptus globulus stand using transition equations for basal area, dominant average height and mortality, using tle dummy variables approach. The data used are from 168 permanent plots. The equations evaluated individually were then adjusted simultaneously by seemingly unrelated regression (SUR). Based on the analysis of goodness of fit and predictive ability, the model proposed by Korf, as amended by Cieszewski (2004), for the dominant average height was selected; Levakovic model (Zeide, 1993) for the basal area and the model Pienaar and Shiver (1981) for mortality. The simulation model developed is more flexible and can lift some of the limitations of the model used previously. Its integration into a decision support system (DSS), constitute a useful tool for planning and decision making in the forestry sector.
650 $aFORESTACIÓN
653 $aADA
653 $aDYNAMIC EQUATIONS
653 $aECUACIONES DINÁMICAS
653 $aEUCALYPTUS GLOBULUS
653 $aGADA
653 $aSUR
700 1 $aFRANCO, J.
700 1 $aDIÉGUEZ, U.
773 $tAgrociencia Uruguay$gv. 22(1): p. 63-80, 2018.
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INIA Tacuarembó (TBO) |
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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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