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| 72. | | LAGOMARSINO, X.; LUZARDO, S.; MONTOSSI, F.; MEDEROS, A.; BOTTERO, S.; MARTÍNEZ, Y.; CARRACELAS, E.; ZAMIT, W.; BENTANCURT, M. Suplementación infrecuente con afrechillo de arroz a terneros pastoreando praderas mejoradas: ¿Es necesario suplementar todos los dias? ln: INIA Tacuarembó. Unidad Experimental Glencoe. Día de campo, setiembre 2011, Paysandú, Uruguay. Propuestas tecnológicas para el incremento de la productividad, la valorización y el ingreso económico para sistemas ganaderos de basalto. Tacuarembó (Uruguay): INIA, 2011. p. 27-30 (INIA Serie Actividades de Difusión ; 657)| Biblioteca(s): INIA Tacuarembó. |
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| 73. | | CAZZULI, F.; BREMM, C.; JAURENA, M.; POPPI, D.; DURANTE, M.; BENVENUTTI, M.A.; SAVIAN, J.V.; DEVINCENZI, T.; ROVIRA, P.J.; LAGOMARSINO, X.; HIRIGOYEN, A.; LATTANZI, F. The defoliation dynamics of a stockpiled native grassland pasture follow similar patterns between supplemented and unsupplemented beef calves. Grass and Forage Science, 2023, Vol.78, Issue 2, pages 306-316. doi: https://doi.org/10.1111/gfs.12608 Article history: Received 24 November 2022; Revised 14 March 2023; Accepted 15 March 2023. -- Correspondence author: Cazzuli, F.; Instituto Nacional de Investigación Agropecuaria (INIA), Programa Pasturas y Forrajes, Estación...| Biblioteca(s): INIA Las Brujas. |
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| 75. | | BALDI, F.; BANCHERO, G.; LA MANNA, A.; FERNÁNDEZ, E.; PÉREZ, E.; BRITO, G.; LAGOMARSINO, X.; SAN JULIAN, R.; LUZARDO, S.; DEL CAMPO, M.; MONTOSSI, F. Efecto del manejo nutricional post-destete y durante el período de terminación sobre las características de crecimiento, calidad de la canal y la carne. ln: INIA TACUAREMBÓ. INSTITUTO NACIONAL DE CARNES. Calidad de carnes. SEMINARIO DE ACTUALIZACIÓN TÉCNICA, 20-21 de setiembre, Tacuarembó, 2010. Montevideo (Uruguay): INIA, 2010. Módulo A| Biblioteca(s): INIA Tacuarembó. |
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| 76. | | CAZZULI, F.; JAURENA, M.; DURANTE, M.; CARDOZO, G.; RUGGIA, A.; BREMM, C.; LAGOMARSINO, X.; CUADRO, R.; ROVIRA, P.J.; POPPI, D.P.; LATTANZI, F. The final winter sward height of a native grassland pasture affects the quantity of the green biomass in spring. [abstract]. In: AUPA, Proceedings del VII Congreso Uruguayo de Producción Animal. Sección Cambio Climático y Producción Sostenible (Climate Change Section), 14 y 15 diciembre 2021. Archivos Latinoamericanos de Producción Animal, 29(Supl.1), p.2. (Archivos Latinoamericanos de Producción Animal, Vol.29, Supl.1) Corresponding author: F. Cazzulli, INIA Tacuarembó, Ruta 5 km 386, Tacuarembó, Uruguay. mailto:fcazzuli@inia.org.uy| Biblioteca(s): INIA Las Brujas. |
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| 77. | | JAURENA, M.; DE BARBIERI, I.; LAGOMARSINO, X.; LIMA, G.; PIÑEIRO, A.; LIMA, D.; SUÁREZ, M.; MEROLA, R.; GUTIERREZ, D.; ROVIRA, F. Impacto de la asignación de pastura y nivel de suplementación en la sustentabilidad de pasturas naturales sobre suelos superficiales de basalto. ln: INIA TACUAREMBÓ. ESTACIÓN EXPERIMENTAL GLENCOE. Día de campo. Producción animal, pasturas. Estación Experimental Glencoe, Paysandú, 14 noviembre, 2006. Tacuarembó (Uruguay): INIA, 2006. p. 5-6 (INIA Serie Actividades de Difusión ; 473)| Biblioteca(s): INIA Tacuarembó. |
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| 78. | | JAURENA, M.; AYALA, W.; TERRA, J.A.; BERMÚDEZ, R.; BARRIOS, E.; LAGOMARSINO, X.; CORREA, J.; AVILA, P.; SALVO, L.; HERNÁNDEZ, J. Impactos de la fertilización fosfatada y la inclusión de leguminosas en pasturas. ln: Día de Campo, 2010, Treinta y Tres, Uruguay Día de Campo. Treinta y Tres (Uruguay): INIA, 2010. p. 11-13 INIA Treinta y Tres. Unidad Experimental Palo a Pique (UEPP)| Biblioteca(s): INIA Tacuarembó. |
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| 79. | | CAZZULI, F.; DURANTE, M.; HIRIGOYEN, A.; SÁNCHEZ, J.; ROVIRA, P.J.; BERETTA, V.; SIMEONE, A.; JAURENA, M.; SAVIAN, J.V.; POPPI, D.; MONTOSSI, F.; LAGOMARSINO, X.; LUZARDO, S.; BRITO, G.; VELAZCO, J.I.; LATTANZI, F.; BREMM, C. Beef cattle grazing native grasslands may follow three different supplement response patterns. Grasses. 2023, Volume 2, Issue 3, pages 168-184. https://doi.org/10.3390/grasses2030014 --- OPEN ACCESS. Article history: Received 3 May 2023; Revised 1 July 2023; Accepted 13 July 2023; Published 7 August 2023. -- Academic Editor: Fabio Gresta. -- FUNDING: This research was funded by INIA Uruguay. -- LICENSE: This article is an open access...| Biblioteca(s): INIA Las Brujas. |
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| 80. | | PITTALUGA, O.; BRITO, G.; SOARES DE LIMA, J.M.; DEL CAMPO, M.; ZAMIT, W.; DA CUÑA, K.; PIÑEIRO, J.; PIÑEIRO, A.; LAGOMARSINO, X.; OLIVERA, J.; TRINDADE, G.; ARRIETA, G.; MOREIRA, R. Efecto de diferentes dietas sobre el crecimiento animal, el rendimiento carnicero y la calidad de la carne. ln: INIA TACUAREMBÓ. ESTACIÓN EXPERIMENTAL GLENCOE. Día de campo. Producción animal, pasturas y forestal. Estación Experimental Glencoe, Paysandú, octubre, 2005. Tacuarembó (Uruguay): INIA, 2005. p. 43-50 (INIA Serie Actividades de Difusión ; 431)| Biblioteca(s): INIA Tacuarembó. |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
18/01/2022 |
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Actualizado : |
18/01/2022 |
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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 : |
RIZZO, G.; MAZZILLI, S.R.; ERNST, O.; BAETHGENI, W.E.; BERGER, A. |
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Afiliación : |
GONZALO RIZZO, Departamento de Producción Vegetal, Facultad de Agronomía, Estación Experimental Mario Alberto Cassinoni, Universidad de la República, Paysandú, Uruguay; SEBASTIAN R. MAZZILLI, Departamento de Producción Vegetal, Facultad de Agronomía, Estación Experimental Mario Alberto Cassinoni, Universidad de la República, Paysandú, Uruguay; OSWALDO ERNST, Departamento de Producción Vegetal, Facultad de Agronomía, Estación Experimental Mario Alberto Cassinoni, Universidad de la República, Paysandú, Uruguay; WALTER E. BAETHGEN, International Research Institute for Climate and Society (IRI), Columbia University, 61 Route 9W, Palisades 9, 10964, NY, United States; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Season-specific management strategies for rainfed soybean in the South American Pampas based on a seasonal precipitation forecast. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Agricultural Systems, 2022, volume 19, Article number 103331. doi: https://doi.org/10.1016/j.agsy.2021.103331 |
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ISSN : |
0308-521X |
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DOI : |
10.1016/j.agsy.2021.103331 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 8 September 2021; Received in revised form 16 November 2021; Accepted 17 November 2021; Available online 25 November 2021.
Editor: Guillaume Martin.
Corresponding author: Rizzo, G.; Departamento de Producción Vegetal, Facultad de Agronomía, Estación Experimental Mario Alberto Cassinoni, Universidad de la República, Paysandú, Uruguay; email:grizzo2@huskers.unl.edu --
This project was partially funded on a fellowship from the Instituto Nacional de Investigaci?n Agropecuaria de Uruguay (INIA) granted to the senior author, and by the INIA project #INIA_CS_48_0_00. Authors wish to thank Deborah Gasso and Cristina Capurro (INIA) for providing access to their field experiment data to evaluate crop model performance. We also acknowledge Mariana Hill, Carlos Clerici, and Martin Dell'Aqua (Ministry of Livestock, Agriculture, and Fisheries of Uruguay) for providing soil and cropping sequence data. We are grateful to FUCREA for providing access to the farmers' database. |
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Contenido : |
ABSTRACT. - CONTEXT Global climate change is resulting in more frequent and more damaging extreme events affecting the performance of production systems. It is imperative to develop good season-specific crop management recommendations to help farmers to improve their adaptive capacity to a changing climate one season at a time. OBJECTIVE: We aimed to evaluate the skill of the International Research Institute for Climate and Society (IRI) seasonal precipitation forecasts and the interaction between the forecasted seasonal precipitation scenarios and management practices for rainfed soybean cropping systems using a crop simulation model. METHODS: We used a crop simulation model (CROPGRO-Soybean) coupled with weather data to assess the potential use of the IRI seasonal precipitation forecasts as a tool to optimize season-specific management strategies for rainfed soybean in Uruguay. We used a total of 620?668 IRI seasonal precipitation forecasts released from 2003 to 2016 for each of the five weather stations located in the main soybean producing area. The analysis was performed for two soybean cropping systems (i.e., sown as a single crop or as double-cropped soybean), for which we considered combinations of sowing dates and maturity groups (11 sowing dates × 3 maturity groups combinations for each soybean cropping system). RESULTS AND CONCLUSIONS: The IRI seasonal precipitation forecasts were able to successfully forecast below-normal precipitation scenarios in 77% of the total predictions developed for this scenario considering all weather stations during the study period (2003?2016), while it was less accurate in forecasting above-normal precipitation scenarios (60% of success). We found that earlier sowing dates were a better strategy for years when an above-normal precipitation forecast was released for the December?January-February period (4.7 Mg ha−1 average seed yield). In contrast, delayed sowing dates were more appropriate for below-normal precipitation forecasts (3.7 Mg ha−1 average seed yield). Applying season-specific management practices farmers could potentially increase their soybean yields by up to 0.6 and 1.6 Mg ha−1, in years with below- or above-normal forecasted precipitations, respectively. The benefit of season-specific management will depend on the interaction among all management practices, the effective capacity of farmers to implement it, and the risk profile the farmer adopts and it is exposed to. SIGNIFICANCE: Here we built a novel approach to assess the impact of considering seasonal precipitation forecasts for optimizing crop production. This assessment provided insights on how farmers can use seasonal precipitation forecasts to optimize rainfed soybean yield for a specific cropping season.
© 2021 Elsevier Ltd MenosABSTRACT. - CONTEXT Global climate change is resulting in more frequent and more damaging extreme events affecting the performance of production systems. It is imperative to develop good season-specific crop management recommendations to help farmers to improve their adaptive capacity to a changing climate one season at a time. OBJECTIVE: We aimed to evaluate the skill of the International Research Institute for Climate and Society (IRI) seasonal precipitation forecasts and the interaction between the forecasted seasonal precipitation scenarios and management practices for rainfed soybean cropping systems using a crop simulation model. METHODS: We used a crop simulation model (CROPGRO-Soybean) coupled with weather data to assess the potential use of the IRI seasonal precipitation forecasts as a tool to optimize season-specific management strategies for rainfed soybean in Uruguay. We used a total of 620?668 IRI seasonal precipitation forecasts released from 2003 to 2016 for each of the five weather stations located in the main soybean producing area. The analysis was performed for two soybean cropping systems (i.e., sown as a single crop or as double-cropped soybean), for which we considered combinations of sowing dates and maturity groups (11 sowing dates × 3 maturity groups combinations for each soybean cropping system). RESULTS AND CONCLUSIONS: The IRI seasonal precipitation forecasts were able to successfully forecast below-normal precipitation scenarios in 77% of the tot... Presentar Todo |
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Palabras claves : |
Agricultural management; Climate change; Crop production; Cropping practice; Cropping system; El Niño/southern oscillation; Glycine max; Maturity group; Precipitation (climatology); Seasonal variation; Soybean; Strategic approach. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 04915naa a2200349 a 4500
001 1062640
005 2022-01-18
008 2022 bl uuuu u00u1 u #d
022 $a0308-521X
024 7 $a10.1016/j.agsy.2021.103331$2DOI
100 1 $aRIZZO, G.
245 $aSeason-specific management strategies for rainfed soybean in the South American Pampas based on a seasonal precipitation forecast.$h[electronic resource]
260 $c2022
500 $aArticle history: Received 8 September 2021; Received in revised form 16 November 2021; Accepted 17 November 2021; Available online 25 November 2021. Editor: Guillaume Martin. Corresponding author: Rizzo, G.; Departamento de Producción Vegetal, Facultad de Agronomía, Estación Experimental Mario Alberto Cassinoni, Universidad de la República, Paysandú, Uruguay; email:grizzo2@huskers.unl.edu -- This project was partially funded on a fellowship from the Instituto Nacional de Investigaci?n Agropecuaria de Uruguay (INIA) granted to the senior author, and by the INIA project #INIA_CS_48_0_00. Authors wish to thank Deborah Gasso and Cristina Capurro (INIA) for providing access to their field experiment data to evaluate crop model performance. We also acknowledge Mariana Hill, Carlos Clerici, and Martin Dell'Aqua (Ministry of Livestock, Agriculture, and Fisheries of Uruguay) for providing soil and cropping sequence data. We are grateful to FUCREA for providing access to the farmers' database.
520 $aABSTRACT. - CONTEXT Global climate change is resulting in more frequent and more damaging extreme events affecting the performance of production systems. It is imperative to develop good season-specific crop management recommendations to help farmers to improve their adaptive capacity to a changing climate one season at a time. OBJECTIVE: We aimed to evaluate the skill of the International Research Institute for Climate and Society (IRI) seasonal precipitation forecasts and the interaction between the forecasted seasonal precipitation scenarios and management practices for rainfed soybean cropping systems using a crop simulation model. METHODS: We used a crop simulation model (CROPGRO-Soybean) coupled with weather data to assess the potential use of the IRI seasonal precipitation forecasts as a tool to optimize season-specific management strategies for rainfed soybean in Uruguay. We used a total of 620?668 IRI seasonal precipitation forecasts released from 2003 to 2016 for each of the five weather stations located in the main soybean producing area. The analysis was performed for two soybean cropping systems (i.e., sown as a single crop or as double-cropped soybean), for which we considered combinations of sowing dates and maturity groups (11 sowing dates × 3 maturity groups combinations for each soybean cropping system). RESULTS AND CONCLUSIONS: The IRI seasonal precipitation forecasts were able to successfully forecast below-normal precipitation scenarios in 77% of the total predictions developed for this scenario considering all weather stations during the study period (2003?2016), while it was less accurate in forecasting above-normal precipitation scenarios (60% of success). We found that earlier sowing dates were a better strategy for years when an above-normal precipitation forecast was released for the December?January-February period (4.7 Mg ha−1 average seed yield). In contrast, delayed sowing dates were more appropriate for below-normal precipitation forecasts (3.7 Mg ha−1 average seed yield). Applying season-specific management practices farmers could potentially increase their soybean yields by up to 0.6 and 1.6 Mg ha−1, in years with below- or above-normal forecasted precipitations, respectively. The benefit of season-specific management will depend on the interaction among all management practices, the effective capacity of farmers to implement it, and the risk profile the farmer adopts and it is exposed to. SIGNIFICANCE: Here we built a novel approach to assess the impact of considering seasonal precipitation forecasts for optimizing crop production. This assessment provided insights on how farmers can use seasonal precipitation forecasts to optimize rainfed soybean yield for a specific cropping season. © 2021 Elsevier Ltd
653 $aAgricultural management
653 $aClimate change
653 $aCrop production
653 $aCropping practice
653 $aCropping system
653 $aEl Niño/southern oscillation
653 $aGlycine max
653 $aMaturity group
653 $aPrecipitation (climatology)
653 $aSeasonal variation
653 $aSoybean
653 $aStrategic approach
700 1 $aMAZZILLI, S.R.
700 1 $aERNST, O.
700 1 $aBAETHGENI, W.E.
700 1 $aBERGER, A.
773 $tAgricultural Systems, 2022, volume 19, Article number 103331. doi: https://doi.org/10.1016/j.agsy.2021.103331
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