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| 99. |  | MÉNDEZ, R.; DEAMBROSI, E.; BLANCO, P.H.; SALDAIN, N.E.; PÉREZ DE VIDA, F.; GAGGERO, M.; LAVECCHIA, A.; MENDEZ, J.; MARCHESI, C. Reducción de laboreo y siembra directa en el cultivo de arroz Montevideo (Uruguay): INIA, 2001. 17 p. (INIA Serie Técnica ; 122)| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó; INIA Treinta y Tres. |
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| 100. | | MÉNDEZ, R.; DEAMBROSI, E.; BLANCO, P.H.; SALDAIN, N.E.; PÉREZ DE VIDA, F.; GAGGERO, M.; LAVECCHIA, A.; MENDEZ, J.; MARCHESI, C. Reducción de laboreo y siembra directa en el cultivo de arroz [País Agropecuario 79]. El País Agropecuario, 2001, v. 7, no. 79, p. 25-28.| Biblioteca(s): INIA Tacuarembó. |
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| Registros recuperados : 111 | |
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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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