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| Registros recuperados : 9 | |
| 1. |  | ESPIGOLAN, R.; BALDI, F.; BANCHERO, G.; BRITO, G.; LA MANNA, A.; ALBUQUERQUE, L.G. Aplicação de modelos não-lineares para o ajuste de características. REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 47., 2010, Salvador, BA. Empreendedorismo e Progresso Científicos na Zootecnia
Brasileira de Vanguarda Brasília, DF: Sociedade Brasileira de Zootecnia: UEM, 2010. Anais| Biblioteca(s): INIA La Estanzuela. |
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| 2. | | ESPIGOLAN, R.; BALDI, F.; BANCHERO, G.; BRITO, G.; LA MANNA, A.; ALBUQUERQUE, L.G. Efeito da estratégia de crescimento sobre as características de carcaça e da carne em novilhos da raça Hereford. REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 47., 2010, Salvador, BA. Empreendedorismo e Progresso Científicos na Zootecnia
Brasileira de Vanguarda Brasília, DF: Sociedade Brasileira de Zootecnia: UEM, 2010. Anais| Biblioteca(s): INIA La Estanzuela. |
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| 3. | | ESPIGOLAN, R.; BALDI, F.; BOLIGON, A.A.; BANCHERO, G.; BRITO, G.; LA MANNA, A.; MONTOSSI, F.; FERNANDEZ, E.; ALBUQUERQUE, L.G. DE Aplicação de modelos não-lineares para descrever a evolução de características de crescimento e carcaça em bovinos da raça Hereford. [Application of nonlinear models to describe the evolution of growth and carcass traits in hereford cattle.] Ciência Rural, Santa Maria, v. 43, n. 3, p. 513-519, 2013. Article history: Recebido para publicação 16 agosto 2012 / Aprovado em 25 setembro 2012 / Devolvido pelo autor 21 novembro 2012 / CR-2012-0685.| Biblioteca(s): INIA La Estanzuela; INIA Tacuarembó. |
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| 4. | | PESSOA, D. A. N.; SILVA, L. C. A.; MENDONÇA, F. S.; ALMEIDA, V. M.; LOPES, J. R. G.; ALBUQUERQUE, L. G.; SILVA, A. .; RIET-CORREA, F. Evaluation of resistance to natural poisoning by Amorimia septentrionalis in goats which had received sodium monofluoroacetate degrading bacteria. [Avaliação da resistência à intoxicação natural por Amorimia septentrionalis em caprinos que receberam bactérias degradadoras de monofluoroacetato de sódio.] Pesquisa Veterinaria Brasileira 38(10):1913-1917, 1 October 2018. OPEN ACCESS Article history: Received on June 5, 2018. // Accepted for publication on June 13, 2018.| Biblioteca(s): INIA La Estanzuela. |
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| 6. | | TORO OSPINA, A.M.; AGUILAR, I.; VARGAS DE OLIVEIRA, M.H.; CRUZ DOS SANTOS CORREIA, L. E.; VERCESI FILHO, A. E.; ALBUQUERQUE, L.G.; JOSINEUDSON AUGUSTO II DE VASCONCELOS SILVA Assessing the accuracy of imputation in the Gyr breed using different SNP panels. Genome, 2021 Oct, Volume 64 , Issue 10, pag 893-899. Open Acces. Doi: https://doi.org/10.1139/gen-2020-0081 Article history: Received 22 May 2020./ Accepted 17 April 2021. Corresponding author: Alejandra Maria Toro Ospina (email: toroospina92@gmail.com)| Biblioteca(s): INIA La Estanzuela. |
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| 7. | | LEMOS, M.V.A.; CHIAIA, H.L.J.; BERTON, M.P.; FEITOSA, F.L.B.; ABOUJAOUD, C.; CAMARGO, G.M.F.; PEREIRA, A.S.C.; ALBUQUERQUE, L.G.; FERRINHO, A.M.; MUELLER, L.F.; MAZALLI, M.R.; FURLAN, J.J.M.; CARVALHEIRO, R.; GORDO, D.M.; TONUSSI, R.; ESPIGOLAN, R.; SILVA, R.M.O.; DE OLIVEIRA, H.N.; DUCKETT, S.; AGUILAR, I.; BALDI, F. Genome-wide association between single nucleotide polymorphisms with beef fatty acid profile in Nellore cattle using the single step procedure. BMC Genomics, 2016, v. 17:213. Received: 19 November 2015 / Accepted: 23 February 2016 /Published: 9 March 2016| Biblioteca(s): INIA Las Brujas. |
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| 8. | | CHIAIA, H.L.J.; PERIPOLLI, E.; DE OLIVEIRA SILVA, R.M.; FEITOSA, F.L.B.; DE LEMOS, M.V.A.; BERTON, M.P.; OLIVIERI, B.F.; ESPIGOLAN, R.; TONUSSI, R.L.; GORDO, D.G.M.; DE ALBUQUERQUE, L.G.; DE OLIVEIRA, H.N.; FERRINHO, A.M.; MUELLER, L.F.; KLUSKA, S.; TONHATI, H.; PEREIRA, A.S.C.; AGUILAR, I.; BALDI, F. Genomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes. Journal of Applied Genetics, 1 November 2018, volume 59, Issue 4, pages 493-501. Article history: Received: 15 May 2018 // Revised: 28 August 2018 // Accepted: 17 September 2018.| Biblioteca(s): INIA Las Brujas. |
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| 9. | | FEITOSA, F. L. B.; OLIVIERI, B. F.; ABOUJAOUDE, C.; PEREIRA, A. S. C.; DE LEMOS, M. V. A.; CHIAIA, H. L. J.; BERTON, M. P.; PERIPOLLI, E.; FERRINHO, A. M.; MUELLER, L. F.; MAZZALI, M. R.; DE ALBUQUERQUE, L. G.; DE OLIVERA, H. N.; TONHATI, H.; ESPIGOLAN, R.; TONUSSI, R. L.; DE OLIVIERA SILVA, R. M.; GORDO, D. G. M.; MAGALHAES, A. F. B.; AGUILAR, I.; BALDI, F. S. B. Genetic correlation estimates between beef fatty acid profile with meat and carcass traits in Nellore cattle finished in feedlot. Journal of Applied Genetics, 2017, 58 (1), 123-132. Article history: Received: 15 December 2015 /Revised: 10 March 2016 /Accepted: 5 July 2016 / Published Online: 30 July 2016| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 9 | |
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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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