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| 1. |  | PORTUGAL, T. B.; SZYMCZAK, L. S.; DE MORAES, A.; FONSECA, L.; MEZZALIRA, J.C.; SAVIAN, J.V.; ZUBIETA, A. S.; BREMM, C.; DE FACCIO CARVALHO, P. C.; MONTEIRO, A. L. G. Low-intensity, high-frequency grazing strategy increases herbage production and beef cattle performance on sorghum pastures. Animals 2022, volume 12, number 1, 13 pages. OPEN ACCESS. doi: https://doi.org/10.3390/ani12010013 Article history: Received: 17 October 2021 / Revised: 8 November 2021 / Accepted: 10 November 2021 / Published: 22 December 2021 .| Biblioteca(s): INIA Treinta y Tres. |
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| 2. | | PORTUGAL, T. B.; DE FACCIO CARVALHO, P. C.; DE CAMPOS, B.M.; SZYMCZAK, L.S.; SAVIAN, J.V.; ZUBIETA, A.S.; DE SOUZA FILHO, W.; ROSSETTO, J.; BREMM, C.; DE OLIVEIRA, L.B.; DE MORAES, A.; BAYER, C.; GOMES MONTEIRO, A.L. Methane emissions and growth performance of beef cattle grazing multi-species swards in different pesticide-free integrated crop-livestock systems in southern Brazil. Journal of Cleaner Production, 15 August 2023, Volume 414, Article 137536. https://doi.org/10.1016/j.jclepro.2023.137536 Article history: Received 28 December 2022; Received in revised form 16 May 2023; Accepted 19 May 2023; Available online 22 May 2023. -- Correspondence author: Portugal, T.B.; Department of Crop Production and Protection, Federal...| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 2 | |
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 | Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
16/10/2018 |
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Actualizado : |
11/02/2019 |
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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 : |
BORGES, A.; GONZÁLEZ-REYMUNDEZ, A.; ERNST, O.; CADENAZZI, M.; TERRA, J.A.; GUTIÉRREZ, L. |
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Afiliación : |
ALEJANDRA BORGES, Departamento de Estadística. Facultad de Agronomía, UdelaR.; AGUSTÍN GONZÁLEZ-REYMUNDEZ, Departamento de Estadística. Facultad de Agronomía, UdelaR.; OSVALDO, ERNST, Departamento de Producción de Cultivos. EEMAC, Facultad de Agronomía, UdelaR.; MÓNICA CADENAZZI, Departamento de Estadística. Facultad de Agronomía, UdelaR.; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZ, Department of Agronomy, University of Wisconsin. |
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Título : |
Can spatial modeling substitute experimental design in agricultural experiments? |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Crop Science, 2018, v. 59, no. 1, p. 1-10. |
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DOI : |
10.2135/cropsci2018.03.0177 |
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Idioma : |
Inglés |
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Notas : |
Article history: Accepted paper, posted 10/05/18. Published online December, 13. 2018. |
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Contenido : |
Abstract:
One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effect of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of one hundred independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), alpha-lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design. MenosAbstract:
One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effect of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of one hundred independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), alpha-lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitut... Presentar Todo |
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Palabras claves : |
EFFICIENCY STATISTICS; EXPERIMENTAL DESIGN; FIELD VARIABILITY; SPATIAL MODELS; UNIFORMITY TRIAL. |
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Thesagro : |
DISENO ESTADISTICO; DISENO EXPERIMENTAL; MODELOS ESTADISTICOS; VARIABILIDAD. |
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Asunto categoría : |
U30 Métodos de investigación |
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Marc : |
LEADER 02512naa a2200313 a 4500
001 1059193
005 2019-02-11
008 2018 bl uuuu u00u1 u #d
024 7 $a10.2135/cropsci2018.03.0177$2DOI
100 1 $aBORGES, A.
245 $aCan spatial modeling substitute experimental design in agricultural experiments?$h[electronic resource]
260 $c2018
500 $aArticle history: Accepted paper, posted 10/05/18. Published online December, 13. 2018.
520 $aAbstract: One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effect of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of one hundred independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), alpha-lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design.
650 $aDISENO ESTADISTICO
650 $aDISENO EXPERIMENTAL
650 $aMODELOS ESTADISTICOS
650 $aVARIABILIDAD
653 $aEFFICIENCY STATISTICS
653 $aEXPERIMENTAL DESIGN
653 $aFIELD VARIABILITY
653 $aSPATIAL MODELS
653 $aUNIFORMITY TRIAL
700 1 $aGONZÁLEZ-REYMUNDEZ, A.
700 1 $aERNST, O.
700 1 $aCADENAZZI, M.
700 1 $aTERRA, J.A.
700 1 $aGUTIÉRREZ, L.
773 $tCrop Science, 2018$gv. 59, no. 1, p. 1-10.
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