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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
21/02/2014 |
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Actualizado : |
02/08/2017 |
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Tipo de producción científica : |
Artículos en Revistas Agropecuarias |
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Autor : |
CUADRO, R.; MOLFINO, J.H.; QUINCKE, A. |
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Afiliación : |
WASHINGTON ROBIN CUADRO LOPEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Ing. Agr. Consultor Independiente.; Ing. Agr. Consultor Independiente. |
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Título : |
Sistema de soporte para la recomendación de fertilización fosfatada en pasturas. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Agro temario, 2017, v. 16, no. 64, p. 4-5. |
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Páginas : |
p. 4-5 |
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Idioma : |
Español |
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Contenido : |
La competitividad de la producción de carne y leche de Uruguay se basa en gran medida en la rentabilidad del sistema pastoril. A su vez, es ampliamente conocido que se necesita
contar con especies leguminosas forrajeras para aumentar la producción de forraje, tanto en cantidad como en calidad, y así lograr intensificar la producción pecuaria (Millot et al., 1987). Sin embargo, los suelos del Uruguay son naturalmente deficientes en su capacidad de suministro de fósforo (P) (Morón, 2008, Barbazán et al., 2007). Por lo tanto, el fertilizante fosfatado es un insumo central en pasturas a base de leguminosas. Varios autores nacionales han revisado y resumido la trayectoria de experimentación e investigación sobre el tema (Morón, 2007; Bordoli, 2007). En general, se reconoce que las pautas y recomendaciones vigentes presentan limitantes para implementar un adecuado manejo de la fertilización fosfatada en forma generalizada a nivel de país. El objetivo general es desarrollar un sistema de soporte para la recomendación de fertilización de pasturas. |
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Thesagro : |
FERTILIZACION FOSFATADA; PASTURA. |
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Asunto categoría : |
A50 Investigación agraria |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/7154/1/Cuadro-Quincke-Sistema-soporte-fertilizacion-P.pdf
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Marc : |
LEADER 01559naa a2200181 a 4500
001 1028633
005 2017-08-02
008 2017 bl uuuu u00u1 u #d
100 1 $aCUADRO, R.
245 $aSistema de soporte para la recomendación de fertilización fosfatada en pasturas.
260 $c2017
300 $ap. 4-5
520 $aLa competitividad de la producción de carne y leche de Uruguay se basa en gran medida en la rentabilidad del sistema pastoril. A su vez, es ampliamente conocido que se necesita contar con especies leguminosas forrajeras para aumentar la producción de forraje, tanto en cantidad como en calidad, y así lograr intensificar la producción pecuaria (Millot et al., 1987). Sin embargo, los suelos del Uruguay son naturalmente deficientes en su capacidad de suministro de fósforo (P) (Morón, 2008, Barbazán et al., 2007). Por lo tanto, el fertilizante fosfatado es un insumo central en pasturas a base de leguminosas. Varios autores nacionales han revisado y resumido la trayectoria de experimentación e investigación sobre el tema (Morón, 2007; Bordoli, 2007). En general, se reconoce que las pautas y recomendaciones vigentes presentan limitantes para implementar un adecuado manejo de la fertilización fosfatada en forma generalizada a nivel de país. El objetivo general es desarrollar un sistema de soporte para la recomendación de fertilización de pasturas.
650 $aFERTILIZACION FOSFATADA
650 $aPASTURA
700 1 $aMOLFINO, J.H.
700 1 $aQUINCKE, A.
773 $tAgro temario, 2017$gv. 16, no. 64, p. 4-5.
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Registro original : |
INIA Tacuarembó (TBO) |
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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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