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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
01/12/2021 |
Actualizado : |
01/12/2021 |
Tipo de producción científica : |
Actividades de Difusión |
Autor : |
CARRA, B.; DINI, M. (Ed.). |
Afiliación : |
BRUNO CARRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MAXIMILIANO ANTONIO DINI VIÑOLY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Seminario de actualización técnica en frutales de pepita. (Ciclo Destacadas INIA 2021). |
Complemento del título : |
Seminario técnico. Las Brujas, Canelones (UY), 22 y 29 julio 2021. |
Fecha de publicación : |
2021 |
Fuente / Imprenta : |
Canelones (UY): INIA, 2021. |
Páginas : |
70 p. |
Serie : |
(Serie Actividades de Difusión ; 798) |
ISSN : |
1688-9258 |
Idioma : |
Español |
Notas : |
Institución Organizadora: INIA; Instituciones participantes: Universidad de la República, Facultad de Agronomía, IRTA, MGAP-Dirección Nacional de la Granja, MGAP-Dirección General de Servicios Agrícolas.
Comité Organizador: Maximiliano Dini, Bruno Carra, Danilo Cabrera, Valentina Mujica, Natalia Martínez, Mónica Trujillo, Irvin Rodríguez. |
Contenido : |
CONTENIDO - Módulo clima -- Módulo manejo -- Módulo cultivares, portainjertos y sistemas de conducción -- Módulo Manejo regional de plagas -- |
Palabras claves : |
FRUTALES DE HOJA CADUCA. |
Thesagro : |
FITOMEJORAMIENTO; FRUTALES; FRUTALES DE HUESO; FRUTALES DE PEPITA; FRUTICULTURA; MANEJO DEL CULTIVO; MANZANA; PERA. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16125/1/SAD-798-frutales-pepita-2021-nov.pdf
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Marc : |
LEADER 01254nam a2200277 a 4500 001 1062557 005 2021-12-01 008 2021 bl uuuu u00u1 u #d 022 $a1688-9258 100 1 $aCARRA, B. 245 $aSeminario de actualización técnica en frutales de pepita. (Ciclo Destacadas INIA 2021).$h[electronic resource] 260 $aCanelones (UY): INIA$c2021 300 $a70 p. 490 $a(Serie Actividades de Difusión ; 798) 500 $aInstitución Organizadora: INIA; Instituciones participantes: Universidad de la República, Facultad de Agronomía, IRTA, MGAP-Dirección Nacional de la Granja, MGAP-Dirección General de Servicios Agrícolas. Comité Organizador: Maximiliano Dini, Bruno Carra, Danilo Cabrera, Valentina Mujica, Natalia Martínez, Mónica Trujillo, Irvin Rodríguez. 520 $aCONTENIDO - Módulo clima -- Módulo manejo -- Módulo cultivares, portainjertos y sistemas de conducción -- Módulo Manejo regional de plagas -- 650 $aFITOMEJORAMIENTO 650 $aFRUTALES 650 $aFRUTALES DE HUESO 650 $aFRUTALES DE PEPITA 650 $aFRUTICULTURA 650 $aMANEJO DEL CULTIVO 650 $aMANZANA 650 $aPERA 653 $aFRUTALES DE HOJA CADUCA 700 1 $aDINI, M.
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INIA Las Brujas (LB) |
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| Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
16/10/2018 |
Actualizado : |
11/02/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
BORGES, A.; GONZÁLEZ-REYMUNDEZ, A.; ERNST, O.; CADENAZZI, M.; TERRA, J.A.; GUTIÉRREZ, L. |
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. |
Título : |
Can spatial modeling substitute experimental design in agricultural experiments? |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
Crop Science, 2018, v. 59, no. 1, p. 1-10. |
DOI : |
10.2135/cropsci2018.03.0177 |
Idioma : |
Inglés |
Notas : |
Article history: Accepted paper, posted 10/05/18. Published online December, 13. 2018. |
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 |
Palabras claves : |
EFFICIENCY STATISTICS; EXPERIMENTAL DESIGN; FIELD VARIABILITY; SPATIAL MODELS; UNIFORMITY TRIAL. |
Thesagro : |
DISENO ESTADISTICO; DISENO EXPERIMENTAL; MODELOS ESTADISTICOS; VARIABILIDAD. |
Asunto categoría : |
U30 Métodos de investigación |
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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