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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 : |
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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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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Registro original : |
INIA Treinta y Tres (TT) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
15/05/2019 |
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Tipo de producción científica : |
Documentos |
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Autor : |
QUERO, G.; BONNECARRERE, V.; FERNÁNDEZ, S.; SILVA, P.; SIMONDI, S.; BORSANI, O. |
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Afiliación : |
GASTÓN QUERO CORRALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA VICTORIA BONNECARRERE MARTINEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SEBASTIÁN FERNÁNDEZ; PEDRO SILVA; SEBASTIÁN SIMONDI; OMAR BORSANI. |
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Título : |
La eficiencia del uso de luz y la partición energética en arroz es genotipo dependiente. [resumen] |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
In: INIA (Instituto Nacional de Investigación Agropecuaria); INIA Las Brujas; Biotecnología. Jornada de Agrobiotecnología, XI. Encuentro Nacional de REDBIO, III. Jornada técnica. Las Brujas, Canelones (UY): INIA, 2018. |
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Páginas : |
p.8-9 |
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Serie : |
(Serie Actividades de Difusión; 786) |
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Idioma : |
Español |
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Contenido : |
Una de las principales limitantes del rendimiento de arroz en regiones de alta performance productiva, como el Uruguay, es la eficiencia del uso de la luz (EUL). La EUL puede determinarse a nivel de planta entera o a nivel del aparato fotosintético. En este último caso la EUL es medida como rendimiento cuántico del fotosistema II (PSII).
El objetivo de este estudio fue analizar la dependencia genotípica de la EUL a nivel de planta entera y del rendimiento cuántico, usando cuatro genotipos de arroz (INIA Parao, INIA Tacuarí, INIA Olimar y El Paso 144) y cuatro ambientes lumínicos diferentes. |
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Palabras claves : |
ANÁLISIS DE QUENCHING; ANÁLISIS DE RELAJACIÓN; DISIPACIÓN DE LA ENERGÍA; RENDIMIENTO CUÁNTICO. |
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Thesagro : |
ARROZ. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12349/1/sad-786-p.8-9.pdf
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Marc : |
LEADER 01552nam a2200253 a 4500
001 1012414
005 2019-05-15
008 2018 bl uuuu u0uu1 u #d
100 1 $aQUERO, G.
245 $aLa eficiencia del uso de luz y la partición energética en arroz es genotipo dependiente. [resumen]$h[electronic resource]
260 $aIn: INIA (Instituto Nacional de Investigación Agropecuaria); INIA Las Brujas; Biotecnología. Jornada de Agrobiotecnología, XI. Encuentro Nacional de REDBIO, III. Jornada técnica. Las Brujas, Canelones (UY): INIA$c2018
300 $ap.8-9
490 $a(Serie Actividades de Difusión; 786)
520 $aUna de las principales limitantes del rendimiento de arroz en regiones de alta performance productiva, como el Uruguay, es la eficiencia del uso de la luz (EUL). La EUL puede determinarse a nivel de planta entera o a nivel del aparato fotosintético. En este último caso la EUL es medida como rendimiento cuántico del fotosistema II (PSII). El objetivo de este estudio fue analizar la dependencia genotípica de la EUL a nivel de planta entera y del rendimiento cuántico, usando cuatro genotipos de arroz (INIA Parao, INIA Tacuarí, INIA Olimar y El Paso 144) y cuatro ambientes lumínicos diferentes.
650 $aARROZ
653 $aANÁLISIS DE QUENCHING
653 $aANÁLISIS DE RELAJACIÓN
653 $aDISIPACIÓN DE LA ENERGÍA
653 $aRENDIMIENTO CUÁNTICO
700 1 $aBONNECARRERE, V.
700 1 $aFERNÁNDEZ, S.
700 1 $aSILVA, P.
700 1 $aSIMONDI, S.
700 1 $aBORSANI, O.
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INIA Las Brujas (LB) |
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