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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
09/09/2020 |
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Actualizado : |
05/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
RAEGAN HOEFLER; GONZALEZ-BARRIOS , P.; MADHAV BHATTA; NUNES, J.A.R.; BERRO, I.; NALIN, R.S.; BORGES, A.; COVARRUBIAS, E.; DIAZ-GARCIA, L.; QUINCKE, M.; GUTIERREZ, L. |
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Afiliación : |
HOEFLER, R., Department of Agronomy, University of Wisconsin?Madison, 1575 Linden Dr., Madison, WI, 53706, USA.; PABLO GONZALEZ-BARRIOS, Dpartment of Agronomy, University of Wisconsin?Madison, 1575 Linden Dr., Madison, WI, 53706, USA.; BHATTA, M., Department of Agronomy, University of Wisconsin?Madison, 1575 Linden Dr., Madison, WI, 53706, USA.; JOSE A. R. NUNES, Department of Agronomy, University of Wisconsin?Madison, 1575 Linden Dr., Madison, WI, 53706, USA.; INES BERRO, Department of Agronomy, University of Wisconsin–Madison, 1575 Linden Dr., Madison, WI, 53706, USA; RAFAEL S. NALIN, Department of Genetics, Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Piracicaba, São Paulo, 131418-900, Brazil.; ALEJANDRA BORGES, Statistics Department, Facultad de Agronomía, Univesidad de la República, Garzón 780, Montevideo, Uruguay.; EDUARDO COVARRUBIAS, CGIAR Excellence in Breeding Platform (EiB), El Batan, Mexico International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico.; LUIS DIAZ-GARCIA, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias, 20676, Aguascalientes, Mexico.; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCIA GUTIERREZ, Department of Agronomy, University of Wisconsin–Madison, 1575 Linden Dr., Madison, WI, 53706, USA. |
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Título : |
Do Spatial Designs Outperform Classic Experimental Designs?. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Journal of Agricultural, Biological, and Environmental Statistics, 1 December 2020, volume 25, number 4, pag.523-552, 1 December 2020. OPEN ACCESS. Doi: https://doi.org/10.1007/s13253-020-00406-2 |
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DOI : |
10.1007/s13253-020-00406-2 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 15 October 2019/Accepted 01 July 2020/Published 29 August 2020. This project was partially funded through a USDA_AFRI_NIFA_2018-67013-27620 award and by the Hatch Act Formula Fund WISO1984 and WIS03002. Additionally, JARN received funding from CAPES CAPES_PrInt_UFLA 88887.318846_2019-00 as Senior Visiting Professor at the University of Wisconsin-Madison. |
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Contenido : |
Controlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a twodimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments.
However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1×AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments. Supplementary materials accompanying this paper appear online. MenosControlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a twodimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments.
However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1×AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments. Supplementary materials ... Presentar Todo |
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Palabras claves : |
AUTOREGRESSIVE PROCESS; EXPERIMENTAL DESIGN; PREDICTION ACCURACY; RANDOMIZATION-BASED EXPERIMENTAL DESIGNS; RESPONSE TO SELECTION; SPATIAL CORRECTION. |
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Thesagro : |
DISENO EXPERIMENTAL. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16700/1/JABES-2020.pdf
https://link.springer.com/content/pdf/10.1007/s13253-020-00406-2.pdf
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Marc : |
LEADER 03067naa a2200349 a 4500
001 1061304
005 2022-09-05
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1007/s13253-020-00406-2$2DOI
100 1 $aRAEGAN HOEFLER
245 $aDo Spatial Designs Outperform Classic Experimental Designs?.$h[electronic resource]
260 $c2020
500 $aArticle history: Received 15 October 2019/Accepted 01 July 2020/Published 29 August 2020. This project was partially funded through a USDA_AFRI_NIFA_2018-67013-27620 award and by the Hatch Act Formula Fund WISO1984 and WIS03002. Additionally, JARN received funding from CAPES CAPES_PrInt_UFLA 88887.318846_2019-00 as Senior Visiting Professor at the University of Wisconsin-Madison.
520 $aControlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a twodimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments. However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1×AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments. Supplementary materials accompanying this paper appear online.
650 $aDISENO EXPERIMENTAL
653 $aAUTOREGRESSIVE PROCESS
653 $aEXPERIMENTAL DESIGN
653 $aPREDICTION ACCURACY
653 $aRANDOMIZATION-BASED EXPERIMENTAL DESIGNS
653 $aRESPONSE TO SELECTION
653 $aSPATIAL CORRECTION
700 1 $aGONZALEZ-BARRIOS , P.
700 1 $aMADHAV BHATTA
700 1 $aNUNES, J.A.R.
700 1 $aBERRO, I.
700 1 $aNALIN, R.S.
700 1 $aBORGES, A.
700 1 $aCOVARRUBIAS, E.
700 1 $aDIAZ-GARCIA, L.
700 1 $aQUINCKE, M.
700 1 $aGUTIERREZ, L.
773 $tJournal of Agricultural, Biological, and Environmental Statistics, 1 December 2020, volume 25, number 4, pag.523-552, 1 December 2020. OPEN ACCESS. Doi: https://doi.org/10.1007/s13253-020-00406-2
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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
18/02/2019 |
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Tipo de producción científica : |
Documentos |
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Autor : |
DE BARBIERI, I.; MONTOSSI, F.; BERRETTA, E.; RISSO, D.; CUADRO, R.; DIGHIERO, A.; URRESTARAZÚ, A.; NOLLA, M.; LUZARDO, S.; MEDEROS, A.; MARTÍNEZ, H.; ZAMIT, W.; LEVRATTO, J.; BENTANCURT, M.; GARÍON, M.; ZARZA, A.; PRESA, O. |
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Afiliación : |
LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELBIO JOAQUIN BERRETTA CARVALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DIEGO FERNANDO RISSO RIET, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WASHINGTON ROBIN CUADRO LOPEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SANTIAGO FELIPE LUZARDO VILLAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; AMERICA ESTHER MEDEROS SILVEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WILFREDO SHAMIL ZAMIT DUARTE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JUAN CARLOS LEVRATTO CORTES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MAURO ANDRES BENTANCURT PONTTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SUL; OROSILDO RODOLFO PRESA SEMPER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Alternativas de manejo y alimentación para la producción de lanas finas y superfinas en la región de Basalto. |
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Fecha de publicación : |
2004 |
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Fuente / Imprenta : |
ln: INIA Tacuarembó. Sociedad Criadores Merino Australiano del Uruguay. SUL. Proyecto Merino Fino del Uruguay: quinta distribución de carneros generados en el núcleo fundacional de merino fino de la Unidad Experimental Glencoe, INIA Tacuarembó, 1999 - 2004. Glencoe, Paysandú, 10 diciembre, 2004. Tacuarembó (Uruguay): INIA, 2004. |
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Páginas : |
p. 19-40 |
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Serie : |
(INIA Serie Actividades de Difusión ; 392) |
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Idioma : |
Español |
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Contenido : |
En la Unidad Experimental Glencoe de INIA Tacuarembó, se comenzó una serie de trabajos experimentales (parte de los cuales se
desarrollarán en el presente artículo) orientados a diseñar y evaluar sistemas de producción de lanas f inas y superf inas sobre campo natural y mejoramientos de campo principalmente a desarrollarse en la región de Basalto. El objetivo principal de estos trabajos es aportar inf ormación científ ico-técnica que permita evaluar el impacto de determinadas medidas de manejo, de pasturas y animales, sobre la cantidad y calidad del producto y la sustentabilidad de las mismas. A continuación se presentan los resultados obtenidos de dos trabajos experimentales realizados desde f ines del año 2001 hasta f ines del año 2003. |
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Palabras claves : |
SHEEP. |
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Thesagro : |
MANEJO DEL GANADO; MERINO; OVINOS; PRODUCCION DE LANA; RAZAS (ANIMALES); SISTEMAS DE PASTOREO; SUELO BASALTICO; URUGUAY. |
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Asunto categoría : |
L01 Ganadería |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/9799/1/SAD392p19-40.pdf
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Marc : |
LEADER 02327naa a2200445 a 4500
001 1021637
005 2019-02-18
008 2004 bl uuuu u00u1 u #d
100 1 $aDE BARBIERI, I.
245 $aAlternativas de manejo y alimentación para la producción de lanas finas y superfinas en la región de Basalto.
260 $c2004
300 $ap. 19-40
490 $a(INIA Serie Actividades de Difusión ; 392)
520 $aEn la Unidad Experimental Glencoe de INIA Tacuarembó, se comenzó una serie de trabajos experimentales (parte de los cuales se desarrollarán en el presente artículo) orientados a diseñar y evaluar sistemas de producción de lanas f inas y superf inas sobre campo natural y mejoramientos de campo principalmente a desarrollarse en la región de Basalto. El objetivo principal de estos trabajos es aportar inf ormación científ ico-técnica que permita evaluar el impacto de determinadas medidas de manejo, de pasturas y animales, sobre la cantidad y calidad del producto y la sustentabilidad de las mismas. A continuación se presentan los resultados obtenidos de dos trabajos experimentales realizados desde f ines del año 2001 hasta f ines del año 2003.
650 $aMANEJO DEL GANADO
650 $aMERINO
650 $aOVINOS
650 $aPRODUCCION DE LANA
650 $aRAZAS (ANIMALES)
650 $aSISTEMAS DE PASTOREO
650 $aSUELO BASALTICO
650 $aURUGUAY
653 $aSHEEP
700 1 $aMONTOSSI, F.
700 1 $aBERRETTA, E.
700 1 $aRISSO, D.
700 1 $aCUADRO, R.
700 1 $aDIGHIERO, A.
700 1 $aURRESTARAZÚ, A.
700 1 $aNOLLA, M.
700 1 $aLUZARDO, S.
700 1 $aMEDEROS, A.
700 1 $aMARTÍNEZ, H.
700 1 $aZAMIT, W.
700 1 $aLEVRATTO, J.
700 1 $aBENTANCURT, M.
700 1 $aGARÍON, M.
700 1 $aZARZA, A.
700 1 $aPRESA, O.
773 $tln: INIA Tacuarembó. Sociedad Criadores Merino Australiano del Uruguay. SUL. Proyecto Merino Fino del Uruguay: quinta distribución de carneros generados en el núcleo fundacional de merino fino de la Unidad Experimental Glencoe, INIA Tacuarembó, 1999 - 2004. Glencoe, Paysandú, 10 diciembre, 2004. Tacuarembó (Uruguay): INIA, 2004.
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INIA Tacuarembó (TBO) |
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