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Biblioteca (s) : |
INIA La Estanzuela; INIA Las Brujas; INIA Salto Grande; INIA Treinta y Tres. |
Fecha : |
21/02/2014 |
Actualizado : |
22/06/2016 |
Autor : |
Corsi, W. |
Afiliación : |
WALTER CORSI, CENTRO DE INVESTIGACIONES AGRÍCOLAS "ALBERTO BOERGER". |
Título : |
Caracteristicas del clima y de las cuencas naturales que influyen en los procesos hidrologicos |
Fecha de publicación : |
1984 |
Fuente / Imprenta : |
ln: Taller Nacional de Investigación sobre Cuencas Experimentales, 1 : 1984 : Montevideo Memorias. Montevideo (Uruguay): MAP ; IICA, 1984. |
Páginas : |
p49-83 |
Idioma : |
Español |
Notas : |
Ejemplar de Biblioteca INIA Treinta y Tres, donación familia Dr. Juan Carlos Scarsi. |
Thesagro : |
BALANCE HIDRICO; BALANCE HIDRICO DEL SUELO; CONTENIDO DE AGUA EN EL SUELO; CUENCAS HIDROGRAFICAS; DEFICIT DE HUMEDAD EN EL SUELO; EVAPORACION; EVAPOTRANSPIRACION; FACTORES CLIMATICOS; LLUVIA; MANEJO DEL SUELO; MOVIMIENTO DEL AGUA EN EL SUELO; TIPOS DE SUELOS; URUGUAY. |
Asunto categoría : |
-- F06 Riego |
Marc : |
LEADER 01025naa a2200289 a 4500 001 1011563 005 2016-06-22 008 1984 bl uuuu u00u1 u #d 100 1 $aCORSI, W. 245 $aCaracteristicas del clima y de las cuencas naturales que influyen en los procesos hidrologicos 260 $c1984 300 $ap49-83 500 $aEjemplar de Biblioteca INIA Treinta y Tres, donación familia Dr. Juan Carlos Scarsi. 650 $aBALANCE HIDRICO 650 $aBALANCE HIDRICO DEL SUELO 650 $aCONTENIDO DE AGUA EN EL SUELO 650 $aCUENCAS HIDROGRAFICAS 650 $aDEFICIT DE HUMEDAD EN EL SUELO 650 $aEVAPORACION 650 $aEVAPOTRANSPIRACION 650 $aFACTORES CLIMATICOS 650 $aLLUVIA 650 $aMANEJO DEL SUELO 650 $aMOVIMIENTO DEL AGUA EN EL SUELO 650 $aTIPOS DE SUELOS 650 $aURUGUAY 773 $tln: Taller Nacional de Investigación sobre Cuencas Experimentales, 1 : 1984 : Montevideo Memorias. Montevideo (Uruguay): MAP ; IICA, 1984.
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INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha actual : |
09/09/2020 |
Actualizado : |
05/09/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
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. |
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. |
Título : |
Do Spatial Designs Outperform Classic Experimental Designs?. |
Fecha de publicación : |
2020 |
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 |
DOI : |
10.1007/s13253-020-00406-2 |
Idioma : |
Inglés |
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. |
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 |
Palabras claves : |
AUTOREGRESSIVE PROCESS; EXPERIMENTAL DESIGN; PREDICTION ACCURACY; RANDOMIZATION-BASED EXPERIMENTAL DESIGNS; RESPONSE TO SELECTION; SPATIAL CORRECTION. |
Thesagro : |
DISENO EXPERIMENTAL. |
Asunto categoría : |
-- |
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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