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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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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
22/02/2021 |
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Actualizado : |
22/02/2021 |
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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 : |
ZOPPOLO, R.; RODRIGUEZ, P.; UBERTI, A.; SANTANA, A. S.; CONIBERTI, A.; CABRERA, D. |
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Afiliación : |
ROBERTO JOSE ZOPPOLO GOLDSCHMIDT, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO ANDRES RODRIGUEZ BRUNO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; A. UBERTI, Universidad Federal da Fronteira Sul - UFFS, Chapeco?, Brazil; A. S. SANTANA, Universidad Federal da Fronteira Sul - UFFS, Chapeco?, Brazil; ANDRES CONIBERTI MUNDY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS DANILO CABRERA BOLOGNA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Influence of climatic factors on productivity of 'Williams' pear trees on different rootstocks. [Conference paper]. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Acta Horticulturae, February 2021, N°1303, p. 251-258. DOI: https://doi.org/10.17660/ActaHortic.2021.1303.36 |
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ISSN : |
0567-7572 (print); 2406-6168 (electronic) |
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DOI : |
10.17660/ActaHortic.2021.1303.36 |
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Idioma : |
Inglés |
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Notas : |
Article history: Published 5 February 2021. In: Acta Horticulturae (ISHS) 1303: XIII International Pear Symposium, Montevideo, Uruguay. Conveners: Roberto Zoppolo, Danilo Cabrera. Editors: Roberto Zoppolo, Danilo Cabrera, D. Granatstein. |
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Contenido : |
Abstract:
Pear (Pyrus communis) is reasonably well-adapted to the average climatic conditions of southern Uruguay, but the climatic variables are not always agreeable for satisfactory production. The objective of this research was to evaluate the impact of climatic factors, on yield components (average fruit weight and productivity) of 'Williams' pears on two different rootstocks ('OH×F40' and 'BA29'). The experiment was established in July 2003 at Instituto Nacional de Investigación Agropecuaria - INIA Las Brujas (34°67?S; 56°37?W). According to Köppen-Geiger classification, the climate of the studied region is ?Cfa? and the soil type is a Typic Argiudoll. To analyze the contribution of climatic factors on productivity of pear trees, a principal component analysis (PCA) was applied using the statistical software R. The correlations between yield components and precipitation, cold units, chill hours (≤7.2°C), relative humidity, evapotranspiration and average temperature were studied from the growing season 2014/15 until 2017/18. Climatic data were collected from the meteorological station at INIA Las Brujas, located less than 500 m from the trial plot. Climatic factors had a more significant effect on 'Williams' productivity than the rootstock factor. One main factor affecting seasonal productivity was chill hours. The average productivity values for the two rootstocks during the cycles 2015/16 and 2017/18 was zero and 7.2 t ha‑1, respectively. During the seasons where chilling was not the limiting factor (>500 chill hours), productivity was significantly higher (25.3 and 42.8 t ha‑1 on average for 2014/15 and 2016/17 seasons, respectively). Precipitation during fruit growth and flower induction and differentiation was another main factor affecting productivity in the current and next season. Even though cumulative yield was significantly higher in OH×F40 compared to BA29 (85.9 vs. 64.8 t ha‑1, respectively) no consistent differences were detected between rootstocks seasonally.
@ International Society for Horticultural Science. MenosAbstract:
Pear (Pyrus communis) is reasonably well-adapted to the average climatic conditions of southern Uruguay, but the climatic variables are not always agreeable for satisfactory production. The objective of this research was to evaluate the impact of climatic factors, on yield components (average fruit weight and productivity) of 'Williams' pears on two different rootstocks ('OH×F40' and 'BA29'). The experiment was established in July 2003 at Instituto Nacional de Investigación Agropecuaria - INIA Las Brujas (34°67?S; 56°37?W). According to Köppen-Geiger classification, the climate of the studied region is ?Cfa? and the soil type is a Typic Argiudoll. To analyze the contribution of climatic factors on productivity of pear trees, a principal component analysis (PCA) was applied using the statistical software R. The correlations between yield components and precipitation, cold units, chill hours (≤7.2°C), relative humidity, evapotranspiration and average temperature were studied from the growing season 2014/15 until 2017/18. Climatic data were collected from the meteorological station at INIA Las Brujas, located less than 500 m from the trial plot. Climatic factors had a more significant effect on 'Williams' productivity than the rootstock factor. One main factor affecting seasonal productivity was chill hours. The average productivity values for the two rootstocks during the cycles 2015/16 and 2017/18 was zero and 7.2 t ha‑1, respectively. During the seasons... Presentar Todo |
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Palabras claves : |
ALTERNATE BEARING; PCA - PRINCIPAL COMPONENT ANALYSIS; PRECIPITATION. |
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Thesagro : |
PYRUS COMMUNIS. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
LEADER 03222naa a2200265 a 4500
001 1061742
005 2021-02-22
008 2021 bl uuuu u00u1 u #d
022 $a0567-7572 (print); 2406-6168 (electronic)
024 7 $a10.17660/ActaHortic.2021.1303.36$2DOI
100 1 $aZOPPOLO, R.
245 $aInfluence of climatic factors on productivity of 'Williams' pear trees on different rootstocks. [Conference paper].$h[electronic resource]
260 $c2021
500 $aArticle history: Published 5 February 2021. In: Acta Horticulturae (ISHS) 1303: XIII International Pear Symposium, Montevideo, Uruguay. Conveners: Roberto Zoppolo, Danilo Cabrera. Editors: Roberto Zoppolo, Danilo Cabrera, D. Granatstein.
520 $aAbstract: Pear (Pyrus communis) is reasonably well-adapted to the average climatic conditions of southern Uruguay, but the climatic variables are not always agreeable for satisfactory production. The objective of this research was to evaluate the impact of climatic factors, on yield components (average fruit weight and productivity) of 'Williams' pears on two different rootstocks ('OH×F40' and 'BA29'). The experiment was established in July 2003 at Instituto Nacional de Investigación Agropecuaria - INIA Las Brujas (34°67?S; 56°37?W). According to Köppen-Geiger classification, the climate of the studied region is ?Cfa? and the soil type is a Typic Argiudoll. To analyze the contribution of climatic factors on productivity of pear trees, a principal component analysis (PCA) was applied using the statistical software R. The correlations between yield components and precipitation, cold units, chill hours (≤7.2°C), relative humidity, evapotranspiration and average temperature were studied from the growing season 2014/15 until 2017/18. Climatic data were collected from the meteorological station at INIA Las Brujas, located less than 500 m from the trial plot. Climatic factors had a more significant effect on 'Williams' productivity than the rootstock factor. One main factor affecting seasonal productivity was chill hours. The average productivity values for the two rootstocks during the cycles 2015/16 and 2017/18 was zero and 7.2 t ha‑1, respectively. During the seasons where chilling was not the limiting factor (>500 chill hours), productivity was significantly higher (25.3 and 42.8 t ha‑1 on average for 2014/15 and 2016/17 seasons, respectively). Precipitation during fruit growth and flower induction and differentiation was another main factor affecting productivity in the current and next season. Even though cumulative yield was significantly higher in OH×F40 compared to BA29 (85.9 vs. 64.8 t ha‑1, respectively) no consistent differences were detected between rootstocks seasonally. @ International Society for Horticultural Science.
650 $aPYRUS COMMUNIS
653 $aALTERNATE BEARING
653 $aPCA - PRINCIPAL COMPONENT ANALYSIS
653 $aPRECIPITATION
700 1 $aRODRIGUEZ, P.
700 1 $aUBERTI, A.
700 1 $aSANTANA, A. S.
700 1 $aCONIBERTI, A.
700 1 $aCABRERA, D.
773 $tActa Horticulturae, February 2021, N°1303, p. 251-258. DOI: https://doi.org/10.17660/ActaHortic.2021.1303.36
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