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6. | | BERRUETA, C.; GIMÉNEZ, G.; GALVÁN, G.; BORGES, A. New sources of partial resistance to bacterial spot race T2 in processing tomatoes. [Novas fontes de resistência parcial à mancha bacteriana raça T2 de tomate para processamento.] Horticultura Brasileira, 2016, v. 34, no. 3, p.326-332. Article history: Received on March 16, 2015 / Accepted on February 10, 2016.Biblioteca(s): INIA Las Brujas. |
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9. | | BORGES, A.; GONZÁLEZ-REYMUNDEZ, A.; ERNST, O.; CADENAZZI, M.; TERRA, J.A.; GUTIÉRREZ, L. Can spatial modeling substitute experimental design in agricultural experiments? Crop Science, 2018, v. 59, no. 1, p. 1-10. Article history: Accepted paper, posted 10/05/18. Published online December, 13. 2018.Biblioteca(s): INIA Treinta y Tres. |
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10. | | GUTIERREZ, L.; BORGES, A.; QUERO, G.; GONZALEZ-REYMUNDEZ, A.; BERRO, I.; LADO, B.; CASTRO, A. Biostatistical tools for plant breeding in the genomics era. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p.46-57. (INIA Serie Técnica; 241).Biblioteca(s): INIA La Estanzuela. |
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20. | | PELUFFO, S.; GONZÁLEZ IDIARTE, H.; BORGES, A.; GALVÁN, G.A.; ARBOLEYA, J. Producción de semilla de cebolla (Allium cepa L.) mediante el método semilla-bulbillo-semilla para tres cultivares de Uruguay (Abs.11, Sección: Mejoramiento genético, cultivares y Producción de semillas de hortalizas). IN: CONGRESO NACIONAL DE HORTI-FRUTICULTURA, 13. AÑO DE LA AGRICULTURA FAMILIAR DE LA FAO. "SOSTENIBILIDAD DE LA PRODUCCIÓN HORTIFRUTÍCOLA FAMILAR". 3-6 SETIEMBRE 2014, MONTEVIDEO (UY). Trabajos presentados. Montevideo (UY): INIA; SUHF, 2014. p.27Biblioteca(s): INIA Las Brujas. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
04/08/2020 |
Actualizado : |
04/08/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - 1 |
Autor : |
GONZÁLEZ BARRIOS, P.; BORGES, A.; TERRA, J.A.; PÉREZ BIDEGAIN, M.; GUTIÉRREZ, L. |
Afiliación : |
PABLO GONZÁLEZ BARRIOS, Facultad de Agronomía, UdelaR, UY.; ALEJANDRO BORGES, Facultad de Agronomía, UdelaR, UY.; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIO PÉREZ BIDEGAIN, Facultad de Agronomía, UdelaR, UY.; LUCÍA GUTIÉRREZ, Department of Agronomy, University of Wisconsin, Madison, USA. |
Título : |
Spatio-temporal modeling and competition dynamics in forest tillage experiments on early growth of Eucalyptus grandis L. |
Fecha de publicación : |
2020 |
Fuente / Imprenta : |
Forest Science, 28 March 2020, Volume 20, Pages 1-11. Doi: https://doi.org/10.1093/forsci/fxaa007 |
DOI : |
10.1093/forsci/fxaa007 |
Idioma : |
Inglés |
Notas : |
Article history: Received: 30 July 2019// Accepted: 30 January 2020// Published: 28 March 2020. |
Contenido : |
Forest tillage experiments regularly use long-term evaluations of large plots creating temporal and/or spatial correlations among observations. Not modeling these correlations could compromise treatment comparisons. The aim of this study was to evaluate the effect of modeling spatio-temporal (ST) variability in forest tillage experiments. We used different strategies that incorporate spatial and/or temporal correlations in the evaluation of tillage intensity effect in initial Eucalyptus growth as well as evaluate the effect of intraplot mortality and competition dynamics. Three tillage intensities in two contrasting soil conditions were compared for tree height and wood volume. Additionally, we compared the use of three individual growth curves for plant height to evaluate the time needed to reach 2 m in height (T2m). We modeled the spatial correlation of T2m using mixed models. In both sites, ST models were superior for plant height and wood volume per hectare, whereas for individual-tree wood volume, temporal models were superior. Pit planting always had a lower performance than disk harrowing and subsoiler, which behaved similarly. The competition dynamics within the plot because of tree mortality was affected by treatments and site. Modeling ST variability is key to improving treatment comparisons in forest experiments. |
Palabras claves : |
GROWTH CURVES; SITE PREPARATION; SPATIO-TEMPORAL VARIABILITY; TILLAGE INTENSITY. |
Asunto categoría : |
U10 Métodos matemáticos y estadísticos |
Marc : |
LEADER 02236naa a2200241 a 4500 001 1061260 005 2020-08-04 008 2020 bl uuuu u00u1 u #d 024 7 $a10.1093/forsci/fxaa007$2DOI 100 1 $aGONZÁLEZ BARRIOS, P. 245 $aSpatio-temporal modeling and competition dynamics in forest tillage experiments on early growth of Eucalyptus grandis L.$h[electronic resource] 260 $c2020 500 $aArticle history: Received: 30 July 2019// Accepted: 30 January 2020// Published: 28 March 2020. 520 $aForest tillage experiments regularly use long-term evaluations of large plots creating temporal and/or spatial correlations among observations. Not modeling these correlations could compromise treatment comparisons. The aim of this study was to evaluate the effect of modeling spatio-temporal (ST) variability in forest tillage experiments. We used different strategies that incorporate spatial and/or temporal correlations in the evaluation of tillage intensity effect in initial Eucalyptus growth as well as evaluate the effect of intraplot mortality and competition dynamics. Three tillage intensities in two contrasting soil conditions were compared for tree height and wood volume. Additionally, we compared the use of three individual growth curves for plant height to evaluate the time needed to reach 2 m in height (T2m). We modeled the spatial correlation of T2m using mixed models. In both sites, ST models were superior for plant height and wood volume per hectare, whereas for individual-tree wood volume, temporal models were superior. Pit planting always had a lower performance than disk harrowing and subsoiler, which behaved similarly. The competition dynamics within the plot because of tree mortality was affected by treatments and site. Modeling ST variability is key to improving treatment comparisons in forest experiments. 653 $aGROWTH CURVES 653 $aSITE PREPARATION 653 $aSPATIO-TEMPORAL VARIABILITY 653 $aTILLAGE INTENSITY 700 1 $aBORGES, A. 700 1 $aTERRA, J.A. 700 1 $aPÉREZ BIDEGAIN, M. 700 1 $aGUTIÉRREZ, L. 773 $tForest Science, 28 March 2020, Volume 20, Pages 1-11. Doi: https://doi.org/10.1093/forsci/fxaa007
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