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| 68. |  | CASTERÁ, F.; ROEL, A.; FUREST, J.M. Agroclimatología ln: INIA TREINTA Y TRES. Arroz: resultados experimentales 1999-2000. Treinta y Tres (Uruguay): INIA, 2000. cap. 1, p. 1-2. (INIA Serie Actividades de Difusión; 224)| Biblioteca(s): INIA Treinta y Tres. |
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| 69. | | MENDEZ, R.; ROEL, A.; FUREST, J. Agroclimatología. ln: INIA TREINTA Y TRES. Arroz: resultados experimentales 2000-2001. Treinta y Tres (Uruguay): INIA, 2001. cap. 1, p. 1-2. (INIA Serie Actividades de Difusión; 257)| Biblioteca(s): INIA Treinta y Tres. |
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| 70. | | MENDEZ, R.; ROEL, A.; FUREST, J. Agroclimatología. ln: INIA TREINTA Y TRES. Arroz: resultados experimentales 2001-2002. Treinta y Tres (Uruguay): INIA, 2002. cap. 1, p. 1-2. (INIA Serie Actividades de Difusión; 292)| Biblioteca(s): INIA Treinta y Tres. |
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| 71. | | MÉNDEZ, R.; ROEL, A.; FUREST, J.M. Agroclimatología. ln: INIA TREINTA Y TRES. PROGRAMA NACIONAL DE ARROZ. Investigaciones agronómicas: reporte técnico anual 2003. Área cultivos. Treinta y Tres (Uruguay): INIA, 2003. cap. 1, p. 1-2. (INIA Reporte Técnico Anual; 01)| Biblioteca(s): INIA Treinta y Tres. |
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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 actual : |
18/09/2014 |
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Actualizado : |
11/10/2019 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
A - 2 |
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Autor : |
ROEL, A.; PLANT, R.E. |
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Afiliación : |
ALVARO ROEL DELLAZOPPA, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay. |
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Título : |
Spatiotemporal analysis of rice yield variability in two California fields |
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Fecha de publicación : |
2004 |
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Fuente / Imprenta : |
Agronomy Journal, 2004, v. 96, no. 1, p. 77-90 |
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ISSN : |
0002-1962 |
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DOI : |
10.2134/agronj2004.7700 |
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Idioma : |
Inglés |
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Contenido : |
Currently, little is known about the spatial and temporal variability of rice (Oryza sativa L.) yield patterns. This information is needed before implementing any site-specific management strategy. The objective of this research was to characterize the spatial and temporal yield variability of rice grown in commercial fields in California. Rice cultivars M-202 and Koshihikari were grown and managed by a cooperating farmer, who collected yield monitor data over a 4-yr period. Alternative methods of data quality analysis were applied to the data. To evaluate temporal variability, yields from different years must be placed on a common grid. The appropriate size for these grids was tested. Large-scale spatial structure was determined using median polish while small-scale spatial structure was evaluated by computing variograms of the yield residuals after subtracting the trends. Temporal variability was determined using two approaches: (i) computation of the variance among years of the original, trend, and residual yield values at fixed points in the field and (ii) cluster analysis of the standardized trend yield values. Results from the study showed that the grid density necessary to capture the spatial variability depended on site and year. Trend surface spatial behaviors depended on year, indicating a lack of temporal stability. Variograms showed strong spatial structure of yield residuals. Cluster analysis reduced the considerable complexity in a sequence of yield maps of these fields to a few general patterns of variations among years. MenosCurrently, little is known about the spatial and temporal variability of rice (Oryza sativa L.) yield patterns. This information is needed before implementing any site-specific management strategy. The objective of this research was to characterize the spatial and temporal yield variability of rice grown in commercial fields in California. Rice cultivars M-202 and Koshihikari were grown and managed by a cooperating farmer, who collected yield monitor data over a 4-yr period. Alternative methods of data quality analysis were applied to the data. To evaluate temporal variability, yields from different years must be placed on a common grid. The appropriate size for these grids was tested. Large-scale spatial structure was determined using median polish while small-scale spatial structure was evaluated by computing variograms of the yield residuals after subtracting the trends. Temporal variability was determined using two approaches: (i) computation of the variance among years of the original, trend, and residual yield values at fixed points in the field and (ii) cluster analysis of the standardized trend yield values. Results from the study showed that the grid density necessary to capture the spatial variability depended on site and year. Trend surface spatial behaviors depended on year, indicating a lack of temporal stability. Variograms showed strong spatial structure of yield residuals. Cluster analysis reduced the considerable complexity in a sequence of yield maps of the... Presentar Todo |
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Palabras claves : |
AGRICULTURA DE PRESICION; ARROZ. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 02085naa a2200181 a 4500
001 1050379
005 2019-10-11
008 2004 bl uuuu u00u1 u #d
022 $a0002-1962
024 7 $a10.2134/agronj2004.7700$2DOI
100 1 $aROEL, A.
245 $aSpatiotemporal analysis of rice yield variability in two California fields$h[electronic resource]
260 $c2004
520 $aCurrently, little is known about the spatial and temporal variability of rice (Oryza sativa L.) yield patterns. This information is needed before implementing any site-specific management strategy. The objective of this research was to characterize the spatial and temporal yield variability of rice grown in commercial fields in California. Rice cultivars M-202 and Koshihikari were grown and managed by a cooperating farmer, who collected yield monitor data over a 4-yr period. Alternative methods of data quality analysis were applied to the data. To evaluate temporal variability, yields from different years must be placed on a common grid. The appropriate size for these grids was tested. Large-scale spatial structure was determined using median polish while small-scale spatial structure was evaluated by computing variograms of the yield residuals after subtracting the trends. Temporal variability was determined using two approaches: (i) computation of the variance among years of the original, trend, and residual yield values at fixed points in the field and (ii) cluster analysis of the standardized trend yield values. Results from the study showed that the grid density necessary to capture the spatial variability depended on site and year. Trend surface spatial behaviors depended on year, indicating a lack of temporal stability. Variograms showed strong spatial structure of yield residuals. Cluster analysis reduced the considerable complexity in a sequence of yield maps of these fields to a few general patterns of variations among years.
653 $aAGRICULTURA DE PRESICION
653 $aARROZ
700 1 $aPLANT, R.E.
773 $tAgronomy Journal, 2004$gv. 96, no. 1, p. 77-90
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