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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 : |
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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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 : |
Factors underlying yield variability in two California rice fields. |
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Fecha de publicación : |
2004 |
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Fuente / Imprenta : |
Agronomy Journal, 2004, v. 96, no. 5, p. 1481-1494. |
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DOI : |
10.2134/agronj2004.1481 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: Feb 4, 2004 // Published: Sept, 2004. |
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Contenido : |
Modern technologies associated with precision agriculture provide the opportunity to more precisely measure yield variability and the ecological processes underlying this variability. Effective analysis of data from these measurements requires statistical methods different from those traditionally employed on data from controlled agronomic experiments. Our objective was to develop and test multivariate statistical methods appropriate for use in analyzing precision agriculture data. We analyzed a data set taken from two commercial California rice fields and consisting of yield spatial trends together with soil core data from a grid of sample points. We used cluster analysis to discern spatiotemporal patterns in grain yield. We applied a Monte Carlo randomization process to the generation of clusters to analyze cluster stability. We then used classification and regression trees (CART) to determine the factors underlying cluster distribution. The clustering procedure successfully identified stable, physically meaningful clusters with recognizable spatial and temporal structure. Thus, the randomization procedure may present an attractive alternative to fuzzy clustering. The CART analysis identified some but not all of the factors underlying the cluster patterns. The number of available data values may have been too small to take advantage of the CART partitioning capabilities. |
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Palabras claves : |
AGRICULTURA DE PRECISION; ARROZ; CALIFORNIA. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
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024 7 $a10.2134/agronj2004.1481$2DOI
100 1 $aROEL, A.
245 $aFactors underlying yield variability in two California rice fields.$h[electronic resource]
260 $c2004
500 $aArticle history: Received: Feb 4, 2004 // Published: Sept, 2004.
520 $aModern technologies associated with precision agriculture provide the opportunity to more precisely measure yield variability and the ecological processes underlying this variability. Effective analysis of data from these measurements requires statistical methods different from those traditionally employed on data from controlled agronomic experiments. Our objective was to develop and test multivariate statistical methods appropriate for use in analyzing precision agriculture data. We analyzed a data set taken from two commercial California rice fields and consisting of yield spatial trends together with soil core data from a grid of sample points. We used cluster analysis to discern spatiotemporal patterns in grain yield. We applied a Monte Carlo randomization process to the generation of clusters to analyze cluster stability. We then used classification and regression trees (CART) to determine the factors underlying cluster distribution. The clustering procedure successfully identified stable, physically meaningful clusters with recognizable spatial and temporal structure. Thus, the randomization procedure may present an attractive alternative to fuzzy clustering. The CART analysis identified some but not all of the factors underlying the cluster patterns. The number of available data values may have been too small to take advantage of the CART partitioning capabilities.
653 $aAGRICULTURA DE PRECISION
653 $aARROZ
653 $aCALIFORNIA
700 1 $aPLANT, R.E.
773 $tAgronomy Journal, 2004$gv. 96, no. 5, p. 1481-1494.
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