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| Registros recuperados : 16 | |
| 10. |  | BALMELLI, G.; RESQUÍN, F.; SIMETO, S.; GONZÁLEZ, M.; SCOZ, R.; BRITO, G.; ROSSI, C.; MARANGES, F. Montes con INIA Sombra: protección del ganado y diversificación productiva. En: DÍA DE CAMPO, 2019, UNIDAD EXPERIMENTAL PALO A PIQUE (UEPP), TREINTA Y TRES, UY. p. 15.| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 11. | | BALMELLI, G.; RESQUÍN, F.; SIMETO, S.; GONZÁLEZ, M.; SCOZ, R.; BRITO, G.; ROSSI, C.; MARANGES, F. INIA SOMBRA: una alternativa para la protección del ganado y para la diversificación productiva. Revista INIA Uruguay, 2019, no. 56, p. 79-83. (Revista INIA; 56).| Biblioteca(s): INIA Tacuarembó. |
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| 12. | | MARANGES, F.; DO CANTO, J.; REYNO, R.; GUTIERREZ, F.; ROSSI, C.; DÍAZ, J.; STEWART, A. Raigrás anual Winter Star 3. Revista INIA Uruguay, 2018, no.52, p.18-20. (Revista INIA; 52)| Biblioteca(s): INIA Treinta y Tres. |
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| 13. | | CERETTA, S.; SASTRE, M.; MONTAÑA, M.; CASTRO, B.; ROSSI, C.; STEWART, S.; VÁZQUEZ, D.; MARANGES, F. Programa de mejoramiento genético de soja: variedad de Soja No-GM SJC13625. Cultivos. Revista INIA Uruguay, Diciembre 2023, no.75 p.51-53. (Revista INIA; 75).| Biblioteca(s): INIA Las Brujas. |
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| 14. | | PORCILE, V.; REYNO, R.; MARANGES, F.; NOLLA, F.; BECOÑA, G.; DE BRUM RODRÍGUEZ, F.; LLOVET, P.; GUTIERREZ, F.; BARAIBAR, N.; LATTANZI, F.; SOTELO, D.; ROSSI, C. ¿Conocemos los materiales forrajeros generados por la investigación uruguaya en los últimos años? Informe especial. Revista INIA Uruguay, Setiembre 2021, no.66, p. 68-77. (Revista INIA; 66).| Biblioteca(s): INIA Treinta y Tres. |
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| 15. | | REYNO, R.; DO CANTO, J.; SILVEIRA, F.; MEROLA, R.; JAURENA, M.; GIORELLO, D.; CUADRO, R.; DEVINCENZI, T.; MONTOSSI, F.; MARANGES, F.; ROSSI, C. Lotus INIA Basalto: potenciando los mejoramientos de campo de la ganadería extensiva del Uruguay. Pasturas. Revista INIA Uruguay, Marzo 2023, no.72, p.35-39. (Revista INIA; 72).| Biblioteca(s): INIA Las Brujas. |
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| 16. | | GIORELLO, D.; DO CANTO, J.; PORCILE, V.; DE BARBIERI, I.; SOARES DE LIMA, J.M.; MONTOSSI, F.; ROSSI, C.; MARANGES, F.; REYNO, R. Paspalum notatum INIA Sepé: una gramínea nativa de alta productividad y persistencia. Revista INIA Uruguay, Setiembre 2021, no.66, p. 51-54. (Revista INIA; 66).| Biblioteca(s): INIA Treinta y Tres. |
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| Registros recuperados : 16 | |
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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. |
|
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 : |
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. |
|
Palabras claves : |
AGRICULTURA DE PRECISION; ARROZ; CALIFORNIA. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 02000naa a2200193 a 4500
001 1050377
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008 2004 bl uuuu u00u1 u #d
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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