Environmental variables for modeling wheat yields in the southwest pampa region of Argentina
Two types of time scales - 10-day intervals (D) and phenological phases (P) - were applied to environmental variables for the development of statistical regression models relating to the southwest pampa region of Argentina, with the aim of detecting the effects of weather on wheat yields for the per...
| Autor: | |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2004 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/71462 |
| Acceso en línea: | http://hdl.handle.net/11336/71462 |
| Access Level: | acceso abierto |
| Palabra clave: | Environmental Variables Multivariate Regression Pampa Region Wheat Modeling https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 |
| Sumario: | Two types of time scales - 10-day intervals (D) and phenological phases (P) - were applied to environmental variables for the development of statistical regression models relating to the southwest pampa region of Argentina, with the aim of detecting the effects of weather on wheat yields for the period 1977-1999. The parameters were grouped as meteorological and processed variables and indices. The processed variables used were total soil water availability (SWA) and the ratio of actual evapotranspiration to potential evapotranspiration (α), obtained from a water-balance model in which the moisture anomaly index (Z) and the Palmer drought severity index (PDSI) were calculated according to Palmer's model. For these parameters it was possible to establish the times of year and the phenological phases with the best correlation to grain yield. The regression equation for meteorological variables on a 10-day scale provides one of the best fits. Using mixed parameters, the two models, D and P, give rise to a standard error of estimate of approximately 200 kg ha-1. Truncated models perform better on a P scale than on a D scale. The use of phenological stages improved yield assessment, particularly for those years with extreme meteorological conditions. The optimum models were tested and root-mean-square errors (RMSE) of 440 kg ha-1 and 470 kg ha -1 were obtained for P and D scales respectively. |
|---|