Abstract

Planning agricultural procedures needs to take into account meteorological conditions. However, because of high associated costs, the density of meteorological stations is often not enough to cover all the cultivated or potentially cultivated areas. In this article we present a methodology to estimate seasonal maximum and minimum mean temperature in cultivated area using data registered in a sole or a few meteorological stations. The procedure is based on mesoscale modeling, which allows meteorological variables to be spatially distributed considering synoptic data and local characteristics.

Simulated daily cycle of temperature was compared with data registered at six meteorological stations located in the cultivated floor of the semiarid Limari Valley (Chile, 31°S). Although in some cases the simulated temperature differs in about 2°C with the observed one, a good fit between model results and experimental data was observed. Using the simulated seasonal minimum and maximum mean temperature fields, maps of temperature differences with respect to a reference station were drawn. In order to observe the influence of the orography on the lapse rate around a station, the methodology was applied for two reference stations located in places with different orographic characteristics. Results for winter and summer seasons are shown.

These generated maps can be used by farmers and agricultural planners to obtain information of seasonal minimum and maximum mean temperature from a station in any site of the cultivated area. This technique is a good alternative to obtain meteorological information at low costs, contributing to territorial planning for climate resilient agriculture sustainability.

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