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In order to carry out an optimal daily irrigation scheduling for a grapevine in a pot under drip irrigation by lysimeter, a great amount of data have to be treated. The majority of data are about monitored weight variations. The rest of the data are about some climatic variables (reference evapotranspiration ETo and rainfall). This daily irrigation programming is traditionally based on physicalmathematical models (soil water balance, radiation or drainage among others). This is a Model-Driven methodology. In this paper, several Model-Driven methodologies for data management are compared and the appropriate methodology is proposed. The experiment was developed in an experimental vineyard located in Orihuela (SE Spain). For this purpose, a lysimeter designed for crops in a pot with three cell charges and a datalogger was installed. Representative data of a vineyard variety (Bobal in 110R) were collected for three months during 2012. In the experiment, drip irrigation of a grapevine in a pot was monitored. Data about weight variations of the crop and irrigation water management were collected. These data were subsequently analysed by statistical techniques that are oriented to determine the adequate data treatment. This methodology will be used in the development of applied algorithms in software for decision tools in irrigation programming for vineyard production.

Data management in drip irrigation monitoring for vineyards in pot by weighing lysimeter