Irrigation-Advisor-A Decision Support System for Irrigation of Vegetable Crops
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AuthorMiras Avalos, José Manuel; Rubio Asensio, José Salvador; Ramírez Cuesta, Juan Miguel; Maestre Valero, José Francisco; Intrigliolo Molina, Diego Sebastiano
Knowledge AreaEdafología y Química AgrícolaIngeniería del Terreno
SponsorsThe authors thank the owners of the experimental fields (Madrid and Galindo families) and David Hortelano (CEBAS-CSIC) for his help with field determinations. This research: including the APC, was funded by the Spanish MINISTERIO DE ECONOMÍA Y COMPETITIVIDAD (MINECO) with FEDER co-financing, projects “RiegoAsesor” and “Preciriego” (grant numbers RTC-2015-3453-2 and RTC-2017-6365-2) and by the European Commission with project “SHui” (grant number: 773903).
Realizado en/conUniversidad Politécnica de Cartagena; Consejo Superior de Investigaciones Científicas
Bibliographic CitationMirás-Avalos JM, Rubio-Asensio JS, Ramírez-Cuesta JM, Maestre-Valero JF, Intrigliolo DS. Irrigation-Advisor—A Decision Support System for Irrigation of Vegetable Crops. Water. 2019; 11(11):2245. https://doi.org/10.3390/w11112245
Soil water balance
Climate change will intensify water scarcity, and therefore irrigation must be adapted to save water. Operational tools that provide watering recommendations to end-users are needed. This work presents a new tool, Irrigation-Advisor (IA), which is based on weather forecasts and is able to separately determine soil evaporation and crop transpiration, and thus is adaptable to a broad range of agricultural situations. By calculating several statistical indicators, IA was tested against the FAO-56 crop evapotranspiration (ETcFAO) methodology using local crop coefficients. Additionally, IA recommendations were compared with current standard practices by experienced farmers (F). Six field experiments with four widely cultivated species (endive, lettuce, muskmelon and potato) were performed in Southeast Spain. Irrigation water applied, crop yield, aboveground biomass and water productivity were determined. Crop water needs underestimations (5%–20%) were detected when comparing IA against ...
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