LCIS DSS-An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study

The efficient use of water in agriculture is one of the most significant agricultural challenges that modern technologies are helping to cope with through Irrigation Advisory Services (IAS) and Decision Support Systems (DSS). These last are considered powerful management instruments able to help farmers achieve the best efficiency in irrigation water use and to increase their incomes through obtaining the highest possible crop yield. In this context, within the project "An advanced low cost system for farm irrigation support - LCIS" (a joint Italian-Israeli R&D project), a fully transferable DSS for irrigation support, based on three different methodologies representative of the state of the art in irrigation management tools (W-Tens, in situ soil sensor; IRRISAT (R), remote sensing; W-Mod, simulation modelling of water balance in the soil-plant and atmosphere system), has been developed. These three LCIS-DSS tools have been evaluated, in terms of their ability to support the farmer in irrigation management, in a real applicative case study on maize grown on Andosols in a private farm in southern Italy in the 2018 season. The evaluation considered the predictive performance of the tools and also the pros and cons of their application, due their different spatial scale applicability, costs and complexity of use. The results have shown that all three approaches are able to realise the maximum obtainable maize production. However, the method based on in situ soil sensor (W-Tens) supplied 40% more water compared to the other two methods, whereas the IRRISAT (R) and W-Mod approaches represent the best solution in terms of irrigation water use efficiency (IWUE). Moreover, IRRISAT (R) has the advantage of being able to work without soil spatial information, although unlike W-Tens both the latter methods need a high level of user expertise and consequently support of external service providers. Integration between different tools represents an opportunity for improved water use efficiency in agriculture (e.g., field sensors and remote sensing).