Optimal water-need estimation and irrigation scheduling significantly reduce the use of water in agriculture: insights from the SWAMP Italian (Reggio-Emilia) pilot

The objective of the SWAMP Italian pilot was the development and test IoT solutions for precise water-need estimation and optimal irrigation management to improve irrigation efficiency, reducing water usage and energy costs. The peculiarity of the Italian pilot lies on the presence of a Water District Manager (WDM), that is a public or private entity in charge of the management of a complex canal network devoted at allocating the water to several farmers (e.g., Consorzio di Bonifica dell’Emilia Centrale in the case of this pilot). In such context (common in many parts of Italy and other countries), a proper definition of the irrigation scheduling, together with the use of a water balance model for estimating crops water requirements, represent two pillars to try to reduce water usage in agriculture. IoT solutions developed during the project can serve as the framework collecting data from sensors in the fields, from infrastructures or external services (e.g., weather forecast, drone surveys) and providing tools and indications to farmers and WDMs.

These solutions have been implemented in the Italian pilot (892 ha, 320 ha of which are irrigated), which is located in the Po valley, near-by the city of Reggio-Emilia. Although COVID-19 pandemic significantly affected the project plans limiting real trials, SWAMP solutions proven their potential in water saving.

The precise water-need estimation is pursued within the SWAMP platform implementing the CRITERIA model, developed by ARPAE, thanks to which the amount of water saved in a pear orchard is estimated to be on average equal to 80%, without having any yield loss. In particular, the following figure shows precipitations and total irrigation water volume in the two irrigation seasons (2019 and 2020, panel a) and b), respectively), where orange and blue areas represent the cumulative irrigation water requirement simulated by CRITERIA and used by the farmer, respectively. Although not proven by real tests carried out in the field, these results shade some light on the potential of such solutions.

The optimization of the water distribution among multiple farmers and crops served by a common irrigation network is carried out adopting a mixed integer linear programming solution (MILP). MILP collects all water requests and provides gatekeeper with the optimal irrigation scheduling minimizing water loss by infiltration, delays on water delivery and field operations. The impact of the optimization algorithm has been evaluated referring to a series of real irrigations managed by the WDM during a 15-day period of 2020 season. The efficiency improvement results in a 32% reduction of the overall inflow volume required by the district. Grey curve in the following figure represents the irrigation request from farmers, while the blue one represents the volume used to satisfy such irrigation needs as recorded at the district inlet. Benefit associated to the adoption of the MILP algorithm is evident looking at the orange curve, which shows the volume that would have been required by the district, during the same period, in case of adopting the optimal scheduling.

Not withstanding the difficulties that characterized the final phases of the project due to COVID-19, the Italian pilot fully succeeded in proving the potential of IoT-based solutions to sustain both optimal water-need estimation and scheduling optimization approaches.




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