Water and energy savings using variable rate sprinkler irrigation on a large maize farm in northern Italy

Abstract

Maize is a key crop both globally and in Italy. In the Po Valley, it is cultivated on 500,000 ha, primarily for use in livestock production. Here, maize cultivation is highly dependent on irrigation, traditionally performed using border irrigation. However, due to increasing water scarcity, more efficient irrigation strategies will be required in the future. This study develops and tests an innovative integrated framework combining soil characterisation, in-field monitoring devices, agro-hydrological modelling and remote sensing to save water and energy. In 2021, a variable rate (VR) irrigation strategy was implemented in a 15-ha center pivot in a large livestock farm in northern Italy using: i) soil mapping based on an electromagnetic induction (EMI) sensor to delineate homogeneous zones, ii) a modelling workflow coupling soil moisture probes and weather forecasts to determine irrigation timing and amounts, and iii) a speed-controlled pivot for spatially variable application. This approach reduced water and energy use by 20 %, while maintaining yield and reducing grain moisture at harvest, although operational constraints imposed by the tenant limited the achievable savings. The framework was then scaled up to the entire farm for the 2016–2021 period using a semi-distributed agro-hydrological model supported by remote sensing data. Simulations indicated a mean reduction of 19 % in irrigation and energy use, consistent with field results. Overall, the developed modelling framework proved to be effective in optimizing irrigation and can be transferred to other crop-growing areas relying on sprinkler systems. • The developed variable-rate (VR) irrigation strategy integrates measures, models and weather forecasts. • VR irrigation reduced maize water use by 20 % without affecting yield on a 15 ha pivot in 2021. • Energy saving was consistent with water saving (20 %). • VR simulations for a farm (300 ha) showed an average (6 years) water and energy saving of 19 %. • Crop phenometrics and parameters retrieved by Sentinel-2 were crucial at the farm level.

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