Efficient Irrigation of Maize Through Soil Moisture Monitoring and Modeling

Abstract

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Agriculture is the major user of water resources, accounting for 70% of global freshwater demand. As the demand for clean water increases, so does the need to implement more efficient strategies for water management in irrigated agriculture. While the benefits of precision irrigation in high-value crops, such as cannabis, tomatoes, and potatoes, are fully recognized, there is still need to investigate and implement cheap and efficient irrigation strategies for widespread low-value crops such as maize. In this study, the soil moisture dynamics in a sprinkler-irrigated maize field in Veneto (Northeastern Italy) was monitored using six time domain reflectometry (TDR) probes for the entire growing season. The TDR sensors were positioned at different depths into two separate sites: an Uninformed Site irrigated based on the farmer's experience and an Informed Site in which a water balance irrigation strategy was applied based on soil moisture measurements. A parsimonious hydrological model was then implemented and calibrated to quantify the different water balance terms (precipitation, evapotranspiration, lateral fluxes, and deep percolation). The comparison between the water budget terms in the two sites highlights that soil moisture monitoring during agriculture activities leads to substantial savings in terms of irrigation water volumes requirements and cost, without compromising the productivity of the crop field. A simplified upscaling of the results at the regional scale, assuming average conditions as in this study site and growing season, reveals that potentially significant economic savings, compared to the total profits linked to maize crops, could be possible.

Keywords

• time domain reflectometry
• hydrological modeling
• Markov chain Monte Carlo
• water balance
• irrigation costs