Optimizing Irrigation Water Use Efficiency for Tomato and Maize Fields across Italy Combining Remote Sensing Data and the AquaCrop Model

Marco Mancini,Imen Ben Charfi,Chiara Corbari

doi:10.3390/hydrology8010039

Abstract

Remote sensing data of canopy cover and leaf area index are used together with the AquaCrop model to optimize irrigation water use efficiency for tomato and maize fields across Italy, which differ in climate, soil types and irrigation technique. An optimization irrigation strategy, “SIM strategy”, is developed based on crop stress thresholds and then applied to all the analyzed fields in different crop seasons, evaluating the effect not only on irrigation volume and number of irrigations but also on crop yield and canopy cover, and on the drainage flux which represents the main water loss. Irrigation volume reduction is found to be between 200 and 1000 mm, mainly depending on the different soil types within the climate, irrigation technique and crop type. This is directly related to the drainage flux reduction which is of a similar entity. The SIM strategy efficiency has then been quantified by different indicators, such as the irrigation water use efficiency (IWUE) which is higher than with the observed irrigations (around 35% for tomato fields in Southern Italy, between 30 and 80% for maize in Northern Italy), and the percolation deficit and irrigation efficiency. The AquaCrop model has been previously calibrated against canopy cover and leaf area index (LAI) data, producing errors between 0.7 and 5%, while absolute mean errors (MAE) between 0.015 and 0.04 are obtained for soil moisture (SM). The validation of the AquaCrop model has been performed against evapotranspiration (ET) ground-measured data and crop yields producing MAE values ranging from 0.3 to 0.9 mm/day, and 0.9 ton/ha for maize and 10 ton/ha for tomatoes, respectively.

Highlights

The conflictual use of water is becoming more and more evident, even in regions traditionally rich in water
The AquaCrop model has been previously calibrated against canopy cover and leaf area index (LAI) data, producing errors between 0.7 and 5%, while absolute mean errors (MAE) between 0.015 and 0.04 are obtained for soil moisture (SM)
In the first field (CA1), soil moisture is sensibly underestimated before calibration with a negative efficiency index (EI) (−0.78) and an MAE of 0.045, while after the calibration a good agreement is found with an EI of 0.48 and an MAE of 0.026

Summary

Introduction

The conflictual use of water is becoming more and more evident, even in regions traditionally rich in water. Improving water use efficiency in agriculture is an immediate requirement of human society for sustaining global food security, to preserve quality and quantity of water resources and to reduce causes of poverty, migrations and conflicts among states, which depend on trans-boundary river basins [3]. Among the existing crop models, the AquaCrop [5] model, developed by FAO (Food and Agriculture Organization), has been widely used for estimating crop yields as well as crop water requirements and optimal irrigation scheduling for different crops in different climatic environments owing to the balance between simplicity, accuracy and robustness [9,10,11,12,13,14]. The calibration of model parameters has traditionally been performed against observed ground punctual measurements of crop yield, soil moisture (SM), leaf area index (LAI) or canopy cover (CC) [15]

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