A Novel Decision Support System for Generating Irrigation Ecolabels Based on the Resource Overutilization Ratio

Abstract

As a crucial economic activity, agriculture must consider factors that impact business viability, such as environmental conditions and climate. In planning an agricultural field, particularly for woody crops, it’s crucial to recognize that plants are reliable indicators of irrigation efficiency. Unlike herbaceous crops, woody crops are typically planted with an irrigation system that remains unchanged for the duration of the life of the crop. Initially, the irrigation system is designed based on the conditions of the first year, but after several years, it is essential to reassess the design using new data, such as plant development, to evaluate whether the original setup was effective. This static approach, if uncorrected, cannot account for the developing needs of the crop, such as plant mortality, changes in soil water availability, topographical influences, and proper management by the farmer. Therefore, regular assessment and adjustment of the irrigation system are necessary to ensure long-term efficiency and sustainability. To this end, this paper introduces a novel DSS supported by two novel concepts for improving water management in these systems: the \(\:Irrigation\:Ecolabel\), based on the Resource Overutilization Ratio (\(\:ROR\)). By using the FAO-56 Penman-Monteith method to calculate the current water needs of plants through the crop coefficient (Kc) and leveraging technologies like drones to measure key factors such as canopy cover, the DSS gathers essential data. Then, it compares this information with the existing irrigation system, which, for woody crops, is often based on the original design. A new index is developed: the \(\:ROR\), which evaluates the level of excess water usage and assigns an efficiency label to the system. A case study in a vineyard in northern Spain revealed opportunities for resource savings and efficiency improvements. Implementing this labeling system could optimize resource management and reduce environmental impact. The datasets can be found in public repositories, and the software is open-source.