Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

Panagiotis Christias,Thrassyvoulos Manios,Ioannis N Daliakopoulos,Mariana Mocanu

doi:10.3390/math8050717

Panagiotis Christias, Thrassyvoulos Manios + Show 2 more

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https://doi.org/10.3390/math8050717

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Abstract

This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue.

Highlights

The agricultural sector consumes the large majority (70%) of water abstractions [1], the resulting water productivity ranges widely even for similar climates, locations and crops [2]
Since their advent [9], irrigation decision support systems (IDSS) aim to facilitate “smart” water use that allows water users to decouple water consumption from yields, achieving economic growth with a lower environmental footprint [10]. These systems produce irrigation schedules based on deterministic models such as AquaCrop [11,12,13] or IRRINET [14] that eventually rely on the Food and Agriculture Organization (FAO) of the United Nations FAO-56 method [15] to estimate crop yield response to temperature and water availability
Motivated by the relatively poor adoption of IDSS [17,32,33], despite the global impact that efficient irrigation could achieve, here, we aimed to develop an innovative irrigation decision support system for tree crops that can accept multiple conflicting objectives while considering a wide but very relevant range of system parameters such as soil moisture, plant evapotranspiration, weather conditions, agricultural input and water costs, as well as yield market value

Summary

Introduction

The agricultural sector consumes the large majority (70%) of water abstractions [1], the resulting water productivity ranges widely even for similar climates, locations and crops [2]. In the face of increasing demand [5] and climate change, both at the global [6] and the local scale [7], pressure on agricultural systems is expected to increase, and water scarcity will become a major limitation for sustainable development, especially in semi-arid regions [8]. Since their advent [9], irrigation decision support systems (IDSS) aim to facilitate “smart” water use that allows water users to decouple water consumption from yields, achieving economic growth with a lower environmental footprint [10].

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