Irrigation and crop management using multi-objective optimization – A case study

Abstract

Irrigation and crop management practices play a crucial role in ecology and agrohydrology and can have significant environmental and socio-economic impacts. The important decision factors include soil moisture and water salinity, particularly in arid and semi-arid regions. In this study, the effects of these two factors on maize are quantitatively measured. To collect data, design of experiments is utilized at a greenhouse in a research center. The dataset consists of eighteen variables, including sixteen qualitative, quantitative, and morphological maize characteristics, as well as two decision variables, i.e., water depletion and salinity. This endeavor aims to promote the use of multi-objective decision-making techniques in the water management context to improve agricultural practices in regions facing water scarcity.

To ensure appropriate data analysis, besides data visualization, multivariate and repeated measures analysis of variance are employed. Subsequently, a repeated measures model is used to fit statistical models and construct the corresponding responses. Furthermore, to determine the optimal levels of soil moisture and water salinity, which are often in conflict with each other, the problem is approached within a multi-objective framework. The weighted p-norm method is employed to incorporate management priorities in the decision-making process and facilitate tradeoffs between these two factors. Policy implications based on the findings are provided, offering valuable insights for improving irrigation and crop management practices. Detailed guidelines are provided on how to effectively analyze a problem, construct a model, and verify its assumptions.