## 1 Introduction  
As climate change accelerates, many regions are beginning to experience less rainfall than their historical average, which means governments worldwide are facing major issues in water resource management [1; 2; 3; 4]. Therefore, researchers have begun designing various decision support systems [5; 6] to aid governments in distributing limited water resources; among these, decision support for irrigation systems is the most widely discussed [7; 8]. For this topic, scholars usually develop methodology in two stages. They first use the agricultural knowledge of professionals to calculate the objective values of different irrigation solutions and then apply decision support algorithms to obtain a small selection of optimal solutions. For example, [PERSON] et al. [9] used the agronomic standards of each crop, such as crop coefficient, root depth, and soil water balance to evaluate the degrees of water wastage; a top-\(k\) query was then applied to select the solution with the least wastage. [PERSON] et al. [10] employed various crop-growth models to estimate total crop profit and found the solutions with the most profit using top-k. More recently, [PERSON] et al. [11] used the agronomic standards of the crops, soil water, and salt balance limits to construct a two-layer multi-objective agricultural water allocation model, which evaluates economic benefits and irrigation water productivity. Solutions were also obtained in this case using a top-\(k\) query. These examples illustrate the extensive development of the first stage of irrigation decision support, i.e., adding elements and developing models to obtain the objective values. However, all these examples simply applied a top-\(k\) query in the second stage of the algorithm to filter for feasible solutions. The current paper seeks to optimize irrigation decision support further by improving upon the second stage of the algorithm. In the early stages of development, decision support systems used a single objective value as the selection criterion, which means that the best solution was simply the one with the highest objective value. This was the procedure followed by top-\(k\) query algorithms. Later, the concept of multiple objective values was proposed. Multiple target values are integrated into one score, and the one with the highest score is selected as the best solution. However, this approach is flawed in cases where objectives conflict with each other. If the parameters of top-\(k\) integration are not adjusted well, the algorithm will only return the best solution for one of the conflicting parties, and the user will lose the opportunity to learn about other solutions that may meet current needs [12; 13]. Therefore, in 2001, [PERSON] et al. [12] proposed the concept of skyline query, which is able to find good solutions to the target problem with seemingly contradictory components and return them all to the user. This concept overcomes the shortcomings of the top-\(k\) query. Many scholars have, thus, abandoned top-\(k\) in favor of the skyline query. For example, [PERSON] et al. [14], [PERSON] et al. [15], and [PERSON] et al. [16] used skyline queries to support path decisions. [PERSON] and [PERSON] [17] and [PERSON] et al. [18] used the skyline query to assist in travel planning. [PERSON] and [PERSON] [19] and [PERSON] et al. [20] used the skyline query to assist social networks in critical person selection. In this paper, we are, thus, prompted to verify the effectiveness of the skyline query for irrigation decision support. The role of this paper is to explore upgrading the second stage of existing irrigation decision support systems, so we do not focus on the first stage of the decision-making process, i.e., evaluating the objective values. Rather, we aim to adapt the skyline query to the field of irrigation, as existing skyline query algorithms cannot be directly applied to the irrigation decision support systems.