The use of inappropriate irrigation water may lead to unwanted consequences for the soil, plants, irrigation system equipment and more. Ideally, the assessment of water fitness should include all influential factors and their interdependence. Because that is rarely the case, as an attempt to satisfy such requirements we propose a new approach based on the FAO guidelines and Bayesian network (BN) modelling. Fifteen factors recommended by the FAO were used to create a network to assess irrigation water fitness. The usability of the BN was tested on two water samples from two measuring points in Vojvodina Province, Serbia. The results obtained from the same sample are diverse and depend on the plants' tolerance to salt and ionic toxicity, and on soil texture. For the different conditions at the measuring point Hetin, the probability of no restriction on use varied from 50 to 84%. In the case of the Bačko Gradište measuring point, the probability varied from 66 to 91.5%, and the strongest fitness depends on the soil texture. Comparison of the obtained results with the FAO classification shows high correlation and points to flexibility and adaptability of the created network to change data or working with missing data.

中文翻译：

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基于贝叶斯网络和粮农组织指南的灌溉水适宜性评估

使用不适当的灌溉水可能会对土壤、植物、灌溉系统设备等造成不良后果。理想情况下，对水适宜性的评估应包括所有影响因素及其相互依存性。因为这种情况很少发生，为了满足这些要求，我们提出了一种基于粮农组织指南和贝叶斯网络 (BN) 建模的新方法。粮农组织推荐的 15 个因素被用于创建一个评估灌溉水适宜性的网络。BN 的可用性在塞尔维亚伏伊伏丁那省两个测量点的两个水样上进行了测试。从同一样品中获得的结果是多种多样的，取决于植物对盐和离子毒性的耐受性，以及土壤质地。对于Hetin测点的不同条件，不限制使用的可能性从 50% 到 84% 不等。在 Bačko Gradište 测量点的情况下，概率从 66% 到 91.5% 不等，最强的适应度取决于土壤质地。获得的结果与粮农组织分类的比较显示出高度相关性，并指出所创建的网络在更改数据或处理缺失数据方面具有灵活性和适应性。