Institute of Land Use, Technical and Precision Technology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032, Debrecen, Hungary; Institutes for Agricultural Research and Educational Farm, University of Debrecen, Böszörményi 138, 4032, Debrecen, Hungary.
Department of Geography, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
J Environ Manage. 2024 Nov;370:122640. doi: 10.1016/j.jenvman.2024.122640. Epub 2024 Sep 27.
Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its underlying factors at a regional scale. In this context, the present study aimed to evaluate soil health in the eastern Mediterranean region by using the Sodium Adsorption Ratio (SAR) as an indicator of soil salinity in three distinct soil horizons. The main objective of the research was to evaluate the performance of four machine learning (ML) models, including Random Forest (RF), Nu Support Vector Regression (NuSVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Gradient Boosting Regression (GBR), for accurate prediction of SAR following the Recursive Feature Elimination (RFE) as a feature selection method. Moreover, SHapely Additive exPlanations (SHAP) was applied as sensitivity analysis to identify the most influential covariates. Main findings of the research revealed that the average clay content in the surface horizon (H-) was 50.5% ± 10.4, which significantly increased to 57.5% ± 8.7 (p < 0.05). No significant mean differences were detected between the studied horizons for SAR and Na. ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is ranked as NuSVR > GBR > ANN-MLP > RF. Lastly, SHAP sensitivity analysis identified CEC, Ca, Mg, and Na as the most influential variables for SAR prediction in both the training and testing stages. Hence, the research yielded valuable insights for efficient agricultural soil management at a regional level using state-of-the-art technology.
土壤盐渍化是可持续农业面临的全球性重大问题，它影响作物产量，对实现确保粮食安全的可持续发展目标（SDG）构成威胁。因此，有必要在区域尺度上详细监测土壤盐渍化并揭示其潜在因素。在此背景下，本研究旨在通过使用钠离子吸附比（SAR）作为土壤盐分的指标，评估东地中海地区的土壤健康状况，研究分别在三个不同土壤层次中进行。研究的主要目的是评估四种机器学习（ML）模型的性能，包括随机森林（RF）、核支持向量回归（NuSVR）、人工神经网络-多层感知机（ANN-MLP）和梯度提升回归（GBR），以在递归特征消除（RFE）作为特征选择方法的情况下，准确预测 SAR。此外，还应用 SHapely Additive exPlanations（SHAP）作为敏感性分析，以确定最具影响力的协变量。研究的主要结果表明，表层土壤（H-）的平均粘粒含量为 50.5%±10.4，显著增加到 57.5%±8.7（p<0.05）。在所研究的土壤层次中，SAR 和 Na 没有显著的平均差异。ML 输出结果表明，NuSVR 在训练和测试阶段都能更准确地预测结果。此外，在 RFE 方法选择的七个特征的情景 2（SC2）下，有利于 SAR 的高度准确预测。总体而言，ML 模型的性能排名为 NuSVR>GBR>ANN-MLP>RF。最后，SHAP 敏感性分析确定 CEC、Ca、Mg 和 Na 是训练和测试阶段 SAR 预测的最具影响力变量。因此，本研究利用最先进的技术为区域水平的农业土壤管理提供了有价值的见解。
