{"title":"酸性土壤复杂改良剂对土壤质量综合预测的通用模型","authors":"Pengshun Wang , Prakash Lakshmanan , Qichao Zhu , Siwen Zhang , Donghao Xu , Shuihan Yuan , Fusuo Zhang","doi":"10.1016/j.geoderma.2025.117495","DOIUrl":null,"url":null,"abstract":"<div><div>Soil acidification poses a serious threat to sustainable use of arable land. Applying soil amendments are effective measures to counteract the widely occurred soil acidification. However, a method to accurately predict the amendment effects on soil acidification is lacking. In this study, a total of 41 soil culture treatments, covering organic, inorganic, and their combinations, were conducted to compile a comprehensive dataset, which was further used to establish a model to predict the performance of amendments. The random forest (RF) and multiple linear regression (MLR) were adopted to model soil quality changes due to varying amendments application. 30 % percent of the culture treatment dataset and field observations were used to validate the model performance. The results demonstrate that MLR models are less robust in predicting the change in soil indicators, with the R<sup>2</sup> varying 0.6∼0.82. For some soil indicators, such as exchangeable acid (Ex-Acid), exchangeable calcium and cation exchange capacity (CEC), due to weak adherence to key assumptions such as linearity, homoscedasticity, and normality, which likely impaired their predictive reliability. Such limitations could reduce the model’s fitting accuracy and predictive stability for certain soil properties. The RF model is excellent at reconstructing changes in all soil chemical properties, with R<sup>2</sup> greater than 0.80. This includes soil pH, Ex-Acid, exchangeable calcium, and exchangeable magnesium, except for changes in CEC, which rarely changed after amendments application. Validation of model predictions through multi-site field observations further confirmed the robust predictions for multiple types of amendments. In particular, the prediction results of RF for Ex-acid are better than those of MLR. The overall outcomes suggest that RF model demonstrated greater reliability and adaptability, highlighting its practical value for guiding amendment selection in acid soil management.</div></div>","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"461 ","pages":"Article 117495"},"PeriodicalIF":6.6000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Universal models for comprehensive prediction of soil quality responses to complex amendments in acidic soil\",\"authors\":\"Pengshun Wang , Prakash Lakshmanan , Qichao Zhu , Siwen Zhang , Donghao Xu , Shuihan Yuan , Fusuo Zhang\",\"doi\":\"10.1016/j.geoderma.2025.117495\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Soil acidification poses a serious threat to sustainable use of arable land. Applying soil amendments are effective measures to counteract the widely occurred soil acidification. However, a method to accurately predict the amendment effects on soil acidification is lacking. In this study, a total of 41 soil culture treatments, covering organic, inorganic, and their combinations, were conducted to compile a comprehensive dataset, which was further used to establish a model to predict the performance of amendments. The random forest (RF) and multiple linear regression (MLR) were adopted to model soil quality changes due to varying amendments application. 30 % percent of the culture treatment dataset and field observations were used to validate the model performance. The results demonstrate that MLR models are less robust in predicting the change in soil indicators, with the R<sup>2</sup> varying 0.6∼0.82. For some soil indicators, such as exchangeable acid (Ex-Acid), exchangeable calcium and cation exchange capacity (CEC), due to weak adherence to key assumptions such as linearity, homoscedasticity, and normality, which likely impaired their predictive reliability. Such limitations could reduce the model’s fitting accuracy and predictive stability for certain soil properties. The RF model is excellent at reconstructing changes in all soil chemical properties, with R<sup>2</sup> greater than 0.80. This includes soil pH, Ex-Acid, exchangeable calcium, and exchangeable magnesium, except for changes in CEC, which rarely changed after amendments application. Validation of model predictions through multi-site field observations further confirmed the robust predictions for multiple types of amendments. In particular, the prediction results of RF for Ex-acid are better than those of MLR. The overall outcomes suggest that RF model demonstrated greater reliability and adaptability, highlighting its practical value for guiding amendment selection in acid soil management.</div></div>\",\"PeriodicalId\":12511,\"journal\":{\"name\":\"Geoderma\",\"volume\":\"461 \",\"pages\":\"Article 117495\"},\"PeriodicalIF\":6.6000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoderma\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0016706125003362\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOIL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoderma","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0016706125003362","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOIL SCIENCE","Score":null,"Total":0}
引用次数: 0
摘要
土壤酸化对耕地的可持续利用构成严重威胁。施用土壤改良剂是防治土壤酸化的有效措施。然而,目前还缺乏一种准确预测土壤酸化效果的方法。本研究通过41个土壤培养处理,包括有机、无机及其组合处理,构建了综合数据集,并利用该数据建立了修正效果预测模型。采用随机森林(random forest, RF)和多元线性回归(multiple linear regression, MLR)模型对不同改良剂施用量引起的土壤质量变化进行了模拟。30%的培养处理数据集和实地观察用于验证模型的性能。结果表明,MLR模型在预测土壤指标变化方面的鲁棒性较差,R2为0.6 ~ 0.82。对于一些土壤指标,如交换性酸(Ex-Acid)、交换性钙和阳离子交换容量(CEC),由于对线性、均方差和正态性等关键假设的依从性较弱,可能会损害其预测的可靠性。这些限制可能会降低模型的拟合精度和对某些土壤特性的预测稳定性。RF模型对所有土壤化学性质的变化具有较好的重建效果,R2大于0.80。这包括土壤pH, Ex-Acid,交换性钙和交换性镁,除了CEC的变化,在改剂应用后很少改变。通过多站点现场观测对模式预测的验证进一步证实了对多种修正类型的稳健预测。特别是RF对Ex-acid的预测结果优于MLR。结果表明,RF模型具有较高的可靠性和适应性,在指导酸性土壤改良剂选择方面具有重要的实用价值。
Universal models for comprehensive prediction of soil quality responses to complex amendments in acidic soil
Soil acidification poses a serious threat to sustainable use of arable land. Applying soil amendments are effective measures to counteract the widely occurred soil acidification. However, a method to accurately predict the amendment effects on soil acidification is lacking. In this study, a total of 41 soil culture treatments, covering organic, inorganic, and their combinations, were conducted to compile a comprehensive dataset, which was further used to establish a model to predict the performance of amendments. The random forest (RF) and multiple linear regression (MLR) were adopted to model soil quality changes due to varying amendments application. 30 % percent of the culture treatment dataset and field observations were used to validate the model performance. The results demonstrate that MLR models are less robust in predicting the change in soil indicators, with the R2 varying 0.6∼0.82. For some soil indicators, such as exchangeable acid (Ex-Acid), exchangeable calcium and cation exchange capacity (CEC), due to weak adherence to key assumptions such as linearity, homoscedasticity, and normality, which likely impaired their predictive reliability. Such limitations could reduce the model’s fitting accuracy and predictive stability for certain soil properties. The RF model is excellent at reconstructing changes in all soil chemical properties, with R2 greater than 0.80. This includes soil pH, Ex-Acid, exchangeable calcium, and exchangeable magnesium, except for changes in CEC, which rarely changed after amendments application. Validation of model predictions through multi-site field observations further confirmed the robust predictions for multiple types of amendments. In particular, the prediction results of RF for Ex-acid are better than those of MLR. The overall outcomes suggest that RF model demonstrated greater reliability and adaptability, highlighting its practical value for guiding amendment selection in acid soil management.
期刊介绍:
Geoderma - the global journal of soil science - welcomes authors, readers and soil research from all parts of the world, encourages worldwide soil studies, and embraces all aspects of soil science and its associated pedagogy. The journal particularly welcomes interdisciplinary work focusing on dynamic soil processes and functions across space and time.