The effects of climate and soil properties on the magnitude of the visual soil quality indicators: a logistic regression approach

IF 0.9 Q4 GEOSCIENCES, MULTIDISCIPLINARY AIMS Geosciences Pub Date : 2023-01-01 DOI:10.3934/geosci.2023027
F. Teixeira
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Abstract

Understanding how different climates and soil properties affect the soil processes requires quantifying these effects. Visual soil quality indicators have been proposed to assess the robustness of the soil processes and infer their ability to function. The scores of the visual soil quality indicators covary with climate features and soil properties, and their magnitude is different in acid-to-neutral and alkaline soils. These variables show collinearities and interactions, and the assessment of the individual effect of each variable on the scores of the visual indicators and the selection of the best set of explanatory variables can only be made with a definite set of variables. Logistic regression was used to calculate the effects of six climate variables and four soil properties, and their interactions, on the scores of eight visual soil quality indicators. Simple models featuring climate and soil variables explained a substantial part of the variation of the visual indicators. Models were fitted for each visual indicator for acid-to-neutral and alkaline soils. The sample size needed was calculated, and the method and its validity were discussed. For two possible outcomes, the sample size using the events per variable (EPV) criterium ranges between 62 and 183 observations, while using one variable and a variance inflation factor, it ranges between 22 and 234. Except for the model of soil structure and consistency for acid-to-neutral soils, with a C statistic of 0.67, all others had acceptable to excellent discrimination. The models built are adequate, for example, for the large-scale spatial outline of the soil health indices, to couple with soil morphological-dependent pedotransfer functions, and so on. Future models should consider (test) other explanatory variables: other climate variables and indices, other soil properties and soil management practices.
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气候和土壤性质对目视土壤质量指标大小的影响:逻辑回归方法
了解不同的气候和土壤性质如何影响土壤过程需要量化这些影响。视觉土壤质量指标已被提出,以评估土壤过程的稳健性和推断其功能的能力。土壤质量目视指标的得分随气候特征和土壤性质的变化而变化,在酸碱土壤和中性土壤中,其大小不同。这些变量表现出共线性和相互作用,评估每个变量对视觉指标得分的个别影响和选择最佳解释变量集只能用一组确定的变量来进行。采用Logistic回归计算了6个气候变量和4种土壤性质及其相互作用对8个目视土壤质量指标得分的影响。以气候和土壤变量为特征的简单模型解释了视觉指标变化的很大一部分。对酸转中性和碱性土壤的各视觉指标进行了模型拟合。计算了所需的样本量,讨论了该方法及其有效性。对于两种可能的结果,使用每个变量事件(EPV)标准的样本量范围在62到183个观察值之间,而使用一个变量和方差膨胀因子,其范围在22到234之间。除酸中性土壤结构和一致性模型的C统计量为0.67外,其余均为可接受的极优判别。所建立的模型能够较好地反映土壤健康指数的大尺度空间轮廓,能够与土壤形态相关的土壤转移函数相耦合等。未来的模型应该考虑(测试)其他解释变量:其他气候变量和指数、其他土壤特性和土壤管理实践。
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来源期刊
AIMS Geosciences
AIMS Geosciences GEOSCIENCES, MULTIDISCIPLINARY-
自引率
7.70%
发文量
31
审稿时长
8 weeks
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