Increase of temperature exacerbates the conversion of P fractions in organic horizon

Luo, Chaoyi; Wu, Yanhong; He, Qingqing; Wang, Jipeng; Bing, Haijian

doi:10.1016/j.soilbio.2024.109368

Cited by 2 publications

(1 citation statement)

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“…PET describes the maximum evapotranspiration rate occurring under specific environmental conditions (e.g., temperature, humidity, solar radiation, and wind speed), when there is no limitation of water supply [ 62 ]. The phosphorus dynamics in the soil can be indirectly affected during evapotranspiration [ 63 ]. For example, strong evapotranspiration is likely to cause a decrease in the SM content, which in turn affects the dissolution and transport of phosphorus in the soil.…”

Section: Discussionmentioning

confidence: 99%

Mapping Soil Properties in the Haihun River Sub-Watershed, Yangtze River Basin, China, by Integrating Machine Learning and Variable Selection

Huang,

Liu,

et al. 2024

Sensors

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Mapping soil properties in sub-watersheds is critical for agricultural productivity, land management, and ecological security. Machine learning has been widely applied to digital soil mapping due to a rapidly increasing number of environmental covariates. However, the inclusion of many environmental covariates in machine learning models leads to the problem of multicollinearity, with poorly understood consequences for prediction performance. Here, we explored the effects of variable selection on the prediction performance of two machine learning models for multiple soil properties in the Haihun River sub-watershed, Jiangxi Province, China. Surface soils (0–20 cm) were collected from a total of 180 sample points in 2022. The optimal covariates were selected from 40 environmental covariates using a recursive feature elimination algorithm. Compared to all-variable models, the random forest (RF) and extreme gradient boosting (XGBoost) models with variable selection improved in prediction accuracy. The R2 values of the RF and XGBoost models increased by 0.34 and 0.47 for the soil organic carbon, by 0.67 and 0.62 for the total phosphorus, and by 0.43 and 0.62 for the available phosphorus, respectively. The models with variable selection presented reduced global uncertainty, and the overall uncertainty of the RF model was lower than that of the XGBoost model. The soil properties showed high spatial heterogeneity based on the models with variable selection. Remote sensing covariates (particularly principal component 2) were the major factors controlling the distribution of the soil organic carbon. Human activity covariates (mainly land use) and organism covariates (mainly potential evapotranspiration) played a predominant role in driving the distribution of the soil total and soil available phosphorus, respectively. This study indicates the importance of variable selection for predicting multiple soil properties and mapping their spatial distribution in sub-watersheds.

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Section: Discussionmentioning

confidence: 99%