Unraveling the threshold and interaction effects of environmental variables on soil organic carbon mapping in plateau watershed
[Display omitted] •Landscape metrics were used as environmental variables to estimate and map SOC.•Environmental variables have threshold and interaction effects in SOC mapping.•The two effects were investigated with combined GBDT and partial dependence analysis.•Soil moisture is the most important...
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Veröffentlicht in: | Geoderma 2024-10, Vol.450, p.117032, Article 117032 |
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Sprache: | eng |
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•Landscape metrics were used as environmental variables to estimate and map SOC.•Environmental variables have threshold and interaction effects in SOC mapping.•The two effects were investigated with combined GBDT and partial dependence analysis.•Soil moisture is the most important factor that affects the spatial variation of SOC.•High soil moisture and high landscape contagion favour SOC accumulation.
Understanding the spatial distribution and mechanisms driving soil organic carbon (SOC) is crucial for assessing soil carbon stocks and implementing effective carbon sequestration strategies in agricultural landscapes. The linear and nonlinear relationships between environmental variables and SOC have been extensively documented, but the threshold and interaction effects among multiple covariates on SOC remain underexplored. This study focused on farmland within the Qilu Lake watershed in Yunnan Province, China, which is characterized by complex surface conditions shaped by both climate change and anthropogenic activities. Utilizing 216 soil samples from the watershed, this research aimed to investigate the threshold and interaction effects of environmental variables on SOC. To achieve this, gradient boosted decision tree (GBDT) combined with partial dependence analysis were employed to elucidate the spatial distribution of SOC and the intricate relationships between environmental factors and SOC. In order to enhance the accuracy of SOC prediction, we employed the landscape metrics as environmental variables, thereby facilitating a more comprehensive description of the landscape. The results indicated that GBDT (R2 = 0.47) outperformed random forest (R2 = 0.38), achieving higher accuracy and lower uncertainty, indicated by a narrower 90% prediction interval. The SOC distribution was predominantly influenced by soil moisture, elevation, and the contagion index (CONTAG), with threshold effects observed at relatively high soil moisture levels (>50%), CONTAG levels (>85%), and relatively low elevations ( |
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ISSN: | 0016-7061 1872-6259 |
DOI: | 10.1016/j.geoderma.2024.117032 |