Exploring variability in rangeland soil organic carbon stocks across California (USA) using a voluntary monitoring network

Geoderma Regional(2020)

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摘要
Rangelands are an economically and ecologically important land type that make up more than 40% of California's land area. Protecting and, where appropriate, increasing soil organic carbon (SOC) stocks across these rangelands can help to promote on-site and public ecosystem services including forage production and climate change mitigation. A necessary step toward appropriately and effectively stewarding SOC is understanding baseline variability in SOC stocks and identifying how that variability relates to factors such as climate, soil texture, and topography. While these relationships have been examined at broader spatial scales and in other contexts, analysis for California's rangelands remains lacking. We leveraged a voluntary ecological monitoring program consisting of 45 working ranches to explore how SOC varies with geographic distance, climate, vegetation, soil type, and topography within and across three regions of California. We found that there was large variability in soil C stocks across the state, with the Central Coast region storing more SOC than the San Joaquin and Sacramento Valleys. Network-wide, SOC stocks ranged from 4.83 to 63.54 Mg ha−1 for 0–10 cm depth and 12.15–205.37 Mg ha−1 for 0–40 cm depth. As hypothesized, climate was the strongest predictor of SOC stocks across the entire network, and in general other predictor groups became more important at the regional level. Within each region, we found on average a sixfold difference in SOC stocks, with much of the variation unexplained by the predictor variables included. While some differences in SOC likely result from unaccounted variables such as soil mineralogy, conceivably some of the variation is also due to differences in past and current land use—indicating that there is potential to rebuild SOC through management.
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关键词
Climate,Mediterranean-type climate,Grazing lands,Random Forest,Soil texture,Variation partitioning analysis
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