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• W2509721063 abstract "Soil organic carbon (SOC), a generic term for the carbon found in soil's organic matter, is one of earth's most dynamic carbon reservoirs and is relevant for climate science, forestry, agriculture, and soil science. Accurate estimates of SOC are important for verifying carbon sequestration in soil, national carbon inventories, and climate change mitigation programs like carbon trading. Unfortunately, collection of SOC data is limited by time and cost constraints; therefore, statistical methods are necessary for estimating the spatial distribution of SOC. In this paper, we develop a spatial statistical method that involves modeling the second-order nonstationarity in SOC via relevant covariate processes. As these processes are not fully observed, we incorporate Bayesian CART for treed covariate segmentation in a manner that allows the first- and second-order properties of SOC to be similar in regions where the distribution of a covariate is homogeneous. Using Bayesian model averaging to account for uncertainty in the segmentation process and to accommodate multiple covariate processes, we outline an algorithm for generating predictions of SOC at unobserved locations based on our covariate-driven nonstationary model. Compared to related approaches, our method yields moderate improvements for out-of-sample cross validation and much faster computation relative to traditional spatial models." @default.
• W2509721063 created "2016-09-16" @default.
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• W2509721063 date "2016-08-19" @default.
• W2509721063 modified "2023-09-27" @default.
• W2509721063 title "Nonstationary Spatial Process Modeling Via Treed Covariate Segmentation, with Application to Soil Organic Carbon Stock Assessment" @default.
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