English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. The status of the soil organic carbon (SOC) stock at any position in the landscape is subject to a complex interplay of soil state factors operating at different scales andregulating multiple processes resulting either in soils acting as a net sink or net source of carbon. Forest landscapes are characterized by high spatial variability, and key drivers of SOC stock might be specific for sub-areas compared to those influencing the whole landscape. Consequently, separately calibrating models for sub-areas (local models) that collectively cover a target area can result in different prediction accuracy and SOC stock drivers compared to a single model (global model) that covers the whole area. The goal of this study was therefore to (1) assess how global and local models differ in predicting the humus layer, mineral soil, and total SOC stock in Swedish forests and (2) identify the key factors for SOC stock prediction and their scale of influence. We used the Swedish National Forest Soil Inventory (NFSI) database and adigital soil mapping approach to evaluate the prediction performance usingrandom forest models calibrated locally for the northern, central, andsouthern Sweden (local models) and for the whole of Sweden (global model).Models were built by considering (1) only site characteristics which arerecorded on the plot during the NFSI, (2) the group of covariates (remote sensing, historical land use data, etc.) and (3) both site characteristics and group of covariates consisting mostly of remote sensing data. Local models were generally more effective for predicting SOC stock aftertesting on independent validation data. Using the group of covariatestogether with NFSI data indicated that such covariates have limitedpredictive strength but that site-specific covariates from the NFSI showedbetter explanatory strength for SOC stocks. The most important covariatesthat influence the humus layer, mineral soil (0–50 cm), and total SOCstock were related to the site-characteristic covariates and include thesoil moisture class, vegetation type, soil type, and soil texture. This study showed that local calibration has the potential to improve predictionaccuracy, which will vary depending on the type of available covariates.

Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data