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Soil erodibility (susceptibility of soil to be lost to erosion) is one of the components of the universal soil loss equation (USLE). In the USLE, erodibility is known as the K factor, which in turn is a function of these soil properties: particle size distribution, organic matter content, structure, and permeability. The traditional approach for estimating soil erodibility does not account for spatial variability of individual soil properties or spatial correlation among those properties. Our objectives in this study were to evaluate the use of joint sequential simulation for mapping soil erodibility, as well as to partition the individual and joint variance contribution of soil properties for predicting soil erodibility. We collected 192 usable soil samples across Fort Hood, Texas in the summer of 1999. For each of those samples, we obtained an estimate of particle size distribution, organic matter content, structure, permeability, and calculated soil erodibility. We carried out both independent and joint sequential simulation to generate spatially explicit predictions and variance of all soil properties as well as covariance between pairs of soil properties for each cell within our simulation area. We used the program GCOSIM3D to conduct those simulations. On average, joint sequential simulation resulted in a K factor variance of less than half the variance obtained from independent simulation. Using the results from joint sequential simulation, we partitioned the contribution of each soil property and pair of properties using first-order Taylor series expansion of the soil erodibility function. Individually, Very-Fine-Sand-and-Silt contributed the most (46.19%), whereas Structure contributed the least (6.53%) to the K factor variance. Jointly, Permeability/Structure contributed the most (9.32%), whereas Sand/Very-Fine-Sand-and-Silt caused the largest reduction (−19.19%) in the K factor variance. We conclude that joint sequential simulation provided approximately twice as much precision as independent simulation for the spatially explicit prediction of soil erodibility. Likewise, first-order Taylor series expansion offered an accurate approach for partitioning the individual and joint contribution of soil properties to soil erodibility variance. This partitioning allowed us to identify large sources of uncertainty and suggest efficient approaches for further improving the precision of K value predictions. [Copyright &y& Elsevier]

Soil organic carbon (SOC) is an important soil property relating to soil formation, structure, and water-holding capacity. Remote sensing has been used for predicting SOC but remains a challenge due to the complex and indirect relationship between SOC and remote sensing variables. In this research we explored the approaches of predicting SOC distribution in a hickory plantation region using random forest (RF) and geographically weighted regression (GWR) based on Landsat and ancillary data, analyzed SOC spatial distribution and dynamic change between 2008 and 2013 through a thresholding approach, and examined major factors that resulted in SOC degradation in the young and mature hickory plantations using a logistic regression. The results showed that RF outperformed GWR in the prediction of SOC and provided stable and reliable SOC predictions with root mean squared errors of 4.6 g kg −1 in 2008 and 4.4 g kg −1 in 2013. A large area of hickory plantation was experiencing SOC decrease. The analysis of major factors causing SOC degradation indicated that steep slope and high proportion of silt component in soil resulted in SOC decrease in the young hickory plantations, and high elevation, high proportion of silt component in the soil, and the increase of soil fraction in the ground cover led to SOC decrease in the mature hickory plantations. This research provides valuable approaches to spatially predict SOC and identify major factors driving SOC degradation, which will be useful for adopting better measures to improve management of hickory plantations. [ABSTRACT FROM AUTHOR]

Abstract: Zoigê alpine wetland located at the Qinghai–Tibet Plateau, China, is the largest alpine wetland in the world and very sensitive to global climate changes. However, little is known of the dynamics of soil water soluble organic carbon (WSOC), light fraction organic carbon (LFOC), soil organic carbon (SOC) and its indicators in this alpine wetland. In this study, four wetland sites and one degraded wetland site in the Zoigê alpine wetland were selected to investigate the relationships of stable isotope and labile organic carbon dynamics with groundwater elevation or water table level. We also examine if the δ13C abundance, WSOC, and LFOC can be used as potential indicators of SOC dynamics in these wetland sites. The results showed that the plant dominant species in the degraded wetland site were different from those in the non-degraded wetland sites. Moreover, the declining water table did not significantly lower foliar δ13C, C and N concentrations and C/N ratio of dominant plant species, but led to a significant decrease of soil SOC, WSOC and LFOC concentrations and an increase of soil δ13C. In the wetland sites, SOC was significantly correlated with soil δ13C, WSOC, and LFOC. Our regression analysis showed that LFOC was the best potential indicator of SOC dynamics with WSOC and soil δ13C being less useful as indicators, respectively. In addition, the absolute slope coefficients of the regression models increased as the soil depth increased, which indicated that the SOC turnover increased with soil depth. [Copyright &y& Elsevier]