Soil-gas diffusivity plays a fundamental role on diffusion-controlled migration of climate impact gases from different terrestrial ecosystems including managed pasture systems. Soil-gas diffusivity has a strong bearing on soil type/texture and soil structure (e.g., density) and typically shows a depth-dependent behavior in subsurface. This study investigated the gas diffusivity in soils sampled from a managed pasture site at Ambewela, Sri Lanka at 0-5 cm depth range along a downgrading transect. The soils were pre-characterized for particle-size distribution, organic matter content, dry density and particle density. Soil-gas diffusivity was measured using one-chamber diffusion apparatus using N2 and O2 as experimental gases. The measured diffusivity, together with selected intact and repacked soil data from literature, were tested against the existing predictive gas diffusivity models. We used a generalized descriptive parametric two-region model to represent bimodal/two-region behaviour of selected soils which was able to statistically outperform the predictive models for both intact and repacked soils and hence demonstrated its applicability to better characterize site-specific greenhouse gas emissions with useful implications for pasture management.

The cosmic-ray neutron probe (CRNP) provides continuous monitoring of average near-surface soil water content (SWC) on a hectometer scale. However, the performance of the CRNP in surfaces of highly heterogeneous vegetation and SWC caused by the highly eroded terrain remains uncertain. This study evaluated three different vegetation calibration methods developed for CRNP on the Loess Plateau, compensating for the privious lack of vegetation correction for CRNP in this area. The landscape biomass and its distribution within the CRNP footprint of a small watershed on the Chinese Loess Plateau were measured using an unmanned aerial vehicle (UAV) equipped with a visible light camera and a Rededge multispectral camera. Three CRNPs were installed at three different locations including loamy sand hills (LSH) plot, silty loam dam (SLD) plot, and a sandy loam slope (SLS) plot and electronic sensors of SWC were buried at various depths in each of the three plots. We found the parameter, N0, was best represented by the biomass at the average growth conditions of the monitoring period, yielding the lowest RMSE (0.068). The variation of spatio-temporal distribution of SWC reduced the representativeness of the CRNP to the regional average SWC. The CRNP SWC calibrated using the three methods at three sites yielded similar temporal changes with precipitation. The three methods each reduced the RMSE between the estimated CRNP SWC and the kriging weighted SWC with the BWE (biomass water equivalent) corrected method, Veg-N0 method having the lowest RMSE. The correction of the spatial distribution of vegetation using the three methods further reduced the RMSE, in the order: LSH< SLS< SLD. The cooperation between CRNP and UAV could obtain regional averaged SWC accurately, which should be useful for formulation sustainable vegetation management strategies on the Loess Plateau and regions with water scarcity possibly around the world.