Soil-gas diffusivity and its variation with soil moisture plays a fundamental role in diffusion-controlled migration of climate-impact gases from different terrestrial agroecosystems including cultivated soils and managed pasture systems. The wide contrast in soil texture and structure (e.g., density, soil aggregation) in agriculture topsoils (0–10 cm) makes it challenging for soil-gas diffusivity predictive models to make accurate predictions across different moisture conditions. This study characterized gas diffusivity and gas-phase tortuosity in soils sampled from managed pasture and cultivated sites in Sri Lanka at 0–10 cm depth, together with selected soil-gas diffusivity data from the literature. Soil-gas diffusivity was measured using a one-chamber diffusion apparatus using N₂ and O₂ as experimental gases. The measured diffusivity, together with literature data representing both intact and repacked soils, were tested against five existing widely known gas diffusivity predictive models. The tested models tended to mischaracterize the two-region behavior in some of the aggregated soils, suggesting the need of soil-specific diffusivity models to better describe gas diffusivity in agricultural soils. We suggested a new parametric two-region model, developed in line with literature-based models, to represent both unimodal and bimodal/two-region behavior of selected soils. The new model statistically outperformed the existing predictive models for both intact and repacked soils and, hence, demonstrated its applicability to better characterize site-specific greenhouse gas emissions under different soil water regimes.

Hydrological isolation of abandoned aquaculture ponds is a principal barrier to coastal wetland recovery across South and Southeast Asia. However, sediment assessment rarely accompanies rehabilitation interventions, due to high monitoring costs and data scarcity for modelling. This study presents a low-cost framework combining a self-constructed depth-integrating sampler with a parsimonious one-dimensional (1-D) advection model to assess relative patterns of suspended sediment concentration (SSC) in restoration canals, when constrained by granular input data. 264 SSC samples across 23 locations in linear and contoured palmate canals constructed at the Anawilundawa mangrove restoration site, were collected over six tidal phases (high and low across three sampling days). The sampler achieved 85.5% coarse particle exclusion (>0.5 mm) and resolved vertical stratification. Near-bed SSC averaged 66% higher than surface SSC, while attenuated tidal energy and local geometry interactions governed SSC. When parameterised with geometry-specific diffusivity multipliers, the 1-D model achieved a root mean square error of 0.048 g L-1, with leave-one-pond-out validation demonstrating stable performance across canal geometries. High sedimentation event identification sensitivity was 74.1%. A two-dimensional model did not improve performance due to interpolation artefacts (92.3% of nodes with Péclet number>2), demonstrating an “optimal complexity frontier” where manual sampling invalidates accuracy gains from two and three-dimensional models. This framework provides a practical, cost-effective decision-support tool for sediment-aware mangrove restoration, replicable in similar microtidal, fine-sediment-dominated systems.