Potential soil carbon sequestration in overgrazed grassland ecosystems

Excessive grazing pressure is detrimental to plant productivity and may lead to declines in soil organic matter. Soil organic matter is an important source of plant nutrients and can enhance soil aggregation, limit soil erosion, and can also increase cation exchange and water holding capacities, and is, therefore, a key regulator of grassland ecosystem processes. Changes in grassland management which reverse the process of declining productivity can potentially lead to increased soil C. Thus, rehabilitation of areas degraded by overgrazing can potentially sequester atmospheric C. We compiled data from the literature to evaluate the influence of grazing intensity on soil C. Based on data contained within these studies, we ascertained a positive linear relationship between potential C sequestration and mean annual precipitation which we extrapolated to estimate global C sequestration potential with rehabilitation of overgrazed grassland. The GLASOD and IGBP DISCover data sets were integrated to generate a map of overgrazed grassland area for each of four severity classes on each continent. Our regression model predicted losses of soil C with decreased grazing intensity in drier areas (precipitation less than 333 mm yr(-1)), but substantial sequestration in wetter areas. Most (93%) C sequestration potential occurred in areas with MAP less than 1800 mm. Universal rehabilitation of overgrazed grasslands can sequester approximately 45 Tg C yr(-1), most of which can be achieved simply by cessation of overgrazing and implementation of moderate grazing intensity. Institutional level investments by governments may be required to sequester additional C.

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.