Net ecosystem productivity and carbon dynamics of the traditionally managed Imperata grasslands of North East India

Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China

Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (εmax), was optimized by using vegetation classification data. Then, the NEP was estimated by coupling the optimized CASA model, geostatistical model of soil respiration (GSMSR) and the soil respiration–soil heterotrophic respiration (Rs-Rh) relationship model. The ground observations from ChinaFLUX were used to verify the NEP estimation accuracy. The results showed that the R2 of the optimized CASA model increased from 0.411 to 0.774, and RMSE decreased from 21.425 gC·m−2·month−1 to 12.045 gC·m−2·month−1, indicating that optimizing CASA model by vegetation classification data was an effective method to improve the estimation accuracy of NEP. On this basis, the spatial and temporal distribution of NEP in China was analyzed. The research indicated that the monthly variation of NEP in China was a single peak curve with summer as the peak, which generally presented the pattern of southern region > northern region > Qinghai–Tibet region > northwest region. Furthermore, from 2001 to 2016, most regions of China showed a non-significant level upward trend, but main cropland (e.g., North China Plain and Northeast Plain) and some grassland (e.g., Ngari in Qinghai–Tibet Plateau and Xilin Gol League in Inner Mongolia) showed a non-significant-level downward trend. The study can deepen the understanding of the distribution of carbon sources/sinks in China, and provide a reference for regional carbon cycle research.

Water stable aggregates and the associated active and recalcitrant carbon in soil under rubber plantation

Rehabilitation of the degraded soil is imperative to minimize the effects of soil degradation. It is in this context that stable soil aggregates, essential to providing physical protection to the organic residues, are important indicators of soil restoration or degradation. Thus, the present study was aimed at determining the soil aggregate stability and associated carbon fractions under rubber (Hevea brasiliensis) plantations. The study was conducted on 10, 15, 25, and 34-year-old rubber plantation established on Imperata grassland. Soil samples were collected from 0 to 10, 10-20, 20-50, 50-100 cm depths from different aged rubber plantation and native forest (NF) using a soil core of 5.6 cm inner diameter. Soil aggregates from each depth were separated by the wet-sieving technique, and grouped into three fraction size classes: macro-aggregates (>2 mm), meso-aggregates (0.25-2 mm), and micro-aggregates (<0.25 mm), and analyzed for carbon concentrations. The results showed that macro-aggregates dominated soil under different plantation ages and decreased with an increase in soil depth. The Mean Weight Diameter (MWD) and the Geometric Mean Diameter (GMD) increased with an increase in the age of the plantation and decreased with increase in soil depth. The MWD was the highest in the forest soil (5.8 mm) and the lowest (3.0 mm) under 10-year-old rubber plantation. The highest GMD was found under 34-year-old rubber plantation (2.1 mm) and the lowest under 10-year (1.4 mm) plantation. The SOC concentration under the recalcitrant pool increased with the increase in plantation age, and the highest amount was observed under 34-year old plantation. The increase in aggregate stability, recalcitrant carbon pool, and SOC stock with age chronosequence suggests the ecological role of mature rubber plantations in soil rehabilitation by minimizing the process of soil degradation.