Spatial assessment of carbon stocks of living vegetation at the national level in Lao PDR

The international mechanism for Reducing Greenhouse Gas Emissions from Deforestation and Forest Degradation (REDD) supposedly offers new opportunities for combining climate mitigation, conservation of the environment, and socio-economic development for development countries. In Laos REDD is abundantly promoted by the government and development agencies as a potential option for rural development. Yet, basic information for carbon management is missing: to date no knowledge is available at the national level on the quantities of carbon stored in the Lao landscapes. In this study we present an approach for spatial assessment of vegetation-based carbon stocks. We used Google Earth, Landsat and MODIS satellite imagery and refined the official national land cover data to assess carbon stocks. Our study showed that more than half (52%) of carbon stock of Laos is stored in natural forests, but that 70% of this stock is located outside of national protected areas. On the basis of two carbon-centered land use scenarios we calculated that between 30 and 40 million tons of carbon could be accumulated in shifting cultivation areas; this is less than 3% of the existing total stock. Our study suggests that the main focus of REDD in Laos should be on the conservation of existing carbon stocks, giving highest priority to the prevention of deforestation outside of national protected areas.

Carbon stocks, tree diversity, and the role of organic certification in different cocoa production systems in Alto Beni, Bolivia

This study compares aboveground and belowground carbon stocks and tree diversity in different cocoa cultivation systems in Bolivia: monoculture, simple agroforestry, and successional agroforestry, as well as fallow as a control. Since diversified, agroforestry-based cultivation systems are often considered important for sustainable development, we also evaluated the links between carbon stocks and tree diversity, as well as the role of organic certification in transitioning from monoculture to agroforestry. Biomass, tree diversity, and soil physiochemical parameters were sampled in 15 plots measuring 48 × 48 m. Semi-structured interviews with 52 cocoa farmers were used to evaluate the role of organic certification and farmers’ organizations (e.g., cocoa cooperatives) in promoting tree diversity. Total carbon stocks in simple agroforestry systems (128.4 ± 20 Mg ha−1) were similar to those on fallow plots (125.2 ± 10 Mg ha−1). Successional agroforestry systems had the highest carbon stocks (143.7 ± 5.3 Mg ha−1). Monocultures stored significantly less carbon than all other systems (86.3 ± 4.0 Mg ha−1, posterior probability P(Diff > 0) of 0.000–0.006). Among shade tree species, Schizolobium amazonicum, Centrolobium ochroxylum, and Anadenanthera sp. accumulated the most biomass. High-value timber species (S. amazonicum, C. ochroxylum, Amburana cearensis, and Swietenia macrophylla) accounted for 22.0 % of shade tree biomass. The Shannon index and tree species richness were highest in successional agroforestry systems. Cocoa plots on certified organic farms displayed significantly higher tree species richness than plots on non-certified farms. Thus, expanding the coverage of organic farmers’ organizations may be an effective strategy for fostering transitions from monoculture to agroforestry systems.

Simulating Carbon Stocks and Fluxes of an African Tropical Montane Forest with an Individual-Based Forest Model

Tropical forests are carbon-dense and highly productive ecosystems. Consequently, they play an important role in the global carbon cycle. In the present study we used an individual-based forest model (FORMIND) to analyze the carbon balances of a tropical forest. The main processes of this model are tree growth, mortality, regeneration, and competition. Model parameters were calibrated using forest inventory data from a tropical forest at Mt. Kilimanjaro. The simulation results showed that the model successfully reproduces important characteristics of tropical forests (aboveground biomass, stem size distribution and leaf area index). The estimated aboveground biomass (385 t/ha) is comparable to biomass values in the Amazon and other tropical forests in Africa. The simulated forest reveals a gross primary production of 24 tcha-1yr-1. Modeling above- and belowground carbon stocks, we analyzed the carbon balance of the investigated tropical forest. The simulated carbon balance of this old-growth forest is zero on average. This study provides an example of how forest models can be used in combination with forest inventory data to investigate forest structure and local carbon balances.

Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks

Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements. Using data from an extensive national survey of English grasslands, we show that surface soil (0–7 cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions. Soil C stocks in the largest pool, of intermediate particle size (50–250 μm), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0·45–50 μm), was explained by soil pH and the community abundance-weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N-rich vegetation. The C stock in the small active fraction (250–4000 μm) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves. Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate. Synthesis and applications. Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1–100 000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration.