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SUMMARY China’s growing presence in Africa’s extractive industries has been the subject of much debate in recent years, reflecting concerns about both environmental sustainability and the governance of resource wealth for long-term benefit. In Cameroon, since 2000 the largest timber concession in the country has been held by a Chinese company. This provides an opportunity to take a deeper look at corporate practices in the extractive industry and explore the extent to which corporate behaviour varies between Chinese and non-Chinese companies. Through a general analysis of Cameroonian timber production and trade, and a detailed analysis of two European companies (one FSC-certified) and one Chinese company (without FSC certification), this paper assesses the effects of Chinese capital and China-related trade on rural livelihoods and forest condition in the Cameroonian forestry sector. Our findings suggest that while the Chinese market shapes the trade patterns and management activities of logging companies, it does so irrespective of the companies’ nationality. Also, findings suggest that nationality of firms has a weak influence on the impacts on local livelihoods around the sampled logging concessions in Cameroon.

The basins of the Indus and Ganges rivers cover 2.20 million km2 and are inhabited by more than a billion people. The region is under extreme pressures of population and poverty, unregulated utilization of the resources and low levels of productivity. The needs are: (1) development policies that are regionally differentiated to ensure resource sustainability and high productivity; (2) immediate development and implementation of policies for sound groundwater management and energy use; (3) improvement of the fragile food security and to broaden its base; and (4) policy changes to address land fragmentation and improved infrastructure. Meeting these needs will help to improve productivity, reduce rural poverty and improve overall human development.

Ethiopia's policy of large dam construction in the Blue Nile River basin is evaluated by simulating the impact of one downscaled midrange climate change scenario (A1B) on the performance of existing and planned irrigation and hydropower schemes. The simulation finds that by 2100: 1) average basin-wide irrigation demand will increase; 2) annual hydroelectricity generation will be just 60% of potential; and 3) flow at the Ethiopia-Sudan border will be reduced from 1661 m3/s to 1301 m3/s as a consequence of climate change in combination with upstream water resource development. Adaptation to climate change and development must be considered together.

Microinsurance is widely considered an important tool for sustainable poverty reduction, especially in the face of increasing climate risk. Although index-based microinsurance, which should be free from the classical incentive problems, has attracted considerable attention, uptake rates have generally been weak in low-income rural communities. We explore the purchase patterns of index-based livestock insurance in southern Ethiopia, focusing in particular on the role of accurate product comprehension and price, including the prospective impact of temporary discount coupons on subsequent period demand due to price anchoring effects. We find that randomly distributed learning kits contribute to improving subjects' knowledge of the products; however, we do not find strong evidence that the improved knowledge per se induces greater uptake. We also find that reduced price due to randomly distributed discount coupons has an immediate, positive impact on uptake, without dampening subsequent period demand due to reference-dependence associated with price anchoring effects.

Numerous authors have stressed the importance of guaranteeing and protecting the tenure and human rights of indigenous and other forest-based communities under schemes for reducing emissions from deforestation and forest degradation (REDD, or REDD+); and important international indigenous organizations have spoken out strongly against REDD+. This article examines two specific issues that present risks for local communities: rights to forests and rules for resource use. It draws on the findings of a study conducted by the Center for International Forestry Research (CIFOR) on forest tenure reforms in selected countries in Asia, Africa and Latin America from 2006 to 2008. The study underlines the numerous obstacles faced by communities after rights are won, in moving from statutory rights to their implementation and to access to benefits on the ground. It argues that there is currently little reason to expect better results from national policies under REDD+ without binding agreements to protect local rights.

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.