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Most of the climate change models for South Africa predict a reduction in freshwater availability by 2050, which implies that water availability for sectoral production activities is expected to decline. This decline has an impact on sectoral output, value added and households’ welfare. Using a computable general equilibrium approach, this study investigates the possible impact of global change on households’ welfare. The simulation results show that water scarcity due to global change can potentially lead to a general deterioration in households’ welfare. The poor households, whose incomes are adversely impacted, are the most vulnerable to the consequences of the impact of global change on water resources in South Africa. This vulnerability can only be reduced if welfare policies that maintain food consumption levels for the least and low-income households are implemented

In the agricultural sector, climate change (CC) affects multiple weather variables at different stages of crop cycles. CC may influence the mean level or affect the distribution of events (e.g., rainfall, temperature). This work evaluates the economic impact of CC-related changes in multiple climatic components, and the resulting uncertainty. For this purpose, a three-stage discrete stochastic programming model is used to represents farm sector of an irrigated area of Italy and to examine the influence of CC on rainfall and on maximum temperature. These variables affect the availability of water for agriculture and the water requirements of irrigated crops. The states of nature, and their change, are defined more broadly than in previous analyses; this allows examining the changes of more climatic variables and crops cultivation. The effect of CC is obtained by comparing the results of scenarios that represent the climatic conditions in the current situation and in the future. The results show that the agricultural sector would seek to lower costs by modifying patterns of land use, farming practices and increasing the use groundwater. The overall economic impact of these changes is small and due primarily to the reduced availability of water in the future. The temperature increase is, in fact, largely offset by the effects of the increase in CO2 levels, which boosts the yield of main crops of the irrigated zone. Therefore, availability and water management becomes a crucial factor to offset the increase of evapotranspiration and of water stress resulting from the increase of temperature. However, the costs of CC are very high for some types of farming, which suffer a large reduction in income.

Waste with high biomass content generated in cities in developing countries is sent to landfills or open dumps. This research aims to degrade biomass content in urban waste through cultivation, at pilot scale, of the edible mushroom Pleurotus ostreatus. First, the number of diapers used by one baby per week was measured with a survey in day care facilities. Then, cellulose content of diapers was assessed. Finally, cultivation of P. ostreatus was carried out using as substrate a mixture of diapers with gardening waste, a co-substrate readily available at urban settings. The factors assessed were strain of P. ostreatus (grey BPR-81, white BPR-5), conditioning of the substrate (diapers with and without plastic) and co-substrate (wheat straw, grass, and withered leaves). Results show that diapers are a valuable source of biomass, as generation of diapers with urine is 15.3 kg/child/month and they contain 50.2% by weight of cellulose. The highest reductions in dry weight and volume (>64%) of substrates was achieved with the substrate diaper without plastic and co-substrate wheat straw. Although diapers with plastic and grass and leaves showed lower degradation, they achieved efficiencies that make them suitable as a co-substrate (>40%), considering that their biomass is currently confined in landfills.

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.

So far, various studies assessed global biomass potentials and came up with widely varying results. Existing potential estimates range from 0 EJ/a up to more than 1,550 EJ/a which corresponds to about three times the current global primary energy consumption. This paper provides an overview of the available research on bioenergy potentials and reviews the different assessments qualitative way with the objective to interpret previous research in an integrated way. In the context of this paper we understand bioenergy as energy from biomass sources including energy crops, residues, byproducts and wastes from agriculture, forestry, food production and waste management. In this review special attention was paid to the difference between residue and energy potentials, land availability estimates, and the geographical resolution of existing potential estimates. The majority of studies concentrate on energy crop potentials retrieved from surplus agricultural land and only few publications assess global potentials separated by different world regions. It results that land allocated to the exclusive production of energy crops varies from 0 to 7,000 ha, depending on land category and scenario assumptions. Only a small number of available potential assessments consider residue potentials as well as energy crop potentials from degraded land. Future energy crop potentials are assumed to vary in the mean from 200 to 600 EJ/yr. In contrast residue potentials are expected to contribute between 62 and 325 EJ/yr. The highest potentials are assigned to Asia, Africa and South America while Europe, North America and the Pacific region contribute minor parts to the global potential.

To reduce national oil dependency, ethanol has been given a center stage of U.S. energy sources. The Renewable Fuel Standard (RFS) program was launched to increase the volume of renewable gasoline from 9 billion gallons in 2008 to 36 billion gallons by 2012, among which 15 billion are corn-based ethanol, while U.S. corn-based ethanol can hardly achieve this level. There is a trend that indicates U.S. importing ethanol from other countries, so a bilateral trade system has been established between U.S. and Brazil since 2003. The annual import is 211 million gallons in 2008 (USDC, 2009). Nevertheless, this amount is far away from the target, and the worldwide food shortage called us to divert our attention from fuel to food. China, as the third largest ethanol producer, has extreme ethanol growth potential with low production costs and large sources of cassava, which is a non-food feedstock for ethanol. This paper uses Data Envelopment Analysis (DEA) to measure and compare the efficiency of ethanol production in China and Brazil. To estimate the extent output can be proportionally expanded without altering the input quantities employed in each country. The output orientated method has been developed with annual ethanol production from the inputs-- land for ethanol crops, agricultural labor force and capacity of ethanol production. The DEA results show that China has been more efficient in ethanol production than Brazil since the year 2007. This means China has comparative advantage over Brazil in producing ethanol, hence U.S. can import from China instead of Brazil in the future.

According to the United Nations Framework Convention on Climate Change (UNFCCC) and Kyoto Protocol under it, industrial countries have to estimate their greenhouse gas emissions annually, and assess the uncertainties in these estimates. In Finland, agricultural methane (CH4) and nitrous oxide (N2O) emissions represent 7% of anthropogenic greenhouse gas emissions, and globally the share is much higher. Agriculture is one of the most uncertain emission categories (representing over 20% of greenhouse gas inventory uncertainty in Finland), due to both high natural variability of the emission sources and poor knowledge of the emission-generating processes. In this paper, we present an uncertainty estimate of agricultural CH4 and N2O emissions from Finland in 2002. Uncertainties were estimated based on measurement data, literature and expert judgement, and total uncertainty in agriculture was calculated using Monte Carlo simulation. According to the calculations, agricultural CH4 and N2O emissions from Finland were 3.7 to 7.8 Tg carbon dioxide (CO2) equivalents, 5.4 Tg being the mean value.Estimates of CH4 emissions are more reliable than those of N2O. N2O from agricultural soils was the most uncertain emission category, and the uncertainty was not reduced by using available national measurement data of N2O fluxes. Sensitivity study revealed that the uncertainty in total agricultural inventory could be 7% points lower, if more accurate emission estimation methods were used, including 1) improved data collection in area estimates of organic soils, 2) climate-specific methods for N2O from agricultural soils as already presented in literature, and 3) more detailed CH4 estimation methods for enteric fermentation which can be achieved by investigating national circumstances and digestible systems of animals in more detail.