Adaptation to climate change in Ethiopia and South Africa: options and constraints

Climate change is expected to adversely affect agricultural production in Africa. Because agricultural production remains the main source of income for most rural communities in the region, adaptation of the agricultural sector is imperative to protect the livelihoods of the poor and to ensure food security. A better understanding of farmers’ perceptions of climate change, ongoing adaptation measures, and the decision-making process is important to inform policies aimed at promoting successful adaptation strategies for the agricultural sector. Using data from a survey of 1800 farm households in South Africa and Ethiopia, this study presents the adaptation strategies used by farmers in both countries and analyzes the factors influencing the decision to adapt. We find that the most common adaptation strategies include: use of different crops or crop varieties, planting trees, soil conservation, changing planting dates, and irrigation. However, despite having perceived changes in temperature and rainfall, a large percentage of farmers did not make any adjustments to their farming practices. The main barriers to adaptation cited by farmers were lack of access to credit in South Africa and lack of access to land, information, and credit in Ethiopia. A probit model is used to examine the factors influencing farmers’ decision to adapt to perceived climate changes. Factors influencing farmers’ decision to adapt include wealth, and access to extension, credit, and climate information in Ethiopia; and wealth, government farm support, and access to fertile land and credit in South Africa. Using a pooled dataset, an analysis of the factors affecting the decision to adapt to perceived climate change across both countries reveals that farmers were more likely to adapt if they had access to extension, credit, and land. Food aid, extension services, and information on climate change were found to facilitate adaptation among the poorest farmers. We conclude that policy-makers must create an enabling environment to support adaptation by increasing access to information, credit and markets, and make a particular effort to reach small-scale subsistence farmers, with limited resources to confront climate change.     

Amine reclaiming technologies in post-combustion carbon dioxide capture

Amine scrubbing is the most developed technology for carbon dioxide (CO₂) capture. Degradation of amine solvents due to the presence of high levels of oxygen and other impurities in flue gas causes increasing costs and deterioration in long term performance, and therefore purification of the solvents is needed to overcome these problems. This review presents the reclaiming of amine solvents used for post combustion CO₂ capture (PCC). Thermal reclaiming, ion exchange, and electrodialysis, although principally developed for sour gas sweetening, have also been tested for CO₂ capture from flue gas. The three technologies all have their strengths and weaknesses, and further development is needed to reduce energy usage and costs. An expected future trend for amine reclamation is to focus on process integration of the current reclaiming technologies into the PCC process in order to drive down costs.     

Impacts from Above-Ground Activities in the Eagle Ford Shale Play on Landscapes and Hydrologic Flows,La Salle County,Texas

We assess the spatial and geomorphic fragmentation from the recent Eagle Ford Shale play in La Salle County, Texas, USA. Wells and pipelines were overlaid onto base maps of land cover, soil properties, vegetation assemblages, and hydrologic units. Changes to continuity of different ecoregions and supporting landscapes were assessed using the Landscape Fragmentation Tool (a third-party ArcGIS extension) as quantified by land area and continuity of core landscape areas (i.e., those degraded by “edge effects”). Results show decreases in core areas (8.7 %; ~33,290 ha) and increases in landscape patches (0.2 %; ~640 ha), edges (1.8 %; ~6940 ha), and perforated areas (4.2 %; ~16230 ha). Pipeline construction dominates landscape disturbance, followed by drilling and injection pads (85, 15, and 0.03 % of disturbed area, respectively). An increased potential for soil loss is indicated, with 51 % (~5790 ha) of all disturbance regimes occurring on soils with low water-transmission rates (depth to impermeable layer less than 50 cm) and a high surface runoff potential (hydrologic soil group D). Additionally, 88 % (~10,020 ha) of all disturbances occurred on soils with a wind erodibility index of approximately 19 kt/km²/year (0.19 kt/ha/year) or higher, resulting in an estimated potential of 2 million tons of soil loss per year. Results demonstrate that infrastructure placement is occurring on soils susceptible to erosion while reducing and splitting core areas potentially vital to ecosystem services.     

Citizens’ perceptions of trust relationships in the environmental management process in North Lebanon

This research investigates how trust plays a role in environmental management in North Lebanon, which has suffered repeated episodes of armed conflict in recent times. Previous studies have shown that environmental problems have increased and that the government has been unable to address these, even during periods of relative peace. We examined trust as a factor that contributes to, or hampers, environmental management. Our analysis drew on a survey in 2011 involving 499 citizens. The results demonstrated that, according to citizens, the lack of trust between citizens, and between citizens and the public sector, is a key factor obstructing effective environmental management. The results indicate the level of correlation between how trusting people are; how citizens participate; and how people perceive government legitimacy.     

Fire severity and ecosytem responses following crown fires in California shrublands

Chaparral shrublands burn in large high-intensity crown fires. Managers interested in how these wildfires affect ecosystem processes generally rely on surrogate measures of fire intensity known as fire severity metrics. In shrublands burned in the autumn of 2003, a study of 250 sites investigated factors determining fire severity and ecosystem responses. Using structural equation modeling we show that stand age, prefire shrub density, and the shortest interval of the prior fire history had significant direct effects on fire severity, explaining > 50% of the variation in severity. Fire severity per se is of interest to resource managers primarily because it is presumed to be an indicator of important ecosystem processes such as vegetative regeneration, community recovery, and erosion. Fire severity contributed relatively little to explaining patterns of regeneration after fire. Two generalizations can be drawn: fire severity effects are mostly shortlived, i.e., by the second year they are greatly diminished, and fire severity may have opposite effects on different functional types. Species richness exhibited a negative relationship to fire severity in the first year, but fire severity impacts were substantially less in the second postfire year and varied by functional type. Much of this relationship was due to alien plants that are sensitive to high fire severity; at all scales from 1 to 1000 m2, the percentage of alien species in the postfire flora declined with increased fire severity. Other aspects of disturbance history are also important determinants of alien cover and richness as both increased with the number of times the site had burned and decreased with time since last fire. A substantial number of studies have shown that remote-sensing indices are correlated with field measurements of fire severity. Across our sites, absolute differenced normalized burn ratio (dNBR) was strongly correlated with field measures of fire severity and with fire history at a site but relative dNBR was not. Despite being correlated with fire severity, absolute dNBR showed little or no relationship with important ecosystem responses to wildfire such as shrub resprouting or total vegetative regeneration. These findings point to a critical need for further research on interpreting remote sensing indices as applied to postfire management of these shrublands.     

Diversity-invasibility across an experimental disturbance gradient in Appalachian Forests

Research examining the relationship between community diversity and invasions by nonnative species has raised new questions about the theory and management of biological invasions. Ecological theory predicts, and small-scale experiments confirm, lower levels of nonnative species invasion into species-rich compared to species-poor communities, but observational studies across a wider range of scales often report positive relationships between native and nonnative species richness. This paradox has been attributed to the scale dependency of diversity-invasibility relationships and to differences between experimental and observational studies. Disturbance is widely recognized as an important factor determining invasibility of communities, but few studies have investigated the relative and interactive roles of diversity and disturbance on nonnative species invasion. Here, we report how the relationship between native and nonnative plant species richness responded to an experimentally applied disturbance gradient (from no disturbance up to clearcut) in oak-dominated forests. We consider whether results are consistent with various explanations of diversity-invasibility relationships including biotic resistance, resource availability, and the potential effects of scale (1 m2 to 2 ha). We found no correlation between native and nonnative species richness before disturbance except at the largest spatial scale, but a positive relationship after disturbance across scales and levels of disturbance. Post-disturbance richness of both native and nonnative species was positively correlated with disturbance intensity and with variability of residual basal area of trees. These results suggest that more nonnative plants may invade species-rich communities compared to species-poor communities following disturbance.     

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.