ABSTRACT. The Sudano-Sahelian zone of West Africa has experienced recurrent droughts since the mid-1970s and today there is considerable concern for how this region will be able to adapt to future climate change. To develop well targeted adaptation strategies, the relative importance of climate factors as drivers of land use and livelihood change need to be better understood. Based on the perceptions of 1249 households in five countries across an annual rainfall gradient of 400-900 mm, we provide an estimate of the relative weight of climate factors as drivers of changes in rural households during the past 20 years. Climate factors, mainly inadequate rainfall, are perceived by 30-50% of households to be a cause of decreasing rainfed crop production, whereas a wide range of other factors explains the remaining 50-70%. Climate factors are much less important for decreasing livestock production and pasture areas. Increases in pasture are also observed and caused by improved tenure in the driest zone. Adaptation strategies to declining crop production include 'prayer' and migration in the 400-500 mm zone; reforestation, migration, and government support in the 500-700 mm zone; and soil improvement in the 700-900 mm zone. Declining livestock holdings are countered by improved fodder resources and veterinary services. It is concluded that although rainfed crop production is mainly constrained by climate factors, livestock and pasture are less climate sensitive in all rainfall zones. This needs to be reflected in national adaptation strategies in the region.

Environmental change in the Sudan-Sahel region of West Africa (SSWA) has been much debated since the droughts of the 1970s. In this article we assess climate variability and environmental stress in the region. Households in Senegal, Mali, Burkina Faso, Niger, and Nigeria were asked about climatic changes and their perceptions were compared across north-south and west-east rainfall gradients. More than 80% of all households found that rainfall had decreased, especially in the wettest areas. Increases in wind speeds and temperature were perceived by an overall 60-80% of households. Contrary to household perceptions, observed rainfall patterns showed an increasing trend over the past 20 years.

Abstract-In current, typical software development projects, hundreds of developers work asynchronously in space and time and may introduce anti-patterns in their software systems because of time pressure, lack of understanding, communication, and-or skills. Anti-patterns impede development and maintenance activities by making the source code more difficult to understand. Detecting anti-patterns incrementally and on subsets of a system could reduce costs, effort, and resources by allowing practitioners to identify and take into account occurrences of anti-patterns as they find them during their development and maintenance activities. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently four limitations: (1) they require extensive knowledge of anti-patterns, (2) they have limited precision and recall, (3) they are not incremental, and (4) they cannot be applied on subsets of systems. To overcome these limitations, we introduce SMURF, a novel approach to detect anti-patterns, based on a machine learning technique-support vector machines-and taking into account practitioners' feedback. Indeed, through an empirical study involving three systems and four anti-patterns, we showed that the accuracy of SMURF is greater than that of DETEX and BDTEX when detecting anti-patterns occurrences. We also showed that SMURF can be applied in both intra-system and inter-system configurations. Finally, we reported that SMURF accuracy improves when using practitioners' feedback.

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