Abstract. Recent disease epidemics and their spread around the world have illustrated the weaknesses of disease surveillance and early warning systems (EWS), both at national and international levels. These diseases continuously threaten the livestock sector on a worldwide basis, some with major public health impact. EWS and accurate forecasting of new outbreaks of epidemic livestock diseases that may also affect wildlife, and the capacity for spread of such diseases to new areas is an essential pre-requisite to their effective containment and control. Because both the geographical and seasonal distribution of many infectious diseases are linked to climate, the possibility of using climaterelated environmental factors as predictive indicators, in association with regular disease surveillance activities, has proven to be relevant when establishing EWS for climate-related diseases. This article reviews the growing importance of using geographical information systems in predictive veterinary epidemiology and its integration into EWS, with a special focus on Rift Valley fever. It shows that, once fully validated in a country or region, this technology appears highly valuable and could play an increasing role in forecasting major epidemics, providing lead time to national veterinary services to take action to mitigate the impact of the disease in a cost-effective manner.
Soil, vegetation, climate and management are the main factors affecting environmental sensitivity to degradation, through their intrinsic characteristics or by their interaction with the landscape. Different levels of degradation risks may be observed in response to particular combinations of the aforementioned factors. For instance, the combination of inappropriate management practices and intrinsically weak soil conditions will result in a degradation of the environment of a severe level, while the combination of the same type of management with better soil conditions may lead to negligible degradation. The objective of this study was to identify the factors responsible for land degradation processes in Basilicata and to simulate through the adoption of the SALUS soil-plant-atmosphere system model potential measures to mitigate the processes. Environmental sensitive areas to desertification were first identified using the Environmental Sensitive Areas (ESAs) procedure. An analysis for identifying the weight that each contributing factor (climate, soil, vegetation, socio-economic management) had on the ESA was carried out and successively the SALUS model was executed to identify the best agronomic practices. The best agronomic management practice was found to be the one that minimized soil disturbance and increased soil organic carbon. Two alternative scenarios with improved soil quality and subsequently improving soil water holding capacity were used as mitigation measures. The new ESA were recalculated and the effects of the mitigation suggested by the model were assessed
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