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
Interpreting and predicting the evolution of non-point source (NPS) pollution of soil and surface and subsurface water from agricultural chemicals and pathogens, as well as overexploitation of groundwater resources at regional scale, are continuing challenges for natural scientists. Accordingly, in this study we present a regional-scale modeling approach for vadose zone solute leaching that is based on stochastic application of a deterministic vadose zone model describing the water flow and solute transport processes in the unsaturated zone using the Richards equation (RE) and the advective-dispersive equation (ADE), respectively. The stochastic framework (Monte Carlo technique) allows accounting for uncertainty in the vulnerability outputs. As the approach is built on physically-based equations, it may be extended to the predictions of water fluxes (i.e., groundwater recharge) in the vadose zone. The approach relies on available datasets coming from different sources (detailed pedological information, hydrological properties in different soil horizons, water table depth, spatially distributed climatic temporal series and land use) and offers quantitative answers to soil and groundwater vulnerability to NPS of chemicals at regional scale within a defined confidence interval. Interpolation of these local distributions by geostatistical tools provides areal distributions of the statistical moments of solute and water fluxes. A preliminary evaluation of methodology was carried out for quantifying contaminant transport and groundwater recharge profile in the Metaponto plain in Southern Basilicata, Italy. Results showed large differences in the magnitude of the different travel times and related uncertainties among different profiles. The lower or higher vulnerability was found to be mainly related to the average silt content of the soil profiles.
In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization’s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets.
, 2016. A new Global Agro-Environmental Stratification (GAES).Wageningen, Wageningen Environmental Research, Report 2761. 70 pp.; 46 fig.; 12 tab.; 71 ref.The GAES database (Version 01a) is a newly developed Global Agro-Environmental Stratification within the EU SIGMA (Stimulating Innovation for Global Monitoring of Agriculture) project. GAES will serve as a new agro-environmental stratification for better global monitoring of the agricultural production on the basis of Earth Observation data and crop growth models. It is anticipated that GAES will be exploited for a wider range of applications, some within SIGMA, towards data gap analysis that identifies agro-environmental strata with limited capacity and monitoring data on agricultural production. GAES was produced by applying segmentation techniques to newly available global agroenvironmental data with a high spatial resolution re-sampled to 1 km spatial resolution. The datasets were able to stratify the agricultural production zones according to the region's agro-environmental characteristics, including climatic regimes, soil, terrain, elevation conditions, water availability and land cover proprieties. The GAES strata obtained by segmentation at four different spatial levels (with Level 4 as the most detailed) have been further characterised and described in terms of phenology (e.g. start and peak of the growing season), agricultural (water) management practices, field size, biotic constraints, national and sub-national crop production statistics, GDP, transport infrastructure conditions or market accessibility. The GAES database has four hierarchical layers, with 92 attributes.GAES Level 1 has 194 agro-environmental (AE) types (818 strata); GAES Level 2 has 300 AE types (1,688 strata); GAES Level 3 has 374 AE types (2,087 strata); GAES Level 4 has 516 AE types (3,208 strata). GAES typology is a combination of temperature, altitude, parent material and land cover characteristics. GAES Version 01 has become freely available.
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