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Background: To-date, Rift Valley fever (RVF) outbreaks have occurred in 38 of the 69 administrative districts in Kenya. Using surveillance records collected between 1951 and 2007, we determined the risk of exposure and outcome of an RVF outbreak, examined the ecological and climatic factors associated with the outbreaks, and used these data to develop an RVF risk map for Kenya. Methods: Exposure to RVF was evaluated as the proportion of the total outbreak years that each district was involved in prior epizootics, whereas risk of outcome was assessed as severity of observed disease in humans and animals for each district. A probability-impact weighted score (1 to 9) of the combined exposure and outcome risks was used to classify a district as high (score ≥ 5) or medium (score ≥2 - <5) risk, a classification that was subsequently subjected to expert group analysis for final risk level determination at the division levels (total = 391 divisions). Divisions that never reported RVF disease (score < 2) were classified as low risk. Using data from the 2006/07 RVF outbreak, the predictive risk factors for an RVF outbreak were identified. The predictive probabilities from the model were further used to develop an RVF risk map for Kenya. Results: The final output was a RVF risk map that classified 101 of 391 divisions (26%) located in 21 districts as high risk, and 100 of 391 divisions (26%) located in 35 districts as medium risk and 190 divisions (48%) as low risk, including all 97 divisions in Nyanza and Western provinces. The risk of RVF was positively associated with Normalized Difference Vegetation Index (NDVI), low altitude below 1000m and high precipitation in areas with solonertz, luvisols and vertisols soil types (p <0.05). Conclusion: RVF risk map serves as an important tool for developing and deploying prevention and control measures against the disease. [ABSTRACT FROM AUTHOR]

The use of night-time livestock enclosures, often referred to as "bomas", "corrals", or "kraals", is a common practice across African rangelands. Bomas protect livestock from predation by wildlife and potential theft. Due to the concentration of animal faeces inside bomas, they not only become nutrient-rich patches that can add to biodiversity, but also hotspots for the emission of nitrous oxide (N 2 O), an important greenhouse gas, especially when animals are kept inside for long periods. To provide an accurate estimate of such emissions for wider landscapes, bomas need to be accounted for. Moreover, initial experiments indicated that more frequent shifts in the boma locations could help to reduce N 2 O emissions. This stresses the need for better understanding where bomas are located, their numbers, as well as when they are actively used. Given the recent advances in satellite technology, resulting in high-frequent spectral measurements at fine spatial resolution, solutions to address these needs are now within reach. This study is a first effort to map and monitor the appearance of bomas with the use of satellite image time series. Our main dataset was a dense times series of 3 m resolution PlanetScope multispectral imagery. In addition, a reference dataset of boma and non-boma locations was created using GPS-collar tracking data and 0.5 m resolution Pléiades imagery. The reduction of vegetation cover and increase of organic material following boma installation result in typical spectral changes when contrasted against its surroundings. Based on these spectral changes we devised an empirical approach to infer approximate boma installation dates from PlanetScope's near infrared (NIR) band and used our reference dataset for setting optimal parameter values. A NIR spatial difference index resulted in clear temporal patterns, which were more apparent during the wet season. At landscape scale our approach reveals clear spatio-temporal patterns of boma installation, which could not be revealed from less frequent sub-meter resolution imagery alone. While further improvements are possible, we show that small-sized (150–500 m2) temporary surface changes, such as those that occur when pastoralists use mobile bomas, can be detected with dense image time series like those offered by the PlanetScope constellation. In future, this could lead to better assessment of a) spatio-temporal livestock distribution, b) the contribution of bomas to N 2 O emissions and soil fertility at landscape scale, and c) the uptake of enclosure rotation practices at large spatial scales. • Livestock enclosures, or 'bomas', are hotspots of nutrients and N 2 O emissions. • We made a dataset on boma location/timing from GPS collars and Pléiades images. • We then used dense PlanetScope time series to assess the appearance of mobile bomas. • An automatic boma detection approach was developed using a spatial difference index. • We revealed clear spatio-temporal patterns of boma installation at landscape scale. [ABSTRACT FROM AUTHOR]

Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near real-time, and that strongly relates to livestock mortality. Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related insurance premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record (1981–2011) to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near-real time NDVI products: five from MODIS and one from SPOT-VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R 2 statistics (leave-one-year-out) for the overlapping period 2002–2011. We found that division-specific models were more effective than a global model for linking the division-level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes. We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index. [ABSTRACT FROM AUTHOR]

• We propose a new design that strengthens the ecological significance of an currently operational forage scarcity index for livestock insurance. • The proposed design has an improved relationship with livestock mortality data. • It provides a flexible structure for indemnity calculation, allowing also per pixel análisis of forage. • Improvements are relevant for the geographic expansion of the insurance scheme. • The proposed design can be adopted for other NDVI-based early warning monitoring systems. Recurrent drought represents a major threat in arid and semi-arid regions of East Africa where pastoralists depend on their livestock for subsistence. In Kenya and southern Ethiopia, an existing satellite-based index insurance scheme aims to protect pastoralists against the adverse effects of drought. Under that scheme, payouts are made based on an area-aggregated seasonal forage scarcity index derived from remotely-sensed Normalized Difference Vegetation Index (NDVI). NDVI values are directly averaged per unit areas of insurance (UAI), which are based on administrative borders but take into limited account the ecological variability within the unit. The choice of administrative boundaries at the onset of the analysis may negatively impact the performance of the product. Our study explores an alternative index design based on an ecological stratification of the study area. First, we performed an unsupervised classification of NDVI time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) to group pixels with similar temporal NDVI trajectories. Next, we used average NDVI profiles and ancillary data to discard areas deemed insignificant for forage production. We then transformed NDVI values into z-scores to assess how each pixel relates to the multi-year distribution of NDVI values per class and season. In the final step, we calculated the alternative forage scarcity index, which is the percentage of pixels with anomalously low NDVI values per season and UAI (i.e. z-score ≤ −1.0). To evaluate its performance, we compared unit-level results for both the original and alternative designs against spatially-aggregated monthly household survey data on livestock mortality from 16 sample sites corresponding to eight administrative units within the study area. Besides performing better in predicting livestock mortality (i.e. increases of 53% and 39% in correlation coefficients as measured by Pearson's r and Spearman's ρ, respectively) and strengthening the ecological significance of the index, the proposed design has a number of other advantages: 1) the index is calculated using a much larger statistical basis, 2) it allows for analysis of forage conditions at sub-unit level, and 3) it offers a more flexible structure for payout calculation. These advantages could be of particular relevance for expanding the index-insurance scheme to agro-pastoral regions characterized by more heterogeneous landscapes. Finally, we propose to consider using this approach to better account for the ecological variability of rangelands in other NDVI-based early warning and monitoring systems. [ABSTRACT FROM AUTHOR]