Land-use change can have profound effects on forest communities, compromising seedling recruitment and growth, and long-term persistence of forests on the landscape. Continued forest conversion to agriculture causes forest fragmentation which decreases forest size, increases edge effects and forest isolation, all of which negatively impact forest health. These fragmentation effects are magnified by human use of forests, which can compromise the continued persistence of species in these forests and the ability of the forests to support the communities that depend on them. We examined the extent and influence of human disturbance (e.g. weedy taxa, native and exotic tree plantations, clearings, buildings) on the ecological status of sacred church forests in the northern highlands of South Gondar, Ethiopia and hypothesized that disturbance would have a negative effect. We found that disturbance was high across all forests (56%) and was negatively associated with tree species richness, density, and biomass and seedling richness and density. Contrary to expectation, we found that forests < 15.5 ha show no difference in disturbance level with distance from population center. Based on our findings, we recommend that local conservation strategies not only protect large forests, but also the small and highly used forests in South Gondar which are critical to the needs of local people, including preserving large trees for seed sources, removing exotic and weedy species from forests, and reducing clearings and trails within forests.

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SUMMARYAquaCrop, the FAO water productivity model, is used as a tool to predict crop production under water limiting conditions. In the first step AquaCrop was calibrated and validated for barley (Hordeum vulgare L.). Data sets of field experiments at seven different locations in four countries (Ethiopia, Italy, Syria and Montana, USA) with different climates in different years and with five different cultivars were used for model calibration and validation. The goodness-of-fit between observed and simulated soil water content, green canopy cover, biomass and grain yield was assessed by means of the coefficient of determination (R 2 ), the Nash-Sutcliff efficiency (E), the index of agreement (d) and the root mean square error (RMSE). The statistical parameters indicated an adequate accuracy of simulations (validation regression of yield: R 2 = 0.95, E = 0.94, d = 0.99, RMSE = 0.34). Subsequently, sowing strategies in the semi-arid environment of northern Ethiopia were evaluated with the validated model. Dry sowing had a probability of 47% germination failure attributable to false start of the rainy season. On the other hand, delay sowing at the start of the rainy season to eliminate germinating weeds should be kept as short as possible because grain yields strongly reduce in the season due to water stress when sowing is delayed on shallow soils. This research demonstrates the ability of AquaCrop to predict accurately crop performance with only a limited set of input variables, and the robustness of the model under various environmental and climatic conditions.

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