In response to the 2011 Bonn Challenge, Ethiopia has committed to restoring 15 million ha of degraded forest and savannah. This study focuses on rehabilitation of communal lands in Tigray through the use of exclosures. Exclosures, often established by using so-called social fences in Ethiopia, are widely recognized as effective in restoring vegetation. This study identified factors contributing to the success of exclosures. After selecting nine successful exclosures from three agro-ecological zones, data were collected through a formal survey of 324 randomly selected households, and from focus group discussions and key informant interviews. Local communities recognize the role of exclosures in increasing site productivity and vegetation cover. However, this positive attitude is often challenged by shortages of livestock feed as a competing priority. Results of our analysis are presented here, bringing insights on factors affecting successful planning and implementation of exclosures and their wider adoption as a means of landscape rehabilitation.

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Dry Afromontane forests form the largest part of the existing natural vegetation in Ethiopia. Nevertheless, models for quantifying aboveground tree biomass (AGB) of these forests are rare. The objective of this study was, therefore, to develop local multispecies and species-specific AGB models for dry Afromontane forests in northern Ethiopia and to test the accuracy of some potentially relevant, previously developed AGB models. A total of 86 sample trees consisting of ten dominant tree species were harvested to develop the models. A set of models relating AGB to diameter at breast height (DBH) or at stump height (DSH), height (H), crown area (CA), and wood basic density (ρ) were fitted. Model evaluation and selection was based on statistical significance of model parameter estimates, relative mean root-square-error (rMRSE), relative bias (rBias), and Akaike Information Criterion (AIC). A leave-one-out cross-validation procedure was used to compute rMRSE and rBias. The best multispecies model, which includes DSH, CA, and ρ as predictors, explained more than 95% of the variability in AGB. The best species-specific models for the two dominant species, with DBH or DSH as the sole predictor, also explained more than 96% of the variability in AGB. Higher biases from the previously published models compared to the best models from this study show the need to develop local models for more accurate biomass estimation. The developed models allow to quantify AGB with a high level of accuracy for our site, and they can potentially be applied in dry Afromontane forests elsewhere in Ethiopia if species composition and growing conditions are carefully evaluated before an application is done.

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