No abstract

The southwestern Ethiopian montane forests are one of the most species‐rich ecosystems and are recognised globally as a priority area for the conservation of biodiversity. Particularly, in contrast to the drier central and northern Ethiopian highlands, they have received little attention by researchers. Here, we review changes to agricultural systems in and around these forests that are known as the genetic home of coffee (Coffea arabica L.) and that are important to the livelihoods of many rural people who have developed traditional management practices based on agro‐ecological knowledge, religious taboos and customary tenure rights. We explored the impacts of conversions to agroforestry and cereal‐based cropping systems on biodiversity, soil fertility, soil loss and the socio‐economic conditions and culture. The increasing trend of cereal cropping, resettlement and commercial agriculture causes the deterioration of natural forest cover in the region and threatens biodiversity, land quality, sustainable, traditional farming practices and the livelihood of the local community. Large‐scale plantations of tea, coffee, soapberry locally known as endod (Phytolacca dodecandra L'Hér.) and cereals have resulted in biodiversity loss. Following the conversion of forests, cultivated fields exhibit a significant decline in soil fertility and an increase in soil loss as compared with the traditional agroforestry system. The establishment of a sustainable agricultural system will require a change in paradigm, whereby the intrinsic values of the traditional forest‐based agricultural system are recognised, rather than the ongoing mimicking of agricultural policies that were developed for the open fields of central Ethiopia. Copyright © 2016 John Wiley & Sons, Ltd.

No abstract

No abstract

Policies, measures, and models geared towards flood prevention and managing surface waters benefit from high quality data on the presence and characteristics of drainage ditches. As a cost and labour effective alternative for acquiring such data through field surveys, we propose a method (a) to extract vector data representing ditch drainage networks based on local morphologic features derived from high resolution digital elevation models (DEM) and (b) to identify possible connections in the ditch network by calculating a probability of the connectivity using a logistic regression where the predictor variables are characteristics of the ditch centre lines or derived from the DEM. Using Light Detection and Ranging (LiDAR) derived DEMs with a 1 m resolution, the method was developed and tested for a mixed agricultural residential area in north-eastern Belgium. The derived ditch segments had an error of omission of 8% and an error of commission of 5%. The original positional accuracy of the centre lines of the extracted ditches was 0.6 m and could be improved to 0.4 m by shifting each vertex to the position of the lowest LiDAR point located within a radius equal to the spatial resolution of the used DEM. About 69% of the false disconnections in the network were identified and corrected leading to a reduction of the unconnected parts of the ditch network by 71%. The extracted and connected network approximated the reference ditch network fairly well.