To monitor land-use/land-cover (LULC) change and assess its impact on the soil property, the availability of benchmark data is indispensable, which is hardly available in the intensively cultivated regions of developing countries. Our study attempts to solve this problem by generating a benchmark soil data through the development of modified spatial analogue (MSA) method in the context of the Upper Dijo River catchment, south-central Ethiopia. The magnitude and patterns of LULC changes were extracted from air photos and satellite imageries, along with the acquisition of soil samples from the reference and target sites through ground survey. Analysis of digital image processing shows significant LULC changes in a period that spanned three decades. The impact of LULC change on soil quality was assessed by comparing the soil physico-chemical properties sampled from the reference and target sites. The result shows a decline in total nitrogen, organic matter, available potassium and pH levels in soils collected from target sites, which conforms to results reported by studies conducted in data-rich environment. With careful validation, MSA could be useful for monitoring soil property changes in data-scarce environment and generate soil-related parameters for agro-ecological models.

Characterization of coffee terroirs is important to determine authenticity and provide confidence for consumers to select the right product. In this regard, Amhara Region, which is located at the northwestern part of Ethiopia, produces various local coffee types with distinct cup qualities. The coffees are, however, not yet registered with certification marks or trademarks for indications of their geographical origins. This study was aimed at developing analytical methodology useful to determine the geographical origin of green coffee beans produced in Amhara Region based on multi-element analysis combined with multivariate statistical techniques. For this, a total of 120 samples of green coffee beans, collected from four major cultivating zones (West Gojjam, East Gojjam, Awi, and Bahir Dar Especial Zones) were analyzed for K, Mg, Ca, Mn, Fe, Cu, Fe, Co, Ni, Zn, Si, Cr, Cd, and Pb using inductively coupled plasma-optical emission spectroscopy. The elemental analysis data were subjected to principal component analysis (PCA) and linear discriminant analysis (LDA). PCA was used to explore the natural groupings of samples and the discriminatory ability of elements. Accordingly, the elements K, Mg, Ca, and Na were found to be the main discriminators among samples. LDA provided a model to classify the coffee samples based on their production zones with an accuracy of 94.2% and prediction ability of 93.4%. Thus, the elemental composition of green coffee beans can be used as a chemical descriptor in the authentication of coffee produced in Amhara Region.