Multi-Scale analysis of the impacts of soil and water conservation practices and landscape on grain yield and return on investment in the sub-humid ethiopian highlands.

Ethiopia's sub-humid highlands face a critical challenge in balancing agricultural productivity with land degradation. This study explores the effectiveness of soil and water conservation practices (SWCPs) in addressing this challenge. We investigated the interaction effects of types of SWCPs, landscape positions, and location on Teff (Eragrostis teff) and wheat (Triticum aestivum) yield. In addition, we assessed the economic viability of SWCPs using cost-benefit analysis with farmer-funded and cost-sharing scenarios. The results indicated that yield was significantly affected by the interactions between factors like SWCP type and landscape position. Soil bunds consistently increased crop yield across diverse locations and landscapes, indicating superior erosion control benefits. Lower landscape positions on foot slopes benefited most from SWCP implementation. Teff yield increased by 188 % and wheat yield by 181 % under soil bunds. The cost-benefit analysis confirmed the financial viability of SWCPs, particularly for Teff (NPV = 4499.35 USD, IRR = 50 %, and BCR = 1.51) and wheat (NPV = 544.35 USD, IRR = 16 %, and BCR = 1.06) grown on lower landscapes with farmer-funded investment scenarios. Positive return on investment was observed in both scenarios, with cost-sharing offering greater economic benefits for farmers. These findings highlight the importance of an integrated approach to SWC implementation for achieving multiple Sustainable Development Goals (SDGs) by enhancing food security, improving farmer incomes, and promoting sustainable and productive landscape management practices. Future research should explore the long-term sustainability of SWCPs, their adaptation across diverse agroecological zones and landscapes, the incorporation of various crops, the broader socioeconomic impacts, and the development of effective extension programs for wider adoption by farmers.

Evaluating land suitability and water availability for surface irrigation in the Abbay basin of Ethiopia.

This study was conducted in the Abbay basin of Ethiopia to evaluate land suitability for irrigation considering both surface and groundwater sources using the analytic hierarchy process. Multiple factors which affect irrigated agriculture productivity were considered, and an 85% threshold was applied to identify irrigable land. The suitability result was validated using ground truth data from existing irrigation projects for surface water sources and depth to groundwater data for groundwater sources. The low flow potential of rivers, which is dependable for surface irrigation, was evaluated against suitable land considering the most dominant crops. The result showed that nearly 10% of the basin area (19 192 km2) and 5.3% of the basin (10 364 km2) were found suitable for surface irrigation from rivers and groundwater, respectively. South Gojam was found to be the most suitable sub-basin (approx. 3880 km2) for surface irrigation, whereas Muger was found to be the most suitable sub-basin (approx. 2105 km2) for surface irrigation from rivers and groundwater, respectively. Depth to groundwater was shallow for Muger as compared with other sub-basins. The validation result depicted more than 83% and 73% overlap for surface and groundwater sources, respectively. Land suitability and water availability assessment result in the Abbay basin shows a promising result for surface irrigation developments.

Dataset for mapping groundwater contaminant risk using the DRASTIC model for a case study in Ethiopia.

This paper contains the data that helps in mapping groundwater contaminant risk (nitrate and phosphate) using the DRASTIC model for a case study in Ethiopia. The data contains a total of 9 parameters, each having data points ranging from 10 to 33 (196 records in total). About 52 % of the data points from 7 parameters (depth to water table, aquifer media, soil, types of vadose, transmissivity, aquifer thickness, and hydraulic conductivity) allows the model to predict contaminant risk levels in the groundwater. About 48% of the data points from direct records of 2 variables (nitrate and phosphate) help to validate model predictions and spatial mapping of contaminant risk levels. A brief description of the model development can be found by Alamne, Assefa, Belay and Hussein [1]. This data helps to associate and develop relations between contaminant risk with aquifer characteristics, soil, and water table. A principal component analysis can be performed to identify essential parameters in the prediction of groundwater contaminant risk levels. In addition, the dataset can be used as a baseline or reference point for trend analysis on contaminant risk with the addition of a new dataset.