Land-use changes under poor management practices are among the major causes of decline in soil fertility and agricultural productivity in south-central Ethiopia. Therefore, the study of soil properties variations across land-use types will have implications for planning proper soil management strategies. This study assessed the effects of land-use types on soil physicochemical properties in the Cheha district, south-central Ethiopia. Seven land-use types, eucalyptus plantation, cultivated, grazing, shrub, enset (Ensete ventricosum), khat (Chata edulis), and natural forest, were considered in describing soil fertility. A total of 21 soil samples (0–20 cm depth) were collected randomly from three representative soil sampling plots across each land-use type and examined for their analysis of soil physicochemical properties. Results showed that land-use changes induced significant (p < 0.05) differences in the soil OC, total N, CEC, pH, aluminum saturation, and Fe among land-use types were observed. Soils under eucalyptus plantation were considerably low in the sand (23.44%), silt (24.00%), and available water content (111.13 mm/m), whereas high in clay (52.56%) content and bulk density (1.39 g cm⁻³) as compared to enset land-use type. Compared to natural forest, soils under enset land-use type were high in soil OC (2.41%), total N (0.23%), and CEC (37.61 cmol (+) kg⁻¹) followed by khat land-use. Correlation analysis also showed a highly significant positive relationship between soil OC and CEC but negative with bulk density, soil pH, and aluminum saturation. Total N in eucalyptus plantation, cultivated, khat, and enset lands were depleted by 56.0%, 40.0%, 36.0%, and 8.0%, respectively. Relative to forest land, lower exchangeable Ca, Mg, and Na contents were recorded in soils of cultivated land use. The extractable Fe, Mn, and Zn contents of soils were generally high and are not limiting for crop production. The intensive exploitation of land resources and improper soil management systems in the area could be the major factors for variations in soil properties across land-use types. Therefore, there is a need to develop integrated soil management practices that could help to restore soil nutrients and thereby minimize the continuing soil degradation for sustainable agricultural productivity.

Up-to-date digital soil resource information and its comprehensive understanding are crucial to supporting crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, and is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small scaled (1 : 2 M), which limit its practical applicability. Yet, a large legacy soil profile dataset accumulated over time and the emerging machine-learning modeling approaches can help in generating a high-quality quantitative digital soil map that can provide better soil information. Thus, a group of researchers formed a Coalition of the Willing for soil and agronomy data-sharing and collated about 20 000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and 14 681 profile data were prepared for modeling. Random forest was used to develop a continuous quantitative digital map of 18 World Reference Base (WRB) soil groups at 250 m resolution by integrating environmental covariates representing major soil-forming factors. The map was validated by experts through a rigorous process involving senior soil specialists or pedologists checking the map based on purposely selected district-level geographic windows across Ethiopia. The map is expected to be of tremendous value for soil management and other land-based development planning, given its improved spatial resolution and quantitative digital representation.

Summary: Meeting the burgeoning global demand for both food and energy requires substantial yield increases through the efficient use of inputs like fertilizers. Prompted by the result of a soil survey expedition in the late 1950s, which signaled a widespread deficiency of nitrogen (N) and phosphorus (P), plant nutrition research in Ethiopia began in the 1960s, focusing on the response of prioritized cereals – tef (Eragrostis tef), wheat (Triticum aestivum), and maize (Zea mays) – to the application of N and P fertilizers. Nationwide on-farm trials conducted in the early 1970s led to a blanket recommendation of 64 kg N ha⁻¹ and 20 kg P ha⁻¹, irrespective of the crop and soil types, which were applied in the form of di-ammonium phosphate (18-46-0) and urea (46-0-0), respectively. Research conducted in the 1980s across agro-ecological and edaphic spectrum recommended 30–138 kg N ha⁻¹ and 0–50 kg P ha⁻¹, respectively. However, studies show that only 30–40% of the smallholder farmers use fertilizers at a rate less than recommended (on average at 37–40 kg ha⁻¹). This rate reflects limited supply, high prices, and the low and declining crop response to fertilizers. As a result, cereal yields increased only 10% despite a fivefold increase in fertilizer application since the 1980s. Owing to the limited and declining crop response and the increased price of fertilizer in the 1990s, research on the integrated application of inorganic and organic sources of fertilizers was initiated. Although the integrated use resulted in increased yield and better economic benefits, it was not mainstreamed into the national agricultural extension system. The soil survey expedition that began in 2011 culminated in the mapping of the soil nutrient status using literature-based critical limits. The maps have persistently revealed the deficiency of N, P, potassium, sulfur, zinc, and boron across the surveyed areas. Despite the above efforts, the data sets generated through the soil surveys conducted at different times during the last half-century and the agronomic research during the same period have never been fully exploited. It is believed that the recent development in data mining and machine-learning approaches creates the opportunities to use the data sets in conjunction with other covariates in order to generate evidence that helps to make better decisions both at strategic and operational levels. The development of decision support tools based on such large datasets and analytical capacity is believed to facilitate better-informed decisions that lead to increased resource use efficiency and sustainability.

Degradation of crop and grazing lands is a pervasive problem that negatively impacts agricultural productivity and livelihoods of crop-livestock farmers in the Blue Nile basin of Ethiopia. Area enclosure together with a cut and carry livestock feeding system is often advocated as an approach for the regeneration of degraded grazing lands. This paper reports the results of a two-year farmer participatory study conducted to assess the effects of infiltration trenches (ITs) and Chloris gayana Kunth (Rhodes grass; cultivar Masaba; tetraploid; C4 grass species) reseeding on restoration of degraded grazing lands. A split plot design was used with IT as the main plot and C. gayana reseeding as a sub-plot on 28 private grazing plots under enclosure. The results showed that IT alone increased soil moisture content and prolonged the growing period. IT and C. gayana reseeding together significantly (P ≤ 0.05) increased herbage dry matter yield and improved soil chemical properties. The highest mean herbage dry matter yield (21 Mg ha⁻¹ per cut) was recorded for plots treated with IT and reseeded with C. gayana. The higher herbage dry matter yield was attributed to increased soil moisture and the resultant prolonged growing period induced by the trenches coupled with the ability of C. gayana to effectively utilize the retained water. The results suggest that an integrated land management approach involving enclosure, in-situ water conservation and C. gayana reseeding can rapidly increase biomass productivity on degraded grazing lands while also enhancing soil quality with concomitant livelihood benefits for farmers.

Up-to-date digital soil resources information, and its comprehensive understanding, is crucial to support crop production and sustainable agricultural development. Generating such information through conventional approaches consumes time and resources, which is difficult for developing countries. In Ethiopia, the soil resource map that was in use is qualitative, dated (since 1984), and small-scale (1:2 M) which limits its practical applicability. Yet, a large legacy soil profile data accumulated over time and the emerging machine learning modelling approaches can help in generating a high-quality quantitative digital soil map that can provide accurate soil information. Thus, a group of researchers formed a coalition of the willing for soil and agronomy data sharing and collated about 20,000 soil profile data and stored them in a central database. The data were cleaned and harmonized using the latest soil profile data template and prepared 14,681 profile data for modelling. Random Forest was used to develop a continuous quantitative digital map of 18 WRB reference soil groups at 250 m resolution by integrating environmental variables-covariates representing major Ethiopian soil-forming factors. The validated map will have tremendous significance in soil management and other land-based development planning, given its improved spatial nature and quantitative digital representation.