Abstract This study sought to analyze the degree of spatial association of soil texture with agro-climatic zones and slope classes on the farmlands of the Jema watershed, in the Northwestern Highlands of Ethiopia. The agro-climatic zones (elevation zones) determine the micro-climate and biota of the study area. Thirty six soil composite samples for texture (the proportion of clay, silt and sand) analysis from four agro-climatic (elevation) zones and seven slope classes were collected. One-Way-ANOVA was employed to compute the mean variability of texture among the identified terrain classes, and linear regression was used to analyze the degree of association between texture and the terrain attributes. The measured values of sand, silt and clay in the watershed ranged from 11.4 to 43.4, 6.0 to 34.8, and 21.8 to 77.8, respectively. The One-Way-ANOVA indicated a significant (p < 0.05) soil texture variation in both slope and agro-climatic zone classes. Heavy clay, clay and clay loam were identified as the major texture classes in the lower, middle and upper parts of the watershed, respectively. The regression analysis showed that texture was more influenced by the difference in the elevation values than in slope values in the watershed. The standardized beta coefficients of slope and elevation for clay particles were 0.499 and 0.767, respectively. For sand, the regression coefficients for slope and agro-climatic zone were 0.485 and 0.812, respectively. This implies that an interactive effect of micro-climate and biota governed by elevation influenced the spatial distribution of soil texture more than slope.

Abstract The association between elevation (agro-climatic zones, ACZs) and the mean annual total rainfall (MATRF) is not straightforward in different parts of the world. This study sought to estimate the amount of MATRF across four elevation zones of Jema watershed, which is situated in the northwestern highlands of Ethiopia, by employing an appropriate interpolation method. The elevation of the watershed ranges from 1895 to 3518 m a.s.l. For the sake of this study, 34 sample MATRF data were extracted from satellite and nearby gauge stations that were recorded from 1983 to 2010. These data sources were reconstructed by International Research Institute for Climate and Society at Columbia University, USA, at a scale of 10 km by 10 km. An elevation data set generated from a digital elevation model with 30-m resolution (DEM 30 m) was considered as a covariable to estimate the MATRF. To identify the optimal interpolation model, mean errors were computed using cross-validation statistics. The root-mean-square error (RMSE) analysis showed that ordinary cokriging (OCK) was the most accurate model with a predictive power of 87.3%. The root-mean-square standardized (RMSSE) analysis showed that the best precision value (0.72) occurred in OCK. Stable and Gaussian trend lines together with local polynomial types of trend removal, and an elliptical neighborhood search function could perform best to maximize the accuracy and the precision of estimating MATRF. Elevation, as a covariable, enhanced the degree of accuracy and precision of estimation. The value of the trend line function (least square) between the MATRF and elevation was very weak (R2 = 0.07), whereas the value of trend line function (least square) between the MATRF and the longitude coordinates (east–west direction) was medium (R2 = 0.34). The estimated MATRF for the entire watershed under study ranged from 1228 to 1640 mm. To conclude, elevation could contribute to the estimation of the MATRF. The value of the MATRF showed a declining pattern from the lower to higher elevation areas of the watershed.

Abstract Generating land capability class guidelines at a watershed scale has become a priority in sustainable agricultural land use. This study analyzed the area of cultivated land use situated on the non-arable land-capability class in the Jema watershed in the Upper Blue Nile River Basin. Soil surveys, meteorological ground observations, a digital elevation model (DEM) at 30 m, Meteosat at 10 km × 10 km and Landsat at 30 m were used to generate the sample soil texture class, average annual total rainfall (ATRF in mm), terrain, slope (%), elevation (m a.s.l) and land-use land cover (%). The land capability class was analyzed by considering raster layers of terrain, the average ATRF and soil texture. Geo-statistics was employed to fit a surface of soil texture and average ATRF estimates. An overlay technique was used to compute the proportion of cultivated land placed on non-arable land. As per the results of the terrain analysis, the elevation (m a.s.l) of the watershed is in the range of 1895 to 3518 m. The slope was found to be in the range of 0 to 45%. The amount of estimated rainfall ranged from 1640 to 131 mm with value declined from the lower to the higher elevation. Clay loam, clay and heavy clay were found to be the major soil texture classes. Four land capability classes, i.e., II, III, IV (arable) and V (non-arable), were identified with proportions of 28.56%, 45.74%, 22.16% and 3.54%, respectively. Seven land-use land covers were identified, i.e., annual crop land, grazing land, bush land, bare land, settlement land, forestland and water bodies, with proportions of 42.1, 35.9, 8.90, 8.3, 2.6, 2.1, and 0.2, respectively. Around 1707.7 ha of land in the watershed is categorized under non-arable land that cannot be used for annual crop cultivation at any level of intensity. Around 437 ha (3.5%) of land was cultivated on non-arable land. To conclude, the observed unsustainable crop land use could maximize soil loss in upstream regions and siltation and flooding downstream. The annual crop land use that was observed on non-arable land needs to be replaced with perennial crops, pasture and/or forest land uses.

Abstract The objective of the study was to analyze the variability of various climate indicators across the agro-climatic zones (ACZs) of the Jema watershed. The variability was analyzed considering mean annual rainfall (MARF, mm), mean daily minimum temperature (MDMinT, °C), and mean daily maximum temperature (MDMaxT, °C). A one-way analysis of variance (ANOVA) was employed to test whether group mean differences exist in the values of the indicated climatic indicators among the ACZs of the watershed. The coefficient of variation was computed to analyze the degree of climate variability among the ACZs. Rainfall and temperature data sets from 1983 to 2017 were obtained from nearby meteorological stations. The effect of climate variability in the farming system was assessed with reference to local farmers’ experience. Ultimately, the values of the stated indicators of exposure to climate variability were indexed (standardized) in order to run arithmetic functions. The MARF decreases towards sub-alpine ACZs. Based on the result of the ANOVA, the two-tailed p-value (≤ 0.04) was less than 0.05; that is, there was a significant variation in MARF, MDMaxT (°C), and MDMinT (°C) among the ACZs. The coefficient of variation showed the presence of variations of 0.18–0.88 for MARF, 0.18 to 0.85 for MDMaxT, and 0.02–0.95 for MDMinT across the ACZs. In all of the indicators of exposure to climate variability, the lowest and highest indexed values of coefficient of variation were observed in the moist–cool and sub-alpine ACZs, respectively. Overall, the aggregate indexed values of exposure to various climate indicators ranged from 0.13–0.89 across the ACZs. The level of exposure to climate variability increased when moving from moist–cool to sub-alpine ACZs. The overall crop diversity declined across the ACZs of the watershed. Nevertheless, mainly because of the rise in temperature, the climate became suitable for cultivating maize and tef even at higher elevations. In order to adapt to the inter-annual variability of the rainy season, the process of adapting early-maturing crops and the use of improved seeds needs to be enhanced in the watershed, especially in the higher-elevation zones. It is also essential to revise traditional crop calendars and crop zones across the ACSz.

Abstract The objective of this study was to evaluate the performance of satellite rainfall estimates (Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2) and Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEPv2) from 1981 to 2018 for monthly meteorological drought analysis over the Upper Blue Nile (UBN) basin. The reference for the performance evaluation was rainfall measured in situ selected with reference to the elevation zones of the basin: Highland, midland, and lowland. Both the measured and estimated rainfall datasets were aggregated by month at a spatial resolution of 10 km × 10 km with a temporal coverage of 38 years from 1981 to 2018 and evaluated with respect to raw precipitation statistics and the standardized precipitation index (SPI). The values of SPI were validated with reference to documented meteorological drought records of the country. The mean bias, correlation coefficient, probability of bias (PBias, %), mean error (ME, mm), and root mean square error (RMSE, mm) values across the elevation zones for CHIRPSv2 were found to be 1.07, 0.91, 6.75, 7.74, and 122.34, respectively. The corresponding values were 1.19, 0.87, 18.56, 19.54, and 130.26 for MSWEPv2. Based on this result, CHIRPSv2 was employed to analyze the magnitude of drought in the different elevation zones of the UBN. The magnitude (SPI) of monthly meteorological drought over the entire UBN basin from 1981 to 2018 ranged from 0 to −3.74. The strongest negative SPI value (−3.74) was observed in August 1984 in midland areas. The highest magnitude of drought was −3.0 in July 2015 over the highland and −3.03 in June 2015 over the lowland during 2014–2017. The observed drought was characterized by extreme, severe, and moderate levels. The mean frequency of severe/extreme meteorological drought in the 38-year period over the highland, midland, and lowland parts of the UBN ranged from 7% to 11%. The average of severe/extreme drought events in each of the elevation zones of the basin was 9%, that is, drought occurred almost every 10 years for all elevation zones of the basin. Over the 38-year period, severe/extreme drought occurred at the onset and/or offset time of rainy season over all elevation zones of the basin. The UBN is characterized as a drought-prone basin. However, the frequency and magnitude of drought could neither be described as a decreasing nor as an increasing linear trend. Thus, the farming practices in the basin need to be enhanced with an improved early warning system and drought-resistant seed technologies.

Abstract The study analyzed the level of agro–ecosystem sensitivity to climate change among the agro–climatic zones (ACZs) that are situated in the highlands of Lake Tana sub–basin. The analyses considered the actual land capability class (LCC in % considering soil texture, slope and elevation zone), crop diversity (count), length of growing period (LGP, month), and inter–annual variability of climate (mean annual rainfall–MARF in mm, mean monthly minimum temperature–MMMinT in ºC, and mean monthly maximum temperature–MMMaxT in ºC). For comparison purpose, it was essential to index/standardize the values of specified indicators. The proportion of arable land varied from 13.30% (in the Sub-Alpine) to 93.00% (in the Moist–Cold). The value of coefficient of variation showed the presence of variations of 7.85–11.21 (%), 7.21–10.34 (%), 16.37–39.61 (%) for MARF (mm), MMMaxT (ºC), and MMMinT (ºC), respectively across the ACZs. The inter–annual variability of both onset and offset time of rainy season was found to be in the range of 0.3–1.25 months. The LGP (month) was in the range of 3.25–6.25 across the ACZs; whereas crop diversity (count) ranged from 2–7. The production of red onion (allium cepa), oat (Avena sativa), local wheat (Triticum), and pea (Pisum sativum) was abandoned in the Sub–Alpine; whereas the production of linseed (Linmu usitatisimum), barley (Hordeum vulgare), and niger (Guizotia abyssinica) in the Moist–Cool. Yet, crops like maize and tef became the common crops in the Cold, possibly because of global warming. The indexed value of agro–ecosystem sensitivity to climate change ranged from 0.14–0.71. The level of agro–ecosystem sensitivity was higher towards the Sub–Alpine. The local development interventions to be made in the various ACZs need to be determined/prioritized considering the level of agro–ecosystem sensitivity.