Variability analysis of soil properties, mapping, and crop test responses in Southern Ethiopia

Agricultural productivity is significantly impacted by soil properties, which vary spatially from a small to a larger area. This variation may be caused by a combination of intrinsic and extrinsic factors, including human activities like soil management practices. The aim of the current study was to analyze soil spatial variability, create a Digital Soil Map (DSM), and test map information with crop in Southern Ethiopia. A total of 18 geo-referenced surface soil samples at depth of 20 cm were collected. Selected soil Physico-chemical properties such as soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av. P), sulfur (S), exchangeable bases [calcium (Ca), magnesium (Mg), and potassium (K)], soil micronutrients [boron (B), copper (Cu), iron (Fe), manganese (Mn) and zinc (Zn)] and cation exchange capacity (CEC) were analyzed. The results revealed clay texture with a mean pH value of 4.6 (strong acidity). About 50% of essential nutrients [N, P, S, Ca, Mg, B & Fe] were deficient. The geostatistical analysis has shown that the best-fitted models were exponential for (OC, TN, available P, S, Mg, CEC, B, Fe, and Zn), spherical for (pH, Ca, Cu and Mn), and Gaussian for (C:N, K, K:Mg, and PBS). The range of all soil properties varied from 50 m to 84 m which was above the actual distance between soil samples (i.e., 46 m). The result showed that the spatial dependence values for soil properties of [OC, TN, CEC, PBS, ESP, and Cu]; [pH, C: N ratio, available P, S, Ca, Mg, K, Na, K: Mg ratio and Zn] and [B, Fe, M n] were strong (<25%]; weak (>75%) and moderate (25%-75%), respectively. Model performance using indicators such as prediction mean error (PME), root mean square standardized error (RMSSE), mean standard error (MSE), and root-mean-square error (RMSE) also confirmed the acceptable prediction. The DSM demonstrated the limitation of N, P, S, and B nutrients for intervention. The DSM information was tested under field conditions using haricot bean (Phaseolus vulgaris) with lime and organic fertilizers as treatments. The experiment consists of lime rates (0, 3, 6 t/ha), rhizobium inoculation (inoculated and non-inoculated), and fertilizer types (0, 150 kg ha^-1 NPSB, 5 t/ha vermi compost, 10t/ha farmyard manure (FYM)) in Randomized Complete Block Design with three replications. The result exhibited interaction effects of lime, inoculation, and fertilizer types significantly influenced (p < 0.05) biomass and grain yield of haricot beans. Rhizobium inoculation x 6t/ha lime x 150 kg ha^-1 NPSB recorded the maximum grain yield (3186.1 kg/ha) which was 26.3 fold over the non-treated soil (117 kg ha^-1). In conclusion, the DSM classified the area into distinct management zones which were tested with a crop trial. The results of the trial confirm the importance of site-specific nutrients/amendment application for sustainable soil management.

Optimization of bacterial cellulose production by Gluconacetobacter xylinus using carob and haricot bean

Bacterial cellulose (BC) can be used in medical, biomedical, electronic, food, and paper industries because of its unique properties distinguishing it from plant cellulose. BC production was statistically optimized by Gluconacetobacter xylinus strain using carob and haricot bean (CHb) medium. Eight parameters were evaluated by Plackett-Burman Design and significant three parameters were optimized by Central Composite Design. Optimal conditions for production of BC in static culture were found as: 2.5g/L carbon source, 2.75g/L protein source, 9.3% inoculum ratio, 1.15g/L citric acid, 2.7g/L Na2HPO4, 30°C incubation temperature, 5.5 initial pH, and 9days of incubation. This study reveals that BC production can be carried out using carob and haricot bean extracts as carbon and nitrogen sources, and CHb medium has higher buffering capacity compared to Hestrin and Schramm media. Model obtained from this study is used to predict and optimize BC production yield using CHb medium.