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Soil management decisions should consider physical potential of the environment, weather variability, and requirements of crops to maximize production to the potential limits. This calls for characterization of environments using selected input variables. Such studies are scanty in southern central Rift Valley of Ethiopia due to which the area is considered homogeneous and identical for agricultural planning, extension, and input delivery programs. Thus, to investigate the scenario, we employed principal component, clustering, and GIS analysis on geo-referenced physiographic and climatic attributes, and their statistical variables obtained from 43 stations with the objective of identifying homogeneous management units with similar physiography, weather pattern, and production scheduling. The analysis of principal components (PCs) indicated that three PCs explained 74.7% of variance in October, November, December, and January (ONDJ), four PCs explained 79.3% of variance in February, March, April, and May, and four PCs explained 80.5% of variance in June, July, August, and September (JJAS). Cluster-I was characterized by high altitude and low temperature in ONDJ season. Cluster-II was characterized by low altitude and high temperature across most seasons. Cluster-III was intermediate in altitude, temperature, and rainfall. Cluster-IV was characterized by high rainfall in JJAS. In all the clusters, PC1 was the mean rainfall component with strong association with altitude and longitude, while PC2 was the temperature component. PC3 is the statistical component with strong influence from mean rainfall. Thus the factors that determine the formation of clusters are reduced from 12 to 5 (T mean, latitude, longitude, altitude, and RFmean) and 43 stations are grouped into 4 clusters (Shamana, Bilate, Hawassa, and Dilla) which are geographically and ecologically distinct. These clusters require different sets of agro-meteorology advisory, maize management, and input delivery strategies.

While pulses are staple food-legumes in Ethiopia, their productivity is low due to low soil fertility. Elite rhizobial strains that significantly increased shoot dry weight and nitrogen (N) contents of common beans and soybeans in greenhouse were selected for two-year field trials to evaluate their effect on yields of the pulses in the field. Each pulse had six treatments, namely four rhizobial inoculants, uninoculated control, and synthetic N fertilizer. In the drought-affected year 2015, inoculated pulses tolerated moisture stress better than non-inoculated controls. Inoculation was conducive to higher or equivalent yields compared to synthetic N fertilizer. At Halaba, bean inoculated with strain HAMBI3562 gave the highest grain yield (1500 ± 81 kg ha−1; mean±SE) while the control yielded only 653 ± 22 kg ha−1. At Boricha, HAMBI3570 gave a grain yield (640 ± 35 kg ha−1) comparable to synthetic N. When rainfall was optimal in 2016, inoculation with HAMBI3562 and HAMBI3570 gave grain yields (around 4300 kg ha−1) equivalent to synthetic N. With soybean, strain HAMBI3513 produced consistently higher or comparable biomass and grain yields compared to synthetic N. In conclusion, HAMBI3562 and HAMBI3570 for beans and HAMBI3513 for soybeans can serve as inoculants for areas having similar conditions as the test areas. [ABSTRACT FROM AUTHOR]