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 droughtaffected 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.

Sweet potato (Ipomoea batatas (L.) Lam) is economically important food crop in Ethiopia. Since its introduction, numerous agronomic research activities were carried out in agricultural research centers, non-governmental organizations and universities. The objective of this piece of work is to document available research findings in a usable manner and present it to first national sweet potato workshop held between 6-7 June 2013, SARI head quarter, Awassa, Ethiopia. Agronomic research has been carried out on preparation of planting materials, curing vine cuttings, planting methods, depth of planting, plant density, planting time, cropping systems, and etc. Results showed that sweet potato crop has a potential of 50 to 60 t/ha in Ethiopia and the length of vines to be used for planting shall be 30 to 40 cm. Weeding trials elaborated that weeding twice on 30-40DAS and 70DAS regardless of the study sites would suffice for the crop. Planting date experiments justified early planting with the onset of rainfall in non irrigated fields. However, the results of population density and fertilizer regimes varied across locations and varieties. Thus generation and promotion of site specific recommendations are of paramount importance for root crops like sweet potato.

The paper describes research results of obtained in the process of generation of appropriate cultural practices, cropping systems and other agronomic studies of cassava in Ethiopia. Based on plant population studies carried out for three consecutive years on major cassava growing areas of SNNPR, plant spacing of 100 cm x 80, 120 cm x 80 and 80 cm x 80 was recommended for Awassa, Amaro kele and Amaro Jijola areas, respectively. The moisture content of cassava roots were invariably higher in varieties Qule, Kele and local in 12th and 15th MAP; But, decreases there after markedly in a linear way. Conversely, viscosity and root yield of the cassava increases significantly (P < 0.05) starting the 12th MAP. Results of intercropping maize and cassava showed that alternate rows (1C:1M) had LER advantage of 71%. Similarly, growing cassava intercropped with haricot bean, cow pea, soy bean and mung bean resulted in LER of 1.82, 1.49. 1.48 and 1.62 compared to sole beans in Amaro area. In one study that investigated planting positions and planting parts, slant and vertical plantings are suitable in Awassa sandy soils compared to horizontal planting. Similarly, this study revealed that planting materials shall be taken from middle and top part of the main stem of cassava compared to its branches. In another study that investigated land preparation methods, it was found that furrow and ridge planting produced comparable results of 48t/ha and 43t/ha compared to 38t/ha produced by flat plantings. Planting date trial carried out for three consecutive seasons elaborated that rainfed cassava planting could be carried out from mid April to early May based on optimum marketable and total tuber yield obtained. Thus, the crop responded very well to crop management practices like plant density, land preparation methods, planting positions, intercropping, etc.and farmers or investors could optimize production by using these recommendations.

Understanding rainfall variability is important to establish crop calendar related agronomic decisions. To this end, we defined start and end of seasons, analyzed dry spell and evaluated conditional risks of alternative planting dates using a thirty years daily rainfall data across southern central rift valley of Ethiopia. Results showed that the probability of annual rainfall being greater than 1000 mm was 97, 24, 94, and 61%, in Dilla, Bilate, Shamana, and Hawassa clusters, respectively. The variability of annual total rainfall in the lowland areas of Dilla and Bilate was above 25%, whereas for Shamana and Hawassa was below 20%. Variability of seasonal rainfall during FMAM was 33.7%, which is higher than ONDJ (27.1%) and JJAS (27.9%), which could lead to maize plants suffering moisture stress during FMAM season. The onset of rains had variability of 29.2, 19.5, 17.5 and 26.5%, and also LGP showed variability of 22.8, 22.1, 21.2 and 20.3% in Shamana, Bilate, Hawassa, and Dilla clusters, respectively. Moreover Shamana, Bilate, Hawassa and Dilla clusters are hit by agricultural drought in one out of 2.61, 2.3, 2.5 and 2.5 years, respectively. Model based analysis of conditional risk of farmers planting dates also showed a success rate of less than 10, 7, 40 and 63% for maize variety in Shamana, Bilate, Hawassa and Dilla clusters, respectively. However, the success rate of risk taker farmers’ is higher than anticipated by the model. The farmers who take risk were encouraged in Shamana cluster by local edaphic, physiographic, socioeconomic and climatic differences. Hence, there is a need to seek real time local agro-metrological advisory and follow the necessary tactical and strategic farming decisions. Moreover, there is also a need to incorporate local factors with modern climate models to obtain synchronized calendar estimates.

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.