Sorghum (Sorghum bicolor [L.] Moench), a main food for more than 500 million impoverished and food insecure people in arid and semi-arid regions of Sub-Saharan Africa (SSA) and South Asia, is an important crop for food and nutritional security (SA). Sorghum has the most acceptance in these drought-prone areas due to its good tolerance to harsh settings, high yield, and use as a good source of forages. In this review, the objective of this study is to document the production and use Sorghum in improvement programmed through a literature review, we used publications from journals to explore gene families, how they evolved, gene family theories, how gene families influenced agronomic features in sorghum, and in-depth studies of the key ten gene families in sorghum. The future prospects on sorghum enhancement include genomic selections and gene families, as well as comparative genomic selections. Furthermore, understanding the mechanism of these gene families is important for addressing problems that plague sorghum production, including as infections, drought, and heat stress.  We can accurately improve traits using modern techniques such as marker-assisted selection, Genomic selections (GS), Marker-assisted backcrossing (MABC), Marker-assisted recurrent selection (MARS), Marker-assisted selections (MAS), and Genome-wide selections (GWAS) if we have the above gene families of interest (GWAS). Sorghum as a desirable breed: future paths and prospects.

The pricing system for soybean is complex because it involves interactions between the markets for soybean grain, soybean meal and soybean oil. The study was undertaken with the objective of identifying and describing the constraints, challenges and opportunities of soybean production and productivity and its impact on the livelihoods of smallholder producer farmers in the area. A multi-stage random sampling techniques were employed to select a total of 153 farmers from four Kebeles. Data were collected from both primary and primary secondary sources. Descriptive statistical analysis, Strengths Weaknesses, Opportunities and Threats (SWOT) analysis, econometrics analysis and value chain analysis were used to analyze the data. Soybean value chain analysis of the study area revealed that the main value chain actors are input suppliers, direct market actors and chain supporters. The major constraints identified are input supply constraints viz., rhizobium inoculants and different pesticides; lack of collateral to get credit, poor storage facilities, low price of the produce in market, and low negotiation (bargaining) power of producers. Moreover, the opportunities are the availability of Union and different NGOs working in soybean, strong community based seed system in the area, wide arable land for soybean production, government’s policy support for soybean sub-sector and establishments of soybean based agro-industry. Therefore, improving extension services of soybean, minimizing the transaction cost of soybean, improving the transportation access, to link producers to chain actors and facilitators, to set up demand driven soybean improvement, increase land allocation for large scale production and market information dissemination are require to improve productivity and profitability of soybean farming in the region and Ethiopia at large. Practical use of trade and marketing policies (including subsidy policies) are needed in this country to compete for the export market.

Acidic soils limit the productive potential of crops because of low availability of basic cations and excess of hydrogen and aluminium in exchangeable forms. At the study area, soil acidity is a well-known problem limiting crop productivity. Therefore, this study was conducted to identify common bean variety that tolerate acid soil or low pH soil. Fifteen (15) common bean variety were grown in split plot design under four soil amendments (limed alone, phosphorus alone, both lime and phosphorus treated, and no any amendment) with three replications at three locations in Western and South Western Ethiopia. Data on growth and yield were collected and analyzed using SAS version 9.3 software. Treatment means were compared at 5% level of significance using List significant Different. The results revealed that variety X amendments X locations X seasons interactions were significant (p<0.01) for both grain yield and plant height. Availability of varietal difference among common bean varieties under both amended and unamended acid soil conditions was observed. The highest grain yield (1.043 t/ha) under control soil conditions obtained from this result is still below the national average (1.59t/ha), but more than the national average under lime and phosphorus treated plots (1.989t/ha), which shows that the selected variety is responded to lime and phosphorus than tolerant to acid soil. SER 119 variety is selected for those farmers who have the capacity to apply lime with phosphorus based on the yield performance at both locations and also this variety is included in the future work of further selection trials. However, further study is required including considering additional genotypes, at least for three or four years to determine the residual effect of phosphorus and lime to reach at a conclusive recommendation.

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For grain yield stability analysis, genotype by environment interactions are crucial in properly identifying and discriminating between varieties and locations. Hence, this experiment was conducted with the objectives to evaluate the stability of soybean using additive main effects and multiplicative interaction (AMMI), multi‐trait stability index (MTSI), weighted average absolute scores biplot (WAASB), Eberhart and Russell regression model, and genotype plus genotype by environment interaction (GGE) biplot analysis for grain yield of soybean genotypes and identified stable genotypes in the different soybean agroecologies of Ethiopia. Twenty‐four soybean genotypes were planted at six soybean environments with RCBD in three replications in the 2015/2016 cropping season. Stability measures, namely, AMMI, AMMI stability value, and GGE biplot analysis were used to identify the high‐yielding and stable genotypes across the testing environments. AMMI‐1 biplot showed Pawe as the ideal environment; Bako as a favorable environment; Asosa an average environment; and the rest namely, Dimtu, Jimma, and Metu as unfavorable environments. On the other hand, AMMI‐2 biplot analysis certain genotypes like Prichard, Spry, Delsoy 4710, and Croton 3.9 were identified as stable genotypes. Bako and Metu were identified as the most discriminating environments. Mega environments and the best yielding soybean genotypes on each mega environment were revealed by the GGE biplot analysis model. For other multivariate statistics used for this study, MTSI, WAASB, and regression models, stable and superior varieties for grain yield were revealed. Through the MTSI, the four genotypes, namely, Liu yue mang, SCS‐1, Clarck‐63k, and AFGAT, were found to be stable and superior over the rest tested genotypes. Overall, the genotypes SCS‐1 and AGS‐7‐1 were stable across soybean growing environments and are recommended for mega environment production.