In spite of the availability of several improved agricultural technologies generated by the research system in Ethiopia over the last four decades, adoption of these innovations by smallholder farmers has been very low. This has led to stagnation of agricultural productivity and low crop yields, exposing the country to recurrent food shortfalls and national food insecurity. The old approach to agricultural research emphasized developing new technologies mainly through onstation research that were then supposed to reach farmers through the public-sector extension system. The Ethiopian Institute of Agricultural Research (EIAR) has in recent years introduced a shift in agricultural research for development, which is based on the innovation systems approach that involved cultivating partnerships with several actors along the value chain, especially farmers, farmers' cooperatives and input suppliers. This paper presents the methodology used to facilitate agricultural innovations and the diffusion of new technologies and illustrates the outcomes of this initiative with regard to technology adoption, productivity growth and the market orientation of production. The authors use examples from experiences in scaling up three grain legumes. Compared to the three-year baseline average , crop output increased nationally by 89%, 85% and 97% in 2008 for common bean, chickpea and lentil respectively. Nationally, 53-59% of the output growth is attributable to yield growth due to technological change, while the balance is due to area expansion. These results affirm that the new approach has led to accelerated adoption of new and high-yielding or low-risk varieties.

Common bean (Phaseolus vulgaris L.) is an important staple crop for smallholder farmers, particularly in Eastern and Southern Africa. To support common bean breeding and seed dissemination, a high throughput SNP genotyping platform with 1500 established SNP assays has been developed at a genotyping service provider which allows breeders without their own genotyping infrastructure to outsource such service. A set of 708 genotypes mainly composed of germplasm from African breeders and CIAT breeding program were assembled and genotyped with over 800 SNPs. Diversity analysis revealed that both Mesoamerican and Andean gene pools are in use, with an emphasis on large seeded Andean genotypes, which represents the known regional preferences. The analysis of genetic similarities among germplasm entries revealed duplicated lines with different names as well as distinct SNP patterns in identically named samples. Overall, a worrying number of inconsistencies was identified in this data set of very diverse origins. This exemplifies the necessity to develop and use a cost-effective fingerprinting platform to ensure germplasm purity for research, sharing and seed dissemination. The genetic data also allows to visualize introgressions, to identify heterozygous regions to evaluate hybridization success and to employ marker-assisted selection. This study presents a new resource for the common bean community, a SNP genotyping platform, a large SNP data set and a number of applications on how to utilize this information to improve the efficiency and quality of seed handling activities, breeding, and seed dissemination through molecular tools.Electronic supplementary materialThe online version of this article (10.1007/s10722-019-00746-0) contains supplementary material, which is available to authorized users.

Understanding the nature and extent of association between yield and yield related traits is the prerequisite study for any underutilized crop improvements of sustainable genetic enhancement. However, there is a lack of sufficient information on seed yield and related trait correlation and path coefficient analysis of cowpea in Ethiopia. To fill the existing knowledge gap, the present study was conducted to determine the nature and extent of phenotypic and genotypic correlation and path coefficient analysis among 18 quantitative traits. A total of 324 cowpea landraces were tested in 18 × 18 simple lattice design at Melkassa Agricultural Research Center and Miesso sub center during 2016 cropping season. The magnitude of genotypic correlations was higher than phenotypic correlations in most traits at both locations; this implies that the traits under consideration were genetically controlled. Seed yield was positively and highly significantly correlated with most of the traits at phenotypic and genotypic levels, indicating the presence of strong inherited association between seed yield and the other 17 traits. Almost all traits genotypic direct and indirect effects were higher than the phenotypic direct and indirect effects; this indicated that the other traits had a strong genetically inherited relationship with seed yield. Genotypic path coefficient analysis revealed that days to flowering, biomass and harvest index at Miesso, and seed thickness, plant height, days to maturity and biomass at Melkassa had relatively high positive direct effect on seed yield. However, seed width and hundred seed weight had exerted negative direct effect on seed yield at each location. Phenotypic path coefficient analysis showed that biomass and harvest index had exerted high positive direct effect on seed yield at both locations.

This experiment was conducted to evaluate 36 common bean genotypes including seven released varieties to generate information on the extent of genetic variability, heritability and expected genetic advance of yield and yield related traits. The field experiment was conducted in 2015 at two locations (Abaya and Yabello) and genotypes were planted in triple lattice design. Data were collected on yield and important agronomic traits. The estimated genotypic (GCV) and phenotypic (PCV) coefficient of variations ranged from 4.82 to 9.85% and 7.03 to 12.93%, respectively for combined analyses. The PCV values were relatively greater than GCV in magnitude for all traits, of which the magnitude of the differences were large for grain yield, seeds number per plant and number of primary branches, but was relatively low for plant height and number of seeds per pod. Broad sense heritability ranged from 18.29 to 58.6%, and genetic advance as percentage of mean ranged from 4.25 to 14.42%. Only plant height and seed number per pod had moderate heritability coupled with relatively high genetic advance values.

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Ethiopia is claimed to be a center of diversity for cowpea production. The crop is the most drought tolerant and could help the country overcome the recurrent drought problem; however, the yield is very low due to lack of effort to develop varieties. This research was conducted to evaluate the stability of cowpea genotypes and to estimate the magnitude of genotypes by environment interaction (GEI) effect on grain yield. Sixteen cowpea genotypes were tested at seven environments in an experiment laid out in a 4 × 4 triple lattice design during 2016/17 cropping season. The combined analysis of variance over environments showed significant differences among genotypes and environments, along with significant effect of GEI on grain yield, days to flowering, days to maturity, plant height and pods per plants. Analysis of variance for grain yield from AMMI model indicated the contribution of genotype and environment, with GEI accounting for about 63.3, 5.3 and 29.7% of the total sum of squares, respectively. The result indicated that environments contributed much to the observed variations suggesting the need to test cowpea genotypes in diverse environments. Considering all stability parmeters, viz; deviation from regression (S 2 di), coefficient of regression (bi) from ER's model, IPCA1, IPCA2 and AMMI stability value (ASV) from AMMI model, GGE biplot and variety TVU was identified as the most stable with mean yield above the mean grain yield of genotypes. Two genotypes: IT-99K-1060a (1398.8 kg/ha) and 86D-378 (1377.1 kg/ha) had first and second highest yield, identified as responsive to both environments but more to favorable environments suggesting the need to further test and develop as varieties. The other two genotypes: 95K-1095-4A and 93K-619-1, identified as unstable and highly responsive to environments suggested to consider the genotypes as candidate varieties where they performed best. Melkassa, Sekota and Jinka were identified as more descrimnating environments, whereas Arbaminch and Kobo were ideal for selecting superior genotypes; however, Babile and Meisso were non descrimnating environments. Key words:Additive main effects and multiplicative interaction (AMMI) stability value, Eberhart and Russell, deviation from regression and triple lattice.

Cowpea is one of the most important indigenous food and forage legumes in Africa. It serves as a primary source of protein for poor farmers in drought-prone areas of Ethiopia. The crop is used as a source of food, and insurance crop during the dry season. Cowpea is adaptable to a wide range of climatic conditions. Despite this, the productivity of the crop is generally low due to lack of stable and drought tolerant varieties. In this study, 25 cowpea genotypes were evaluated in ve environments using a triple lattice design during the 2017 and 2018 main cropping seasons. The objectives of this study were to estimate the magnitude of genotype by environment interaction (GEI) and grain yield stability of selected drought tolerant cowpea genotypes across different environments. The additive main effect and multiplicative interaction (AMMI) model indicated the contribution of environment, genotype and GEI as 63.98 6%, 2.66% and 16.30% of the total variation for grain yield, respectively. The magnitudes of the GEI sum of squares were 6.12 times that of the genotypes for grain yield. The IPCA1, IPCA2 and IPCA3 were all signi cant and explained 45.47%, 28.05% and 16.59% of the GEI variation, respectively. The results from AMMI, cultivar superior measure (Pi), genotype plus genotype-by-environment (GGE) biplot yield stability index (YSI), and AMMI stability value (ASV) analyses identi ed NLLP-CPC-07-145-21, NLLP-CPC-103-B and NLLP_CPC-07-54 as stable and high yielding genotypes across environments. Thus, these genotypes should be recommended for release for production for drought prone areas. NLLP-CPC-07-143, Kanketi and CP-EXTERETIS were the least stable. The AMMI1 biplot showed that Jinka was a high potential and favorable environment while Babile was an unfavorable environment for cowpea production.GGE biplots help visualize and compare the distance between each genotype and the ideal genotype located at the center of the concentric circle (Yan and Rajcan 2002). The ideal genotypes, based on proximity to the center of the concentric circle of the GGE biplot were ACC-216-747 and NLLP-CPC-07-145-21, with high yield and stability (Figure 2). In addition, NLLP-CPC-07-10 was located on the next homocentric circle and might be considered as a desirable genotype. In principle, the ideal genotype should have the longest vector, highest mean performance and with zero GEI, and/or it should perform consistently in all environments. Because of the genetic background or nature of the traits (yield) and level of expression, the ideal genotype does not always exist in reality. Therefore, such like genotypes the breeders can be used as a reference for genotype for further study. Genotypes which were high yielding but were not stable across environments could be recommended for a particular environment. Yan & Rajcan (2002) and Yan et al. (2007) speci ed that the environments with long vectors (PC1 scores) and relatively small angles or absolute with the AEC abscissa are valuable for greater discriminatory capacity (in terms of the geno...

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