Ethiopian subsistence farmers traditionally store their grain harvests, leaving them open to storage pests and fungi that can cause contamination of major staple crops. Applying the most effective strategy requires a precise understanding of the insect species, infestation rates, storage losses, and storage conditions in the various types of farmers’ grain stores. This study did a complete literature analysis on post-harvest pest and management measures with a focus on Ethiopia. The most frequent insect pests of stored cereals in this study were weevils (Sitophilus spp.), the lesser grain borer (Rhyzopertha dominica), rust-red flour beetle (Tribolium sp.), sawtoothed grain beetle (Oryzaephilus sp.), grain beetle (Cryptolestes spp.), Indian meal moth (Plodia interpunctella), and Angoumois grain moth (Sitotroga cerealella). Flour beetles (Tribolium spp.), sawtoothed beetles (Oryzaephilus sp.), flat grain beetles (Cryptolestes pusillus), and some moths have been identified as common stored product pests of stored oil seed, while bruchid beetles (Callosobruchus chinensis) and the moths were reported for pulses. Additionally, the storage pests in Ethiopia under varied conditions caused storage losses of 9–64.5%, 13–95%, 36.9–51.9%, and 2–94.7% in maize, sorghum, chickpeas, and sesame, respectively. To reduce the losses incurred, preventative measures can be taken before infestations or as soon as infestations are discovered. A variety of pest population monitoring systems for harvested products and retailers have been developed and recommended. In this context, reducing post-harvest grain losses is an urgent concern for improving food accessibility and availability for many smallholder farmers in Ethiopia and ensuring the nation’s long-term food security.

Background: Sesame is a rare example of non-model and minor crop for which numerous genetic loci and candidate genes underlying features of interest have been disclosed at relatively high resolution. These progresses have been achieved thanks to the applications of the genome-wide association study (GWAS) approach. GWAS has benefited from the availability of high-quality genomes, re-sequencing data from thousands of genotypes, extensive transcriptome sequencing, development of haplotype map and web-based functional databases in sesame.Results: In this paper, we reviewed the GWAS methods, the underlying statistical models and the applications for genetic discovery of important traits in sesame. A novel online database SiGeDiD (http://sigedid.ucad.sn/) has been developed to provide access to all genetic and genomic discoveries through GWAS in sesame. We also tested for the first time, applications of various new GWAS multi-locus models in sesame.Conclusions: Collectively, this work portrays steps and provides guidelines for efficient GWAS implementation in sesame, a non-model crop.

Sesame production under irrigation is limited in Ethiopia because of in availability of high yielding varieties, inadequate and inefficient irrigation schemes, and insignificant awareness of producers. This study, comprising 13 sesame genotypes, was conducted around Humera and Werer during 2018 and 2019 under irrigation. The design was randomized completely block design with three replications and the objectives were to develop high yielding genotypes and identify important agronomic traits. Multivariate statistical methods like Additive Main Effect and Multiplicative Interaction (AMMI) model, Principal Component Analysis, Cluster and factor analyses were used. The genotypes (6.22%), environments (42.62) and Genotype × Environment Interactions (25.09%) were statistically (p < 0.001) significant for the agronomic traits. The grain yield in each observation varied from 383 kg/ha to 2044 kg/ha and the grand mean yield was 820.19 kg/ha. The highest mean yield was recorded from G12 (948.6 kg/ha) followed by G4 (938.9 kg/ha) while the lowest was recorded from G8 (703.1 kg/ha). G1, G4, G12, G5, G8, G11 and G13 are identified as unstable genotypes while G2, G3, G6, and G9 are stable genotypes. The genotypes were grouped in to four clusters and cluster-II was characterized as the high yielding cluster and it was also associated with grain yield, pods per plant, branches per plant and thousand seed weight. Branches per plant, pods per plant and thousand seed weight may be most determinant and crucial in developing high yielding sesame varieties. This finding recommends that G4 and G6 are desirable genotypes and can be used for irrigation production.

The experiment was conducted from 2009–2011 cropping seasons. Six mung bean genotypes viz. SML-668, Black bean, Bored, Local Gofa, SML-32 and Local 2-Sheraro were evaluated to early maturing and high yielding genotypes and the design was RCBD with three replications. The combined analysis of variance reviled that there was highly significant variation (p < 0.01) of grain yield among the genotypes, environments and genotype by environment interaction. The genotypic, environmental and the genotype x environment interaction (GIE) accounted about 30.47%, 45.01% and 11.59% of the total variation. The average grain yield of the genotypes was 2008.17 kg/ha. The highest and the lowest mean yield was obtained from SML-668 (2536.47 kg/ha) and SML-32 (1773.59 kg/ha) respectively. The AMMI bi-plot also depicted that, SML-668 and SML-32 were the high yielding and low yielding genotype, respectively. Similar to the AMMI bi-plot, the GGE bi-plot also confirmed that SML-668 was the winning genotype in most of the environments; whereas, SML-32 and local 2-sheraro, were the low yielding genotypes in some or all of the environments. E1, E2 and E6 are discriminating environments and declared as the most representative than E3, E4 and E5. Generally, SML-668 was the ideal genotype with higher mean yield and relatively good stability; Local-2 Sheraro was the moderately good yielding genotype and the most unstable genotype; Whereas, SML-32 was the poorly yielding and unstable genotype.

This study focuses on optimizing chlorophyll extraction techniques, in which leaf discs are cut from places on the leaf blade to enhance chlorophyll concentration in sesame (Sesamum indicum L.) leaves. Thirty sesame genotypes, categorized into light green (LG), middle green (MG), and deep green (DG) pigment groups based on leaf coloration, were selected from a larger pool of field-grown accessions. The investigation involved determining optimal Soil Plant Analysis Development (SPAD) value index measurements, quantifying pigment concentrations, exploring extraction solvents, and selecting suitable leaf disk positions. Significant variations in chlorophyll content were observed across genotypes, greenness categories, and leaf disk positions. The categorization of genotypes into DG, MG, and LG groups revealed a correlation between leaf appearance and chlorophyll content. The study highlighted a consistent relationship between carotenoids and chlorophyll, indicating their role in adaptation to warm environments. An examination of leaf disk positions revealed a significant chlorophyll gradient along the leaf blade, emphasizing the need for standardized protocols. Chlorophyll extraction experiments identified DMSO and 96% ethanol, particularly in those incubated for 10 min at 85 °C, as effective choices. This recommendation considers factors like cost-effectiveness, time efficiency, safety, and environmental regulations, ensuring consistent and simplified extraction processes. For higher chlorophyll extraction, focusing on leaf tips and the 75% localization along the sesame leaf blade is suggested, as this consistently yields increased chlorophyll content. Furthermore, our examination revealed significant anatomical variations in the internal structure of the mesophyll tissue leaves between deep green and light green sesame plants, primarily linked to chloroplast density and pigment-producing structures. Our findings, therefore, provide insightful knowledge of chlorophyll gradients and encourage the use of standardized protocols that enable researchers to refine their experimental designs for precise and comparable chlorophyll measurements. The recommended solvent choices ensure reliable outcomes in plant physiology, ecology, and environmental studies.

Ethiopia is one of the famous and major producers of sesame in sub-Saharan Africa, and Ethiopian sesame is among the highest quality in the world. The experiment was conducted in Northern Ethiopia for three growing seasons (2013–2015) under a rain fed condition with the objective of identifying high-yielding genotypes and their agronomic traits. The experiment consisted of twelve genotypes laid down in randomized complete block design with three replications. The genotype, year, and genotype × year interaction components showed statistically highly significant variation (p<0.001) for most of the agronomic traits which clearly confirms the presence of genotype × year interaction in this study. The highest combined mean grain yield (906.3 kg/ha) was obtained from Hirhir followed by Serkamo white (756.5 kg/ha), and from the three growing seasons, the highest grain yield (1161.5 kg/ha) was recorded from Hirhir grown in the second growing season (2014). The growing seasons were different from one another in allowing the genotypes to have a different performance, and all of the agronomic traits, except thousand seed weight, were statistically different across the three growing seasons. In the ordination of the genotypes and agronomic traits, PCA1, which accounted for 38.3% of the variation, was positively associated with grain yield, branches per plant, length of the pod-bearing zone, plant height, number of pods per plant, number of seeds per pod, and thousand seed weight. On the contrary, PCA2, which accounted for 19.7% of the variation, was positively associated with days to 50% flowering and days to 50% maturity.