Identification of adaptable sunflower (Helianthus annuus L.) genotypes using yield performance and multiple-traits index.

Sunflower is the most important oil crop ranked as fourth edible oil in the world. The study was conducted in Northern Ethiopia during 2017-2019 cropping seasons using randomized completely block design with three replications. The objective was to decipher the genotype by environment interaction (GEI) in multi-environment trials (MET) and identify adaptable sunflower genotypes. Combined ANOVA, AMMI ANOVA and Eberhart and Rusell regression were analyzed, and GGE bi-plots, AMMI1 and AMMI2 bi-plots, Principal component Analysis (PCA), multi-trait genotype-ideotype distance index (MGIDI), correlation network plot for sunflower traits were sketched. AMMI stability measures, Best Linear Unbiased Prediction (BLUP) based indexes; parametric and non-parametric statistics were computed using R-statistical software. In the AMMI ANOVA the main effects of the environment (E) (54.18 % SS), genotype (G) (16.9 % SS) and GEI (23.50 % SS) were significant (p < 0.001). The genotypic Likely-hood Ratio Test revealed significant for all traits. The AMMI bi-plot and the GGE bi-plots selected G10 and G2 as the most adaptable genotypes. CV, HMGV, RPGV, HMRPGV, Pi, GAI, KRS, S(3) and S(6) also identified G10 as the most stable genotype. Based on the MGIDI, G10 (MGIDI = 1.45) and G5 (MGIDI = 2.19) are selected and these genotypes are recommended for further cultivation in Tigray.

Impact of storage technologies and duration on insect pest population, post-harvest losses, and seed quality of stored chickpea in Ethiopia.

Chickpea (Cicer arietinum L.) is the most important winter season food legume in Ethiopia. Despite being a major producer and consumer of chickpeas, Ethiopia experiences lower yields due to biotic and abiotic stresses, particularly insect pest infestations during storage. This study aimed to evaluate the impact of different storage technologies and durations on the losses of stored chickpea seeds in terms of both quantity and quality. The experiment involved five storage technologies and three durations, spanning a period of 6 months, with data collected at 2-month intervals. The results showed that the Purdue Improved Crop Storage (PICS) and Super GrainPro (SGP) bags effectively maintained intergranular temperature, seed moisture content, and relative humidity throughout the storage period, followed by the modified hermetic metal silo. In contrast, traditional bags exhibited a significant increase in these parameters. The PICS and SGP bags also exhibited the lowest numbers of total insect pests after 6 months, while the jute bags had the highest infestation. Common insect species found in the stored chickpea seeds were Callosobruchus chinensis (L.), Sitophilus oryzae (L.), and Tribolium confusum (duVal). Furthermore, hermetic bags (PICS and SGP) demonstrated the least grain damage and weight loss, while jute bags had relatively higher values. Seed viability was well maintained in hermetic bags but significantly decreased in traditional bags. Overall, hermetic storage technologies, such as the PICS and SGP bags, effectively suppressed insect development, reduced losses, and preserved seed viability without the need for insecticides. It is recommended that farmers use these hermetic storage bags after proper drying to enhance food security and income generation. By implementing these recommendations, Ethiopia can enhance its chickpea storage practices, reduce post-harvest losses, and contribute to improved food security and economic sustainability in the chickpea sector. © 2023 Society of Chemical Industry.

Investigation of insect population density, species composition and associated losses in chickpea seeds stored on farms in Ethiopia.

This survey aimed to investigate the extent of insect infestations, associated losses, and insect species abundance in farm-stored chickpea seeds across five chickpea growing districts in Ethiopia. Despite being the largest producer, consumer, and exporter of chickpea in Africa, insect pest infestations have caused significant losses to Ethiopia's chickpea industry. Results showed that Callosobruchus chinensis (L.) was the most prevalent insect species, followed by Sitophilus oryzae (L.) and Tribolium confusum (J. du Val). The insect pests infested both local and improved chickpea varieties, and traditional containers and polypropylene bags were used for storage. The percentage of insect-damaged seed ranged from 4.61% to 14.48%, while the seed weight loss ranging from 1.13% to 4.55%. The range of seed germination percentages was from 65% to 88%, with a mean rate of 71%. These losses significantly affect the market value of the crop as grain and its use as seed, affecting farmers' income and food security. Therefore, it is crucial to develop effective solutions to prevent the loss of farm-stored chickpea in Ethiopia.

Resequencing of 410 Sesame Accessions Identifies SINST1 as the Major Underlying Gene for Lignans Variation.

Sesame is a promising oilseed crop that produces specific lignans of clinical importance. Hence, a molecular description of the regulatory mechanisms of lignan biosynthesis is essential for crop improvement. Here, we resequence 410 sesame accessions and identify 5.38 and 1.16 million SNPs (single nucleotide polymorphisms) and InDels, respectively. Population genomic analyses reveal that sesame has evolved a geographic pattern categorized into northern (NC), middle (MC), and southern (SC) groups, with potential origin in the southern region and subsequent introduction to the other regions. Selective sweeps analysis uncovers 120 and 75 significant selected genomic regions in MC and NC groups, respectively. By screening these genomic regions, we unveiled 184 common genes positively selected in these subpopulations for exploitation in sesame improvement. Genome-wide association study identifies 17 and 72 SNP loci for sesamin and sesamolin variation, respectively, and 11 candidate causative genes. The major pleiotropic SNPC/A locus for lignans variation is located in the exon of the gene SiNST1. Further analyses revealed that this locus was positively selected in higher lignan content sesame accessions, and the "C" allele is favorable for a higher accumulation of lignans. Overexpression of SiNST1C in sesame hairy roots significantly up-regulated the expression of SiMYB58, SiMYB209, SiMYB134, SiMYB276, and most of the monolignol biosynthetic genes. Consequently, the lignans content was significantly increased, and the lignin content was slightly increased. Our findings provide insights into lignans and lignin regulation in sesame and will facilitate molecular breeding of elite varieties and marker-traits association studies.

Post-Harvest Insect Pests and Their Management Practices for Major Food and Export Crops in East Africa: An Ethiopian Case Study.

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.

Genome-wide association study and its applications in the non-model crop Sesamum indicum.

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.

Multivariate analysis for yield and yield-related traits of sesame (Sesamum indicum L.) genotypes.

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.