Crops

Durum wheat is a vital wheat species cultivated worldwide for human consumption, ranking second to bread wheat. The Ethiopian durum wheat allele pool shows wide gene diversity; however, limited improvement work has been done to exploit this diversity. Thus, this study aimed to assess the genetic variability, heritability, and interrelationship among different phenotypic traits in 210 recombinant inbred lines (RILs) using an alpha lattice design with two replications. The analysis of variance revealed a significant difference for all the measured traits. The phenotypic coefficient of variation (PCV) was greater than the genotypic coefficient of variation (GCV) for all the characters, which reflects that the existing range of variability within the genotypes was not only due to the varying influence of genotype but also the environment. A correlation analysis disclosed that grain yield was positively related to the traits of plant height and 1000-kernel weight, suggesting that selecting these traits could enhance yield. Path analysis revealed that days to booting, maturity, and 1000-kernel weight directly affect grain yield. Among the measured traits, early developmental traits revealed higher broad-sense heritability. The findings of this study highlight high genetic diversity among Ethiopian durum wheat genotypes, opening up opportunities to integrate these materials into future wheat-breeding programs through introgression with other germplasm sources in Ethiopia and beyond.

Elephant grass (Pennisetum purpureum Schumach, EG) is a promising biomass energy crop due to its high productivity and adaptability to harsh environments. In the transition to renewable energy, varietal evaluation is essential to identify cultivars that maximize biomass and energy yield. This study assessed three varieties (VS-19, VA-06, and VDP as control) across three harvest cycles (new planting, first regrowth, and second regrowth) between 2022 and 2024 at the Cotton and Agricultural Development Research Institute, Ninh Thuan Province, Vietnam. The site was characterized by mean temperatures of 25–36 °C, relative humidity of 65–82%, and average precipitation of 75.7 mm per month. Agronomic traits, energy potential (heating oil equivalent per hectare, HOE/ha), forage quality, and soil amendment value of the EG were examined to address the research question whether EG can be integrated into a three-cycle utilization model (energy, forage, soil amendment) to support a circular bioeconomy in Vietnam. All cultivars showed good growth, strong drought tolerance, and resistance to pests and diseases. VS-19 showed superior tillering, strong lodging resistance, and the highest biomass yield (63.8 t/ha) with an energy output of 32,636 HOE/ha, while VA-06 (56.1 t/ha; 28,699 HOE/ha) and VDP (54.7 t/ha; 27,952 HOE/ha) produced slightly lower but comparable outputs. Forage evaluation indicated moderate nutritional quality, while residues from the third cycle showed favorable carbon and nutrients content, making EG suitable as a soil amendment. EG thus demonstrates high biomass and energy yields, forage potential, and soil improvement capacity, reinforcing its role in integrated bioenergy and agricultural systems.

Selection decisions in plant breeding programs are complex, and breeders aim to integrate phenotypic impressions, genotypic data, and agronomic performance across multiple selection stages to develop successful varieties. This study investigates whether such decisions can be predicted in a commercial winter wheat (Triticum aestivum L.) breeding program using elastic net models trained on genome-wide distributed markers and genomic estimated breeding values. For this purpose, a dataset of several thousand lines tested between 2015 and 2019 in preliminary, advanced, and elite multi-environment yield trials was analyzed across three decision-making scenarios. The predictive models achieved a higher precision than random selection in all scenarios, with an increased performance when genomic estimated breeding values were included as predictors. Comparisons of breeder selections and model recommendations in terms of selection differentials for key agronomic traits showed a substantial overlap in breeding objectives, while both the breeder’s decisions and the model’s suggestions maintained similar levels of genetic diversity. Although the precision of the elastic net model was of moderate magnitude, divergent model recommendations often identified promising alternative lines, highlighting the potential of artificial intelligence to support decision-making in plant breeding.