It is a fact that the performance of most crop genotypes varies across environments due to genotype‐environment interactions. Sharing knowledge about maize genotypes that are well‐adapted and high‐yielding in specific environments is essential for smallholder farmers to boost their productivity. A field trial was established in three locations during the 2019 and 2020 growing seasons to identify and select better‐adapted high‐yielding maize genotypes for production. A total of 10 improved varieties were evaluated using a randomized complete block design with three replications. The AMMI analysis of variance showed grain yield significantly (p ≤ 0.001) influenced by genotype‐environment interaction. This suggests that different genotypes showed varying grain yield performance across environments, implying the opportunity to choose effective genotypes suitable for both specific and broad adaptability. Moreover, the combined analysis of variance showed that genotype‐environment interaction significantly influenced plant height, ear height, dry biomass yield, and hundred‐kernel weight. The sum of squares factor explained that grain yield variation was contributed by the genotype (71.22%), followed by the environment (16.54%) and the genotype‐environment interaction effect (12.24%) of the total variation. Genotypes BH‐661, BH‐549, and BH‐546 demonstrated superior performance and stability, surpassing the national maize mean productivity and indicating broad adaptability. The AMMI analysis, GSI, and GGE biplot confirmed their robustness and high yield potential, especially for BH‐661 and BH‐549, making them optimal for variable conditions. Therefore, BH‐661 and BH‐549 are selected as stable genotypes for production in all agroecologies suitable for maize in the Dawuro zone.

Soil acidity and its driven soil fertility problems are causing wide gap between actual farmers yield and research yield in faba bean in the highlands of Dawro zone and Ethiopia

Metagenomics, culture-independent profiling of genetic material recovered from environmental samples, provides a powerful route to characterize microbial communities associated with legumes and to translate their functional potential into breeding targets that enhance resilience and productivity. Across analyses of rhizosphere, endosphere, and seed microbiomes, repeated studies consistently identify a conserved set of microbial functions linked to nutrient cycling, responses to abiotic and biotic stress, and biological control of pathogens, thereby offering mechanistic support that community-level functional capacities can shape host outcomes, including seedling vigor, nutrient-use efficiency, and stress tolerance. To move from descriptive discovery to actionable breeding, three complementary translational strategies have emerged: (i) synthetic microbial communities (SynComs) engineered to deliver targeted metabolic functions while enabling rigorous assessment of community stability and functional consistency; (ii) predictive model systems that integrate metagenomic features with phenotypic measurements to prioritize candidate taxa or functions for subsequent validation; and (iii) precision inoculation approaches that deploy validated microbes or consortia in agronomic settings to test whether metagenome-inferred functions confer robust performance under field-relevant conditions. A critical appraisal of metagenomic, multi-omics, and translational studies indicates that functional-phenotypic mappings are promising, yet substantial barriers continue to constrain reproducibility and scalability, including heterogeneity in sampling and experimental design, biases introduced by DNA extraction and sequencing, variability across bioinformatics workflows and reference databases, and overarching biosafety and regulatory constraints that can obscure true biological signals and weaken the reliability of functional inferences intended to guide selection decisions. To mainstream metagenomics in conventional legume breeding, we propose a breeders' roadmap centered on coordinated standardization and decision-ready analytics, encompassing standardized metagenomics-compatible sampling and sequencing platforms, harmonized computational frameworks and metabolic inference tools to ensure comparable functional calls, high-throughput phenotyping protocols aligned to microbiome-sensitive host traits, and selection frameworks that explicitly incorporate microbiome-oriented decision rules rather than treating microbial signals as ancillary. Finally, integrating machine learning with multi-omics datasets alongside precision delivery systems offers a practical route to generate actionable holobiont-level selection indices, and, when coupled with clearly defined translational pipelines and methodological standardization, metagenomics can broaden breeding gains beyond those achievable using host genomics alone, enabling more reliable, function-driven microbiome-assisted improvement of legume performance.

The traditional plant-breeding toolkit—controlled crosses, iterative phenotypic selection, and multi-environment testing—remains the core engine of crop improvement, reliably delivering field-validated varieties that scale in real farming systems. This review synthesizes how self-pollinating, cross-pollinating, and vegetatively propagated crops confront common constraints while highlighting high-impact accelerators and practical pathways for integration with genomics, cycle compression, digital phenotyping, and participatory approaches. We find that while traditional methods stay essential, accelerators such as phased marker-assisted and genomic selection, doubled haploids, speed breeding, off-season nurseries, and high-resolution field phenotyping markedly boost genetic gains. Crop-specific gains emerge from blending pedigree and genomic selection in self-pollinators, employing recurrent selection and heterotic management in outcrossers, and applying sanitation, indexing, and tissue culture in vegetatively propagated crops. Across crops, managing diversity, engaging farmers in testing, and thoughtfully designing seed systems to enhance scalability and quality are critical. A realistic near-term path integrates genomics, cycle compression, and participation with the enduring cycle of phenotypic evaluation, field testing, and seed-system development— contingent, of course, on seed-system maturation, breeder training, and enabling policy environments.

Evolutionary forces—selection, genetic drift, gene flow, and mutation—exert profound influences on allele and genotype frequencies within populations, thereby shaping genetic diversity and adaptive capacity. This review synthesizes contemporary findings regarding the interplay of these forces in driving evolutionary change, with an emphasis on population genetics, molecular evolution, and the variation of quantitative traits. The discourse delves into selection dynamics, illuminating the significance of positive selection in the emergence of adaptive traits, purifying selection in the preservation of genomic integrity, and balancing selection in the maintenance of genetic diversity. Furthermore, the phenomenon of genetic drift is scrutinized within the context of small populations, where stochastic fluctuations can dramatically alter allele frequencies, frequently culminating in the fixation or loss of alleles. Gene flow and migration are explored as pivotal mechanisms that introduce genetic variation, effectively counteracting the influences of drift and selection pressures. The review also assesses mutation rates and their contributions to genetic innovation, underscoring their pivotal role in fostering evolutionary novelty. Recent investigations that integrate genomic time-series data alongside AI-driven evolutionary modeling yield valuable insights into temporal changes in allele frequencies. Case studies of both plant and animal populations vividly illustrate how evolutionary forces sculpt genetic architecture, influencing phenotypic traits, adaptation to environmental stressors, and the divergence of species. Prospective research avenues underscore the necessity for the integration of multi-omics approaches, encompassing genomics, transcriptomics, and epigenetics, to refine evolutionary models. The review concludes by asserting that a comprehensive understanding of evolutionary forces is indispensable for forecasting genetic trends, conserving biodiversity, and enhancing breeding strategies.

In a specific area, fertilizer application for a crop depends on the soil type and profitability. Optimizing chemical fertilizer utilization in crop production is crucial from an environmental and economic point of view. However, there is limited information available on the optimum NPSB fertilizer rate for the production of wheat and barley in the study area. Thus, the study aimed to establish area-specific NPSB (18.9% N, 16.44% P, 6.95% S, and 0.1% B) blended fertilizer rate recommendations for the optimal grain yield and profitability of wheat and barley. The field experiment was conducted in two locations in the 2021 and 2022 cropping seasons using a randomized complete block design with three replications. The nine treatments included 0, 25, 50, 75, 100, 125, 150, 175, and 200 kg ha⁻¹ NPSB fertilizer rates. Growth, yield, and yield component data in wheat and barley were collected from randomly selected individual plants and the net plot area. An economic analysis was conducted for grain yield using the procedure established by the International Maize and Wheat Improvement Center. The analysis of variance results showed that blended fertilizer significantly (p < 0.01) affected plant height, number of productive tillers, spike length, number of kernels, thousand seed weight, biomass yield, grain yield, and harvest index traits. The application of a 150 kg ha⁻¹ NPSB rate produced the highest grain yield (5419.0 kg ha⁻¹) in wheat, whereas the application of a 125 kg ha⁻¹ NPSB rate gave the maximum grain yield (4708.0 kg ha⁻¹) in food barley. The economic analysis revealed that the application of a 150 kg ha⁻¹ NPSB fertilizer rate for wheat gave the highest net benefits (309711.50 ETB ha⁻¹) and marginal rate of return (MRR) (4096.96%), while the application of a 125 kg ha⁻¹ NPSB fertilizer rate for barley obtained the highest net benefits (184424.00 ETB ha⁻¹) and MRR (2195.39%). Therefore, to optimize the productivity and profitability of wheat and barley in the study area and similar agroecologies, farmers are recommended to use 150 kg ha⁻¹ NPSB for wheat and 125 kg ha⁻¹ NPSB for barley.