Agricultural Horizons: Research, Technology and Sustainable Development Vol. 6

Increasing crop yield remains a central objective of plant breeding, but yield is an emergent, highly polygenic phenotype whose expression depends on development, resource capture, reproductive success, stress response, management and genotype-by-environment interaction. This critical narrative review evaluates how breeding has improved yield, why rates of progress vary among crops and environments, and which combinations of quantitative-genetic, physiological, genomic and phenomic approaches are most likely to sustain future genetic gain. Literature was selected from major open scholarly sources, with emphasis on peer-reviewed evidence published from 1990 to 9 August 2026 and inclusion of earlier foundational work where necessary. Historical evidence shows that conventional selection, hybrid breeding, plant-type modification and adaptation to agronomic intensification have produced substantial gains, yet observed farm-yield trends cannot be attributed to genetics without careful separation of agronomic and environmental effects. The breeder's equation remains a useful organising framework because gain depends jointly on genetic variation, selection accuracy and intensity, and cycle duration. Genomic selection can improve selection efficiency for complex traits when prediction models are trained on relevant, well-phenotyped populations, but prediction accuracy is not a sufficient endpoint: realised gain per unit time and cost is the decisive criterion. High-throughput phenotyping, environmental characterisation, reaction-norm models, physiological breeding, crop growth modelling and speed breeding can increase the value of genomic information by improving trait measurement and shortening cycles. Genome editing and de novo domestication expand the design space but currently complement rather than replace population-based improvement for polygenic yield. The strongest evidence therefore favours integrated breeding systems that optimise the entire genetic-gain pipeline while maintaining genetic diversity, testing across representative target environments and explicitly managing trade-offs among yield, stability, quality, resilience and resource-use efficiency. Future progress will depend less on any single technology than on coordinated improvement of population design, phenotyping, prediction, crossing, testing and deployment.