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Molecular breeding has revolutionized the improvement of forage crops by offering precise tools to enhance the yield, quality, and environmental resilience. This review provides a comprehensive overview of the current technologies, applications, and future directions in the field of forage crop molecular breeding. Technological advancements in the field, including Quantitative Trait Loci (QTL) mapping, Genome-Wide Association Studies (GWASs), genomic selection (GS), and genome-editing tools such as CRISPR-Cas9, have significantly advanced the identification and incorporation of beneficial traits into forage species. These approaches have dramatically shortened the breeding cycles and increased the efficiency of developing cultivars with improved yield, disease resistance, stress tolerance, and nutritional profiles. The implementation of these technologies has led to notable successes, as demonstrated by case studies on various forage crops, showcasing enhanced forage quality and adaptability to challenging environmental conditions. Furthermore, the integration of high-throughput phenotyping with advanced bioinformatics tools has streamlined the management of large-scale genomic data, facilitating more precise selection and breeding decisions. Looking ahead, this review explores the potential of emerging technologies, such as the application of artificial intelligence in predictive breeding, along with the associated ethical and regulatory considerations. While we stand to gain benefit from these innovations, the future of molecular breeding in forage crops must also confront the challenges posed by climate change and the imperative of sustainable agricultural practices. This review concludes by emphasizing the transformative impact of molecular breeding on the improvement of forage crop and the critical need for ongoing research and collaboration to fully realize its potential.

The global growing population is experiencing challenges to satisfy the food chain supply in a world that faces rapid changes in environmental conditions complicating the development of stable cultivars. Emergent methodologies aided by molecular marker information such as marker assisted selection (MAS) and genomic selection (GS) have been widely adopted to assist the development of improved genotypes. In general, the implementation of GS is not straightforward, and it usually requires cross-validation studies to find the optimum set of factors (training set sizes, number of markers, quality control, etc.) to use in real breeding applications. In most cases, these different scenarios (combination of several factors) vary just in the levels of a single factor keeping fixed the levels of the other factors allowing the use of previously developed routines (code reuse). In this study, we present a set of structured modules that are easily to assemble for constructing complex genomic prediction pipelines from scratch. Also, we proposed a novel method for selecting training-testing sets of sizes across different cross-validation schemes (CV2, predicting tested genotypes in observed environments; CV1, predicting untested genotypes in observed environments; CV0, predicting tested genotypes in novel environments; and CV00, predicting untested genotypes in novel environments). To show how our implementation works, we considered two real data sets. These correspond to selected samples of the USDA soybean collection (D1: 324 genotypes observed in 6 environments scored for 9 traits) and of the Soybean Nested Association Mapping (SoyNAM) experiment (D2: 324 genotypes observed in 6 environments scored for 6 traits). In addition, three prediction models which consider the effect of environments and lines (M1: E + L), environments, lines and main effect of markers (M2: E + L + G), and also the inclusion of the interaction between makers and environments (M3: E + L + G + G×E) were considered. The results confirm that under CV2 and CV1 schemes, moderate improvements in predictive ability can be obtained with the inclusion of the interaction component, while for CV0 mixed results were observed, and for CV00 no improvements were shown. However, for this last scenario, the inclusion of weather and soil data potentially could enhance the results of the interaction model.