Biomass yield and quality are the primary targets in forage crop improvement programs worldwide. Low-quality fodder reduces the quality of dairy products and affects cattle’s health. In multipurpose crops, such as maize, sorghum, cowpea, alfalfa, and oat, a plethora of morphological and biochemical/nutritional quality studies have been conducted. However, the overall growth in fodder quality improvement is not on par with cereals or major food crops. The use of advanced technologies, such as multi-omics, has increased crop improvement programs manyfold. Traits such as stay-green, the number of tillers per plant, total biomass, and tolerance to biotic and/or abiotic stresses can be targeted in fodder crop improvement programs. Omic technologies, namely genomics, transcriptomics, proteomics, metabolomics, and phenomics, provide an efficient way to develop better cultivars. There is an abundance of scope for fodder quality improvement by improving the forage nutrition quality, edible quality, and digestibility. The present review includes a brief description of the established omics technologies for five major fodder crops, i.e., sorghum, cowpea, maize, oats, and alfalfa. Additionally, current improvements and future perspectives have been highlighted.

Complex traits influenced by multiple genes pose challenges for marker-assisted selection (MAS) in breeding. Genomic selection (GS) is a promising strategy for achieving higher genetic gains in quantitative traits by stacking favorable alleles into elite cultivars. Resistance to Fusarium oxysporum f. sp. niveum (Fon) race 2 in watermelon is a polygenic trait with moderate heritability. This study evaluated GS as an additional approach to quantitative trait loci (QTL) analysis/marker-assisted selection (MAS) for enhancing Fon race 2 resistance in elite watermelon cultivars. Objectives were to: (1) assess the accuracy of genomic prediction (GP) models for predicting Fon race 2 resistance in a F_2:3 versus a recombinant inbred line (RIL) population, (2) rank and select families in each population based on genomic estimated breeding values (GEBVs) for developing testing populations, and (3) determined how many of the most superior families based on GEBV also have all QTL associated with Fon race 2 resistance. GBS-SNP data from genotyping-by-sequencing (GBS) for two populations were used, and parental line genome sequences were used as references. The GBLUP and random forest outperformed the other three parametric (GBLUP, Bayes B, Bayes LASSO) and three nonparametric AI (random forest, SVM linear, and SVM radial) models, with correlations of 0.48 and 0.68 in the F_2:3 and RIL population, respectively. Selection intensities (SI) of 10%, 20%, and 30% showed that superior families with highest GEBV can also comprise all QTL associated with Fon race 2 resistance, highlighting GP efficacy in improving elite watermelon cultivars with polygenic traits of disease resistance.

Predictive breeding has been proposed as an effective approach to accelerate genetic gain for complex traits. Genomic prediction (GP) models have been developed in alfalfa ( Medicago sativa L.) for key traits in the last decade. More recently, phenomic prediction (PP) models have been proposed as a low‐cost, high‐throughput alternative to GP in several crops. Although PP holds potential for accelerating genetic gains in plant breeding, environmental factors and data acquisition timing substantially influence predictive ability. To assess the viability of phenomics for perennial forage improvement, we compared PP and GP of dry matter yield (DMY) and persistence in alfalfa across multiple environmental conditions, including different harvest schedules and fertilizer management regimes. GP models were developed using the genomic best linear unbiased prediction (GBLUP) approach. For PP models, a similar approach was used by creating a relationship matrix from reflectance data collected with an unmanned aerial vehicle (phenomic best linear unbiased prediction). Different cross‐validations scenarios were implemented to test the performance of prediction models. For DMY, PP models resulted in greater predictive ability than GP for predictions within harvest and within management. For predictions across harvests or management practice, the predictive ability for PP models decreased and was similar or lower than GP. For stand persistence, GP and PP models achieved moderate predictive ability, ranging from 0.34 to 0.4 and 0.25 to 0.52, respectively. For PP optimization, training models built with ground truth data collected from adjacent experimental units had similar predictive ability than models built with random sampling across the whole experiment, which is operationally more effective. These results suggest that GP and PP can be implemented in alfalfa, and other perennial forages, to breed improved cultivars for complex traits.

Meloidogyne enterolobii is a virulent root‐knot nematode (RKN) species posing a significant threat to watermelon production across the United States. The USDA, ARS, Plant Introduction (PI) collection of Citrullus amarus , a wild relative of cultivated watermelon ( Citrullus lanatus ), contains RKN‐resistance. However, incorporating RKN resistance into watermelon cultivars is challenging. Genomic selection could be a useful strategy for improving RKN resistance in watermelon as it is a polygenic trait primarily controlled through additive gene action, as indicated by the broad‐sense heritability being close or equal to the narrow‐sense heritability: H ² = 0.69, h ² = 0.23 for galling and H ² = 0.47, h ² = 0.46 for eggs per gram of root. Here, a genomic selection approach was used to evaluate 97 C. amarus PIs (S3 inbred lines) for RKN resistance based on two criteria: (1) root galling percentage and (2) eggs per gram of root. Variants from whole‐genome resequencing (2.1 million SNPs) of PIs were used here. Genomic prediction (GP) models, including genomic best linear unbiased prediction (GBLUP), sparse GBLUP, and the reproducing kernel Hilbert space (RKHS) regression, were tested. The RKHS model based on 2.1 million SNPs had the highest prediction ability for galling (0.42) and for eggs per gram of root (0.50). The most resistant PIs (galling: PI_596692, PI_532664, PI_288316, PI_271767, and PI_542118; eggs per gram root: PI_271775, PI_485584, and PI_299379) based on both best linear unbiased estimates and genome estimated breeding values under 10% selection intensity were selected and are being used in our breeding program to enhance RKN resistance in watermelon cultivars.

Complex traits influenced by multiple genes pose challenges for marker-assisted selection (MAS) in breeding. Genomic selection (GS) is a promising strategy for achieving higher genetic gains in quantitative traits by stacking favorable alleles into elite cultivars. Resistance to Fusarium oxysporum f. sp. niveum (Fon) race 2 in watermelon is complex and polygenic with moderate heritability. This study evaluated GS as an alternative or additional approach to quantitative trait loci (QTL) analysis/marker assisted selection (MAS) for enhancing Fon race 2-resistance in elite watermelon cultivars. Objectives were to: 1) assess the accuracy of genomic prediction (GP) models for predicting Fon race 2-resistance in F₂ (Pop I) and recombinant inbred line (RIL) (Pop II) populations, 2) rank and select families in each population based on genomic estimated breeding values (GEBVs) for developing testing populations, and 3) verify if major QTL associated with Fon race 2-resistance are present in top selected families with the highest GEBV. Resistance ratings were based on the percentage of healthy plants at the 28-day post-seeding in Fon race 2-inoculated soil. GBS-SNP data from genotyping-by-sequencing (GBS) for 205 F₂:₃ and 204 RIL families were used, and parental line genome sequences were used as references. Six GS models, including parametric (G-BLUP, BayesB, Bayes_LASSO) and non-parametric (Random Forest, SVM Linear, SVM Radial) methods, were tested. G-BLUP and Random Forest outperformed the other models, with correlations of 0.48 in the F_2:3 and 0.68 in the RIL populations, highlighting the GP efficacy in early-stage breeding for improving Fon race 2-resistance in elite watermelon cultivars.