Bermudagrass (Cynodon spp.) is a forage and turf crop commonly used worldwide. The USDA bermudagrass germplasm set is composed of plant introductions (PI’s) collected around the world and contains different Cynodon species, primarily C. dactylon. The collection was screened in a replicated trial in Florida for forage yield, leaf width, nutritive value (NV), and Bermudagrass Stem Maggot (Atherigona reversura) (BSM), which is an invasive pest to the southeastern United States that damages bermudagrass fields. The goal of this research was to determine ploidy level and genome size in this USDA collection, and evaluate the influence of ploidy level in the estimation of genetic parameters for BSM, leaf width, dry matter yield, and NV traits. For chromosome counts using classical cytogenetics techniques, root tips and meristems were collected from a set of PI’s with known ploidy. The PI’s and cultivars with known chromosome counts were used as internal standards to run flow cytometry and estimate genome size of the PI’s with unknown ploidy. Ploidy level was determined for all accessions and were used to estimate genetic parameters of phenotypic traits. By providing information on ploidy levels and genetic parameters, this research will support breeding efforts and future selections for forage bermudagrass.

Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high‐quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full‐sib and 35 half‐sib families was bulk‐genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single‐nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks.

Potato (Solanum tuberosum L.) is one of the most important crops for human consumption worldwide, representing an essential component for the food security of several countries. However, yield and quality are negatively affected by biotic and abiotic stresses. In this sense, the selection of potato cultivars tolerant to heat and resistant to diseases is of great importance. Our goal was to select clones suitable for the chips industry, having heat tolerance and bearing the Rx1 and Ryadg alleles, which confer resistance to Potato virus X and Potato virus Y. We evaluated 491 clones originating from 31 biparental crosses under three different seasons in terms of heat stress (without heat stress [WHS], moderate heat stress [MHS], and high heat stress [HHS]). The evaluated traits were specific gravity (SG), dry matter yield (DMY), and proportion of physiological disorders (PD). After evaluation in the WHS and MHS seasons, the presence of the Rx1 and Ryadg alleles was investigated with the help of molecular markers in the 68 clones showing the best performance, which descended from the DGN2103 and DGN4002 parental clones. To gather the genotypic values for all traits in each season, the clones were ranked according to an index based on the genotype–ideotype distance. On average of all seasons, the top 10% selected clones by the index were 9 and 0.1% higher than the Atlantic cultivar for the traits DMY and SG, respectively, and 80% lower than Atlantic cultivar for PD. In conclusion, we report the selection of potato clones suitable for industrial processing.

A crucial point in agricultural experimentation is to compare treatments with high accuracy. However, agricultural experimentation is prone to field heterogeneity, and a common source of error is the spatial variation between the plots used in an experiment. With plant breeding experiments, the high number of tested genotypes requires breeders to use large areas, which invariably increases the likelihood of spatial variation. The use of models that do not address this variation can lead to errors in selecting the best genotypes. Our goal was to evaluate the effects of two spatial models—first‐order autoregressive (AR1) and spatial analysis of field trials with splines (SpATS)—to control the spatial variation in 30 experiments from potato (Solanum tuberosum L.) breeding programs. Specifically, we sought to control for three traits: total tuber yield (TTY), marketable tuber yield (MTY), and tuber specific gravity (SG). The results obtained with the use of spatial models were compared with the base model (independent errors) based on precision, heritability, and the impact on the selection of the best clones. Spatial models were effective in controlling local and global errors and achieved greater accuracy and efficiency over the base model. The spatial approach also showed greater heritability for all analyzed traits. The spatial models led to differences in the clone ranking and consequently in the selection of the best clones. Thus, spatial analysis has the power to make more precise analyses, which leads to more accurate selections and should be used to analyze phenotype data of potato breeding programs.

Genomic prediction integrates statistical, genomic and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5-20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values at the individual (GEBV) and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations.