Alfalfa is planted in more than 30 million hectares worldwide, but despite its popularity in temperate regions, it is not widely grown in subtropical agroecosystems. It is critical to improve alfalfa for such regions, considering current predictions of global warming and the increasing demands for animal-based products. In this study, we examined the diversity present in subtropical alfalfa germplasm and reported genetic parameters for forage production. An initial screening was performed from 2014 to 2016, evaluating 121 populations from different subtropical origins. Then, a breeding population was created by crossing selected plants, resulting in 145 full-sib and 36 half-sib families, which were planted in a row-column design with augmented representation of three controls (‘Bulldog805′, ‘FL99′ and ‘UF2015′). Dry matter yield (DMY), canopy height (AH), and percentage blooming (BLOOM) were measured across several harvests. Moderate narrow-sense heritability and high genetic correlations between consecutive harvests were estimated for all traits. The breeding line UF2015 produced higher DMY than FL99 and Bulldog805, and it could be a candidate cultivar release. Several families produced higher DMY than all checks, and they can be utilized to develop high yielding and adapted alfalfa cultivars for subtropical agroecosystems.

Estimation of genotype‐by‐environment interaction (GEI) is important in breeding programs because it provides critical information to guide selection decisions. In general, multienvironment trials exhibit heterogeneity of variances and covariances at several levels. Thus, the objectives of this study were (a) to find the best genetic covariance matrix to model GEI and compare changes in genotypic rankings between the best covariance structure against a compound symmetry structure, (b) to define mega‐environments for turfgrass performance across the southeastern United States, and (c) to estimate genetic correlations between drought or nondrought and growing or nongrowing conditions to determine the extent of GEI under specific environments. Three nurseries with 165, 164, and 154 genotypes were evaluated in 2011–2012, 2012–2013, and 2013–2014, respectively. These nurseries were conducted at eight locations (Citra, FL; Hague, FL; College Station, TX; Dallas, TX; Griffin, GA; Tifton, GA; Stillwater, OK; and Jackson Springs, NC). The response variables were averaged turfgrass quality (TQ), TQ under drought (TQD), nondrought TQ (TQND), TQ under actively growing months (TQG), and TQ under nongrowing months (TQNG). This study demonstrated that (a) the best variance structure varied among traits and seasons, and changes in genotype rankings were dependent on GEI; (b) considering TQ and TQND, mega‐environments formed between Jackson Springs and College Station, and between Citra, Dallas, and Griffin, whereas Stillwater, Hague, and Tifton represented unique environments across the southeastern United States; and (c) genetic correlations between drought or nondrought and growing or nongrowing conditions suggested that indirect selection can be efficient in multienvironment trials for contrasting environmental conditions.

In breeding programs, superior parental genotypes are used in crosses to generate novel genetic variability for new selection cycles. Genotypes are usually more adapted to environments where the breeding program is located, since selections are performed under specific agroecosystems. Thus, the objective of this study was to evaluate the performance of bermudagrass (Cynodon Rich. species), St. Augustinegrass [Stenotaphrum secundatum (Walter) Kuntze], seashore paspalum (Paspalum vaginatum Sw.), and zoysiagrass (Zoysia Willd. species) breeding lines from five different breeding programs (North Carolina State University, Oklahoma State University, Texas A&M University System, University of Florida, and University of Georgia) across the southeastern United States. Three breeding nurseries for each species were evaluated for 2 yr at eight locations: Citra and Hague, FL; College Station and Dallas, TX; Griffin and Tifton, GA; Stillwater, OK; and Jackson Springs, NC. Turfgrass quality (TQ) was evaluated (rated on a 1–9 scale) across repeated measurements over time. Data were analyzed using mixed models, and principal component analyses were performed using predicted genotypic values. The narrowest range in variation for TQ performance was observed in seashore paspalum breeding lines, whereas greater variation was observed for St. Augustinegrass and zoysiagrasses. St. Augustinegrass presented the lowest genotype × environment interaction in all nurseries. Specific adaptability was not observed for the lines developed by different breeding programs, with the exception of the bermudagrass lines from Oklahoma State University in Nursery 3.

The selection of sexual genitors in Urochloa decumbens breeding is dependent upon the performance of their progeny for several traits simultaneously. Thus, our objectives were to (i) compare the efficiency of indices to select genitors of U. decumbens, (ii) evaluate the genetic gains obtained through selection intensities, and (iii) evaluate the multivariate pattern of progenies through principal components analysis (PCA). For this purpose, 1415 hybrids from 75 progenies of full siblings were evaluated at Embrapa Beef Cattle (Brazil) using seven cuts for dry matter production, regrowth, protein, fiber, lignin, and percentage digestibility. Statistical analyses were performed using mixed models and PCA. The direct selection for dry matter production provided a 37.51% genetic gain. Agronomic traits using indices provided greater gains. Genitors selected using PCA Biplot were similar to selections using indices. Indices and PCA were proven to be an excellent tool to select multi-traits in U. decumbens.

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St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] is a warm-season turfgrass primarily used for home lawns and commercial landscapes in the southern United States. New cultivars that possess desirable turfgrass quality (TQ) in combination with improved tolerance to diseases, drought and cold are needed to increase the sustainability of St. Augustinegrass production and maintenance in transitional zones. This study's objectives were to evaluate breeding lines in multienvironment trials across North Carolina to (a) assess relationships among economically important traits, and (b) select genotypes with stable performance across environments. Sixtyone St. Augustinegrass genotypes and five commercial checks were established in replicated field trials at three locations across North Carolina. Entries were evaluated for rate of establishment, TQ, turfgrass stand density, genetic color, leaf texture, uniformity, winter survival, fall color, drought tolerance, and gray leaf spot resistance from 2017 to 2020. Best linear unbiased predictions were used to calculate a selection index to identify elite genotypes across traits. The 10 traits were clustered into three groups: winter survival and fall color; genetic color, leaf texture, and gray leaf spot resistance; and establishment rate, TQ, density, uniformity, and drought tolerance. Selection of the top 10 genotypes using the selection index resulted in positive estimated genetic gains for all 10 traits, indicating it is an effective method for simultaneous selection. Line XSA 14271 outperformed 'Palmetto', 'Raleigh', 'Captiva', and 'Seville', for several traits and was the top-ranked line. It will be advanced to on-farm trials to evaluate sod production traits to assess its potential for commercial release.

Cultivated pastures are the basis to livestock production in subtropical and tropical agroecosystems because they are the main and the most economical feed source (Jank et al., 2014;Mganga et al., 2013). Specifically in Brazil, pastures occupy more than 165 Mha, and most of this area (151 Mha) is planted in monoculture grasses, and 14.2 Mha are used in production systems with crops for grains

Cowpea (Vigna unguiculata L. Walp) is a multipurpose crop widely cultivated in tropical and subtropical regions around the world. Characterizing cowpea phenotypes in germplasm collections is a crucial step to ensure future breeding efforts in the species. In this study, we estimated variance components and calculated genetic parameters for eight traits in 292 accessions from the University of California Riverside cowpea mini‐core collection, along with three lines released by the USDA, and seven cultivars. Broad‐sense heritability (H2) ranged from .21 to .83 for all accessions evaluated in Year 1, and from .16 to .83 for traits evaluated in 100 accessions during Year 2. Genotype × year correlations ranged from .45 to .99, illustrating consistency in phenotypic performance for some traits. Positive trait correlations were estimated for plant height and days to flowering, biomass at flowering (R1), and biomass at pod maturity (R6). Biomass at R1 and R6 were highly positively correlated. The USDA line US‐1137 showed high biomass production and can be used as forage and cover crop, whereas US‐1136, US 1138, and the cultivar Iron Clay showed superior traits as dual‐purpose (grain and/or fodder). Several accessions from the mini‐core outperformed cultivars and represent valuable genetic resources that can contribute to cowpea improvement. Moreover, these results showed that multiyear and/or multienvironment studies are needed to reliable select improved germplasm for agronomic traits. This study will provide cowpea breeders and geneticists with valuable phenotypic data to start new breeding efforts, and/or opportunities for gene discovery for key phenological and agronomic traits.