Legumes such as peanut ( Arachis hypogea ) can fulfill most of their nitrogen requirement by symbiotic association with nitrogen-fixing bacteria, rhizobia. Nutrient availability is largely determined by microbial diversity and activity in the rhizosphere that influences plant health, nutrition, and crop yield, as well as soil quality and soil fertility. However, our understanding of the complex effects of microbial diversity and rhizobia inoculation on crop yields of different peanut cultivars under organic versus conventional farming systems is extremely limited. In this research, we studied the impacts of conventional vs. organic cultivation practices and inoculation with commercial vs. single strain inoculum on peanut yield and soil microbial diversity of five peanut cultivars. The experiment was set up in the field following a split-split-plot design. Our results from the 16 S microbiome sequencing showed considerable variations of microbial composition between the cultivation types and inoculum, indicating a preferential association of microbes to peanut roots with various inoculum and cropping system. Alpha diversity indices (chao1, Shannon diversity, and Simpson index) of soil microbiome were generally higher in plots with organic than conventional inorganic practices. The cultivation type and inoculum explained significant differences among bacterial communities. Taxonomic classification revealed two phyla, TM6 and Firmicutes were significantly represented in inorganic as compared to organic soil, where significant phyla were Armatimonadetes, Gemmatimonadetes, Nitrospirae, Proteobacteria, Verrucomicrobia, and WS3. Yields in the organic cultivation system decreased by 10–93% of the yields in the inorganic cultivation system. Cultivar G06 and T511 consistently showed relative high yields in both organic and inorganic trials. Our results show significant two-way interactions between cultivation type and genotype for most of the trait data collected. Therefore, it is critical for farmers to choose varieties based on their cultivation practices. Our results showed that bacterial structure was more uniform in organic fields and microbial diversity in legumes was reduced in inorganic fields. This research provided guides for farmers and scientists to improve peanut yield while promoting microbial diversity and increasing sustainability.

Cold stress (CS) affects the survivability, geographical distribution, and yield stability of crops. Suitable management and agronomic practices can minimize the crop losses associated with cooler environments. However, agronomic practices alone can't support plants adequately to withstand the harsh cold. Therefore, exploring plants cold stress-responsive factors such as genetic, epigenetic, physiological, and cellular is crucial. This report discusses on cold stress effect, signal perception, signal transduction, gene expression, and associated molecular phenomena in plants. Three cold acclimation response pathways: Ca2+ mediated ICE1- CBF/ DREB1, hormonal, and reactive oxygen species (ROS), are elucidated. Also, this report summarizes the latest research work on genetics and genomics of forage species from the perspectives of cold tolerance improvement. In several instances, our hypotheses have been supported by a recent research output from our genetic analysis experiment on alfalfa (Medicago sativa L.) cold tolerance. We further review the importance of high-throughput genomics and phenomics for cold tolerance improvement in forage species and recommended implementing widely recognized techniques such as genomic selection (GS) and genome-wide association studies (GWAS) to develop climate-resilient cultivars. The transgenics and genome-edited cold-tolerant forage cultivars with low or no yield penalty must be the goals of future research.

Abstract 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.

The application of remote sensing in plant breeding is becoming a routine method for fast and non-destructive high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) equipped with sensors. Alfalfa (Medicago sativa L.) is a perennial forage legume grown in more than 30 million hectares worldwide. Breeding alfalfa for herbage accumulation (HA) requires frequent and multiple phenotyping efforts, which is laborious and costly. The objective of this study was to assess the efficiency of UAV-based imagery and spatial analysis in the selection of alfalfa for HA. The alfalfa breeding population was composed of 145 full-sib and 34 half-sib families, and the experimental design was a row-column with augmented representation of controls. The experiment was established in November 2017, and HA was harvested four times between August 2018 and January 2019. A UAV equipped with a multispectral camera was used for HTP before each harvest. Four vegetation indices (VIs) were calculated from the UAV-based images: NDVI, NDRE, GNDVI, and GRVI. All VIs showed a high correlation with HA, and VIs predicted HA with moderate accuracy. HA and NDVI were used for further analyses to calculate the genetic parameters using linear mixed models. The spatial analysis had a significant effect in both dimensions (rows and columns) for HA and NDVI, resulting in improvements in the estimation of genetic parameters. Univariate models for NDVI and HA, and bivariate models, were fit to predict family performance for scenarios with various levels of HA data (simulated in silico by assigning missing values to full dataset). The bivariate models provided higher correlation among predicted values, higher coincidence for selection, and higher genetic gain even for scenarios with only 30% of HA data. Hence, HTP is a reliable and efficient method to aid alfalfa phenotyping to improve HA. Additionally, the use of spatial analysis can also improve the accuracy of selection in breeding trials.

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 (H-2) 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 x 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.

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.