Elucidating the effects of organic vs. conventional cropping practice and rhizobia inoculation on rhizosphere microbial diversity and yield of peanut.

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.

Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks.

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.

Phenomics-Assisted Selection for Herbage Accumulation in Alfalfa (Medicago sativa L.).

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.