Files
Abstract
Alfalfa (Medicago sativa L.) is the most valuable forage crop in the world. Alfalfa production is limited in the southeastern United States due to the prevalence of acidic, aluminum-rich soils. Despite significant efforts, there currently exists no alfalfa cultivar with sufficient agronomic performance under low pH soil conditions. The present research represents a three-pronged approach for improving low pH and high aluminum tolerance in alfalfa. The first characterizes the effect of low pH soils on yield and fall dormancy rating through a replicated, multi-year, multi-location field phenotyping approach in which 138 half-sib families were evaluated in both a natural low pH field soil (4.9-5.2, extractable aluminum=10.41-11.38 mg kg-1) and a field soil amended with lime prior to establishment (pH=6.37-7.07, extractable aluminum=0.01-2.29 mg kg-1). Yield data was used to develop an Acid Soil Adaptation Index (ASAI) for use as a selection criterion for the next round of recurrent phenotypic selection (RPS). The second objective was to develop an efficient greenhouse rhizobox assay to screen acid-soil tolerance, including a high-throughput image analysis procedure. Root system architecture (RSA) traits associated with acid tolerance, as determined by the field evaluation, were identified. Lastly, we performed a genome-wide association study (GWAS) for field and nutrient-related traits using genotype-by-sequencing (GBS) and DArTag sequence data. Twenty-two genetic markers were significantly associated with these traits and candidate genes were annotated. Genomic prediction models were built for both genotyping sets and compared for accuracy. Through this work, we combine traditional plant breeding approaches and technological advances in image processing and genomic analysis to contribute to the development of locally-adapted, acid-tolerant alfalfa cultivars.