This dissertation consists of three studies in risk management and finance applied in agriculture. The studies address several important issues ranging from the provision and innovation of agricultural insurance to the credit risk migration in agricultural lending. The first study proposes a temperature-humidity index insurance product and examines whether this product can effectively protect against the risk of reduced milk production caused by heat stress. Results suggest that even when premiums are loaded and the insurance purchaser is faced with both geographical and temporal basis risk, a temperature-humidity index insurance product would provide risk management benefits to a representative south-central Georgia dairy producer. The second study compares the risk reduction performance between GRP and MPCI for cotton and soybean in Georgia and South Carolina under three premium rating schemes. Results suggest that even in agricultural heterogeneous production regions, GRP is still viable if adverse selection and moral hazard inherent in MPCI create a large positive wedge. The third study introduces two variants of Markov chain models to analyze farm credit risk migration as alternatives to the traditional discrete time model cohort method. Results indicate that Markov chain models provide more accurate and reliable migration probability estimates by capturing indirect and transient changes in farm credit risk ratings that are omitted under the cohort method. Metric comparisons between the cohort migration matrix and each of the variant of Markov chain models are found to be much more substantial in magnitude in farm credit risk transition compared to the comparison results obtained for corporate bond ratings migration.