Observing a fast-evolving world economy and agricultural financial system, this dissertation studies three emerging questions in agricultural finance: agricultural financial technology (FinTech), blockchain and cryptocurrencies; persistence and growth of religious farming communities that limit use of technology in the U.S.; and livestock insurance willingness to offer in China. These emerging questions and developments fit closely into the top priority of the G20 Global Partnership for Financial Inclusion (GPFI), and the vital tools recognized to meet the Sustainable Development Goals (SDGs) set by the United Nations. These emerging agricultural finance strategies are crucial to improve agricultural value chain, reduce inequality, and spur economic growth, especially for rural areas with intensive agricultural activities. These topics are sparsely studied empirically due to limited data, evolving policies, and rapidly changing technologies. In this thesis I take the initiative to understand these both qualitatively and quantitatively. The first chapter discusses the overall importance of these emerging areas of agricultural finance strategies to support the development of the agricultural economy. We provide background domain knowledge on Agricultural FinTech with an emphasis to blockchain and cryptocurrencies, elaborating the applications of blockchain in food and agriculture value chain and supply chain, that improves the transparency and efficiency of tracing and trading. Understanding the relationship between blockchain and cryptocurrencies is crucial to applications of this technology in agriculture, thus we relay quantitative research to the more practically operated cryptocurrencies to understand its market property. Chapter 2 examines the financial property of 3,351 cryptocurrency price series, particularly looking at the long or short memory within its price series and evaluate if they follow a fractional Brownian motion through Hurst estimators and Stepwise autoregression. We investigate the releasing mechanism of Bitcoin and use top 105 coins’ supply structure to explain the memory in their price series, for which we find the % of total coins issued is explanatory. Due to concerns of supply related built-in memory within the price series, we propose a de-mean method to decompose the price series with the time varying mean and the variations, and we found that some previously found long memory was actually spurious. The gradually increasing (time-varying) mean can explain this spuriousness, and we use the market structure such as the 4-year bull circle and the deflationary releasing mechanism to explain this time-varying phenomenon. The findings of this chapter confirm the fractionality of crypto markets generally, and provide clarity of true or spurious long memory using both traditional and advanced methods. This enhances our understanding of these emerging financial assets that could potentially be applied to agricultural value chains. One possibility is a blockchain token system that is linked to warehouse receipts and enables the holding and trading of grains electronically. The third chapter looks at Amish population growth and how that could affect farmland prices. The Amish culture promotes strong family ties and human-nature interactions; thus, they generally use less modern agricultural production technology. We conceptualize the co-existence of different farming styles between Amish farmers and conventional farmers, to explain how the less productive Amish farmers can stay competitive. The main rationale is that the effectively cheaper labor costs of Amish due to their larger families can compensate the disadvantage from less output due to their limited use of farming technologies. Simply speaking, Amish may have lower revenue, though their costs are low as well, so they could maintain the similar level of profits to their non-Amish neighbors. Because farmland is the most important farm asset class, we hypothesize that any differences in profitability of farming systems would be reflected in farmland prices and analyze whether farmland prices are influenced by Amish population growth. We use a standard hedonic approach and unique shift-share like instrumental variable in this empirical model and find no statistically significant relationship between Amish population growth and farmland prices. Therefore, we infer that Amish compete on the farmland markets similar as the conventional farmers, which aligns with our conceptual framework scenario of similar profitability. This chapter does not only offer insights into the coexistence of multiple farming systems in one market, represented in the farmland market; but also, methodologically showcases how an identification strategy from the labor literature can be applied to agriculture finance issues. The fourth chapter presents our work in investigating agricultural insurance agents’ willingness to offer (WTO) livestock insurance in China, through an in-the-field discrete choice experiment (DCE). We include eight main attributes of livestock insurance and contain various combinations of different levels of them on the choice cards. We implemented the analysis in 6 blocks, with 35 insurance agents in each block. We ask each of them to respond to 15 cards, on each card choose from one of the two hypothetically designed livestock insurance. The card choice combinations of various attributes and levels are designed using JMP software through a D-optimal approach, which use limited sets of choices to maximize the choice exposures to participants and reveal their utility changes when they decide the trade-offs between two choices on the cards based on the different attribute levels included. Premium subsidy and insurance types are two out of eight attributes we primarily study and also find strong clear evidence on. We find that a one level (10%) increase of subsidy lead agents’ probability to offer be 3.166 times higher. We also find the more traditional type, mortality insurance, is still strongly preferred than the newer introduced insurance, with weather-based index insurance being the least preferred, because of farmers’ difficulty in understanding and conflicts when basis risk occurs. Through using survey question to generate interaction term model, we also find knowledge to the newer type of the insurance products improves the WTO on that particular insurance type. This chapter is among the very first to study the supply side of agriculture insurance, and the DCE method we use is a recently popularized method in evaluating insurance products and participants preferences with designs of attributes variations. Our research provides important policy implications as the government and insurance companies work out the details in enlarging the take-up of insurance via interventions of subsidy, education, and innovative /diverse product offering.