I explore the impact of the Production Credit Associations (PCAs), an arm of the early Farm Credit System, on agricultural yield and input use following the farm crisis of the 1920s. Like many low-and middle-income countries today, farmers in the early 20th century United States found it difficult to access credit. The PCAs were established in 1933 and significantly increased the supply of short-term credit available to farmers. Using distance from the serving PCA as a proxy for credit access, I find that counties within 30 km of a PCA had 7% to 14% higher crop revenue per acre and 9% higher corn yields than counties more than 60 km from a PCA. These areas also had a small but statistically significant increase in the use of tractors (1% to 2%). These results provide crucial evidence of the impact of governmentsponsored enterprises on the early US agricultural economy and its use as a cost-effective tool to address market frictions.

We discuss a little-known but highly successful approach to innovation and data governance observed in the U.S. dairy sector. The National Cooperative Dairy Herd Improvement Program (NCDHIP) is a century-old institution that coordinates farm data collection to support research on dairy cattle breeding and genetic selection. After discussing the program's history, we discuss how its evolution can inform data governance in agriculture today. We identify three key attributes that make the NCDHIP a successful model in agriculture: overcoming free-riding with member benefits to data providers, ensuring data interoperability with uniform data standards, and controlling data access and use with cooperative governance.

Estimates of productivity growth in the dairy sector attribute as much as half of observed growth to genetic improvement. However, productivity can be over-attributed to the quality of the genetics instead of the skill of the farmer in selecting them when models ignore selection bias in the dairy cow production function. Our work decomposes total productivity change on Wisconsin dairy farms due to genetics into separate effects for genetic improvement and endogenous selection. Using data from a large sample of Wisconsin dairy farms and nationallevel data on dairy sire rankings, we develop and estimate a model that accounts for selection behavior in the animal's production function. We find that selection accounts for as much as 75 percent of the total productivity improvement in our sample. Our results provide evidence for positive assortative matching, whereby farmers who adopt above-average yield genetics also perform better than average for their chosen genetics. Overall, our results indicate that a large portion of productivity growth in dairy farming can be explained by farmers' ability to identify and select genetics well suited to their production environment, and not solely the quality of the genetics they choose.

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