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Farm Credit is a major provider of credit to agricultural producers in Oklahoma and nationally. The decision to place a new Farm Credit office reduces borrower search and travel costs and should increase loan volume. The purpose of this paper is to model the new loan volume as function of distance from east central Oklahoma county centroids to Farm Credit offices. The model is then used to predict the impact of placing new offices in underserved areas.

County aggregate new loan volume is regressed on distances to Farm Credit branch and field offices and other variables expected to impact agricultural loan volume. The estimated model is used to predict new loan volume impact of adding additional branch and field offices in counties that did not have these offices. Confidence intervals are used to measure the significance of predicted loan volumes.

Distances from county centroids to both branch and field offices were found to significantly reduce new loan volume. The results were used to simulate the addition of new branch and field offices. The simulation predicted the added annual new loan volume associated with office additions.

Using spatial models, Farm Credit of east central Oklahoma and other agricultural lenders can better plan for expansion (or consolidation). These models indicate counties where annual new loan volume will likely be higher (or lower for consolidation) than other nearby counties. The result can be improved borrower access and system financial performance.

While spatial modeling has been utilized in other sectors, little has been done relative to agricultural credit access and impact on loan volume. The model here explicitly models the impact that distance to Farm Credit offices have on annual new loan volume.

Here we provide a short reflection on the persistent theme of knowledge in reproductive studies which allows us to draw out further insights from each of the chapters.