Doctor of Philosophy / Department of Agricultural Economics / Vincent Amanor-Boadu / The drop in average U.S. net farm income from 2014 through 2016 has indicated that current risk management options available to farmers have not fully mitigated the risks associated with farming. Although there are more risk management tools available to farmers today than there have been in the past, there is still a need to improve upon the available options and create new ways of securing agricultural production into the future. In an effort to improve how farmers cope with risk and uncertainty, system resilience concepts have started to find applications in production agricultural research. Agricultural resilience can generally be defined as the ability of an agricultural production system to return to normal (or improved) operations after having experienced an unexpected economic or environmental shock. The contribution of this research was to conduct an empirical analysis of farm resilience based on existing theories in system and agricultural resilience. A conceptual model was developed to apply an existing resilience measure, the resilience triangle, to a production agriculture setting and a model of farm resilience was constructed based on the existing literature in agricultural resilience. In this model, farm resilience is driven by three defining capabilities: buffering capability, adaptive capability, and transformative capability. The data for this analysis was obtained from the Kansas Farm Management Association (KFMA). Based on the literature review and the conceptual framework, resilience triangle areas were computed for individual farms during two distinct periods of economic shock, 1980 and 1998. An index of farm resilience was generated from the resilience triangle areas, which were then used as dependent variables in the econometric analysis. A fractional response logit model was estimated to test hypotheses about the impact of the different resilience capabilities on overall resilience index values. The results of the analysis indicated that there are differences in the ways that buffering and adaptive capabilities impact overall farm resilience, however there were not conclusive findings that buffering capabilities were stronger among the resilient farms as compared to the non-resilient farms. These results indicate that farm resilience is driven by both buffering and adaptive capabilities jointly. Even though buffering capabilities are important at the outset of a shock, the farm will then need adaptive capabilities to recover from the initial impact of the shock.