| dc.description.abstract | American farmers agree that the current 2014 Farm Bill Title I safety net programs are effective and have worked as they were designed. However, due to steadily declining crop prices and difficult agricultural economic times, there is a growing need for improvement of the timing of Price Loss Coverage (PLC) and Agricultural Risk Coverage (ARC) payments. In the current state of the American agricultural economy, it is becoming increasingly more difficult for farmers to cash flow and to obtain operating loans from lenders. For this reason, Congress is working to find a solution in the upcoming 2018 Farm Bill so farmers can be paid sooner than they are under the current farm bill. This research was conducted at the request of the Chief Economist of the House Agricultural Committee, Bart Fischer.
The objective of this study was to evaluate alternative payment timing options by calculating a new marketing year average (MYA) price series for determining PLC and ARC payments. A stochastic simulation model was used to estimate the probability of triggering commodity program payments for the baseline and four alternative formulas for calculating MYA prices. Several outcomes were examined with attention primarily focused on the forecasted 2017 MYA prices for the baseline and alternatives, the 2017 forecasted ARC government payments, and the 2017 forecasted PLC government payments.
Stochastic Efficiency with Respect to a Function (SERF) analysis results indicated that most farmers prefer the Last Twelve First Five (L12F5) alternative because it has the overall highest program payments, but overall farmers are undecided on which alternative price series they prefer. Additionally, the results for the 12-month baseline price series is preferred by taxpayers because it has the lowest amount of government payments. It is important to note that there is not a single alternative that both producers and taxpayers agree upon, therefore, no assumptions may be drawn at this point and farther analysis is needed. | en |
