THE ROLE OF AGRICULTURAL POLICY IN FARM RISK MANAGEMENT AND DISASTER MITIGATION

<b>Project Methods</b><br> To address empirical policy objectives outlined, a variety of analytical techniques including econometrics, and mathematical programming.Data – For each of the analyses, data is available either through public portals or through information requests. US Department of Agriculture survey data is publically available for acres planted, yield, and price. Weather data are publically available from the Drought Monitor and the weather station readings. Data on government program payments are publically available for some programs or were obtained through a Freedom of Inform Act (FOIA) request.Methods -Objectives 1, 2 and 3 will use econometric methods. The first objective is motivated by a change in the 2018 Agricultural Improvement Act that eliminated agricultural risk coverage and price loss coverage eligibility for land in grass for an extended period of time and under very specific conditions. Due to grain prices and drought, it is likely that land was transitioned into grass or pasture in the years specified in the bill, creating the potential for sizable impacts in Oklahoma. For the first objective, acreage data from 2009 to 2017 and covariates to explain the transfer of acres from cropland to grass or pasture in Oklahoma–including cattle and crop price, land rental rate, and drought–in that time period are analyzed using econometric techniques. Base acres are available from the 2015-2016 base acre update in the 2014 Agricultural Act and are compared to the land that moved from cropland to grass and pasture prior to 2009.Policies designed for disaster recovery have become more common at the Federal level, particularly given the number of extreme weather events that occurred in the last decade. However, it is unclear the extent to which livestock producers are taking advantage of these programs and whether geographic differences exist in utilization. For the second objective, US county level livestock disaster program payments from 2008 to 2017 were obtained from an FOIA request. Data on extreme temperatures, extreme precipitation, wildfires, and hurricanes will be used to econometrically test the most common causes of livestock disaster program payments in eligible counties.A trade policy change in 2018 that applied tariffs to non-agricultural goods by the US government resulted in retaliatory tariffs on US agricultural exports. This project will particularly focus on US pork exports to China. An ongoing series of African swine fever outbreaks have reduced the domestic supply in China, creating an economic situation in which exports would be used to reduce the price impacts for Chinese consumers of the disease driven supply decline. These two events create a complex, and unique analytical situation with a series of stressors on the world pork market. Objective 3 will use time series data on tariffs, and bilateral export data between the US and China to measure the export changes caused by tariffs. Production and price data, and import quantity data will be used to measure the impact of African swine fever outbreaks in China. The econometric approach to jointly examine exogenous shocks on supply and demand in China and demand in the US will be developed as the project progresses.Foreign animal diseases pose a serious threat to the US livestock industry, in terms of production losses, costs to the industry and export market damages. Various disease response and recovery policies are aimed at quickly eradicating the disease, but part of the decision-making process is reducing the economic damages of the disease outbreak. In recognition of animal health risks, the 2018 Agricultural Improvement Act designated mandatory funds to enhance animal disease preparedness and response capacity. Objective 4 will use mathematical programming and partial equilibrium modeling to examine the market and economic welfare impacts of animal disease response and recovery policies. This objective will be pursued in cooperation with epidemiologists that have built spatial, stochastic disease spread models for foot-and-mouth disease, African swine fever, and highly pathogenic avian influenza in the US. The method developed under this objective will be applied to multiple diseases and scenarios. Scenarios analyzed will include handling of vaccinated animals, the impact of carrier animals, surveillance during and after outbreaks, and market recovery. In each of these scenarios, economic questions answered will advance scientific Results will be provided to animal health authority decision makers.Finally, it is difficult to look forward and say what policy questions will take priority at the State and Federal level over the next 5 years. However, it is important to provide policymakers and official agencies, as well as producers and other agribusiness stakeholders, relevant timely information on the impacts of policy. Thus, objective 5 reflects the dynamic nature of agricultural policies by using a variety of statistical and mathematical modeling techniques to answer public policy research questions that are unanticipated at this time but will arise in the coming years.