Kansas cow-calf production efficiency

Doctor of PhilosophyDepartment of Agricultural EconomicsDustin L. PendellThe beef cattle sector has been, and continues to be, the single largest sector in the Kansas agriculture industry, with cattle and calves generating $8.27 billion in cash receipts in 2017 (KDA 2018). In 2017, Kansas produced nearly 5.69 billion pounds of red meat, or nearly 11 percent of the nation’s total (KDA 2018). According to estimates prepared by the Kansas Department of Agriculture, beef cattle farming and ranching has a direct output of approximately$6.3 billion. The cow-calf sector is the beginning of the beef industry; therefore, understanding the factors influencing profitability, efficiency, and structure is very important. The objective of this study is to examine the efficiency of beef cow-calf production in Kansas. Technical, allocative, and scale efficiencies of cow-calf operations are estimated, as well as, analysis on the relationship between input costs and efficiency and profitability and efficiency. Beef cow-calf operations vary considerably in size, available resources, profitability, and the use of technology. The variability in profitability suggests room to improve both production and financial management practices. In addition to estimating efficiency measures of cow-calf operations, the study identifies how marketing strategies (selling calves vs. selling feeders) impacts efficiency. This study contributes to the existing literature by estimating efficiencies for cow-calf producers and identifying production characteristics that impact efficiencies, in addition to, introducing the use of super-efficiency in the cow-calf industry segment. The nonparametric Data Envelopment Analysis approach, along with regression analysis, is used to determine how marketing strategies and production characteristics are correlated with efficiency and profitability. The Kansas Farm Management Association data are used in this analysis with cow-calf producers analyzed in two groups based on their marketing strategy (sells calves or sells feeders). Three years of whole-farm and enterprise data are included in the study, with a total of 240 producers selling calves and 264 producers selling feeders between 2018 and 2020. An input orientation is applied including feed, labor, utilities, and veterinary costs. Output is defined as the gross farm income (in dollars). Producers selling feeders were more technically efficient than those selling calves in both 2018 and 2019; however, in 2020, those that sold calves were slightly more technically efficient on average (0.840) than those that sell feeders (0.830). Technical efficiency was relatively more important than scale and allocative efficiency for both marketing strategies across almost all years (one exception in 2020, with producers marketing calves, where the allocative efficiency correlation coefficient was higher than technical). Technical efficiency was relatively more important in explaining profitability than either allocative or scale efficiency. Regressions indicated that a 0.10 increase in pure technical efficiency increases net income per cow by $96. A 0.10 increase in allocative and scale efficiencies increases net income per cow by$48 and $97, respectively. This suggests that producers that are experiencing low (or negative) levels of profitability should concentrate on adjusting the size of their herd relative to reducing input use per unit of output. Labor costs had the most impact on technical and allocative efficiency, while feed costs had the greatest impact on scale efficiency. Suggesting that producers wanting to impact their efficiency should focus on feed and labor costs

Parametric and Nonparametric Measurements of Rural and Urban Transit Efficiency

Public transit districts provide services to improve the mobility of individuals. Transportation disadvantaged individuals are especially reliant on these services. Providing transit services, however, is expensive. Previous studies examined urban transit district efficiency using frontier methodologies, such as the parametric stochastic frontier analysis and the nonparametric order-m analysis. Fewer studies examine transit efficiency using frontier methods focusing on rural transit districts (RTDs). RTDs provide critical services to rural individuals. Examining rural and urban transit efficiency helps improve the cost-effectiveness of RTDs. With this background, the objective is to further the understanding of transit efficiency with a focus on RTDs. This includes acquiring a greater understanding of technical efficiency in transit districts and understanding the effects of external variables on efficiency and how those effects differ between urban and rural transit districts. This objective is accomplished using parametric and nonparametric models to estimate transit efficiency. Part of this journey includes the development of a more robust nonparametric estimator, the order-md estimator, in response to weaknesses in a currently used semi-global estimator, order-m. Being a semi-global estimator, order-m weaknesses arise when the data shows a wide dispersion of firm size and returns to scale, both of which are present across the urban-rural transit spectrum. The order-md estimator is a local estimator that proves resilient against these problems. A cross sectional database constructed from 2015-2019 national transit database data and 2016 and 2020 census data is used to estimate transit efficiency. Data are limited to modes that primarily use vehicles common in the rural transit context. Both parametric and nonparametric analysis are conducted to increase the robustness of the transit efficiency inferences but including both exposes inconsistencies in results from the two methods. Including a distance-based output measure alongside a trip-based output measure is important for estimating transit efficiency; without a distance-based measure efficiency estimates may simply measure rurality. Collecting fares decreases inefficiency, even if the fares are small. Parametric and nonparametric estimations disagree about the effects of median household income and population density on transit efficiency. The formal organization type does not appear to have a significant impact on transit district efficiency