Agricultural policy changes directly affect the economic viability of Texas crop producers because government payments make up a significant portion of their net farm income (NFI). NFI projections benefit producers, agribusinesses and policy makers, but an economic model making these projections for Texas did not previously exist. The objective of this study was to develop a model to project annual NFI for producers of major crops in Texas. The Texas crop model was developed to achieve this objective, estimating state prices, yields and production costs as a function of their national counterparts. Five hundred iterations of national price and yield projections from the Food and Agricultural Policy Research Institute (FAPRI), along with FAPRI’s average production cost projections, were used as input to the Texas crop model. The stochastic FAPRI Baseline and residuals for Ordinary Least Squares (OLS) equations relating Texas variables to national variables were used to incorporate the risk left unexplained by OLS equations between Texas and U.S. variables. Deterministic and probabilistic NFI projections for Texas crops were compared under the January 2005 and January 2006 FAPRI Baseline projections. With production costs increasing considerably and prices rising moderately in the January 2006 Baseline, deterministic projections of 2006-2014 Texas NFI decreased by an average of 26 percent for corn, 3 percent for cotton, 15 percent for peanuts, and 12 percent for rice, and were negative for sorghum and wheat. Probability distributions of projected NFI fell for all program crops, especially sorghum and wheat. Higher hay price projections caused deterministic projections of NFI for hay to rise roughly 13 percent, and increased the probability distributions of projected hay NFI. Deterministic and probabilistic projections of total NFI decreased for each year, especially for 2006-2008 when fuel price projections were the highest. The Texas crop model can be used to simulate NFI for Texas crop producers under alternative FAPRI baselines. The model shows the impact of baseline changes on probability distributions of NFI for each crop and for Texas as a whole. It can also be useful as a policy analysis tool to compare impacts of alternative farm and macroeconomic policies on NFI.

A computerized dairy herd simulation was developed to evaluate the economic impact of changing length of lactation relative to length of dry period in a dairy herd. It created weekly production for individual cows in a typical herd. Cows were dried off early if they were producing below a designated daily milk yield. They were replaced with fresh cows to produce more daily milk and increase profit while maintaining a constant number of cows in milk (98 to 102). A two by four factorial of dry off strategies was designed using rates of lactation decline of 6% and 8% and early dry off at 8, 13, 18, and 23 kg. Cows producing less than this for 2 wk consecutively were dried off. There were 100 cows in each herd and each of the eight scenarios was run 10 times (10 herds) for 80 herds total. Dry cow groups at 8, 13, 18, and 23 kg dry off were 14, 17, 23, and 32% of total herds, respectively. Average daily milk (kg) increased for the four dry kg: 30.4, 31.2, 32.3, and 33.7 kg/d per milking cow, whereas RHA decreased. Three different milk-feed income scenarios, (+20%, average, -20%) were combined with three dry cow costs, (+20%, average, and -20%). Nine combinations were analyzed statistically at each rate of decline. Net cash income changed $3561, $1571, and $-3051 from 8 to 13 to 18 to 23 kg dry kg under a normal economic situation. Net farm income under the same scenario changed $3170, $2945, and $-1154. Under the best economic situation, net cash income increased with each successive dry kg, $5086, $4248, and $921. Net farm income also increased by $4695, $5621, and $2819. Net cash income and net farm income were largest at 13 and 18 kg when milk-feed income was low and dry cow cost was high, the worst economy scenario. Only in the most optimistic economic situations does it appear practical for a dairy business to adopt early dry off beyond 13 kg/d per cow given the small gains and the yearly variability. Strategies of dry off at larger dry kg, although not greatly profitable, nevertheless were not extremely unprofitable either. / Master of Science

Master of Science / Department of Agricultural Economics / Mykel R. Taylor / Land value research has been conducted over many decades with efforts being focused on a broad spectrum of topics encompassing many different issues. The research in this thesis will focus on understanding the relationship between net farm income, cash rent, and land value. This research could provide insight and direction in determining future land value behavior. Understanding land prices is important to many different segments of the agricultural industry. Those involved in the industry want to know where land values are going and what the future looks like. Although certain segments may not be directly affected by land value movements, if value decreases the environment of the agriculture industry is changed. Farmers and ranchers are interested in future land values as they make purchase and sale decisions or as they consider future growth of their operation. Agribusinesses understand the affect a decrease in land value would do to farmer’s decisions regarding capital purchases. Additionally, agriculture finance institutions are interested in the future movement of land value as they are concerned about the affects adverse movements in land value would have on their customer’s balance sheet and ultimately their collateral position. In this paper the relationship between land value and cash rent; where land value is a function of historical cash rent and cash rent is a function of net returns to the land will be tested for its’ existence in Kansas. Data were collected for the nine crop reporting districts in Kansas from 1973 through 2012.

This study examines the potential effects of categorical increases in production expenditures on farm income performance according to farm standing. The objective of this study is to expose differences in anticipated net farm income return from production expenditure investments and the optimal expense allocation strategy for each performance level. Studying farm performance through segregation by utilizing a two-tier analysis and quantile regression acknowledges the possibility that managerial strategy can differ based on managerial ability. Study outcomes are useful to farm managers because they offer more prescription style results and interpretations than found in other farm performance studies. Study findings show that as managerial proficiency increases so does a manager’s ability to extract higher returns from additional expenditures in certain input categories. Additionally, better managers are able to produce higher returns from more investment sources than their lower performing peers. Overall, study results and interpretations point to the importance of farm management ability as the key input for improving farm performance.