This thesis studies the off-farm labor supply decision of farm operators and their spouses in the United States. The data used in this study is from the Agricultural Resource Management survey, 2006. The objective of this study is twofold. First, to identify those factors that affect off-farm labor supply of farm operators and their spouses. In particular, this study investigates the impact of human capital of farm operators and spouses, personal, family, farm and location characteristics on labor allocation for on- and off-farm work. Empirical results indicate that farm operators and their spouses human capital are positively correlated with off-farm labor supply. In addition, the number of children in a household is inversely related to a spouses off-farm employment. Similarly, a households net worth and farm size have a negative impact on off-farm labor allocation decisions by both farm operators and their spouses. Payments from government programs have a negative effect on labor allocation for non-farm work. The availability of health insurance to farm operators and their spouses from off-farm employment has a positive effect on labor supply for off-farm work. The second objective of this study is to compare results obtained from a parametric probit model and a semiparametric additive probit model of off-farm labor supply by farm operators and spouses. One of the most important aspects of semiparametric analysis is to identify smoothing or nonparametric variables in a regression model. The Blundell and Duncan (1998) approach shows that farm size is such a smoothing variable in the off-farm labor supply model. A semiparametric additive regression model identifies a few significant covariates as compared to a parametric probit model; however, the Hong and White (1995) specification test and likelihood ratio test favor a semiparametric model in this study. In particular, the graphical plots of fitted values from parametric and semiparametric models also show that a semiparametric model is preferred. The semiparametric model helps to formulate appropriate functional form of off-farm labor supply in the United States, which might be the subject of further study of this research.

A national web survey was used to collected data from 253 households in the United States. Choice-based conjoint analysis was used to investigate which packaging and labeling attributes U.S. consumers consider important when choosing 100% fruit juices, and to estimate their willingness-to-pay. The attributes were packaging material, nutritional and health claims, presence of organic ingredients and whether the product was produced locally. Factor analysis, cluster analysis and a median split technique were used to identify market segments based on consumer preferences, behavior, and lifestyles. Differences between the segments were analyzed. A conditional logit model was used to estimate relative of importance and willingness-to-pay for the different attributes. Results from the study show that consumers place positive values on, and are willing to pay a premium of ($1.04) for the Polyethylene Terephthalate (PET) bottle, and ($0.29) more for the no sugar added claim. Consumers with high environmental interests were more likely to pay a premium for a fruit juice with organic ingredients and a nutritional index on the label. High information seekers are willing to pay ($0.13) more for the nutritional index, compared to ($0.05) that low information seeker will pay. Younger people are willing to pay more for local products and more than double ($1.41) of what older people will pay ($0.67) for a PET bottle.

The search for improved methods of estimating crop yield density functions has been a theme of recurrent research interest in agricultural economics. Crop yield density functions are the statistical instrument that generates probability estimates of yield risk, and risk is an important decision variable in production agriculture. Recent research in crop yield density estimation suggests that yield probability estimates can be sensitive to the way yield data are filtered, and if true, then the search for an adequate filter is warranted. Such a quest is pursued in this study. It is proposed that unit-root tests can be used to identify the time-series properties of yields and that the outcome of these tests makes the choice of an appropriate filter trivial. Once a filter has been chosen, then nonparametric methods can be used to more flexibly fit a crop yield density function. The study uses state and county level (aggregated) yield data for corn and soybeans in Arkansas and Louisiana for the period 1960-2008, comprising 121 yield series. The results identify three main types of yield processes (and filters), namely, a unit-root (first differences), a trend stationary process (detrending), and stationary (remove the mean). More specifically, the study finds that for Louisiana soybeans, for example, 73% of the county yields studied can be represented by a unit-root process, 12% followed a trend stationary process, and the remaining 15% were stationary. One important implication of this finding is that the use of a universal yield filter may generate inaccurate yield probability estimates, which translates into inaccurate estimates of crop insurance risk premia. To shed light into relevance of these findings, yield density functions were estimated under alternative filtering scenarios and pairwise probability estimates compared. In particular, the results suggest sizeable differences in the two estimates, which at times can reach -1,153.65%. In addition to providing a detailed analysis of the findings, the study assessed the relevance of the findings in the context of two current risk management programs, namely a group risk plan (GRP) and average crop revenue election (ACRE) program. Limitations of the study are also highlighted.

Current trends in the U. S. dairy industry show an increase in milk cows per farm and milk production per cow, though the total number of milk cows in the industry is declining. This increase in productivity is attributed to advancements and adoption of modern dairy technologies. Breeding technologies are one of the important components of this structural change. This study analyzed the factors affecting the adoption of modern breeding technologies such as artificial insemination, embryo transplants, and sexed semen, and the impact of these technologies on farm productivity and profitability. Results of a bivariate probit model with selection showed that the adoption decision is affected by different farm and farmer attributes such as age, education, off-farm work, farm size, and specialization. The embryo transplants and/or sexed semen technology adoption decision was also influenced by the farmers planning horizon. Farm impact was assessed by estimating net returns and cost measures using ordinary least squares methods. Endogeneity and self-selection bias issues were also tested and corrected for in the impact models. Both artificial insemination (AI) and embryo transplants and/or sexed semen (ETSS) technologies are found to have significant and positive influences on net returns over total and net returns over operating costs per hundredweight of milk produced. Results also suggest that a higher allocated cost is associated with ETSS adoption. Relatively younger, more highly educated farmers and larger and more specialized farms received higher net returns. Since some part of the costs involved in ETSS may be for conducting artificial insemination, larger farms that had already adopted AI may consider ETSS adoption. Adoption decisions on a farm, however, would be based on the added advantages of ETSS adoption versus the additional costs of adopting these.

The development of an efficient biomass supply chain is pivotal for the cellulosic ethanol industry. The Louisiana Sugarcane Belt, and energy cane are the focus of this study. From both the producer and processor perspectives, cost of production, competitiveness of cellulosic ethanol, biomass pricing, changes in crop mix, and the optimal location for cellulosic ethanol processing facilities are the critical factors evaluated. Educating potential energy cane producers on production costs and agronomic practices is the first step in the biomass supply chain. This study finds that for energy cane producers to breakeven, processors need to pay producers at least $30 per ton of biomass. The breakeven price producers require, decreases if new varieties with higher yields and for a longer sustained production cycle are developed. These new varieties also help to increase the competitiveness of the cellulosic ethanol industry relative to the corn ethanol industry by driving down feedstock and transportation costs. For processors to induce the production of energy cane they have to provide producers with expected net returns per acre that are at least equivalent to that of sugarcane. Numerous methods on pricing biomass exist but this study investigates variable pricing strategies, based on corn, crude oil, and ethanol prices, and a two-tiered hybrid strategy that guarantees a portion of production cost plus a fixed amount per ton of biomass production. Results indicated that none of the pricing strategies induce the production of energy cane relative to sugarcane, but minor adjustments to the ethanol and hybrid strategies makes them viable options for processors. Depending upon the pricing strategy implemented, producers alter crop allocation decisions to maximize net returns per acre. Primarily rice and soybean acres in the region decline allowing for the production of energy cane. As the crop mix changes in the region, the cost minimizing location for a cellulosic ethanol plant changes. Results indicate that for a single processor operating Belt the optimal location is St. Landry Parish. Increasing the number of processors in the region to two, decreases total transportation costs decrease and the optimal locations for the plants are Acadia and Pointe Coupee Parishes.

Recent food recalls and food scares in the United States have increased consumers risk perceptions about food borne illness and decreased their confidence in the safety of the U.S. food supply. Results from a continuous tracking of consumer confidence and media coverage of food safety events over a 67 week period between May 2008 and August 2009 are reported in the study. Factor analysis is performed on consumer characteristic statements to identify seven factors. Factor scores for these seven factors are used as inputs in a consumer segmentation procedure. A two step segmentation approach, hierarchical cluster analysis followed by partition cluster analysis is used to create eight consumer segments. An ordered probit model is used to test the hypothesis that media coverage of food safety events affects consumer confidence in the safety of the U.S. food system. The results show that media coverage significantly and negatively affects consumer confidence in the safety of nations food supply during the sample period. The results also indicate that the effect of media coverage is different for each consumer segment identified in the study. Socioeconomic and demographic factors such as geographic region, media source, household size, age, ethnicity, education, and gender also had significant affects on consumer confidence in the safety of United States food supply. Another finding of study is that media effect varies depending on the media source used by respondents. Television has a negative effect on consumer confidence in the safety of the U.S. food system, while internet and newspapers have a positive effect on consumer confidence in the safety of the U.S. food system relative to the television. The findings of this study are important and helpful for government agencies and private companies to understand the magnitude of consumer response to mass media, and for adjusting their response to food safety incidents and determining the economic downturn in the sale of their products and for how long into the future. The consumer segments developed in the study can be used for integrating better risk communication strategies directed toward a specific consumer segment.

Economic research was conducted to present estimates of costs per acre associated with fallow sugarcane weed control programs for Louisiana in 2010. The 2010 projected costs are associated with the various phases of sugarcane fallow using different machinery, implements, and weed control practices followed by most growers in the main sugarcane production area of Louisiana. For bermudagrass and johnsongrass weed control treatments, the herbicides applied were Roundup Original Max at 46 oz/A, generic glyphosate at 64 oz/A, DuPont K4 60DG, Trifluralin 4EC at 4 qt/A, and EPTC at 3.5 pt/A. Purple nutsedge weed control treatments included Roundup Original Max at 46 oz/A, generic glyphosate at 64 oz/A, Permit 75DF at 1 oz/A, and Yukon 67.5WG at 6 oz/A. Roundup Original Max at 46 oz/A applied for perennial weed control was more expensive by $30.40 and $15.20 per acre compared with generic glyphosate treatments applied at 64 oz/A. Treatments applied with Roundup Original Max had a higher sugarcane fallow cost compared with treatments using generic glyphosate at current fuel, labor and herbicide input prices. A spreadsheet decision aid was developed which summarizes sugarcane fallow field operations and weed control costs, including equipment used, performance rates, and herbicides applied. These data can be entered by the user for specific farm situations, calculating total variable tillage and weed control costs per acre. Binary and non-binary linear programming were utilized to determine optimal sugarcane fallow weed control programs for bermudagrass, johnsongrass, and purple nutsedge control. The non-binary LP model selected treatments to achieve desired control of bermudagrass, johnsongrass and purple nutsedge and minimum cost program. In comparison, the binary LP model selected only one treatment that had minimum fallow field operation and weed control cost while satisfying minimum weed control levels. Generic glyphosate cost was found to be sensitive to price increases to $0.27 oz/A or above for bermudagrass control, and $0.33 oz/A for johnsongrass and purple nutsedge control. Fuel prices, directly impacting tillage costs, were found to not be sensitive in determining optimal weed control choices.

The general objective of the study was to determine the economically optimal crop cycle length for major sugarcane varieties currently being produced in Louisiana. The specific objectives of the project included the specification of the mathematical acreage relationships which directly impact the production of a vegetatively propagated perennial crop in a whole farm context; the development of producer decision rules to be used to determine breakeven sugar levels on third stubble sugarcane crops for major varieties in the state; the evaluation of the impact of changes in production factors on developed crop replacement rules; and the optimal cycle length for current variety combinations in a whole farm context. Third stubble breakeven yield results indicate that on average, third stubble should be kept in production if its production exceeds 5,063 pounds of sugar per acre. If sugar per acre yields of plantcane, first stubble and second stubble were averaged, third stubble should be kept only if its production exceeds 74.3% of that average. Results of changes in production factors such as raw sugar price, diesel price, planting ratio and harvest costs indicated that this 74.3% was not significantly affected when the changes were analyzed in a whole farm context. A maximum net return goal of $147,198 was achieved using variety HoCP 00-950 as the only variety planted in the whole operation, when there were no acre limitations on individual variety. Another scenario where no single variety should exceed 50% of the total planted area of the farm was developed and results showed that a maximum net returns goal of $145,154 was achieved by planting 500 acres of variety L 99-266 and 500 acres of variety HoCP 00-950. Finally, a third scenario where no single variety should exceed 30% of the total planted area was developed and results showed that a maximum net returns goal of $129,104 was achieved by planting 100, 300, 300 and 300 acres of varieties HoCP 96-541, L 99-226, L 99-233 and HoCP 00-950, respectively. For all scenarios, results showed that production should be kept until third stubble; therefore, the crop cycle length should be five years.

Increasing environmental concerns, population, and changing preferences of consumers towards healthier foods, and agronomic practices have all aligned to provide not only food and fiber, but also sustainable practices useful to the environment. Cover crops, a type of agricultural technology, provide private and public benefits, which are vital for organic production. The objectives of this study are: 1) Identify determinants of cover crop adoption; 2) analyze how nitrogen management varies by farm relative to adoption or non-adoption of this technology; 3) estimate the change in the probability of adoption of cover crops due to farm, regional and operator characteristics by non-adopters; and 4) estimate the change in intensity in nitrogen use by cover crop adopters due to farm, regional, and operator characteristics. To address our objectives, we developed a two-stage simultaneous equation model where the first stage provides information on the factors affecting adoption of cover crops using a probit model. To better understand the effects of cover crops on the amount of nitrogen use by producers we use a left-censored tobit model and incorporated the adoption of cover crops as an endogenous variable. To estimate the intensity of the effect of adoption of cover crops, we used the McDonald and Moffitt (1980) decomposition of the marginal effects. The results of the probit model showed that producers age, experience, experience squared, all conservative payments, using other producers (who grow cover crops) and organic fertilizer dealers as information sources when making nitrogen management decisions had a significant effect on cover-crop adoption. The results of the tobit model showed econometrically that cover crop adoption had a significant effect on nitrogen use by producers. Three other variables that had a significant effect on nitrogen use by producers were field slope of 12% or more, rented field, and off-farm work.

The coast of Louisiana, with more than three million wetland acres, accounts for about 40 percent of the nations total wetlands. Louisiana is estimated to have lost more than 1.2 million acres of its coastal wetland in the last century. Although 75% of Louisianas coastal wetlands are privately owned, little has been done to encourage private landowners to undertake wetland restoration projects. This dissertation examines the factors that influence the decisions of the landowners to undertake wetland restoration projects. We develop a theoretical framework for understanding the landowners decision-making process in the presence of high uncertainty and increasing restoration costs. The condition under which landowners will invest in wetland restoration and maintenance is derived under the assumptions of risk aversion and relatively high restoration costs. The validity of the theoretical model is tested using data from a mail survey of private wetland landowners in coastal Louisiana that was conducted in Fall of 2009. Two econometric (Tobit and double hurdle) models are estimated to determine the importance of various factors including risk aversion on the probability and the level of private coastal wetland investments. The Likelihood ratio (LR) test shows that the double hurdle model statistically outperforms the Tobit model. The results suggest that the decision to invest in wetland restoration and how much to invest appear to be determined by different processes. The results of the double hurdle model show that risk plays an important role in landowners decisions to invest in wetland restoration and maintenance activities. Landowners who are risk averse make less investment in wetland restoration and maintenance projects than other landowners, and landowners who own properties that are located in risk prone areas are less likely to invest in wetland restoration than other landowners. In addition, the results show that landowners attitudes toward conservation, income related to the property, participation in government wetland programs, ownership structure, and wetland property size are all important determinants of the landowners investment decisions. The analysis emphasizes the need to incorporate risk into the design of wetland incentive programs to encourage private landowners to undertake wetland restoration projects in coastal Louisiana.