A PRINCIPAL-AGENT MODEL FOR REGIONAL PEST CONTROL ADOPTION

Investigating the underlying producer characteristics associated with regional pest control adoption revealed an interesting proposition. Early adopting producers of firm-specific techniques with characteristics including higher education, more specialized operations, and larger sized business units are dissatisfied with a regional pest control technique. This study provides an explanation of the proposition based on a principal-agent model. Empirical support for the proposition is also presented by developing a multinomial logit model for predicting producers' dissatisfaction with boll weevil eradication.Regional pest control, Principal-agent model, Proposition, Firm-specific, Industry-specific, Crop Production/Industries,

RATIONAL EXPECTATIONS ESTIMATION OF GEORGIA SOYBEAN ACREAGE RESPONSE

The general method of moments procedure is used for estimating a soybean acreage response function assuming the producers hold rational expectations. Results indicate that soybean, corn, and wheat futures prices, lagged acreage, and government programs are significant factors for determining soybean plantings. Implications of the results are that crop acreage selection by Georgia producers is not very responsive to demand shocks. Thus, producers in other regions are more likely to absorb impacts from these shocks on crop acreage selection.Soybeans, GMM, Elasticities, Crop Production/Industries,

ECONOMIC RETURNS TO THE BOLL WEEVIL ERADICATION PROGRAM

The economic viability of the Boll Weevil Eradication program in Alabama, Florida, and Georgia is assessed based on a five-year survey of producers. Results indicate the program increases yield 100 pounds per acre. This implies a 19 percent internal rate of return for producers over a ten year period.Cotton, Pest management, Regional pest control, Crop Production/Industries,

Importance de l'activité NAC (Nouveaux Animaux de Compagnie) dans le département du Rhône (étude expérimentale)

LYON1-BU Santé (693882101) / SudocSudocFranceF

A PRINCIPAL-AGENT MODEL FOR REGIONAL PEST CONTROL ADOPTION

Investigating the underlying producer characteristics associated with regional pest control adoption revealed an interesting proposition. Early adopting producers of firm-specific techniques with characteristics including higher education, more specialized operations, and larger sized business units are dissatisfied with a regional pest control technique. This study provides an explanation of the proposition based on a principal-agent model. Empirical support for the proposition is also presented by developing a multinomial logit model for predicting producers' dissatisfaction with boll weevil eradication

Life-Cycle Costs of Alternative Fuels: Is Biodiesel Cost Competitive for Urban Buses?

The purpose of this paper is to provide an expected cost comparison for operating a transit-bus fleet on three different alternative fuels—biodiesel, compressed natural gas (CNG), and methanol. Petroleum diesel is the base fuel. Infrastructure, refueling, and maintenance costs are all part of running an urban transit bus. Additional expenditures would be needed to change fuel storage and delivery systems, as well as bus engines and fuel systems, to use methanol or CNG. Using a 5-percent discount rate, the present value per bus per mile was calculated for the total cost (the sum of infrastructure, bus-alteration, refueling, and maintenance expenses) of a transit fleet over the estimated 30-year life cycle of a refueling infrastructure. Not surprisingly, diesel buses had the lowest cost at 24.7 cents per mile. As biodiesel is blended with diesel, the cost per mile ranged from 27.9 to 47.5 cents, depending on the amount of biodiesel used and its estimated price. CNG’s cost varied from 37.5 to 42 cents per mile, while methanol’s cost was 73.6 cents per mile. This analysis indicates that, although biodiesel and biodiesel blends have higher total costs than diesel fuel, they have the potential to compete with CNG and methanol as fuels for urban transit buses

Life-Cycle Costs of Alternative Fuels: Is Biodiesel Cost Competitive for Urban Buses?

The purpose of this paper is to provide an expected cost comparison for operating a transit-bus fleet on three different alternative fuels—biodiesel, compressed natural gas (CNG), and methanol. Petroleum diesel is the base fuel. Infrastructure, refueling, and maintenance costs are all part of running an urban transit bus. Additional expenditures would be needed to change fuel storage and delivery systems, as well as bus engines and fuel systems, to use methanol or CNG. Using a 5-percent discount rate, the present value per bus per mile was calculated for the total cost (the sum of infrastructure, bus-alteration, refueling, and maintenance expenses) of a transit fleet over the estimated 30-year life cycle of a refueling infrastructure. Not surprisingly, diesel buses had the lowest cost at 24.7 cents per mile. As biodiesel is blended with diesel, the cost per mile ranged from 27.9 to 47.5 cents, depending on the amount of biodiesel used and its estimated price. CNG’s cost varied from 37.5 to 42 cents per mile, while methanol’s cost was 73.6 cents per mile. This analysis indicates that, although biodiesel and biodiesel blends have higher total costs than diesel fuel, they have the potential to compete with CNG and methanol as fuels for urban transit buses

ECONOMIC RETURNS TO THE BOLL WEEVIL ERADICATION PROGRAM

The economic viability of the Boll Weevil Eradication program in Alabama, Florida, and Georgia is assessed based on a five-year survey of producers. Results indicate the program increases yield 100 pounds per acre. This implies a 19 percent internal rate of return for producers over a ten year period

RATIONAL EXPECTATIONS ESTIMATION OF GEORGIA SOYBEAN ACREAGE RESPONSE

The general method of moments procedure is used for estimating a soybean acreage response function assuming the producers hold rational expectations. Results indicate that soybean, corn, and wheat futures prices, lagged acreage, and government programs are significant factors for determining soybean plantings. Implications of the results are that crop acreage selection by Georgia producers is not very responsive to demand shocks. Thus, producers in other regions are more likely to absorb impacts from these shocks on crop acreage selection