Masters Theses

Date of Award


Degree Type


Degree Name

Master of Science


Agricultural Economics

Major Professor

Dayton M. Lambert

Committee Members

Roland K. Roberts, James A. Larson, Margarita M. Velandia


Soil sampling can help producers gain more accurate knowledge about soil nutrient properties and field-level characteristics. This information aids in the placement and timing of fertilizer application. Optimal input application may lower variable costs, increase economic returns, and moderate off-site environmental impacts of farming. Yet producer decisions to incorporate soil information into management practices and perceptions about the value of soil test information over time depends on a wide range of economic, social, and producer characteristics. Studies examining the value of soil information for optimal nutrient management may help inform producers considering adopting these technologies about the potential benefits of soil testing. This thesis provides two studies examining (1) the factors associated with the adoption of precision soil sampling and the length of time this information is perceived useful by cotton producers, and (2) the value of soil test information with regards to optimal potassium fertilizer management in cotton production over multiple growing seasons.

Perceptions about the usefulness of soil test information over time depend on a variety of factors directly or indirectly related to input management. In the first study, the adoption and frequency of soil testing is examined as a function of off-farm, farm business, information sources, and operator characteristics using a Poisson hurdle regression model. Analyzing data from a survey of cotton farmers in 12 Southern states, the length of time producers perceived soil test information to be useful were influenced by farmer experience, land tenure, and the use of other information gathering technologies such as Greenseeker® and electro conductivity.

In the second study, optimal potassium (K) management with information about fertilizer carryover was analyzed using a dynamic programming model. Monte Carlo simulation results suggest the information site-specific technologies provides with respect to residual fertilizer carryover effects of K are greatest when a producer is able to identify the magnitude of soil carryover capacity and incorporate this information to manage K. The information obtained from this research may provide insight for cotton producers, agribusiness firms, and agricultural service providers about the perception and potential benefits of soil sampling information to manage inputs in cotton production.

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