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We determined the value of soil test information for potassium (K) in upland cotton production using the linear response plateau (LRP) and linear response stochastic plateau (LRSP) functions. A stochastic dynamic programming model was used to determine the net present value to K fertilizer when optimal K was applied with knowledge about K carryover. Using K carryover information for K application decisions increased net present value and helped maintain steady levels of soil K. The LRSP function fit the data better than the LRP, and the value of soil testing was $27 ha-1 lower over ten years using the LRSP.

Factors influencing adoption of variable-rate nutrient management (VRM) and georeferenced precision soil sampling (PSS) for fertilizer management among cotton producers and the factors affecting awareness of and participation in cost-share programs encouraging the adoption of nutrient-management practices were analyzed using multivariate probit regression with sample selection. Data were collected from a fourteen-state cotton producer survey. Factors including farm size, operator age, and farm location were correlated with the adoption of VRM and PSS, awareness of cost-sharing programs, and program participation. The results may help agencies target farms with the specific attributes most likely to participate in cost-share programs.

This research analyzes the adoption patterns among cotton farmers for remote sensing, yield monitors, soil testing, soil electrical conductivity, and other precision agriculture technologies using a Multiple Indicator Multiple Causation regression model. Adoption patterns are analyzed using principle component analysis to determine natural technology groupings. Identified bundles are regressed on farm structure and operator characteristics. The propensity to adopt technology bundles was greater for producers managing relatively larger operations who used a variety of information sources to learn about precision farming, irrigated cotton, practiced crop rotation, and participated in working land conservation programs.

A 2009 survey of cotton farmers in twelve states collected information about the use of georeferenced precision soil testing (PST). Adoption of PST technology and the interval until retesting were examined with a Poisson hurdle regression. Survey data were calibrated using a post-stratification weighting protocol. Farming experience, farm size, land ownership, variable rate fertilizer management plans, and the use of soil electrical conductivity devices were correlated the with period until PST adopters retested soil. Understanding how producers perceive the useful life of soil-test information may be important for monitoring the effectiveness of best nutrient management practice adoption.

Adoption of precision agriculture technology has arrived considerable attention, but abandonment has received little. This paper identified factors motivating adoption and abandonment of precision soils sampling in cotton. Younger producers who farmed more cotton area, owned more of their cropland, planted more non-cotton area, or used a computer were more likely to adopt precision soil sampling. Those with more cotton area or who owned livestock were more likely to abandon, while those who used precision soil sampling longer, or used variable-rate fertilizer application were less likely to abandon precision soil sampling.

Farmers are concerned about the high cost of planting herbicide-resistant cotton with the high plant densities recommended for ultra-narrow-row cotton. This study evaluated the effects on net revenues of four herbicide-resistant technology fee policies used since 1996 by Monsanto, the technology license holder. Results indicate that changes in the technology fee policy by Monsanto have raised the cost of planting herbicide-resistant cotton. As a consequence, farmers may have an incentive to switch from ultra-narrow-row cotton to wide-row cotton and to use a lower plant density when the technology fee is tied to the seeding rate.

Research has evaluated the relative profitability of variable-rate (VRT) versus uniform-rate (URT) application of a single input in fields with multiple management zones. This study addresses map-based VRT decisions for multiple inputs in fields with multiple management zones. The decision-making framework is illustrated for nitrogen and water applied to irrigated cotton in fields with three management zones. Results suggest traditional methods of determining VRT application of a single input may by suboptimal if interactions exist among VRT inputs and URT inputs. Implications are that a systems approach to multiple-input VRT decisions can produce increased net returns to VRT.

By using a stochastic frontier framework, the mutual effect of input use on production risk and inefficiency is investigated. Disentangling this mutual effect proves important for empirical reasons, at least when applied to west Tennessee cotton systems grown after various cover crops. The most striking result is that the stochastic frontier model, when compared with a typical Just-Pope model, reorders the relative riskiness of cover-crop regimes associated with the cotton systems.

Producers in southwest Oklahoma lack adequate information about optimal planting decisions for cotton. This study uses a cotton growth simulation model to evaluate alternative cultivar, planting date, irrigation, and harvest choices. Effects of using information about soil moisture at reproduction and revenue loss at harvest in making cultivar and planting data decisions are evaluated. Using soil temperature information to plant at an early date produced high net revenue some years, but reduced mean net revenue and increased risk. Producers maximizing expected net revenue should plant a short-season cultivar in late May and use soil moisture information to schedule irrigation at reproduction.