Lambert, Dayton M.

By: Holley, Kristen; Jensen, Kimberley L.; Lambert, Dayton M.; Clark, Christopher D.
This study applies a bivariate Multiple Indicator–Multiple Causation model to examine farm and operator characteristics associated with the likelihood of using pasture management (PM) and prescribed grazing (PGR) practices. Data are from a survey of cattle operations. Most commonly used practices included adjusting livestock and pasture fertilization. Least used were geotextiles in trafficked areas and buffering sensitive areas. Use of PM practices was income sensitive. Land stewardship and government conservation incentive views influenced PGR. Results suggest complementarities between most PGR and PM practices. However, those with higher opportunity costs and off-farm benefits (e.g., stream crossings) are not complementary with other practices.
By: Harmon, Xavier; Boyer, Christopher N.; Lambert, Dayton M.; Larson, James A.; Gwathmey, C. Owen
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.
By: Boyer, Christopher N.; Lambert, Dayton M.; Velandia, Margarita; English, Burton C.; Robert, Roland K.; Larson, James A.; Larkin, Sherry L.; Paudel, Krishna P.; Reeves, Jeanne M.
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.
By: Lambert, Dayton M.; Paudel, Krishna P.; Larson, James A.
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.
By: Lambert, Dayton M.; English, Burton; Harper, David; Larkin, Sherry L.; Laron, James; Mooney, Daniel F.; Roberts, Roland; Velandia, Margarita; Reeves, Jeanne
The authors regret that the above paper contained an error in the calculation of the survey expansion weights (Lambert et al., 2014, p. 110). Using the notation of the paper, the expansion factor for the lth stratum was introduced as wl =agbh=ngh, where g indexes states and h indexes farm size class. This is in fact the correct expression if Sinkhorn’s (1964) RAS method were used. However, Ireland and Kullback’s (1968) cross-entropy method was used to estimate the expansion factors, and division of variables a and b by the survey response frequency (ngh) is unnecessary. The typographical error has no bearing on the empirical analysis. References
By: Lambert, Dayton M.; English, Burton C.; Harper, David C.; Larkin, Sherry L.; Larson, James A.; Mooney, Daniel F.; Roberts, Roland K.; Velandia, Margarita; Reeves, Jeanne M.
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.
By: Stewart, Lance A.; Lambert, Dayton M.; Wilcox, Michael D.; English, Burton C.
Industry cluster identification methods determine linkages between purchasers and suppliers at the county level for 447 economic sectors in Tennessee. Using an econometric model, the cluster analysis is extended to estimate which value chains contributed to economic growth between 2001 and 2006. Businesses making up the agriculture and forestry clusters enjoyed increased output per job in 34% and 32%, respectively, of Tennessee's counties. The spatial pattern of these findings was significant, suggesting that some counties may benefit from regional coordination of projects designed to enhance or retain businesses in these industry clusters.
By: Walton, Jonathan C.; Lambert, Dayton M.; Roberts, Roland K.; Larson, James A.; English, Burton C.; Larkin, Sherry L.; Martin, Steven W.; Marra, Michele C.; Paxton, Kenneth W.; Reeves, Jeanne M.
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.