To develop a more complete understanding of the ecological factors that regulate crop productivity, we tested the relative predictive power of yield models driven by five predictor variables: wheat and wild oat density, nitrogen and herbicide rate, and growing-season precipitation. Existing data sets were collected and used in a meta-analysis of the ability of at least two predictor variables to explain variations in wheat yield. Yield responses were asymptotic with increasing crop and weed density; however, asymptotic trends were lacking as herbicide and fertilizer levels were increased. Based on the independent field data, the three best-fitting models (in order) from the candidate set of models were a multiple regression equation that included all five predictor variables (R 2 5 0.71), a double-hyperbolic equation including three input predictor variables (R 2 5 0.63), and a nonlinear model including all five predictor variables (R 2 5 0.56). The double-hyperbolic, threepredictor model, which did not include herbicide and fertilizer influence on yield, performed slightly better than the fivevariable nonlinear model including these predictors, illustrating the large amount of variation in wheat yield and the lack of concrete knowledge upon which farmers base their fertilizer and herbicide management decisions, especially when weed infestation causes competition for limited nitrogen and water. It was difficult to elucidate the ecological first principles in the noisy field data and to build effective models based on disjointed data sets, where none of the studies measured all five variables. To address this disparity, we conducted a five-variable full-factorial greenhouse experiment. Based on our fivevariable greenhouse experiment, the best-fitting model was a new nonlinear equation including all five predictor variables and was shown to fit the greenhouse data better than four previously developed agronomic models with an R 2 of 0.66. Development of this mathematical model, through model selection and parameterization with field and greenhouse data, represents the initial step in building a decision support system for site-specific and variable-rate management of herbicide, fertilizer, and crop seeding rate that considers varying levels of available water and weed infestation. Nomenclature: Imazamethabenz; wild oat, Avena fatua L. AVEFA; wheat, Triticum aestivum L.

Composting is the biological decomposition of organic materials, such as plant tissue, food scraps, paper, animal fodder, and wood chips. The end-product, compost, is a beneficial soil amendment because it can contain a diversity of beneficial microorganisms, has high nutrient and water-holding capacities, can increase total soil porosity, and contains essential plant nutrients that improve soil productivity. Coastal regions of the Gulf of Mexico, as well as the Atlantic and European shorelines, have witnessed a proliferation of brown seaweed (Sargassum sp.). When piled on beaches, tourism appeal is reduced, threatening the local economy. When amassed offshore, thick brown seaweed mats can hinder fishing. Excessive decomposition rates can lead to eutrophication, which threatens coastal areas economically and environmentally. Despite these problems, seaweed may be considered a valuable compost ingredient. Therefore, the purpose of this study was to conduct a market test to determine the potential value of a seaweed-incorporated compost to consumers in Texas and to identify attributes of likely consumers. A marketing survey was developed and distributed to gardeners in the central and south Texas regions. Contingent valuation questions measured participants’ willingness to pay for the seaweed compost products. Participants were able to see, smell, and touch a sample of the compost while completing the survey. Despite 92% of respondents ranking themselves as inexperienced in compost behavior, results indicated a potential for a specialty, competitively priced seaweed-incorporated compost to be introduced to the market. Respondents were most willing to pay $4.00/ft3 to $5.00/ft3 for seaweed-incorporated compost. Additionally, participants who responded positively to buying local, buying compost in the past, having positive environmental attitudes, and buying American were more likely to pay more for the seaweed-incorporated compost. There was not an obvious pattern between willingness to pay for seaweed-incorporated compost and demographic responses.

Empirical models of crop–weed competition are integral components of bioeconomic models, which depend on predictions of the impact of weeds on crop yields to make cost-effective weed management recommendations. Selection of the best empirical model for a specific crop–weed system is not straightforward, however. We used information–theoretic criteria to identify the model that best describes barley yield based on data from barley–wild oat competition experiments conducted at three locations in Montana over 2 yr. Each experiment consisted of a complete addition series arranged as a randomized complete block design with three replications. Barley was planted at 0, 0.5, 1, and 2 times the locally recommended seeding rate. Wild oat was planted at target infestation densities of 0, 10, 40, 160, and 400 plants m−2. Twenty-five candidate yield models were used to describe the data from each location and year using maximum likelihood estimation. Based on Akaike's Information Criterion (AIC), a second-order small-sample version ofAIC(AICc), and the Bayesian Information Criterion (BIC), most data sets supported yield models with crop density (Dc), weed density (Dw), and the relative time of emergence of the two species (T) as variables, indicating that all variables affected barley yield in most locations.AIC,AICc, andBICselected identical best models for all but one data set. In contrast, the Information Complexity criterion,ICOMP, generally selected simpler best models with fewer parameters. For data pooled over years and locations,AIC,AICc, andBICstrongly supported a single best model with variablesDc,Dw,T, and a functional form specifying both intraspecific and interspecific competition.ICOMPselected a simpler model withDcandDwonly, and a functional form specifying interspecific, but no intraspecific, competition. The information–theoretic approach offers a rigorous, objective method for choosing crop yield and yield loss equations for bioeconomic models.

This study investigated the effect of compost tea applications on turf quality and soil microbial activity. Evaluations of turfgrass quality were based on The National Turfgrass Evaluation Program's guidelines while soil samples were analyzed for chemical attributes and microbial activity. Four sites and treatments for the study included: 1) a soil drench compost tea application with irrigation, 2) a soil drench compost tea application with no irrigation, 3) no compost tea application with irrigation, and 4) no compost tea application nor irrigation. Fifteen soil samples and turf quality observations from each treatment were collected for pretest data. Then, post-test data were collected after each additional seasonal test period over the course of one year for each of the four plots. For the four plots, the site which received compost tea applications and regular irrigation received significantly higher turf quality ratings, and compost tea improved turf quality ratings beyond that of regular irrigation. No differences were found in microbial populations given the compost tea application. While the study results provided evidence of the value of compost tea to overall turf quality aesthetics, more research is recommended regarding compost tea applications and beneficial soil microbial populations in turf. Species used in this study: Bermudagrass (Cynodon dactylon) L. Pers.

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