A planning tool for integrating crop choices with weed management in the Northern Great Plains
Abstract Crop production in the Northern Great Plains is rapidly changing because of no-till practices; producers now grow a diversity of crops with winter wheat to increase production as well as to manage weeds. With the multitude of crops available, producers are asking for guidelines to sequence crops in rotations that help weed management. We developed a planning tool that lists various choices with crops when designing a rotation; the choices are arranged by impact on weed dynamics as quantified by research in the region. The tool includes choices among crops, such as varying crops with different life cycles, planting dates, or row spacing, and choices within an individual crop, such as varying cultivar or planting date. Choices among crops impact weeds the most, whereas choices within an individual crop are less effective and usually lead to lower crop yield. For example, rotations comprised of two cool-season crops followed by two warm-season crops can reduce weed populations six- to 12-fold. In contrast, replacing a standard-height cultivar with a taller cultivar that is more competitive with weeds, is often inconsistent for weed management, whereas crop yield may be less. Producers associated with the Northern Plains Sustainable Agricultural Society felt that the planning tool would encourage long-range planning as well as help integrate weed management with the design of cropping systems. The purpose of the tool is to encourage ecologically based weed management, which can reduce herbicide inputs by 50% for Northern Great Plains producers.
Developing an Empirical Yield-Prediction Model Based on Wheat and Wild Oat (Avena fatua) Density, Nitrogen and Herbicide Rate, and Growing-Season Precipitation
Abstract 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 (R2 = 0.71), a double-hyperbolic equation including three input predictor variables (R2 = 0.63), and a nonlinear model including all five predictor variables (R2 = 0.56). The double-hyperbolic, three-predictor model, which did not include herbicide and fertilizer influence on yield, performed slightly better than the five-variable 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 five-variable 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 R2 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
Inhibition of Root Growth and Polyamine Metabolism in Sunflower (Helianthus annuus) Seedlings Under Cadmium and Copper Stress
Although sunflower is usually regarded as a highly tolerant crop, impairment of root growth at initial stages of plant development may result in poor crop establishment and higher susceptibility to pathogen attack. In order to evaluate if Cd2+ and Cu2+ may impact on sunflower germination and initial root development, a pot experiment under controlled conditions was carried out. Possible involvement of polyamine metabolism in sunflower response to these stressors was also investigated. Although Cd2+ and Cu2+ treatments affect neither seed germination nor radicle emergence, sunflower seedlings grown in the presence of these heavy metals showed significant inhibition of root growth, being this inhibition greater for Cd2+. Both metals caused significant increases in proline contents at the highest concentrations tested (0.5 and 1 mM), and these increments were more pronounced for Cd2+ treatments, especially between days 3 and 10. Metals also increased putrescine (Put) contents at all concentrations assayed from the seventh day onward, causing no variations on this polyamine time-course pattern. Spermine and spermidine contents, however, were increased only by 1 mM Cd2+. Arginine decarboxylase seems to have been the enzyme responsible for Put increases under both metal treatments. This work demonstrates that initial root growth of sunflower seedlings may be significantly impaired in Cd2+ or Cu2+ contaminated soils. It also shows that polyamines are key biological compounds, which are probably involved in signaling pathways triggered under stress environmental conditions.