Lori J. Wiles

Weed management decision models: pitfalls, perceptions, and possibilities of the economic threshold approach

Abstract The use of scouting and economic thresholds has not been accepted as readily for managing weeds as it has been for insects, but the economic threshold concept is the basis of most weed management decision models available to growers. A World Wide Web survey was conducted to investigate perceptions of weed science professionals regarding the value of these models. Over half of the 56 respondents were involved in model development or support, and 82% thought that decision models could be beneficial for managing weeds, although more as educational rather than as decision-making tools. Some respondents indicated that models are too simple because they do not include all factors that influence weed competition or all issues a grower considers when deciding how to manage weeds. Others stated that models are too complex because many users do not have time to obtain and enter the required information or are not necessary because growers use a zero threshold or because skilled decision makers can make better and quicker recommendations. Our view is that economic threshold–based models are, and will continue to be, valuable as a means of providing growers with the knowledge and experience of many experts for field-specific decisions. Weed management decision models must be evaluated from three perspectives: biological accuracy, quality of recommendations, and ease of use. Scientists developing and supporting decision models may have hindered wide-scale acceptance by overemphasizing the capacity to determine economic thresholds, and they need to explain more clearly to potential users the tasks for which models are and are not suitable. Future use depends on finding cost-effective methods to assess weed populations, demonstrating that models use results in better decision making, and finding stable, long-term funding for maintenance and support. New technologies, including herbicide-resistant crops, will likely increase rather than decrease the need for decision support.

Exploratory data analysis to identify factors influencing spatial distributions of weed seed banks

Abstract Comparing distributions among fields, species, and management practices will help us understand the spatial dynamics of weed seed banks, but analyzing observational data requires nontraditional statistical methods. We used cluster analysis and classification and regression tree analysis (CART) to investigate factors that influence spatial distributions of seed banks. CART is a method for developing predictive models, but it is also used to explain variation in a response variable from a set of possible explanatory variables. With cluster analysis, we identified patterns of variation with direction of the distance over which seed bank density was correlated (range of spatial dependence) with single-species seed banks in corn. Then we predicted patterns of the seed banks with CART using field and species characteristics and seed bank density as explanatory variables. Patterns differed by magnitude of variation in the range of spatial dependence (strength of anisotropy) and direction of the maximum range. Density and type of irrigation explained the most variation in pattern. Long ranges were associated with large seed banks and stronger anisotropy with furrow than center pivot irrigation. Pattern was also explained by seed size and longevity, characteristics for natural dispersal, species, soil texture, and whether the weed was a grass or broadleaf. Significance of these factors depended on density or type of irrigation, and some patterns were predicted for more than one combination of factors. Dispersal was identified as a primary process of spatial dynamics and pattern varied for seed spread by tillage, wind, or natural dispersal. However, demographic characteristics and density were more important in this research than in previous research. Impact of these factors may have been clearer because interactions were modeled. Lack of data will be the greatest obstacle to using comparative studies and CART to understand the spatial dynamics of weed seed banks. Nomenclature: Corn, Zea mays L.

Effect of Steam Application on Cropland Weeds1

Plot-scale field studies were conducted to evaluate the efficacy of steam for the control of cropland weeds in comparison with common herbicides. Weed densities, biomass, or emergence after treatment were measured. Steam (3,200 kg/ha, energy dosage equivalent to 890 kJ/m2, speed of 0.8 m/s) and glyphosate (560 g ai/ha) gave similar control (> 90%) of seedling common lambsquarters and seedling redroot pigweed. Applied at heading, steam was comparable to glyphosate in reducing green foxtail biomass at heading 2 wk after application. Steam applied at a rate of 3,200 kg/ha significantly reduced weed biomass (mixed stand, treated at seedling stage) 9 wk after application compared with the control, whereas steam applied at a rate of 1,600 kg/ha (1.6 m/s) did not. Biomass of downy brome treated with steam was reduced more at anthesis than at the seedling growth stage. Emergence of common lambsquarters, redroot pigweed, and black nightshade was not affected by steam application. Amount of steam applied, weed species, and growth stage are key factors in determining control effectiveness. Nomenclature: Glyphosate; black nightshade, Solanum nigrum L. #3 SOLNI; downy brome, Bromus tectorum L. # BROTE; common lambsquarters, Chenopodium album L. # CHEAL; green foxtail, Setaria viridis L. # SETVI; redroot pigweed, Amaranthus retroflexus L. # AMARE. Additional index words: Kochia scoparia L. Schrad., KOCSC, paraquat, pelargonic acid, SALIB, Salsola iberica Sennen & Pau.

Infestation and spatial dependence of weed seedling and mature weed populations in corn

Abstract Knowing the distribution of weed seedlings in farmer-managed fields could help researchers develop reliable distribution maps for site-specific weed management. With a knowledge of the spatial arrangement of a weed population, cost effective sampling programs and management strategies can be designed, so inputs can be selected and applied to specific field areas where management is warranted. In 1997 and 1998, weeds were sampled at 612 to 682 sites in two center pivot irrigated corn fields (71 and 53 ha) in eastern Colorado. Weeds were enumerated when corn reached the two-leaf, four-leaf, and physiological maturity stages in a 76.2- by 76.2-m grid, a random-directed grid where sites were established at intervals of 76.2 m, and a star configuration based on a 7.62- by 7.62-m grid within three 23,225 m2 areas. Directional correlograms were calculated for 0, 30, 60, 90, 120, and 150° from the crop row. Fifteen weed species were observed across fields. Spatial dependence occurred in 7 of the 93 samples (a collection of sampling units for a particular weed species that was detected within a field at a particular sampling time and year) for populations of field sandbur, pigweed species, nightshade species, and common lambsquarters. Correlogram analysis indicated that 18 to 72% of the variation in sample density was a result of spatial dependence over a geographic distance not exceeding 5 to 363 m among the examined data. Because of the lack of spatial correlation for weed seedling distributions in these eastern Colorado corn fields, interpolated density maps should be based on grid sizes (separation distances) less than 7.62 m for weed seedling infestations. Nomenclature: Common lambsquarters, Chenopodium album L. CHEAL; field sandbur, Cenchrus longispinus (Hack.) Fern CCHPA; nightshade spp., Solanum spp.; pigweed spp., Amaranthus spp.; corn, Zea mays L.

Modeling With Limited Data: The Influence of Crop Rotation and Management on Weed Communities and Crop Yield Loss

Abstract Theory and models of crop yield loss from weed competition have led to decision models to help growers choose cost-effective weed management. These models are available for multiple-species weed communities in a single season of several crops. Growers also rely on crop rotation for weed control, yet theory and models of weed population dynamics have not led to similar tools for planning of crop rotations for cost-effective weed management. Obstacles have been the complexity of modeling the dynamics of multiple populations of weed species compared to a single species and lack of data. We developed a method to use limited, readily observed data to simulate population dynamics and crop yield loss of multiple-species weed communities in response to crop rotation, tillage system, and specific weed management tactics. Our method is based on the general theory of density dependence of plant productivity and extensive use of rectangular hyperbolic equations for describing crop yield loss as a function of weed density. Only two density-independent parameters are required for each species to represent differences in seed bank mortality, emergence, and maximum seed production. One equation is used to model crop yield loss and density-dependent weed seed production as a function of crop and weed density, relative time of weed and crop emergence, and differences among species in competitive ability. The model has been parameterized for six crops and 15 weeds, and limited evaluation indicates predictions are accurate enough to highlight potential weed problems and solutions when comparing alternative crop rotations for a field. The model has been incorporated into a decision support tool for whole-farm management so growers in the Central Great Plains of the United States can compare alternative crop rotations and how their choice influences farm income, herbicide use, and control of weeds in their fields.

Economics of Weed Management: Principles and Practices1

Abstract Weed scientists and invasive plant biologists must find cost-effective, ecologically based methods to manage undesirable plants. Economic analyses are needed for management, policy making, and setting research priorities. The fundamental economic principle for weed management is simple: act only if the benefits exceed the costs. Implementation of the principle is difficult, however, with the many and typically uncertain costs and benefits of management. The economic threshold is a well-known but not practical implementation of this fundamental economic principle. However, adoption of the threshold concept has spurred the development of decision models and use of methods of decision analysis. With these tools, scientists have quantified some risks of management and the value of information about the weed population in a field for management decisions or the value of specific information about weed biology for identifying new management strategies. Meaningful analysis for economic weed management is currently limited by lack of understanding of weed population and spatial dynamics and problematic communication between weed scientists and agricultural economists. Additional index words: Decision analysis, economic analysis, economic threshold, risk, value of information.

Simulating weed scouting and weed control decision making to evaluate scouting plans

The weed seedling population in a field must be scouted or sampled to choose the most appropriate postemergence control treatment. The cost-effectiveness of a scouting plan must be evaluated to confidently recommend its use. We conducted simulation experiments to evaluate scouting plans for use with a microcomputer postemergence weed control decision model for soybeans. The following were simulated the process of scouting, use of the decision model with the scouting information, and the resulting profit from the decision model’s recommendation. Simulations were based on &ta from 14 North Carolina soybean fields. While scouting is recognized as valuable for determining if control is required, our results highlight the value of scouting for choosing among treatments when the need for control is obvious. Our results also indicate that the scouting plan recommended for use with the decision model is cost-effective. However, some risk averse decision makers may wish to scout more intensively. Use of simulation to evaluate weed scouting plans is currently constrained by the lack of &ta on the cost of scouting and the distribution of weeds within fields.