Spatial distribution of broadleaf weeds within 14 North Carolina soybean fields was characterized by fitting negative binomial distributions to frequency distributions of weed counts in each field. In most cases, the data could be represented by a negative binomial distribution. Estimated values of the parameter K of this distribution were small, often less than one, indicating a high degree of patchiness. The data also indicated that the population as a whole was patchy. Counts of individual species were positively correlated with each other in some fields and total weed count could be represented by a negative binomial for 12 of the 14 fields.

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.

A soil sampler, elutriator, and associated sample flushing device were designed and constructed for an intensive study of weed seedbanks. This equipment was used in 1993 to collect and process 4980 soil samples. The sampler was durable, core size was consistent, and sampling was efficient. Cores were approximately 200 cm3and two people could take 120 cores/h. The elutriator separated weed seeds from 36 of these cores at a time. Washing required 60 to 75 min depending on soil texture. Seeds as small as 0.3 mm in diam were recovered and almost 100% of the seeds were recovered from samples spiked with barnyardgrass, redroot pigweed, velvetleaf, and witchgrass. The flushing device was used to transfer sample contents from strainers of the elutriator to propyltex bags for drying and storing. Equipment like this, plus improved technology for identifying and counting seeds, is needed to make weed seedbank studies more feasible.

No abstract

Broadleaf weeds apparently have patchy distributions within a field while POST control decisions are made assuming a regular spatial distribution. As a result, yield loss from weed competition may be overestimated, possibly leading to mistakes in choosing the optimal control treatment. Data on distribution of broadleaf weeds in 14 soybean fields were used in simulation experiments to investigate the potential for improving decision making with information about weed patchiness. The feasibility of modeling weed distribution in individual fields was also examined. Overall, the cost of assuming a regular distribution when making POST decisions was found to be low. Errors that occurred most often involved recommending more intensive control than was actually required, although in a few cases less intensive control was recommended. Error in the yield loss estimated for the uncontrolled population did not indicate the potential for a mistake in decision making for a field. Accurately modeling distribution of weeds within fields may be difficult as a result of correlations between distributions of individual species within a field and variation in distributions between fields.