There is growing evidence to suggest that there is an increase in species extinction occurring globally. In this article, we briefly review the literature on the economics of species extinction, examining what is meant by extinction before explaining how economics has conceptualised this. The initial economics literature on species extinction focuses largely on renewable resources, in particular fisheries, but has subsequently evolved to cover many aspects of biodiversity across all physical scales, employing an increasing array of methodological tools. We also consider aspects of cultural and societal extinctions (e.g. local languages, local knowledge) and how this is positively correlated with loss of biodiversity, as well as an economist’s outlook on the potential to re-capture value post-extinction.

An intensive survey of two farmer-managed corn and soybean fields in eastern Nebraska was conducted to investigate parametric sequential sampling of weed seedling populations using a multistage procedure to estimate k, of the negative binomial distribution. k is a nonspatial aggregation parameter related to the variance at a given mean value. Mean weed seedling density ranged from 0.18 to 3.11 plants 0.38 m−2 (linear meter of crop row) based on 806 sampling locations. The average value of k, derived from 200 multistage estimation procedures, ranged from 0.17 to 0.32. A sequential sampling plan was developed with the goal of estimating the mean with a coefficient of variation (CV) of 10, 20, 30, and 40% of the sample mean. A sampling plan was also constructed to estimate the mean within a specified distance H of the true mean (H(x̄)= 0.10, 0.50 and 1.0 plants 0.38 m−2) with 80, 85, and 90% confidence. Estimating mean weed seedling density within a specified CV of the true mean CV(x̄) using parametric sequential sampling techniques was superior to estimating the mean within a specified distance (H(x̄)) of the true mean when considering the frequency of sampling and probability of error, especially at intermediate k values. At a k: value of 0.32 and 0.25, the difference between the actual CV(x̄) obtained from sampling and the CV(x̄) specified by the sampler was minimal. However, the accuracy of weed seedling density estimates was reduced with decreasing k values below 0.25, especially as the specified CV(x̄) increased.

Row crop weed management decisions can be complex due to the number of available herbicide treatment options, the multispecies nature of weed infestations within fields, and the effect of soil characteristics and soil-moisture conditions on herbicide efficacy. To assist weed managers in evaluating alternative strategies and tactics, three computer programs have been developed for corn, cotton, peanut, and soybean. The programs, called HADSS (Herbicide Application Decision Support System), Pocket HERB, and WebHADSS, utilize field-specific information to estimate yield loss that may occur if no control methods are used, to eliminate herbicide treatments that are inappropriate for the specified conditions, and to calculate expected yield loss after treatment and expected net return for each available herbicide treatment. Each program has a unique interactive interface that provides recommendations to three distinct kinds of usage: desktop usage (HADSS), internet usage (WebHADSS), and on-site usage (Pocket HERB). Using WeedEd, an editing program, cooperators in several southern U.S. states have created different versions of HADSS, WebHADSS, and Pocket HERB that are tailored to conditions and weed management systems in their locations.

A bioeconomic model is used to estimate how managing glyphosate resistance to horseweed affects short- and long-run profits in corn, soybean, and corn-soybean rotation systems. Model results found that resistance management reduces profits in the first year of implementation, but increases profits in the second and subsequent 18 yr. In all three systems, long run gains begin to outweigh short-run costs (and resistance management “pays for itself”) by the second year. Over a 20-yr horizon, the estimated annual average profit advantage from managing resistance exceeded $158 ha–1 ($64/acre) for corn, $137 ha–1 ($55/acre) for corn-soybean, and $55 ha–1 ($22/acre) for soybean. Seed immigration from a neighbor's field can reduce these gains, but this reduction is negligible if the neighbor also practices resistance management. If the neighbor did not manage resistance, however, the grower's estimated annual profit advantage fell to roughly $123 ha–1 ($50/acre) for corn, $60 ha–1 ($24/acre) for the corn-soybean rotation, and virtually zero for soybean. Methods applied in this study identify corn and corn-soybean rotations as cases where resistance management “pays for itself' quickly and significantly, even if the neighbor does not manage resistance. Continuous soybean presents a more challenging case that may require additional programs and incentives to encourage collective resistance management among growers. Even for continuous soybean, however, joint grower–neighbor resistance management can pay for itself within 2 yr.

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.

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 of AIC (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, and BIC selected 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, and BIC strongly supported a single best model with variables Dc, Dw, T, and a functional form specifying both intraspecific and interspecific competition. ICOMP selected a simpler model with Dc and Dw only, 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.

The utility of solaria (1 by 1-m plastic sheets) to predict densities of a few weed species in summer crops has been demonstrated previously, but needed further research to be adopted by farmers and advisors. We tested the method to detect important weeds in Argentina and Minnesota, and determined the minimum number of solaria required to predict the presence of emerged weed seedlings in the forthcoming growing season. Three experiments were performed in Buenos Aires Province, Argentina, and one in Minnesota. Solaria were placed in fields with different previous crops and soil management: no tillage (two fields) and conventional tillage (two fields). Preceding crops were corn (one field), wheat (one field), and double-cropped wheat/soybean (two fields). After weeds were enumerated, solaria were removed, sunflower (one field) and soybean (three fields) were planted, and weeds later assessed in each crop. Results indicate that one solarium per 1.9 ha can detect common lambsquarters with 95% confidence within the next summer crop. For other species, one solarium per 4.2, 1.2, 1.0, and 1.8 to 2.7 ha (depending upon field site) for large crabgrass, prostrate knotweed, wild buckwheat, and green foxtail, respectively, was required. The low cost and simplicity of assessment make this technique more suitable than that of soil seed-bank samples to predict weed emergence. The number of solaria required to forecast weed infestation levels confidently is sufficiently low that their use may be justified, especially in small fields of high-value crops.