Organic matter is a key element of soil fertility. However, in-soil stocks of organic matter are in decline in specialized crop farms. Reintroducing organic fertilizers could be a way to increase or at least maintain organic matter stocks in these soils. Straw/manure exchanges between crop farms and livestock farms could improve overall land fertility and thus the long-term income of farmers. Here we used a bioeconomic model to estimate the agronomic and economic benefits of straw/manure exchanges as part of a strategy to improve soil fertility. Iterative simulations were run with prices of farmyard manure varying from €0 to €20 ton−1 to identify the price at which a crop farm can buy manure and sell straw without degrading its net income (manure ceiling price) compared to purchasing mineral fertilizer only and ploughing back straw. Parallel simulations were run to identify the price at which a livestock farmer can sell manure and buy straw without degrading its net income (manure floor price) compared to keeping all manure on the farm and buying straw on the market. The key new contributions of this study are that it (i) considers the characteristics of manure beyond its short-term fertilizing value, i.e., better mineralization of humus and a 10% increase in crop yields, (ii) estimates both the economic and agronomic benefits of manure and (iii) assesses the impacts of uncertainties on manure effects and prices. The results show that it is profitable for a French farmer in a conventional field crop system to buy manure at a price between €10 ton−1 in the short term and €18 ton−1 in the long term. The results also show that it is still economically advantageous for a livestock farmer to sell part of their manure, even at a very low price. This study shows that it is possible to better distribute manure resources over a territory in a way that enriches the soils of crop farms without degrading the soils of livestock farms.

In animal breeding, genetic parameters along with economic weights (EWs) of traits are applied. Profit functions currently used to calculate rabbit traits’ EWs do not consider nutrient requirements based on animal weight, growth rate and doe reproductive status. Therefore, the aim of this study was to develop a flexible bioeconomic model of rabbit-production systems and implement it in a computer programme in order to calculate economic values and relative EWs for rabbit traits. The bioeconomic model includes calculation of the doe age structure in the stationary state of a doe population; calculation of progeny structure; modelling growth, digestible energy, feed and water requirements for does in different reproductive statuses and for all progeny groups using a normative approach; calculation of the total feed and non-feed costs, revenues and profit per doe and per year; calculation of marginal economic values for up to 20 production and functional traits and estimation of the relative EWs of selected traits. The application of the programme is shown through an example calculation of trait economic values for a typical Czech commercial rabbit-production system. The trait economic value expresses the change in profit per doe and per year when the trait mean is increased by one unit. The programme developed is primarily useful for selection purposes in rabbit-breeding systems. Using this programme, some economic analyses of the impact of production, management and economic circumstances on the economic efficiency of various rabbit-production systems can also be performed.

The impact of weed density and weed distribution on irrigated corn yield was investigated in Colorado. Weed densities examined were 0,33,50, or 100% of the indigenous weed population. A series of weed distribution treatments were achieved by varying the length of the weed-free and weedy zones within the corn row while maintaining a constant weed population of 33 or 50% of the indigenous weed level. Grain yield was affected by weed density, but not by weed distribution. Each additional weed reduced corn yield 8.5 and 2.3 kg ha−1 in 1991 and 1992, respectively. When corn yields were estimated with a computer weed/corn management model, weed densities 5 to 8 wk after planting provided a better yield reduction estimate than weed densities immediately before harvest.

Bioeconomic weed management models help growers achieve appropriate weed management with less herbicide by matching management to the weed population in a field. Growers, however, will not use bioeconomic models unless cost-effective methods to sample their weed populations are identified. Counting and identifying seeds and seedlings is the most time-consuming and costly part of sampling weed populations. The time required for this process was investigated and modeled as a first step in developing sampling plans for growers using WEEDCAM, a bioeconomic model for weed management in Zea mays L. in Colorado. The time required to count and identify seeds or seedlings was recorded for 9,405 soil cores (5 cm in diameter and 10 cm deep) and 9,726 quadrats (18-cm band over 1.52 m of crop row) collected or examined in eight corn fields in eastern Colorado. The time required to count and identify seeds was best described using a log-linear regression with time increasing with the number of seeds and species and the amount of sand in the core. The average cost of determining there are no seeds in a core is $1.07 for a core from a field with 37% sand and $4.32 if the field has 88% sand. The average cost of counting and identifying 36 seeds of four species is $2.70 and $10.88 for cores with 37 and 88% sand, respectively. The time required to count and identify seedlings was best described using a log-linear regression with time increasing with the number of seedlings and species. Classifying seedlings as grass or broadleaf did not improve the model. The average cost of determining that a quadrat is weed-free is $0.02. The average cost of counting and identifying 37 seedlings is $0.05, $0.07, $0.19, and $0.26 per quadrat for 1, 2, 4, and 6 species, respectively. The cost of identifying seeds and seedlings in eastern Colorado Z. mays fields to use WEEDCAM is estimated as $2.71 per core for the seed bank and $0.08 per quadrat for seedlings.

Previous efforts to model crop yield loss from multiple weed species constructed competitive indices based on yield loss from individual weed species. Our model uses a multispecies modification of Cousens’ rectangular hyperbolic yield function to estimate a nonlinear competitive index for weed-crop interference. Results from 13 Minnesota and Wisconsin data sets provide measures of the relative competitiveness of mixed green and yellow foxtails, common lambsquarters, redroot pigweed, velvetleaf, and several other weed species. Competition coefficient estimates are stable over years, but not locations.

The crop-weed interference relationship is a critical component of bioeconomic weed management models. Multi-year field experiments were conducted at five locations to determine the stability of corn-velvetleaf interference relationships across years and locations. Two coefficients (I and A) of a hyperbolic equation were estimated for each data set using nonlinear regression procedures. The I and A coefficients represent percent corn yield loss as velvetleaf density approaches zero, and maximum percent corn yield loss, respectively. The coefficient I was stable across years at two locations, but varied across years at one location. The coefficient A did not vary across years within locations. Both coefficients, however, varied among locations. Results do not support the use of common coefficient estimates for all locations within a region.

Cooperative research was initiated by a multidisciplinary team of weed scientists and agricultural economists in 1975 that laid the foundation for integrated weed management research in Colorado. Colorado's integrated weed management research has four phases: weed seed bank decline, systems approach to weed management, bioeconomic modeling, and multiple weed management tactics. Each phase considered weed seed bank, weed control efficacy, weed control costs, crop yield, and gross margin. All four phases emphasize the need for a weed management system that minimizes seed production in situations where a high soil weed seed bank exists. However, once the weed seed bank is reduced, a weed management program with less than 100% weed control can be employed without increasing the seed bank. Intensive weed management systems are seldom justified when economics, weed control, and weed seed bank are considered. Integrated weed management approaches require integrating crop and weed biology with management tactics. Integrated weed management requires advanced managerial skills and often more time than traditional approaches.

PALWEED:WHEAT is a bioeconomic decision model for determining profit-maximizing postemergence herbicide treatments for winter wheat in the Washington–Idaho Palouse region. PALWEED:WHEAT performed relatively well economically in 2 yr of on-farm field tests. However, the model was less sensitive than desired in prescribing postemergence broadleaved herbicides in the presence of high densities of broadleaved weed seedlings. Therefore, PALWEED:WHEAT was revised in response to the field testing. This paper compares the revised model's agronomic and economic performance to the original model in computer simulations. The revised model, PALWEED:WHEAT II, differs from the original model in several respects: (1) exponential functions replace linear functions in predicting weed survival, (2) preplant application of a nonselective herbicide is entered as an exogenous binary variable, (3) separate indices of broadleaved and grass competition are substituted for an aggregate weed competition index in the wheat yield function, (4) hyperbolic replaces logistic functional representation of weed damage to wheat yield, and (5) separate models are estimated for winter wheat after spring dry pea and for winter wheat in all examined crop rotation positions. In simulations including a variety of agronomic and economic conditions, PALWEED:WHEAT II recommended postemergence herbicide types and rates that consistently complied with agronomic and economic theory. Furthermore, the revised model, especially when estimated from the relevant wheat after pea data set, was markedly more balanced in recommending both broadleaved and grass herbicides in response to observed densities of both weed groups. The substantial change in herbicide recommendations in response to changes in model functional specifications following field testing confirms the importance of field testing and revision of bioeconomic decision models.

Studies were conducted at East Lansing, MI, in 1994 and 1995 to examine corn yield response to giant foxtail interference and to examine the effect of giant foxtail density on giant foxtail biomass, seed production, and seed germination. Treatments consisted of 0, 10, 30, 60, 84, and 98 giant foxtail plants m−1 of row in 1994 and 0, 10, 27, 30, 60, and 69 plants m−1 of row in 1995. The influence of giant foxtail density on corn yield fit a hyperbolic equation. Corn yields were reduced 13% in 1994 and 14% in 1995 from 10 giant foxtail plants m−1 of row. Corn dry matter at maturity was decreased 24 and 23% from 10 giant foxtail plants m−1 of row in 1994 and 1995, respectively. Giant foxtail seed production increased linearly as inflorescence length increased. The length of a single giant foxtail inflorescence increased as plant density increased and the number of inflorescence produced per plant decreased. Giant foxtail seed production ranged from 518 to 2,544 seeds per plant. Ten giant foxtail plants m−1 of row produced 15,700 seeds m−2. Giant foxtail seed germination was not affected by plant density.

WeedSOFT is a state-of-the-art decision support system for weed management in the north central region of the United States, but its accuracy to predict corn yield loss associated with later-emerging weed communities has not been adequately assessed. We conducted experiments in 2004 and 2005 to compare observed and predicted corn yield related to four establishment times of mixed-species weed communities for validation of competitive index modifier (CIM) values in WeedSOFT. Weed communities were established at VE, V2, V4, and V6 corn (emergence, second-leaf, fourth-leaf, and sixth-leaf stages, respectively), and consisted largely of annual grass and moderately competitive annual broadleaf species. Compared to weed-free corn, yield loss occurred in each of seven site-years for weed communities established at VE corn, but in only one site-year for communities established at V2 corn. No corn yield loss was associated with weed communities established at V4 or V6 corn. For communities established at VE corn, predicted corn yield differed from observed yield in all but one site year, with predicted yield less than observed yield in three site-years, and greater than observed yield in two site-years; however, nonlinear regression analyses of yield data pooled over site-years showed that fitted values were similar between predicted and observed yield. For communities established at V2 and V4 corn, predicted yield was less than observed yield in six and five site-years, respectively. For communities established at V6 corn, predicted yield was less than observed yield in three of six site-years, but was similar to observed yield in three of six site-years. These results indicated that the CIM values in WeedSOFT tended to overestimate the competitiveness of late-emerging weed communities. Corn yield data from a pooled analysis of all site-years were used to generate a revised set of growth stage CIM values, which improved the accuracy of predicted corn yield. These results should improve weed management decisions and reduce the need for herbicide applications to late-emerging weeds.

Potential crop yield loss due to early-season weed competition is an important risk associated with postemergence weed management programs. WeedSOFT is a weed management decision support system that has the potential to greatly reduce such risk. Previous research has shown that weed emergence time can greatly affect the accuracy of corn yield loss predictions by WeedSOFT, but our understanding of its predictive accuracy for soybean yield loss as affected by weed emergence time is limited. We conducted experiments at several sites across the Midwestern United States to assess accuracy of WeedSOFT predictions of soybean yield loss associated with mixed-species weed communities established at emergence (VE), cotyledon (VC), first-node (V1), or third-node (V3) soybean. Weed communities across research sites consisted mostly of annual grass species and moderately competitive annual broadleaf species. Soybean yield loss occurred in seven of nine site-years for weed communities established at VE soybean, four site-years for weed communities established at VC soybean, and one site-year for weed communities established at V1 soybean. No soybean yield loss was associated with weed communities established at the V3 stage. Nonlinear regression analyses of predicted and observed soybean yield data pooled over site-years showed that predicted yields were less than observed yields at all soybean growth stages, indicating overestimation of soybean yield loss. Pearson correlation analyses indicated that yield loss functions overestimated the competitive ability of high densities of giant and yellow foxtail with soybean, indicating that adjustments to competitive index values or yield loss function parameters for these species may improve soybean yield loss prediction accuracy and increase the usefulness of WeedSOFT as a weed management decision support system.

Variation in crop–weed interference relationships has been shown for a number of crop–weed mixtures and may have an important influence on weed management decision-making. Field experiments were conducted at seven locations over 2 yr to evaluate variation in common lambsquarters interference in field corn and whether a single set of model parameters could be used to estimate corn grain yield loss throughout the northcentral United States. Two coefficients (I and A) of a rectangular hyperbola were estimated for each data set using nonlinear regression analysis. The I coefficient represents corn yield loss as weed density approaches zero, and A represents maximum percent yield loss. Estimates of both coefficients varied between years at Wisconsin, and I varied between years at Michigan. When locations with similar sample variances were combined, estimates of both I and A varied. Common lambsquarters interference caused the greatest corn yield reduction in Michigan (100%) and had the least effect in Minnesota, Nebraska, and Indiana (0% yield loss). Variation in I and A parameters resulted in variation in estimates of a single-year economic threshold (0.32 to 4.17 plants m−1 of row). Results of this study fail to support the use of a common yield loss–weed density function for all locations.

Nascent research on the profitability of site-specific weed management has focused on reduced herbicide use, ignoring significant information costs for scouting, making treatment maps, and patch herbicide application. Including these information costs results in few, if any, studies, fully covering added costs with herbicide savings. Real-time, sensor-driven site-specific herbicide management promises to overcome many of the scouting and map-making costs so long as weed species recognition accuracy continues to improve and commercial capital costs to decline. Two means to increase revenues are possible from site-specific weed management: value-added crop products (e.g., herbicide-free, nontransgenic crops) and environmental stewardship payments from government programs. Site-specific ecological weed management offers a more fruitful means to garner both higher prices from “green” consumers and revenues for sound environmental stewardship. It entails using spatial tools to enhance habitat for weed predators and diseases and their vectors, as well as to grow crops that are better able to compete with weeds. The most economically promising areas for future weed management will draw upon both spatial and developmental management tools.

Field studies were conducted in 2000 and 2001 to evaluate corn yield-loss predictions generated by WeedSOFT, a computerized weed management decision aid. Conventional tillage practices were used to produce corn in 76-cm rows in Illinois, Indiana, Kansas, Michigan, Missouri, Nebraska, and Wisconsin. A total of 21 site-years from these seven states were evaluated in this study. At 4 wk after planting, weed densities and size, crop-growth stage, estimated weed-free yield, and environmental conditions at the time of application were entered into WeedSOFT to generate POST treatments ranked by percent maximum yield (PMY). POST treatments were chosen with yield losses ranging from 0 to 20%. Data were subjected to linear regression analysis by state and pooled over all states to determine the relationship between actual and predicted yield loss. A slope value equal to one implies perfect agreement between actual and predicted yield loss. Slope value estimates for Illinois and Missouri were equal to one. Actual yield losses were higher than the software predicted in Kansas and lower than predicted in Michigan, Nebraska, and Wisconsin. Slope value estimate from a data set containing all site years was equal to one. This research demonstrated that variability in yield-loss predictions occurred at sites that contained a high density of a single weed specie (>100/m2) regardless of its competitive index (CI); at sites with a predominant broadleaf weed with a CI greater than five, such as Palmer amaranth, giant ragweed, common sunflower, and common cocklebur; and at sites that experience moderate to severe drought stress.

GAMEFISTO simulation model is presented as a tool to improve the small scale fisheries bioeconomic simulation techniques. The main novelty of the current model is the implementation of game theoretic techniques for forecasting the fishing effort trends and consequently, the fish population levels and the economic outcome, including landings, income and net profits. The model assigns individual fishing strategies to individual vessels according to their technical characteristics. The fishermen within a fishing fleet exploiting a single stock are assumed to be the decision agents, who share not only a fish population (modelled through stock externality) but also a market, through an offer-demand function (market externality). Mediterranean fisheries need to be analysed at vessel level due to the heterogeneity of its fishing fleets. A ten year simulation on red shrimp, Aristeus antennatus, is presented as an application of the presented GAMEFISTO model.

In a multi-gear and multi-species artisanal fishery, the level of technical interactions (i.e. the competitive externalities resulting from a shared exploitation of common resources or fishing grounds) among various fishing units is high. Assessing these technical interactions is of great importance for fishery management, as any control applied to one fishing unit may have positive or negative effects on others. The magnitude and direction of these effects cannot be easily measured, unless all fishing units and species in the fishery are considered simultaneously. Technical interactions are particularly important in the complex artisanal fisheries of the English Channel. Using a bioeconomic model of the English Channel that incorporates all the major fishing units (the BECHAMEL model), we describe a method for measuring and classifying the technical interactions due to the competition for resource (stock externalities). The results are used to develop a typology of métiers and fleets based on their overall level of interaction for the resource. We also define fleets and métiers as structuring, dependent, intermediate or autonomous.