The WEEDSIM bioeconomic model was developed in Minnesota and was designed to support weed management decisions for both soil-applied and postemergence weed control programs in Zea mays and Glycine max. In this research, we adapted the WEEDSIM weed management model to Michigan by modifying the crop yield loss functions and herbicide efficacy ratings. We then validated the components of the model and determined whether WEEDSIM led to more profitable weed management than recommendations from SOYHERB or CORNHERB, computer decision aids based solely on herbicide efficacy and cost. The crop year significantly influenced the weed-free yield in Z. mays and G. max, but the weed—crop interference function did not change each year. Total weed seed increased in the untreated compared with the weed-free control over the 3-yr period. Weed seed did not increase significantly in WEEDSIM preemergence/postemergence (PRE/POST), WEEDSIM postemergence, or CORNHERB or SOYHERB treatments compared with the weed-free control, although annual grass seedling density at the time of postemergence herbicide application had increased by 1995 in the WEEDSIM postemergence treatment in G. max because of a 2,4-D amine application only in Z. mays in 1994. WEEDSIM PRE/POST and CORNHERB provided excellent weed control in all three years, and WEEDSIM PRE/POST resulted in gross margins over weed control costs equal to or greater than CORNHERB recommendations. In G. max, Chenopodium album and annual grass control was excellent in all three years for WEEDSIM PRE/POST, WEEDSIM postemergence, and SOYHERB treatments. The highest average gross margin for the 3-yr study was from mechanical weed control in 76-cm-wide rows of G. max ($806 ha−1) and from SOYHERB in 38- and 19-cm-wide rows of G. max ($776 and $808 ha−1, respectively). WEEDSIM recommendations controlled weeds and maintained crop yield in both Z. mays and G. max.

WEEDSIM is a bioeconomic decision aid for management of annual weeds in corn and soybean. It was field-tested for 4 yr in Minnesota. The decision aid has two categories of management recommendations: soil-applied plus postemergence (PRE+), based on estimated weed seedbank composition and density; and postemergence (POST), based upon observed weed seedling composition and density. Weed densities, weed control, herbicide use, environmental impact of herbicide use, weed management costs, crop yields, and economic returns that resulted from PRE+ and POST recommendations were compared to those associated with herbicide management systems (HERB) that were standard for the region. After 4 yr of applying WEEDSIM recommendations to the same plots, there were no increases in annual weed densities (seedbanks, seedlings, established plants, or seed production) or decreases in weed control or crop (soybean, rotation corn, and continuous corn) yields, compared to HERB. WEEDSIM recommendations resulted in average annual herbicide applications of 1.1 kg ai ha−1 for PRE+ and 1.0 kg ai ha−1 for POST, compared to 3.5 kg ai ha−1 for HERB. Environmental impact indices associated with PRE+, POST, and HERB were 0.75, 0.71, and 0.54, with the lowest value indicating greater environmental risk than the two higher values. Similarly, average weed management costs were $24, $33, and $77 ha−1 for PRE+, POST, and HERB, respectively. Based on crop prices of $94 Mg−1 for corn and $220 Mg-1 for soybean, the average gross margins over weed control costs were higher for PRE+ ($509 ha−1) and POST ($522 ha−1) than for HERB ($455 ha−1). In general, WEEDSIM appeared to make management recommendations that adequately controlled weeds, maintained crop yields, reduced herbicide use, decreased environmental risk, lowered weed management costs, and increased gross margins over weed control costs compared to the use of herbicides standard for the region.

A bioeconomic weed management model was tested as a decision aid for weed control in corn at Rosemount, MN, from 1991 to 1994. The model makes recommendations for preemergence control tactics based on the weed seed content of the soil and postemergence decisions based on weed seedling densities. Weed control, corn yield, herbicide active ingredient applied, and economic return with model-generated treatments were compared to standard herbicide and mechanical control treatments. Effects of these treatments on weed populations and soybean yield the following year were also determined. In most cases, the model-generated treatments controlled weeds as well as the standard herbicide treatment. The quantity of herbicide active ingredient applied decreased 27% with the seed bank model and 68% with the seedling model relative to the standard herbicide treatment. However, the frequency of herbicide application was not reduced. In 1 yr, seed bank model treatments did not control weeds as well as the standard herbicide or seedling model treatments. Corn yields reflected differences in weed control. Net economic return to weed control was not increased by using model-generated control recommendations. Weed control treatments the previous year affected weed density in the following soybean crop. In 2 of 3 yr, these differences did not after weed control or soybean yield. Although tactics differed, the bioeconomic model generally resulted in weed control and corn yield similar to the standard herbicide. The model was responsive to differing weed populations, but did not greatly after economic returns under the weed species and densities in this research.

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.

A bioeconomic model was tested as a decision aid for weed control in soybean at Rosemount, MN, from 1991 to 1994. The model makes recommendations for preplant incorporated and preemergence control tactics based on the weed seed content of the soil and postemergence decisions based on weed seedling densities. Weed control, soybean yield, herbicide use, and economic return with model-generated treatments were compared to standard herbicide and mechanical control systems. Effects of these treatments on weed populations and corn yield the following year were also determined. In most cases, the model-generated treatments controlled weeds as well as a standard herbicide treatment. Averaged over the 3 yr, the quantity of herbicide active ingredient applied was decreased by 47% with the seedbank model and 93% with the seedling model compared with a standard soil-applied herbicide treatment. However, the frequency of herbicide application was not reduced. Soybean yields reflected differences in weed control and crop injury. Net economic return to weed control was increased 50% of the time using model-recommended treatments compared with a standard herbicide treatment. Weed control treatments the previous year affected weed density in the following corn crop but had little effect on weed control or corn yield. The bioeconomic model was responsive to differing weed populations, maintained weed control and soybean yield and often increased economic returns under the weed species and densities in this research.

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.

Weed management decision aids have proliferated in recent years, but none of them have been rigorously compared with actual farmer weed management on farm fields. This research compares the Michigan WEEDSIM/GWM bioeconomic model and the CORNHERB and SOYHERB herbicide selection models with farmer weed management in Michigan. In 19 site-years of research in corn and soybean during 1996 to 1997, we found that crop yield, weed control costs, and gross margin over weed control costs (profitability) with the computerized decision aids were not statistically superior to the farmer treatments, even at a one-sided threshold of P = 0.10. In corn the gross margin of the farmer treatment ranked highest in both years. In soybean the gross margin of the SOYHERB treatment ranked highest in 1996 and that of the WEEDSIM/GWM treatment was highest in 1997. Overall, the farmer treatment had the highest gross margin 5½ times, the CORNHERB–SOYHERB treatment 6 times, and the WEEDSIM/GWM treatment 7½ times (where ties were divided equally to give 1/2 to each). However, none of these rank differences corresponded to a statistically significant gross margin gain over the farmer treatment.

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.

Policies to reduce nitrate leaching are evaluated using a mixed integer linear programming model of a representative Michigan cash grain farm. At spring 1993 prices, elimination of the current deficiency payment program is found to be more efficient at reducing leaching than a nitrogen input tax, a tax credit on biologically fixed nitrogen, a rotation payment, or obligatory use of the Integrated Farm Management Program Option (IFMPO). However, elimination of the deficiency payment program would significantly reduce farm income. Modeling risk management and nitrate leaching dynamics are useful extensions of this research, as is estimating the benefits from averting nitrate leaching.

A long term whole farm analysis comparing conventional and low-input farming systems is reviewed. A computational error led to the mistaken conclusion that conventional farming with government programs is less preferred by risk-averse farmers than the low input alternative. The greater income variance of conventional agriculture need not make it less preferred provided a higher mean income sufficiently offsets the higher variance.

New crop production technologies developed in response to growing concern over environmental contamination from agriculture may be neither more profitable nor higher yielding than the systems they replace, but they often reduce environmental contamination or improve soil and water quality. Systems designed with environmental objectives cannot be evaluated fairly just by productivity, which is what often is done in economic studies of alternative systems. We review 58 recent studies comparing alternative crop production systems to identify the key criteria for system comparisons, the system characteristics important in designing the analysis, and the methods most suited for comparing alternative systems.

The four key criteria we looked for in system comparisons are expected profit, stability of profits, expected environmental impacts, and stability of environmental impacts. Most economic studies of crop production focus exclusively on profitability, and incorporate neither environmental criteria nor the dynamic characteristics inherent in alternative systems. We identify promising new approaches that take account of specific environmental characteristics and attempt to balance the objectives of profitability and environmental risk management. Balanced environmental-economic analysis is most likely to be achieved by integrating biophysical simulation models with economic optimization methods to model the trade-offs among profitability, environmental impact, and system stability (both financial and environmental).

Sustainable agricultural research and education have gained acceptability within the land-grant system, but they still must be fully integrated into its fabric. Challenges remain in three key areas: knowledge generation, research and education, and funding. New biological and ecological knowledge is needed on plant-animal-human-environment interactions from the microbial level on upward so that we can move beyond anecdotal evidence of biological integration efficiencies to scientific understanding of the underlying processes and opportunities for human intervention. Socioeconomic research must address human motivations to change farming methods and the likely impacts of these changes on farmers, consumers, other species, and the quality of the environment.

Generating this knowledge will affect the integration of research and education. Having farmers set the research and outreach agenda dissolves the old distinction between research and extension. This situation is complicated by budgetary stress and uncertainty about the dividing line between public and private responsibilities. The funding of sustainableagriculture creates a dilemma. Earmarked funding has helped legitimize sustainable agriculture in the land-grant university, but if it fails to become integrated into the routine land-grant research and education agenda, it will lose its newly gained momentum in the event those funds disappear.