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.

Field studies were conducted in 1985 and 1986 to evaluate the stability of reciprocal interference relationships between common cocklebur and soybean under high and low soil moisture conditions. A significant soil moisture differential was established with portable rain exclusion shelters. Well-watered and drought-stressed common cocklebur reduced soybean yield 29 and 12%, respectively. Drought-stressed common cocklebur interfered with soybean over a shorter distance and the magnitude of the effect at a given distance was reduced. The reduced common cocklebur interference in drier soils was attributed to both common cocklebur and soybean growth responses to moisture stress. First, moisture stress caused greater reductions in common cocklebur canopy diameter, stem diameter, node number, and plant height than in soybean. Second, the soybean yield potential was reduced by moisture stress. The reduction in yield potential decreased the effect of the weed interference. Third, soybean canopy development was slowed, and canopy closure that occurred in about 12 weeks in well-watered soybeans never occurred in the moisture-stressed soybeans. This reduced the degree of light interference between both the common cocklebur and soybean and among the soybean plants. The results of this study indicate that the reciprocal interference relationships between common cocklebur and soybean are not stable across soil moisture conditions. The implications of unstable competitive parameters must be considered as threshold models are developed for various field crops.

Field experiments were conducted to determine the influence of time of emergence and density of single and multispecies populations of pigweed and barnyardgrass on soybean yield and competitive abilities of pigweed and barnyardgrass. Pigweed and barnyardgrass were established at selected densities within 12.5 cm on either side of the soybean row. Pigweed and barnyardgrass seeds were sown concurrently with soybean and at the cotyledon stage of soybean growth. Time and density of pigweed and barnyardgrass seedling emergence relative to soybean influenced the magnitude of soybean yield loss. Maximum soybean yield loss ranged from 32 to 99%, depending upon time of emergence relative to soybean. Pigweed was more competitive than barnyardgrass across all locations, years, and time of weed emergence. When pigweed was assigned a competitive index of 1 on a scale from 0 to 1, the competitive ability of barnyardgrass ranged from 0.075 to 0.40 of pigweed, depending upon location and time of emergence. This is the first multiple weed species study to include time of weed emergence relative to the crop. Competitive index values for multiple weed species must be calculated from field experiments in which weeds are grown with the crop under differing environmental conditions.

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.

WeedSOFT® is a decision support system that was developed to help farmers and consultants in Nebraska with the selection of optimal weed management strategies. WeedSOFT® evolved from HERB, a bioeconomic model for soybean that was developed in North Carolina. The program is composed of four independent modules, namely, ADVISOR, EnviroFX, MapVIEW, and WeedVIEW. ADVISOR helps the user select a treatment based on maximum yield or maximum net gain. EnviroFX and MapVIEW provide environmentally relevant herbicide information and county soil maps that indicate vulnerability to groundwater contamination. WeedVIEW is a visual library of color images and line drawings of 46 common weed species. Over 500 farmers and consultants in Nebraska and adjacent states use WeedSOFT®. As a result of the current regionalization effort, the user base is expected to increase rapidly during the next 2 or 3 yr. This article explains the algorithms implemented in the current version of WeedSOFT®.

Multiple weed species in the field combine to cause yield losses and can be described using one of several empirical models. Field studies were conducted to compare observed corn yield loss caused by common sunflower and shattercane populations with predicted yield losses modeled using a multiple species rectangular hyperbola model, an additive model, or the yield loss model in the decision support system, WeedSOFT, and to derive competitive indices for common sunflower and shattercane. Common sunflower and shattercane emerged with corn and selected densities established in field experiments at Scandia and Rossville, KS, between 2000 and 2002. The multiple species rectangular hyperbola model fit pooled data from three of five location–years with a predicted maximum corn yield loss of 60%. Initial slope parameter estimate for common sunflower was 49.2 and 4.2% for shattercane. A ratio of these estimates indicated that common sunflower was 11 times more competitive than shattercane. When common sunflower was assigned a competitive index (CI) value of 10, shattercane CI was 0.9. Predicted yield losses modeled for separate common sunflower or shattercane populations were additive when compared with observed yield losses caused by low-density mixed populations of common sunflower (0 to 0.5 plants m−2) and shattercane (0 to 4 plants m−2). However, a ratio of estimates of these models indicated that common sunflower was only four times as competitive as shattercane, with a CI of 2.5 for shattercane. The yield loss model in WeedSOFT underpredicted the same corn losses by 7.5%. Clearly, both the CI for shattercane and the yield loss model in WeedSOFT need to be reevaluated, and the multiple species rectangular hyperbola model is proposed.

Weed competitiveness can be quantified with the concept of competitive index (CI), a relative scale of weed competitiveness. Field studies were conducted in 2002 and 2003 in northeastern and southeastern Nebraska to evaluate the influence of soybean row spacing and relative weed emergence time on the competitiveness of major weed species in soybean. Ten weed species were seeded in soybean spaced 19 and 76 cm apart at the planting, emergence, and first trifoliate leaf stages of soybean. Total weed dry matter (TDM), weed plant volume, and percent soybean yield loss were arbitrarily selected as a base for determining the CI for each weed species. Soybean yield loss was the least variable parameter used to quantify weed competitiveness and rank their CIs. In general, weeds grown with soybean planted in 19-cm rows produced less TDM, plant volume, and reduced soybean yield less than weed species grown in 76-cm rows. Later-emerging weeds produced less TDM, plant volume, and reduced soybean yield less than the early-emerging ones. In general, broadleaf species were more competitive than grass weed species. Common sunflower was the most competitive weed species in this study.