Interference within a mixture of equal densities of common lambsquarters (Chenopodium album L.), kochia [Kochia scoparia (L.) Schrad.], and redroot pigweed (Amaranthus retroflexus L.) in sugarbeets (Beta vulgaris L. ‘Mono Hy D2’) was determined in a 3-yr field study. Yield of sugarbeet roots and sucrose per hectare decreased as intensity of competition from equal populations of these three weeds increased. At densities of 6, 12, 18, and 24 broadleaf weeds per 30 m of row, root yields were reduced 13, 24, 33, and 39%, respectively. Sucrose yields were reduced similarly. Fewer than three weeds per 30 m of row did not significantly reduce root yield. Reduction in root yield (Y) of sugarbeets caused by specific densities (X) of the three broadleaf weeds was predicted by using the linear equation Y = 1.64 + 1.88 X. The actual reductions in yield were always less than the predicted reductions when this equation was tested against 36 weed control systems because the competitive ability of broadleaf weeds that were treated with herbicides, but not killed, was suppressed during the growing season. By harvest, broadleaf weeds present in weed-control-system plots weighed an average of 75 to 85% less than broadleaf weeds present in nontreated plots.

Field studies were conducted in 1986, 1987, 1988, and 1989 to determine the stability of crop loss functions across site by year environments. Environment was a significant source of variation for the soybean crop loss function as influenced by velvetleaf, but not as influenced by tall waterhemp and common sunflower. Weed density was a highly significant source of variation for all weed species studied. Regressions between percent soybean seed yield reductions and weed populations were linear. The velvetleaf interference regression equations were divided into two groups, those with high soybean-yielding intercepts and those with low-yielding intercepts, to explain the variance observed across environments. The regression equation for the high-yielding intercept group was Ŷ = 4.24X while the low-yielding group was Ŷ = 2.14X, where Y is percent soybean yield reduction and X is weed density per 10.7 m of soybean row. Tall waterhemp and common sunflower regression equations were determined to be Ŷ = 1.37X and Ŷ = 6.52X, respectively. Confidence intervals were used to account for the variance associated with the mean regression equation for each model and to develop economic threshold models that include risk aversion principles.

Field experiments were conducted in 1979, 1980, and 1981 to determine the level of interference five broadleaf weed species exert on soybean [Glycine max (L.) Merr.]. Weed species studied were common cocklebur (Xanthium pensylvanicum Wallr. ♯ XANST), common ragweed (Ambrosia artemesiifolia L. ♯ AMBEL), common lambsquarters (Chenopodium album L. ♯ CHEAL), sicklepod (Cassia obtusifolia L. ♯ CASOB), and redroot pigweed (Amaranthus retroflexus L. ♯ AMARE). The following soybean seed yield reductions were observed with a density of 16 weeds/10-m row: redroot pigweed 22%, common lambsquarters 15%, common ragweed 12%, and sicklepod 5%, respectively. At a density of 8 weeds/10-m row, common cocklebur reduced soybean yield 11%. No single weed growth parameter predicted soybean seed yield reduction for all weed species. Soybean height was reduced by sicklepod competition; was not affected by competition from common lambsquarters, common ragweed, or common cocklebur; and was increased in the presence of redroot pigweed at 12 weeks after planting, when measured 30 cm from the weeds. Leaf area of soybean was higher at greater distances from the weed for all weed species. The range of soybean leaf area reductions occasioned by proximity to individual weed species corresponded fairly well with differences in soybean yield reduction.

The number of computer applications for purposes of weed control decisions has increased dramatically in the past eight yr. During this time, many efficacy-, and population-based weed control decision aids have been developed. The complexity of decisions range from those based on optimal weed control (independent of net profitability) to those predicting weed population effects before most profitable treatments are selected. While the number of software applications has increased sharply, national software databases have not been funded and tracking has been made difficult. The purpose of this paper is to review the criteria upon which efficacy- and population-based weed control decision aids are founded, and to identify software currently available in each of the two categories.

Although economic thresholds are often used to make insect control decisions, the use of thresholds for weed management has been limited. Surveys of growers, agricultural chemical dealers, and farm managers/rural appraisers helped identify limitations to grower acceptance of economic thresholds for weed management. Most growers were concerned about harvest problems due to weeds, with 64% identifying this factor as a major limitation. Landlord perception and weed seed production were identified by 38% of the growers as major limitations, while 36% of the growers listed general appearance of the field as reasons. In contrast, 75% of the agricultural chemical dealers and 63% of the farm managers surveyed listed field appearance as a major reason limiting grower acceptance of economic thresholds for weed management. Since grower concerns involve risk management and future profitability, economic weed thresholds that address long-term costs and benefits of weed control decisions may be more fully accepted.

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.

A decision criterion is presented for postemergence johnsongrass control in soybeans. Models derived from actual data describing the relationship between johnsongrass density (X) and soybean yield loss are used to develop the decision criterion for soybeans planted in 0.25- and 1-m row spacings. The yield loss equations are L1 = 424–424 exp (-0.023X2) for 0.25-m row spacing and L2 = 643-643 exp (−0.004X2) for 1-m row spacing. When combined with the cost of control (Px) and the expected price of soybeans (Py), these equations can be used to find the density thresholds where johnsongrass control becomes profitable. For the 0.25-m row spacing, this relationship was Px = Py [424–424 exp (−0.023X2) or more simply Px = Py L1, which equates the cost of control to the value of the soybean yield increase from control. The johnsongrass density (X) that satisfies this equality is the threshold (X*) above which control is profitable.

Barnyardgrass at 10 or more plants per 1 m of row caused over 80% root yield loss in spring-sown sugarbeets in California. The yield loss decreased to between 5 and 20% when barnyardgrass densities were one plant ever 2 to 3 m of crop row. The relationship between crop yield and barnyardgrass density was described by a rectangular hyperbolic function. The economic threshold density was in the range of one barnyardgrass plant every 5 to 20 m or row. Barnyardgrass plants growing at densities of less than one plant per 1 m in sugarbeets experienced only interspecific competition. Such individual barnyardgrass plants were estimated to produce from 30 000 to over 200 000 seeds, with an average of 100 000 seeds per plant. This seed production equated to a seed rain of about 4000 to nearly 20 000 seeds per m from a density of one weed every 10 m of crop row. These data suggest that using single-year economic thresholds for barnyardgrass is not a sound management practice because even low densities can produce large quantities of seed.

Studies were conducted to determine the effect of interference between ladysthumb and cotton on plant growth and productivity. Ladysthumb remained shorter than cotton until at least 70 d after cotton planting. However, ladysthumb grew over twice as tall as cotton and, depending on plant density, produced between 179 and 681 g dry biomass per plant by cotton harvest. Ladysthumb biomass per plant was not affected by weed density when grown with cotton. When grown alone, ladysthumb produced over 2,000 g dry biomass per plant, which was over four times greater than biomass produced by plants grown with cotton. Cotton lint yield decreased between 0.7 and 0.9 kg ha−1 with each gram increase in weed dry biomass per meter of the row. The relationship between ladysthumb density and cotton percent yield loss was described by the rectangular hyperbola model with the asymptote (coefficient a) constrained to 100% maximum yield loss. The estimated coefficient i (yield loss per unit density as density approaches zero) was 35 ± 5 and 14 ± 2 in 1998 and 2000, respectively. Ladysthumb seed production was also described by the hyperbolic function. Estimated seed production at 1 plant m−1 of cotton row was 33,000 and 47,000 seed m−2 in 1998 and 2000, respectively.

A perceived limitation to incorporating herbicide application decision support system (HADSS) into routine peanut weed management decisions is efficient scouting of fields. A total of 52 peanut fields were scouted from 1997 through 2001 in North Carolina to determine the weed density in a 9.3-m2 section for each 0.4-ha grid of the field. These weed populations and their spatial distributions were used to compare theoretical net return (TNR) over herbicide investment for various scouting methods and weed management approaches. HADSS was used to determine the expected net return for each treatment in each 0.4-ha section of every field under differing assumptions of weed size, soil moisture conditions, and pricing structures. The treatment with the highest net return averaged across all 0.4-ha grids was considered to be the optimal whole-field treatment. For all 52 fields, TNR for the best whole-field treatment and for site-specific weed management (applying the most economical recommendation on each 0.4-ha grid) averaged $414 and $435/ha, respectively. Estimated return from the commercial postemergence herbicide program of aciflurofen plus bentazon plus 2,4-DB followed by clethodim (where grass was present) averaged $316/ha across all 52 fields. For fields of 5 ha or more (17 fields) in which 12 or more samples were taken, TNR was $500, $510, and $516/ha for three-sample (one pass through the middle of the field with samples taken on both ends and the center of the field), six-sample (two passes through the field with three stops per pass), and full-sample (one stop for each 0.4 ha) approaches, respectively.

Studies were conducted to determine the effect of interference between tropic croton (Croton glandulosus) and cotton (Gossypium hirsutum) on plant growth and productivity. Tropic croton height was not affected by weed density, but cotton height decreased with increased weed density 10 wk after planting. Tropic croton biomass per plant was not affected by weed density, but total weed biomass per meter of crop row increased with weed density. Cotton lint yield decreased linearly 2 kg ha−1 with each gram increase in weed dry biomass per meter of row. Percent yield loss–density relationship was described by the rectangular hyperbola model. Estimated coefficients A (maximum yield loss) and I (yield loss per unit density as density approaches zero) were 129.6 ± 42.2 and 35.6 ± 8.0%, respectively, when asymptotic iterations were based on least sums of squares. When A was constrained to 100% yield loss, I was 42.5 ± 5.1%. Results indicated that tropic croton was less competitive with cotton than many weeds but represents an economic threat to cotton growers.

Velvetleaf has been a major concern of Southern cotton growers, yet information on its competitiveness and seed production in cotton is lacking. Experiments were conducted in 1997 and 1998 at the Central Crops Research Station in Clayton, NC, to evaluate density-dependent effects of velvetleaf interference and seed production dynamics in conventional tillage cotton. Velvetleaf at densities of up to 3.5 plants m−1 of row did not influence cotton height until at least 4 wk after planting. Velvetleaf height increased as plant density increased throughout the season in 1997, but it was not affected until 9 wk after planting in 1998. Because of differences in environmental conditions, velvetleaf and cotton achieved maximum height later in 1998 than in 1997; however, velvetleaf seed production and cotton yields were higher in 1998 than in 1997 regardless of velvetleaf density. Velvetleaf density had no effect on the fresh weight, dry weight, and stem diameter of velvetleaf plants in 1997. But in 1998, all these parameters decreased significantly with increasing velvetleaf density. Velvetleaf seed production in 1998 was nearly twice that in 1997. Averaged over velvetleaf densities, the greatest number of seed were produced between nodes 6 and 20 in 1997 and between nodes 1 and 10 in 1998. In both years, cotton yield loss increased with velvetleaf density. Maximum yield loss was 84% at 3.5 velvetleaf plants m−1 of row. Yield losses of 5 and 10% were caused by 0.2 and 0.4 velvetleaf plants m−1 of row (1,930 and 4,110 plants ha−1), respectively, in 1997 and by 0.03 and 0.08 velvetleaf plants m−1 of row (360 and 850 plants ha−1), respectively, in 1998. To understand better the applicability of these results, we hypothesized that environmental variation caused differences in measured responses between 1997 and 1998. Therefore, kriging methods were used to fit correlations between observed rainfall and growing degree days (GDD) each year at the experiment site. Results based on climate data from 4 yr at 110 sites indicated that inference space was higher for GDD than for rainfall. The conditions observed at the experiment site in 1997 were deemed most appropriate for the recommendations made in the surrounding area.

Experiments were conducted in 1998 and 1999 at the Central Crops Research Station near Clayton, NC, to evaluate density-dependent effects of Datura stramonium on weed growth and seed rain and Gossypium hirsutum growth and yield. Datura stramonium height was not affected by density in either year. Crop height never exceeded weed height during the growing season, indicating that competition for light occurred between the two species. Eight weeks after planting or later, G. hirsutum height decreased as D. stramonium density increased. An increase in D. stramonium density from 1 to 32 plants (9.1 m of row)−1 resulted in a decrease in capsule production per plant of 92 and 60 in 1998 and 1999, respectively. Total D. stramonium dry weight per 9.1 m of row increased via a quadratic relationship as weed density increased. Gossypium hirsutum lint yields decreased as D. stramonium biomass and density increased in both years. Estimated yield losses of 10 and 25% were caused by D. stramonium at 0.5 and 1.5 plants (9.1 m of row)−1 (572 and 1,716 plants ha−1), respectively, in 1998 and 0.6 and 1.8 plants (9.1 m of row)−1 (690 and 2,060 plants ha−1), respectively, in 1999.

Studies were conducted to determine the effect of interference between Pennsylvania smartweed and cotton on plant growth and productivity. Pennsylvania smartweed remained shorter than cotton until at least 80 d after cotton planting. However, Pennsylvania smartweed produced considerable dry biomass by cotton harvest. Pennsylvania smartweed biomass per plant was not affected by weed density when grown with cotton. When grown alone, Pennsylvania smartweed produced 1,640 and 2,060 g dry biomass plant−1 depending on the year. This biomass was over four times greater than the biomass produced by plants grown with cotton. Cotton lint yield decreased between 1.3 and 1.1 kg ha−1 with each gram increase in weed dry biomass per meter of row. The relationship between Pennsylvania smartweed density and cotton percent yield loss was described by the hyperbolic function. The estimated coefficients a (maximum yield loss as density approaches infinity) and i (yield loss per unit density as density approaches zero) were 102 ± 23 and 51 ± 12, respectively, in 1998 and 53 ± 1 and 98 ± 5, respectively, in 2000. Pennsylvania smartweed achene production was also described by the hyperbolic function. Estimated achene production at 1 plant m−1 cotton row was 18,000 and 26,000 achenes m−2 in 1998 and 2000, respectively.

Field studies were conducted at two North Carolina locations to determine the effect of interference between pale smartweed and cotton on plant growth and productivity. Pale smartweed remained shorter than cotton until at least 70 d after cotton planting. However, pale smartweed grew over twice as tall as cotton and produced considerable dry biomass by cotton harvest. Pale smartweed biomass per plant was not affected by weed density up to 3.5 plants m−1 of row when grown with cotton. Cotton competition reduced pale smartweed dry biomass per plant at least 400%. The relationship between pale smartweed and cotton percent yield loss was described by the rectangular hyperbola model with the asymptote (coefficient a) constrained to 100% maximum yield loss. The estimated coefficient i (yield loss per unit density as density approaches zero) was 29 ± 4 and 23 ± 4 in 1998 and 2000, respectively. Pale smartweed achene production was also described by the hyperbolic function. Estimated achene production of smartweed at 1 plant m−1 cotton row was 63,000 and 25,000 achenes m−2 in 1998 and 2000, respectively.

Studies were conducted to evaluate density-dependent effects of common ragweed on weed growth and peanut growth and yield. Common ragweed height was not affected by weed density and peanut canopy diameter. Weed height exceeded peanut height throughout the growing season, indicating that competition for light occurred between the two species. Common ragweed aboveground dry biomass per plant decreased as weed density increased, but total weed dry biomass per meter of crop row increased with weed density. The rectangular hyperbola model described the effect of weed density on percent peanut yield loss. With the asymptote constrained to 100% maximum yield loss, the I coefficient (yield loss per unit density as density approaches zero) was 68.3 ± 12.2%. Common ragweed did not influence the occurrence of tomato spotted wilt virus, early leaf spot (Cercospora arachidicola), southern stem rot (Sclerotium rolfsii), and Cylindrocladium black rot (Cylindrocladium crotalariae). However, as common ragweed density increased, the incidence of late leaf spot (Cercosporidium personatum) increased. Results indicate that common ragweed is one of the more competitive weeds in peanut and a potential economic threat to peanut growers.

An economic threshold of agricultural pest management is derived. Results provide a method for researchers to use in making improved pest control recommendations to farmers without farm level decisionmaking. An empirical illustration for lesion nematode management in irrigated corn is given and directions for further research are indicated.

The rice stink bug is a major pest of rice in Texas, causing quality related damage. The previous thresholds used for assisting in rice stink bug spray decisions lacked flexibility in economic and production decision variables and neglected the dynamics of the pest population. Using stochastic dynamic programming, flexible economic thresholds for the rice stink bug were generated. The new thresholds offer several advantages over the old, static thresholds, including increased net returns, incorporation of pest dynamics, user flexibility, ease of implementation, and a systematic process for updating.

Introduction. In the southern Sudanian zone of Benin, fruit flies are major pests and responsible for considerable yield losses in mango production. The two main species of economic importance affecting mango trees in Benin are Ceratitis cosyra and Bactrocera invadens. During the 2006 crop year and across seven main cultivars, losses stood at 17% in early April and exceeded 70% in mid-June. By the middle of the crop year, over 50% of losses occurred in mango orchards. Materials and methods. The Stone and Pedigo method was used to compute the economic injury level, using variables such as pest control costs, level of losses and mango prices. Results and discussion. The Economic Injury Level (EIL) was highly variable. For the cultivar Kent, for example, it varies from (30 to 75) fruit flies captured per ha and per week, whereas for Keitt the level ranges from (24 to 57) fruit flies·ha–1·week–1. EIL variability depends among other things on market price fluctuation. This method is based on the early introduction of detection traps in mango orchards. A weekly monitoring of trapped fruit flies is used to compute the EIL and make a decision about pest control. Pest control (GF-120 in this case) is recommended if the gross returns exceed production costs. If the number of trapped fruit flies is below the EIL, a treatment is not recommended because pest control will not be profitable. Conclusion. The EIL is an accurate tool to set the optimal (economic) timing of pest control treatment in mango orchards. These preliminary results should lead to the introduction of integrated pest management (IPM) activities to deal with the fruit fly species that are threatening the profitability of mango production in central and northern Benin.