Data were tested for homogeneity of variance prior to statistical analysis by plotting residuals. Analysis of variance (ANOVA) was performed on peanut yield loss, jimsonweed dry biomass, jimsonweed seed weight and number, and jimsonweed pod weight. Linear, quadratic, and higher-order effects were tested by partitioning sums of squares (Draper and Smith, 1981). Location was considered a random variable, and the weed-density main effects were tested by the error associated with the appropriate location by weed-density interaction (McIntosh, 1983). Significant effects were explained using appropriate regression. Nonlinear models were used if ANOVA indicated that higher-order polynomial effects of jimsonweed density were more significant than linear or quadratic effects. Iterations were performed to determine parameter estimates with least sums of squares for all nonlinear models using the Gauss-Newton method via PRO NLIN in SAS ([SAS] 1998).

Plant height was measured at different time intervals after planting each location. Therefore, the Gompertz equation (Equation 1, Draper and Smith, 1981) was fitted to plant height of each species in each plot: 1 Where Y_h is plant height in centimeters, A is the upper asymptote for late-season plant height, B and K are constants, e is the base of natural logarithms, and T is time in weeks after planting. Multivariate analysis of variance was conducted on the three estimated parameters for each fitted curve to test for location, weed-density, and location by weed-density effects.

The relationship between jimsonweed density per meter of row and percent peanut yield loss was fitted to the rectangular hyperbola (Equation 2) (Cousens, 1998). 2 Yield loss (YL) is based on percent reduction of weed-free yield. A is the asymptote for yield loss and was constrained to 100%. D is the density per meter of crop row and I is the yield loss per weed as weed density approaches zero.

Coefficients of determination (R ²) were calculated for all regressions. For linear equations, R ² values were calculated as 100 times the ratio of regression sums of squares to corrected total sums of squares (Askew et al., 2001; Draper and Smith, 1981). Where a nonlinear equation was fitted to the data, an approximate R ² value, was calculated by other researchers (Askew et al., 2001; Draper and Smith, 1981; Jasieniuk et al., 1999), was obtained by subtracting the ratio of residual sums of squares to corrected total sums of squares from Equation 1. The R ² and residual mean squares were used to determine goodness of fit to nonlinear models.

Peanut diameter, measured as canopy row width at four random locations, increased over time in plots with low jimsonweed density. However, in the two highest density jimsonweed plots, peanut diameters were less than 25 cm. Average peanut diameter at the lower jimsonweed densities was near 85 cm (93% of row width) at harvest. Thus, peanut diameter does not seem a reliable predictor of peanut productivity or jimsonweed interference.

Analysis of variance on estimated parameters of the Gompertz equation (Equation 1) (Draper and Smith, 1981) indicated that jimsonweed and peanut heights were both significantly affected by jimsonweed density while location affects were not significant. Data were therefore pooled to explain the effect of jimsonweed density on jimsonweed and peanut heights (Figure 1). Furthermore, trends in plant height over time indicated that maximum jimsonweed density effects occurred at 11 weeks after planting (WAP). Peanut height increased from 44 to 57 cm as jimsonweed density increased from 0 to 5.25 plants/m of row (Figure 1). Likewise, jimsonweed heights increased from 97 to 139 cm as jimsonweed density increased from 0 to 5.25 plants/m of row.

There was no location effect for jimsonweed plant and seedpod weights; therefore, data were combined over locations. Jimsonweed plant and seedpod weights were significantly influenced by jimsonweed density (Figure 2). As jimsonweed density increased, plant and pod weight decreased logarithmically, indicating intra-specific weed interference weed interference at the higher weed densities. Plant weight in Figure 2 consists of dry vegetative matter and totaled 620 g when plants were grown at the lowest jimsonweed density. The 918 g total plant weight was 68% vegetative matter and 32% pods (Figure 2). Thus, reproductive structures contribute a substantial amount of jimsonweed dry biomass at peanut harvest. When jimsonweed were grown at the highest density, vegetative weight was 121 g (70% of total) and pods contributed 52 g (30% of total). Although both plant weight and pod weight decreased as jimsonweed density increased, jimsonweed's competitive index remained more stable at between 30 and 32% based on trends in Figure 2.

There was no location effect for jimsonweed seed production; therefore, data were combined over locations. Number of seed produced per plant decreased with increasing jimsonweed density (Figure 3). When jimsonweed was grown at lower densities, seed production amounted to nearly 30,000 per plant, which was reduced to 10,000 per plant when densities increased to 5.25 plants per m of row. However, overall seed production increased from 60,0000 seed/plot at the lowest density to 640,000 seed/plot at the highest density (data not shown). Increasing jimsonweed density also decreased seed per plant and increased total seed produced when grown in competition with cotton (Scott et al., 2000).

Analysis of variance on estimated parameters of the rectangular hyperbola equation (Equation 2) (Cousens, 1987) indicated that peanut heights were significantly affected by jimsonweed density while location effects were not significant. Data were therefore pooled to explain the effect of jimsonweed density on peanut yield (Figure 4). Percent peanut yield reduction increased with increasing jimsonweed density. Jimsonweed density on percent peanut yield loss resulted in i and a values of 10.7 and 98, respectively (Figure 4). Jimsonweed's competitiveness with peanut at lower densities is similar to competitiveness in cotton (Scott et al., 2000). As additional comparisons in peanut, the i value for common ragweed was 68 (Clewis et al., 2001) and 149.5 for cocklebur (Royal et al., 1997), indicating common ragweed and cocklebur were more competitive in peanut than jimsonweed. Common cocklebur (Royal et al., 1997), common ragweed (Clewis et al., 2001), Florida beggarweed (Cardina and Brecke, 1989), and tropic croton (Thomas et al., 2004) plant biomass were also inversely related to peanut yield. Similarities between locations reflect the similarity of peanut yield and weed dry biomass accumulation at each location, however it should be noted that weeds affect crop yield more when yield potential is higher (Swanton et al., 1999).

Predicted peanut yield loss from season-long interference of one jimsonweed plant per meter of crop row was 40% (Figure 4). Using the hyperbolic function (Cousens, 1987) and asymptotic values constrained to 100% yield loss, maximum peanut yield loss when grown with one wild poinsettia (Bridges et al., 1992), tropic croton (Thomas et al., 2004), horsenettle (Hacket et al., 1987), and bristly starbur (Walker et al., 1989) plant per meter of crop was 17, 17, 14, and 13%, respectively. Jimsonweed is more competitive than these weeds; however, its interference is similar to common ragweed and less than common cocklebur. Common ragweed reduced peanut pod yield 40% at one weed per meter of crop row (Clewis et al., 2001), while common cocklebur reduced peanut pod yield 70% at the same density (Royal et al., 1997).