Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

Weeds that respond more to nitrogen fertilizer than crops may be more competitive under high nitrogen (N) conditions. Therefore, understanding the effects of nitrogen on crop and weed growth and competition is critical. Field experiments were conducted at two locations in 1999 and 2000 to determine the influence of varying levels of N addition on corn and velvetleaf height, leaf area, biomass accumulation, and yield. Nitrogen addition increased corn and velvetleaf height by a maximum of 15 and 68%, respectively. N addition increased corn and velvetleaf maximum leaf area index (LAI) by up to 51 and 90%. Corn and velvetleaf maximum biomass increased by up to 68 and 89% with N addition. Competition from corn had the greatest effect on velvetleaf growth, reducing its biomass by up to 90% compared with monoculture velvetleaf. Corn response to N addition was less than that of velvetleaf, indicating that velvetleaf may be most competitive at high levels of nitrogen and least competitive when nitrogen levels are low. Corn yield declined with increasing velvetleaf interference at all levels of N addition. However, corn yield loss due to velvetleaf interference was similar across N treatments except in one site–year, where yield loss increased with increasing N addition. Corn yield loss due to velvetleaf interference may increase with increasing N supply when velvetleaf emergence and early season growth are similar to that of corn.

Large crabgrass, redroot pigweed, and hairy galinsoga are three important weed species in bell pepper and other crops in the northeastern United States. Field experiments were conducted in 1998 and 1999 to determine the influence of density and relative emergence time of the three weed species on bell pepper fruit yield. Densities of 0, 1, 2, 4, 8, 16, and 32 plants m−1 row were established for each weed species from naturally occurring weed populations. The effects of relative emergence time were studied by investigating the different yield responses to weeds emerging at two different times: 3 d or 2 wk after transplanting of pepper. Both weed density and relative emergence time affected pepper yield loss. The relative competitive ability of weed species varied between years. Large crabgrass was the most competitive species in 1998 and the measure of yield loss at low weed densities, I, was estimated to be 34% on the basis of the nonlinear hyperbolic equation. Redroot pigweed was most competitive in 1999 with an estimate of 88% for I. Hairy galinsoga was the least competitive weed in both years. Maximum yield loss under 32 plants m−1 row ranged from 19% with late-emerging hairy galinsoga in 1998 to 99% with early-emerging redroot pigweed in 1999. A new equation was proposed to characterize the relation between yield loss and weed pressure by expanding the nonlinear hyperbolic equation to include a parameter to account for the change of maximum yield loss with emergence time. The expanded equation generally provided a more accurate prediction of yield loss. In addition, several models are introduced to describe both the effects of density and relative emergence time of multiple weed species on crop yield. Generally these models provided an adequate fit of the data and a good description of the competitive ability of the mixed population.

Because soil-residual PRE herbicides reduce and delay annual weed emergence and decrease later weed growth, susceptible weeds surviving or recovering from herbicide treatment reduce crop yields less than do untreated weeds. Recently, corn yields were shown to be reduced differently by untreated weeds emerging in and between crop rows. However, equations have not been reported before that relate corn yield to in-row and between-row weed cover of mixed weed populations recovering from PRE soil-residual herbicides. Published data from PRE herbicide screening research for 3 site-yr in Missouri were reanalyzed to characterize this relation. In-row and between-row weed cover of mixed weed populations, chiefly giant foxtail and common waterhemp, were measured from photographs at midsummer. In 2 of 3 site-yr and with the 3 site-yr average, corn yields were a nonlinear function of both in-row and between-row weed cover recovering from various PRE soil-residual herbicide treatments. In 1 of 3 site-yr, corn yields were a nonlinear function of only between-row total weed cover. Subdividing weed cover into in-row and between-row subpopulations in equations accounted for more data variability in yield estimates than including either subpopulation alone. For all 3 site-yr after PRE herbicide treatment, corn yields were a nonlinear function of only between-row visually rated total weed control. Visual evaluation was less sensitive than photographic weed cover for measuring the contribution of in-row weeds to corn yield loss and characterizing the functional form of the equations.

Weeds generally occur in patches in production fields. Are these patches spatially and temporally stable? Do management recommendations change on the basis of these data? The population density and location of annual grass weeds and common ragweed were examined in a 65-ha corn/soybean production field from 1995 to 2004. Yearly treatment recommendations were developed from field means, medians, and kriging grid cell densities, using the hyperbolic yield loss (YL) equation and published incremental YL values (I), maximum YL values (A), and YL limits of 5, 10, or 15%. Mean plant densities ranged from 12 to 131 annual grasses m−2 and < 1 to 37 common ragweed m−2. Median weed densities ranged from 0 to 40 annual grasses m−2 and were 0 for common ragweed. The grass I values used to estimate corn YL were 0.1 and 2% and treatment was recommended in only 1 yr when the high I value and either the mean or median density was used. The grass I values used for soybean were 0.7 and 10% and estimated YL was over 10% all years, regardless of I value. The common ragweed I values were 4.5 and 6% for corn and 5.1 and 15.6% for soybean. On the basis of mean densities, fieldwide treatment would have been recommended in 6 of 9 yr but in no years when the median density was used. Recommendations on the basis of grid cell weed density and kriging ranged from > 80% of the field treated for grass weeds in 3 of 4 yr in soybean to < 20% of the field treated for common ragweed in 2002 and 2004 (corn). Grass patches were more stable in time, space, and density than common ragweed patches. Population densities and spatial distribution generally were variable enough so that site-specific information within this field would improve weed management decisions.

Clopyralid, picloram, triclopyr, metsulfuron, and tebuthiuron were applied to control kudzu on four loblolly pine forest regeneration sites during July 1997. Spot treatments were applied to recovering kudzu in June 1998 and June 1999. Soil leachate was monitored for these five herbicides from July 1997 to December 2000. All herbicides were detected in shallow (51–58 cm deep) and deep lysimeters (84–109 cm deep). Clopyralid was not persistent and limited leaching occurred, with residue levels of 0.4 to 2.8 μg L−1 in 12 of 102 deep lysimeter samples. Picloram was mobile and persisted at 0.6 to 2.5 μg L−1 in shallow and deep lysimeters for at least 10 mo after the initial application. Triclopyr residues were not persistent in shallow lysimeters and remained < 6 μg L−1 during the study. Metsulfuron persisted at < 0.1 μg L−1 for 182 to 353 d in shallow lysimeters and at < 0.07 μg L−1 for 182 to 300 d in the deep lysimeters in various plots. Tebuthiuron peaks in the deep lysimeters ranged from 69 to 734 μg L−1 34 to 77 d after the first spot treatment. In the soil that was essentially a fill area, tebuthiuron residues remained > 400 μg L−1 (402–1,660 μg L−1) in the shallow lysimeter samples and > 180 μg L−1 (181–734 μg L−1) in the deep lysimeters throughout a 354-d period that followed the first spot application. When used as part of a forest regeneration program, the relative potentials of the herbicides to move into shallow groundwater were: tebuthiuron > picloram > metsulfuron > clopyralid > triclopyr.