Modeling Weed Distribution for Improved Postemergence Control Decisions

Wiles, Lori J.; Wilkerson, Gail G.; Gold, Harvey J.; Coble, Harold D.

doi:10.1017/s0043174500058112

Cited by 50 publications

(42 citation statements)

References 13 publications

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“…Although, the sicklepod population may average to only 0.1 weeds sq. m )1 , it has a higher probability of being detected with the coarser grids because of its larger presence in the field and because weeds are not homogenous, but distributed into patches (Marshall, 1988;Thornton et al, 1990;Wiles et al, 1992;Johnson et al, 1995;Cardina et al, 1997). A notable trend in the grid size comparisons results was that as overall population number increased, the prediction accuracy decreased with the coarser grid sizes.…”

Section: Resultsmentioning

confidence: 99%

Comparison of Different Sampling Scales to Estimate Weed Populations in three Soybean Fields

LaMastus

Shaw

2005

Precision Agric

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A study was conducted to evaluate the accuracy of different weed sampling scales to accurately describe populations in soybean. Three soybean fields were sampled at 8 and 6 weeks after planting in 1998 and 1999, respectively. All weed species were counted on a 10 m grid, using a 0.58-m 2 quadrat. Data were eliminated from the original 10 m grid sample of weeds for each field to develop 40 m, 60 m, and 80 m independent data sets. Distribution and population maps were interpolated using an inverse distance weighted method. Data were extracted from the interpolated maps at known coordinates so that the observed population and the predicted population could be compared. The 10 m grid served as a standard to which all others were compared. No differences in population accuracies between each scale were detected when results were compared on a per weed basis, except when weed populations were very high, generally exceeding 400 plants ha )1 . When the weed density was not at an extreme, the results from these data indicate the ability to describe or account for the weed population fairly accurately, when using coarser grid sizes. These results also suggest that when using a regular grid coordinate system as the sampling structure, an increase from a 10 m scale to an 80 m scale will not cause a significant loss of information when weed populations were not extreme and will provide the necessary information for making suitable weed treatment decisions. However, some small weed patches were not detected with the coarser sampling scales, and the larger sampling scales would not meet their needs if the producerÕs objective is complete control of a species.

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Section: Resultsmentioning

confidence: 99%

Comparison of Different Sampling Scales to Estimate Weed Populations in three Soybean Fields

LaMastus

Shaw

2005

Precision Agric

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“…However, as aggregation of weeds increases, overestimation of crop yield losses occurs because of intraspecific competition (Brain and Cousens 1990). Consequently, herbicides may be recommended on a whole-field basis, when in fact they may not be warranted based on more accurate estimates of yield loss in the presence of weed patchiness (Wiles et al 1992).…”

Section: Ecological Principlesmentioning

confidence: 99%

Remote Sensing and Site-Specific Weed Management

Shaw¹

2005

Frontiers in Ecology and the Environment

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H umans have long recognized that weeds, "plants out of place", compete with desirable plants for water, sunlight, nutrients, and space, thereby reducing their productive capacity. Site-specific weed management (SSWM) has been practiced since the beginning of the selective culture of specific plants for human or animal consumption. In its most fundamental form, humans use SSWM to visually detect unwanted plants and use this information to remove them by hand. However, with the advent of mechanized agriculture and chemical herbicides, the approach has shifted to weed management on a whole-field basis, eg plowing or cultivating an entire field or broadcast application of herbicides. This approach has led to increased agricultural productivity, the ability to manage larger acreages effectively, and substantially reduced labor costs (compared to weeding by hand). However, these practices can also lead to increased soil erosion and compaction, contamination of surface and groundwater with sediment and persistent herbicides, and unnecessary expenditures in areas with little or no weed infestation.With the emergence of new technologies came the development of methods for applying herbicides only where needed. One of the earliest of these involved spotspraying a herbicide solution on weeds growing in a field. This was accomplished with hand-held pump-up sprayers, or later by individuals seated on a platform mounted on the front of a tractor ( Figure 1). Typically, this method used a non-selective herbicide such as glyphosate or paraquat, and care was needed to minimize damage to crop plants. However, this practice provided effective management of otherwise difficult-to-control weeds. More recently, the same technique was used to apply expensive selective herbicides such as fluazifop and sethoxydim on johnsongrass (Sorghum halepense). Although designed for broadcast applications to crop fields, producers found spot-spraying to be just as effective and much more economical.A more automated version of the same principle was the rope-wick applicator (Figure 2). This application method relied on a height differential between the crop and the weed. A pipe was fitted with ropes inserted into holes, and the core of the pipe was filled with a concentrated solution of glyphosate. The solution was wicked onto the ropes, and the pipe was mounted horizontally on the front of a tractor and operated just above the height of the crop, brushing tall weeds with the saturated ropes. REVIEWS REVIEWS REVIEWSWeeds typically occur in patches rather than uniformly across a field; however, conventional management practices rely on whole-field management. Site-specific weed management (SSWM), applying control measures only where weeds are located at densities greater than those that cause economic losses, has tremendous potential for economic and environmental benefits. Current commercially available systems can detect green vegetation and activate a herbicide spray nozzle. Researchers are actively exploring how ground-based, aerial, or satellite...

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“…Below the threshold weed density, money is likely to be saved by not taking action; above it, taking action is likely to result in profit. If several courses of action are available, with the more effective actions being more expensive, there may be a series of thresholds (e.g., Wiles et al 1992b).…”

Section: Introductionmentioning

confidence: 99%

“…Spatial pattern has rarely been considered in empirical studies of weed impact on crop yield. Hughes, in his 1996 review of the incorporation of spatial pattern of harmful organisms into crop loss models, states, ' 'Wiles et al (1992b) made what seems to be the only formal attempt to evaluate spatial information about harmful organisms (in this particular case, weeds) in the context of crop protection decision-making.' ' Wiles et al (1992b) looked at the effects of aggregation using 9.1 m length SUs in soybean weed control models and concluded, for their case, that the cost of ignoring spatial pattern in decision models may be low on average, but occasionally great.…”

Section: Introductionmentioning

confidence: 99%

When Does the Spatial Pattern of Weeds Matter? Predictions From Neighborhood Models

Garrett

Dixon

1998

Ecological Applications

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Good management models for postemergence weed control require good estimates of which weed density produces an economic threshold yield. Because intraweed competition increases if weeds are aggregated, weed spatial pattern may be an important factor for inclusion in management models. Mathematical models of weed–crop competition have demonstrated that this may be the case, but the small number of field studies examining the effect of weed spatial pattern have given variable results. These studies have used sampling units at arbitrary spatial scales for determining the level of aggregation in weed counts. We suggest that the neighborhood size for weed–crop competition is a natural scale for considering spatial pattern. We modeled crop yield resulting from weed competition as a function of the economic threshold, the level of competition within the neighborhood, neighborhood size, and the type and scale of weed pattern. From the model results, we predicted which weed traits would produce large shifts in threshold weed density as weed spatial pattern varies. For these weed species, consideration of spatial pattern in weed management models is predicted to be important. The systems most sensitive to weed spatial pattern are those with low economic thresholds, less competitive weeds, smaller neighborhoods, and aggregation at the scale of the neighborhood.

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Modeling Weed Distribution for Improved Postemergence Control Decisions

Cited by 50 publications

References 13 publications

Comparison of Different Sampling Scales to Estimate Weed Populations in three Soybean Fields

Comparison of Different Sampling Scales to Estimate Weed Populations in three Soybean Fields

Remote Sensing and Site-Specific Weed Management

When Does the Spatial Pattern of Weeds Matter? Predictions From Neighborhood Models

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