Spatial weed distribution and economic thresholds for weed control

A practical and objective system is needed to determine the lowest rates of the most efficacious herbicides to meet each producer's specific weed control problems. Determining which method of weed control to utilize is difficult today with increasing product choices, the growing use and complexity of herbicide mixtures, regulatory pressures to reduce rates, and the closer integration of weed control with other crop decisions. Expert computer systems could improve current practices and use herbicide mixtures as a tool to increase herbicide efficiency. Such systems would account for herbicide dose and mixture responses; select most economical herbicides; optimize adjuvants; recommend control at economic thresholds; and vary rates according to weed spectrum, density, and local environmental conditions. An example using chlorimuron and thifensulfuron illustrates how these systems could use quantitative dose response and mixture information.

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Reducing the Risks of Herbicide Resistance: Best Management Practices and Recommendations

Weed Science ◽

10.1614/ws-d-11-00155.1 ◽

2012 ◽

Vol 60 (SP1) ◽

pp. 31-62 ◽

Cited By ~ 463

Author(s):

Jason K. Norsworthy ◽

Sarah M. Ward ◽

David R. Shaw ◽

Rick S. Llewellyn ◽

Robert L. Nichols ◽

...

Keyword(s):

Weed Control ◽

Herbicide Resistance ◽

Best Management Practices ◽

Weed Management ◽

Crop Production ◽

Management Practices ◽

Production Systems ◽

Herbicide Resistant ◽

Economic Thresholds ◽

Soil Seedbank

Herbicides are the foundation of weed control in commercial crop-production systems. However, herbicide-resistant (HR) weed populations are evolving rapidly as a natural response to selection pressure imposed by modern agricultural management activities. Mitigating the evolution of herbicide resistance depends on reducing selection through diversification of weed control techniques, minimizing the spread of resistance genes and genotypes via pollen or propagule dispersal, and eliminating additions of weed seed to the soil seedbank. Effective deployment of such a multifaceted approach will require shifting from the current concept of basing weed management on single-year economic thresholds.

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Augmented reality systems for weed economic thresholds applications

Planta Daninha ◽

10.1590/s0100-83582010000200025 ◽

2010 ◽

Vol 28 (2) ◽

pp. 449-454 ◽

Cited By ~ 7

Author(s):

N.R. Vidal ◽

R.A. Vidal

Keyword(s):

Augmented Reality ◽

Weed Control ◽

Image Recognition ◽

Current Status ◽

Weed Science ◽

Weed Density ◽

Leading Role ◽

Economic Thresholds ◽

Computer Based

The augmented reality (AR) technology has applications in many fields as diverse as aeronautics, tourism, medicine, and education. In this review are summarized the current status of AR and it is proposed a new application of it in weed science. The basic algorithmic elements for AR implementation are already available to develop applications in the area of weed economic thresholds. These include algorithms for image recognition to identify and quantify weeds by species and software for herbicide selection based on weed density. Likewise, all hardware necessary for AR implementation in weed science are available at an affordable price for the user. Thus, the authors propose weed science can take a leading role integrating AR systems into weed economic thresholds software, thus, providing better opportunities for science and computer-based weed control decisions.

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WEEDING: AWeedEcology andEconomicDecision MakingINstructionalGame

Weed Technology ◽

10.1017/s0890037x00034047 ◽

1991 ◽

Vol 5 (4) ◽

pp. 887-893 ◽

Cited By ~ 6

Author(s):

Lori J. Wiles ◽

Gail G. Wilkerson ◽

Harold D. Coble

Keyword(s):

Weed Control ◽

Simulation Models ◽

Production Costs ◽

Weather Data ◽

Weed Species ◽

Weed Ecology ◽

Management Decisions ◽

Model Driven ◽

Economic Thresholds ◽

Process Level

Process-level simulation models can be a unique and effective medium for teaching concepts of crop biology and management which can usually only be studied in the field. WEEDING was developed from several research models to demonstrate concepts of weed ecology, herbicide efficacy and selectivity, and economic thresholds for weed control decisions. Users of WEEDING interactively grow a soybean crop, making management decisions throughout the seaason. The program includes real weather data and a realistic assortment of soybean varieties, soil types, weed species, and weed control options. Production costs and profit are calculated at the end of a simulated season. The structure of a process-level model driven by real weather data simulates the risk involved in management decisions.

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Estimating economic thresholds for site-specific weed control using manual weed counts and sensor technology: An example based on three winter wheat trials

Pest Management Science ◽

10.1002/ps.3545 ◽

2013 ◽

Vol 70 (2) ◽

pp. 200-211 ◽

Cited By ~ 11

Author(s):

Martina Keller ◽

Christoph Gutjahr ◽

Jens Möhring ◽

Martin Weis ◽

Markus Sökefeld ◽

...

Keyword(s):

Winter Wheat ◽

Weed Control ◽

Sensor Technology ◽

Site Specific ◽

Economic Thresholds

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Economic thresholds and response to uncertainty in weed control

Agricultural Systems ◽

10.1016/0308-521x(87)90021-7 ◽

1987 ◽

Vol 25 (3) ◽

pp. 219-227 ◽

Cited By ~ 30

Author(s):

Bruce A. Auld ◽

Clem A. Tisdell

Keyword(s):

Weed Control ◽

Economic Thresholds

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Estimation of economic thresholds for weed control in winter wheat

Weed Research ◽

10.1111/j.1365-3180.1993.tb01962.x ◽

1993 ◽

Vol 33 (6) ◽

pp. 459-467 ◽

Cited By ~ 26

Author(s):

G. ZANIN ◽

A. BERTI ◽

L. TONIOLO

Keyword(s):

Winter Wheat ◽

Weed Control ◽

Economic Thresholds

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Grower Acceptance of Economic Thresholds for Weed Management in Illinois

Weed Technology ◽

10.1017/s0890037x00043517 ◽

1997 ◽

Vol 11 (4) ◽

pp. 828-831 ◽

Cited By ~ 28

Author(s):

George F. Czapar ◽

Marc P. Curry ◽

Loyd M. Wax

Keyword(s):

Risk Management ◽

Weed Control ◽

Weed Management ◽

Insect Control ◽

Costs And Benefits ◽

Weed Seed ◽

Agricultural Chemical ◽

Economic Thresholds ◽

General Appearance

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.

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