title: Benchmark study on glyphosate-resistant cropping systems in the United States. Part 6: Timeliness of economic decision-making in implementing weed resistance management strategies.
abstract: BACKGROUND
  The introduction of glyphosate-resistant (GR) crops in the late 1990s made weed control in maize, cotton and soybean simple. With the rapid adoption of GR crops, many growers began to rely solely on glyphosate for weed control. This eventually led to the evolution of GR weeds. Growers are often reluctant to adopt a weed resistance best management practice (BMP) because of the added cost of additional herbicides to weed control programs which would reduce short-term revenue. This study was designed to evaluate when a grower that is risk neutral (profit maximizing) or risk averse should adopt a weed resistance BMP.
  
  
  RESULTS
  Whether a grower is risk neutral or risk averse, the optimal decision would be to adopt a weed resistance BMP when the expected loss in revenue is greater than 30% and the probability of resistance evolution is 0.1 or greater. However, if the probability of developing resistance increases to 0.3, then the best decision would be to adopt a weed resistance BMP when the expected loss is 10% or greater.
  
  
  CONCLUSIONS
  Given the scenarios analyzed, risk-neutral or risk-averse growers should implement a weed resistance BMP with confidence that they have made the right decision economically and avoided the risk of lost revenue from resistance. If the grower wants to continue to see the same level of return, adoption of BMP is required.
authors.affiliation: Plant and Soil Sciences Department, Mississippi State University, Mississippi State, MS 39762, USA. weirichj@missouri.edu.
authors.forename: Jason W
authors.initials: JW
authors.lastname: Weirich
authors.forename: David R
authors.initials: DR
authors.lastname: Shaw
authors.forename: Keith H
authors.initials: KH
authors.lastname: Coble
authors.forename: Micheal D K
authors.initials: MD
authors.lastname: Owen
authors.forename: Philip M
authors.initials: PM
authors.lastname: Dixon
authors.forename: Stephen C
authors.initials: SC
authors.lastname: Weller
authors.forename: Bryan G
authors.initials: BG
authors.lastname: Young
authors.forename: Robert G
authors.initials: RG
authors.lastname: Wilson
authors.forename: David L
authors.initials: DL
authors.lastname: Jordan
mesh_terms: D000383:Agriculture / Q000191:economics* / Q000379:methods*; D019985:Benchmarking; D018556:Crops, Agricultural / Q000187:drug effects / Q000235:genetics / Q000254:growth & development; D005998:Glycine / Q000031:analogs & derivatives* / Q000494:pharmacology; D053209:Herbicide Resistance; D006540:Herbicides / Q000494:pharmacology*; D030821:Plants, Genetically Modified / Q000187:drug effects / Q000235:genetics / Q000254:growth & development; D013997:Time Factors; D014481:United States; D058728:Weed Control / Q000191:economics* / Q000379:methods*; D000097797:Glyphosate
chemical_list: D005998:Glycine; D006540:Herbicides; D000097797:Glyphosate