Positive and normative modeling for Palmer amaranth control and herbicide resistance management

George B Frisvold, Muthukumar V. Bagavathiannan, Jason K. Norsworthy

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

BACKGROUND: Dynamic optimization models are normative; they solve for what growers 'ought to do' to maximize some objective, such as long-run profits. While valuable for research, such models are difficult to solve computationally, limiting their applicability to grower resistance management education. While discussing properties of normative models in general, this study presents results of a specific positive model of herbicide resistance management, applied to Palmer amaranth control on a representative cotton farm. This positive model compares a proactive resistance management strategy to a reactive strategy with lower short-run costs, but greater risk of herbicide resistance developing. RESULTS: The proactive strategy can pay for itself within 1-4 years, with a yield advantage of 4% or less if the yield advantage begins within 1-2 years of adoption. Whether the proactive strategy is preferable is sensitive to resistance onset and yield losses, but less sensitive to cotton prices or baseline yields. Industry rebates to encourage residual herbicide use (to delay resistance to post-emergence treatments) may be too small to alter grower behavior or they may be paid to growers who would have used residuals anyway. Rebates change grower behavior over a relatively narrow range of model parameters. The size of rebates needed to induce a grower to adopt the proactive strategy declines significantly if growers extend their planning horizon from 1 year to 3-4 years. CONCLUSIONS: Whether proactive resistance management is more profitable than a reactive strategy is more sensitive to biological parameters than economic ones. Simulation results suggest growers with longer time horizons (perhaps younger ones) would be more responsive to rebate programs. More empirical work is needed to determine how much rebates increase residual use above what would occur without them.

Original languageEnglish (US)
JournalPest Management Science
DOIs
StateAccepted/In press - 2017

Fingerprint

Amaranthus palmeri
resistance management
herbicide resistance
growers
cotton
profits and margins
education
herbicides
planning
industry
economics
farms

Keywords

  • Cotton
  • Herbicide resistance
  • Modeling
  • Net returns
  • Palmer amaranth
  • Yield loss

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Insect Science

Cite this

Positive and normative modeling for Palmer amaranth control and herbicide resistance management. / Frisvold, George B; Bagavathiannan, Muthukumar V.; Norsworthy, Jason K.

In: Pest Management Science, 2017.

Research output: Contribution to journalArticle

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abstract = "BACKGROUND: Dynamic optimization models are normative; they solve for what growers 'ought to do' to maximize some objective, such as long-run profits. While valuable for research, such models are difficult to solve computationally, limiting their applicability to grower resistance management education. While discussing properties of normative models in general, this study presents results of a specific positive model of herbicide resistance management, applied to Palmer amaranth control on a representative cotton farm. This positive model compares a proactive resistance management strategy to a reactive strategy with lower short-run costs, but greater risk of herbicide resistance developing. RESULTS: The proactive strategy can pay for itself within 1-4 years, with a yield advantage of 4{\%} or less if the yield advantage begins within 1-2 years of adoption. Whether the proactive strategy is preferable is sensitive to resistance onset and yield losses, but less sensitive to cotton prices or baseline yields. Industry rebates to encourage residual herbicide use (to delay resistance to post-emergence treatments) may be too small to alter grower behavior or they may be paid to growers who would have used residuals anyway. Rebates change grower behavior over a relatively narrow range of model parameters. The size of rebates needed to induce a grower to adopt the proactive strategy declines significantly if growers extend their planning horizon from 1 year to 3-4 years. CONCLUSIONS: Whether proactive resistance management is more profitable than a reactive strategy is more sensitive to biological parameters than economic ones. Simulation results suggest growers with longer time horizons (perhaps younger ones) would be more responsive to rebate programs. More empirical work is needed to determine how much rebates increase residual use above what would occur without them.",
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