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Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health
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Optimizing a Pesticide Fate Model Using Field Dissipation Studies on Alfalfa to Predict Honeybee Exposure and Risk.

Ashlie D Kinross1, Sean Lyons1, Kimberly J Hageman1

  • 1Department of Chemistry and Biochemistry, Utah State University, Logan 84322, Utah, United States.

Environmental Science & Technology
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

Accurate pesticide dissipation predictions require specific photodegradation rates. This study measured these rates for insecticides, improving pesticide fate models and honeybee risk assessments.

Keywords:
chlorpyrifosdissipationindoxacarbphotodegradationλ-cyhalothrin

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Area of Science:

  • Environmental chemistry
  • Insect toxicology
  • Agrochemical science

Background:

  • Pesticide fate modeling aids in understanding spray timing effects on insect populations.
  • Accurate dissipation predictions necessitate chemical-specific foliar photodegradation and penetration rates.

Purpose of the Study:

  • To measure photodegradation rates for insecticides (active ingredient and formulation).
  • To incorporate measured rates into a pesticide dissipation model.
  • To evaluate the model's accuracy and calculate honeybee risk quotients.

Main Methods:

  • Measured photodegradation rates for λ-cyhalothrin and indoxacarb (active ingredient vs. formulation).
  • Incorporated rates into a pesticide dissipation model.
  • Validated the model with field data from alfalfa studies using chlorpyrifos and λ-cyhalothrin.

Main Results:

  • Photodegradation rates for λ-cyhalothrin and indoxacarb showed no significant difference between active ingredient and formulation.
  • Optimized model demonstrated good agreement between predicted and measured insecticide concentrations in alfalfa leaves.
  • Time-dependent honeybee risk quotients were calculated for the 7-day post-application period.

Conclusions:

  • Measured photodegradation rates enhance the accuracy of pesticide dissipation models.
  • Improved models can better predict insecticide fate and inform risk assessments for beneficial insects like honeybees.