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ARS Home » Pacific West Area » Logan, Utah » Pollinating Insect-Biology, Management, Systematics Research » Research » Research Project #440907

Research Project: Using Pesticide Fate Models to Guide Application Timing and Improve Alfalfa Seed Yields

Location: Pollinating Insect-Biology, Management, Systematics Research

Project Number: 2080-21000-019-036-R
Project Type: Reimbursable Cooperative Agreement

Start Date: Oct 1, 2021
End Date: Apr 30, 2023

Objective:
Alfalfa (Medicago sativa) has the third greatest production value of any crop in the United States, and over 16,000 acres were harvested in 2020. Production of alfalfa seed relies on bee pollination to produce marketable yields. Alfalfa seed growers also must manage pest populations that can lead to yield loss. Major pests include lygus bugs (Lygus hesperus) and spider mites (Tetranychus spp.). Lygus bugs in particular can cause significant damage to the reproductive organs of alfalfa flowers, resulting in reduced seed yield. We propose to use predictive pesticide fate models and knowledge of insect behavior to develop best management guidance for timing of pesticide applications in order to maximize efficacy against the target pest(s) while minimizing negative impacts to bees. We will focus on applications of three insecticides that are currently used by seed growers in the pacific northwest, Warrior (active ingredient: lambda-cyhalothrin), Beleaf (AI: flonicamid), and Transform (AI: sulfoxaflor). We will use predictive fate models to predict the best spray timing for each application, and then test these predictions in the field. Warrior (lambda-cyhalothrin) is typically used as a clean-up spray right before bloom. The goal would therefore be to maximize efficacy against pests in order to minimize the need for sprays during bloom. Another goal would be to have accurate predictions for the degradation rates in the field in order to better time placement of alfalfa leafcutting bees to avoid exposure to lambda-cyhalothrin, which is known to be highly toxic to bees (contact LD50 of 0.135 ug/bee for Osmia bicornis). Beleaf (flonicamid) on the other hand has low toxicity for bees (contact LD50 > 100 ug/bee for Apis mellifera), which is why it is a common choice during bloom, and we therefore will focus on maximizing efficacy against the target pests to reduce the need for additional sprays (reducing costs and effort). Lastly, another commonly used bloom-time insecticide is Transform (sulfoxaflor), though it has high toxicity for bees (contact LD50 of 0.379 ug/bee and an oral LD50 of 0.146 ug per bee for Apis mellifera). It is therefore recommended that Transform only be applied at night, at least four hours before sunrise. However, anecdotally, growers have noticed that if they spray Transform after midnight, they see negative impacts on ALCBs. It’s therefore critical that we provide clearer guidance on when and under what environmental conditions it is safest to apply Transform during bloom. Results from this project will allow us to provide clearer guidance to growers on the timing of insecticide sprays in order to maximize efficacy against pests and minimize effects on bees. Ultimately, our hope is that these recommendations will result in fewer sprays needed to control pest populations and healthier bees that provide more effective pollination services. Objectives 1. Use predictive pesticide fate models to determine the optimal timing of sprays. 2. Test predictions from fate models under field conditions. 3. Develop guidance for growers on timing of sprays around bloom.

Approach:
ARS scientists will complete the work proposed in Objectives 2 and 3, and Utah State University researchers will complete the work proposed in Objective 1 (pesticide fate modeling). Obj. 2: Test predictions from fate models under field conditions. Using the predictions from the models, we will select three timed treatments to apply each focal insecticide ranked from first to third in predicted effectiveness. Selection of timed applications will be based on the model predictions under a range of environmental conditions and our knowledge of insect activity (e.g. targeting when lygus are most active, and when bees are least active). Testing will occur at the research alfalfa farm owned by the USDA ARS Pollinating Insects Research Unit in Logan, UT. A planting of alfalfa will be split into 30 blocks. Warrior will be applied to 9 randomly selected blocks, but at three different times with three replicates in each timed application. Applications will be made with a boom sprayer attached to the back of a gator UTV. Rates will follow the label recommendations for alfalfa seed production, using the higher concentrations when a range is given. The three timed applications will be made all within 24 hours during weather conditions when spraying is recommended (low wind, no rain) and timed during pre-bloom, when growers would typically do a clean-up application. The day when applications are made will also be informed by model predictions for when the application would be most efficacious against pests while minimizing risks to bees (insecticide is predicted to significantly degrade before bloom, according to the model). Clippings of alfalfa (flowers and leaves) will be collected in sprayed plots every four hours for 48 hours, and then every eight hours for the following 72 hours (total of 5 days of sampling). Clippings will be random throughout the plot and equate to 5g of material. The same methods will be repeated for an application of Beleaf during peak bloom, and an application of Transform during late bloom. Clippings from control plots (no-spray) will always be collected at the same time as those from treatment plots to control for the effects of drift. Weather conditions will be recorded at each application and sample collection. Plant clippings will be stored in the dark at -20°C, and then residues will be quantified. Obj. 3: Develop guidance for growers on timing of sprays around bloom. Results from the field trial in Obj. 2 will allow us to test if model predictions match field residue degradation rates. Any deviations from predictions will aid in further parameterization of the model to increase accuracy. We can then use these models in conjunction with knowledge of insect behavior to guide recommendations for spray timing. Additionally, we can use the model to modify recommendations based on weather conditions and other local factors that would influence residue degradation. ARS scientits will meet with alfalfa seed growers in Touchet, WA to present on this research and provide recommendations for spray timing as well as present this work at the annual Western Alfalfa Seed Growers Association meeting in January 2023.