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ARS Home » Southeast Area » Stoneville, Mississippi » Pollinator Health in Southern Crop Ecosystems Research » Research » Publications at this Location » Publication #417914
Research Project: Ecological Assessment and Mitigation Strategies to Reduce the Risks of Bees to Stressors in Southern Crop Ecosystems

Location: Pollinator Health in Southern Crop Ecosystems Research

Title: Data and code from: A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health

Author
item Kannan, Narayanan
item Read, Quentin
item Zhang, Weiqiang

Submitted to: Ag Data Commons
Publication Type: Database / Dataset
Publication Acceptance Date: 8/7/2024
Publication Date: 8/7/2024
Citation: Kannan, N., Read, Q.D., Zhang, W. 2024. Data and code from: A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health. Ag Data Commons. Available: https://agdatacommons.nal.usda.gov/articles/.

Interpretive Summary: This is a dataset accompanying a manuscript published in the journal Heliyon. In this dataset, we archive results from several laboratory and field trials testing different adjuvants (spray additives) that are intended to reduce particle drift, increase particle size, and slow down the particles from pesticide spray nozzles. We fit statistical models to the droplet size and speed distribution data and statistically compare different metrics between the adjuvants (sodium alginate, polyacrylamide [PAM], and control without any adjuvants). The results presented here and interpreted in the accompanying manuscript have important implications for protecting pollinator health by reducing undesirable non-target impacts of insecticides applied to crops in the Lower Mississippi Delta.

Technical Abstract: This dataset contains all data and code required to clean the data, fit the models, and create the figures and tables for the laboratory experiment portion of the manuscript: Kannan, N., Q. D. Read, and W. Zhang. 2024. A natural polymer material as a pesticide adjuvant for mitigating off-target drift and protecting pollinator health. Heliyon, in press. https://doi.org/10.1016/j.heliyon.2024.e35510. The raw data and R scripts presented contain code to process data before model fitting, fit Bayesian distributional regressions to the droplet size and speed data, generate expected posterior predictions from the models, and produce tables and graphics of the results.