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Development and application of a numerical dynamic model for pesticide residues in apple orchards.

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BACKGROUND Limited understanding of the fate of pesticides in apple orchards may lead to recurring pests or pose risks to food safety. In this study, through a field experiment conducted… Click to show full abstract

BACKGROUND Limited understanding of the fate of pesticides in apple orchards may lead to recurring pests or pose risks to food safety. In this study, through a field experiment conducted in an apple orchard, a dynamic plant uptake model, coupled with a soil water model, was developed to simulate measured pesticide concentrations in soil and different plant compartments. RESULTS Results showed that the overall model could adequately describe the dataset of four pesticides in the apple orchard. An estimated 15-24.7% of applied pesticides were deposited on leaves and 0.37-0.58% on fruits. Decreasing pesticide concentrations in fruits were observed after pesticide application, with 9.6-64.8% of this decrease can be explained by biodegradation, 29.8-75.8% by fruit growth dilution, and 11.3-47.6% by wash-off. Furthermore, a first estimation of dietary risks indicated that the ingestion of the apples may not represent an acute and chronic risk to human health. CONCLUSION The dynamic plant uptake model, coupled with the tipping buckets soil water model, could successfully be fitted for describing to the dataset for the fate of four pesticides applied in an apple orchard. The contribution of different pathways on pesticide concentration was highly influenced by precipitation, fruit growth dilution, and the characteristics of different pesticides. This model can improve understanding of pesticide fate in apple orchards and has great potential for supporting food safety assessment and decision-making to minimize impacts arising from pesticide applications. This article is protected by copyright. All rights reserved.

Keywords: application; pesticide; apple orchard; apple; model; apple orchards

Journal Title: Pest management science
Year Published: 2022

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