Process-based fungicide degradation model for soybean application intervals

Overview

This research proposes a process-based model capable of quantifying fungicide degradation in soybean crops using weather data, including temperature, solar radiation, and precipitation. The goal is to define precise fungicide application intervals, helping growers optimize disease management while minimizing unnecessary chemical applications. By integrating environmental variables with degradation kinetics, the model aims to provide data-driven recommendations that improve efficiency, reduce costs, and support more sustainable soybean production.

Technical specifications

• Modeling approach: Process-based mathematical functions correlating environmental conditions with fungicide degradation rates
• Input variables: Temperature, solar radiation, and precipitation data
• Target crop: Soybean, with potential adaptation to other crops
• Validation methodology: Three field experiments in the 2020/21 growing season, two in Santa Maria, Rio Grande do Sul, Brazil, and one in Santa Helena, Paraná, Brazil
• Data collection: Soybean leaf sampling every two days after fungicide application, sent to a pesticide residues laboratory in Santa Maria
• Research background: Builds on over two decades of process-based model development, including Simanihot for cassava, SimulArroz for rice, and PhenoGlad for gladiolus

Technology readiness level

The model is currently in early-stage validation with active field experiments underway. The research team is seeking a partnership to support fungicide residue quantification, which is essential for calibrating and validating the model. The full study is expected to take approximately 18 months to complete. Once validated, the model could serve as a decision-support tool for soybean growers, agronomists, and crop protection companies seeking to refine fungicide application timing based on real-time weather conditions.