A decision-oriented framework for optimizing herbicide mixture ratios and dose bands under heterogeneous weed...

Area of Science:

• Agricultural Science
• Agronomy
• Weed Science

Background:

• Herbicide mixtures are vital for broad-spectrum weed control and managing herbicide resistance.
• Current mixture selection often overlooks weed community heterogeneity and worst-case scenarios.
• Lack of quantitative criteria for worst-case efficacy hinders effective weed management.

Purpose of the Study:

• To develop and implement a decision-oriented framework for optimizing ternary post-emergence herbicide mixtures.
• To integrate ratio-dose response-surface modeling, interaction robustness screening, and conservative effective-dose mapping.
• To establish quantitative criteria for worst-case efficacy in herbicide mixture selection.

Main Methods:

• A multi-stage framework was applied to a sugarcane production system.
• Ratio-dose response-surface modeling and interaction robustness screening were employed.
• Conservative effective dose mapping, using ED90_worst, guided dose selection.

Main Results:

• Herbicide mixture interactions showed temporal dependence, with synergistic effects diminishing over time.
• The conservative worst-case effective dose (ED90_worst) ranged from 148.66 to 807.79 g a.i. ha⁻¹.
• A 30:8 ratio mixture demonstrated efficacy against five of six weed groups, with a practical dose band of 652.22-717.44 g a.i. ha⁻¹ ensuring crop safety.

Conclusions:

• The developed framework prioritizes robustness and worst-case performance for optimizing herbicide mixtures.
• This approach offers a reproducible decision-support tool for heterogeneous weed communities.
• The study supports resistance-aware post-emergence weed management strategies.