Predictive models for weed emergence timing and dormancy in agroecological systems

Overview

Weed management is one of the most persistent challenges in agricultural production systems, complicated by increasing herbicide tolerance and resistance. This research delivers population-based predictive models that forecast the timing and magnitude of weed seed germination and emergence by linking seed dormancy behavior to key environmental drivers. By accurately predicting the emergence window—the period when seedlings are most vulnerable to control measures—the models enable more effective, timely, and sustainable weed management interventions.

The approach addresses a critical operational need for agronomists and growers: knowing precisely when to act. Accurate emergence predictions support targeted herbicide application, reduce unnecessary treatments, and contribute to integrated weed management strategies that preserve efficacy of available control tools.

Technical specifications

Core methodology:

• Population-based models that relate weed seed dormancy dynamics to environmental factors
• Inputs include temperature variation, seed water content, light signals, and burial depth
• Models are calibrated to reflect the seasonality of temperate habitats and adjustable to different agroecological scenarios
• Validated under field conditions to ensure practical reliability

Key features:

• Predicts both the timing and size of weed emergence flushes
• Identifies the emergence window when seedlings are most susceptible to control
• Adaptable across multiple weed species and contrasting environmental conditions
• Supports scenario analysis for different latitudes and climate contexts

Future development:

• Collection of ecotypes from six agronomically important weed species across a wide latitude range in the Pampean zone of Argentina (south, center, and north)
• Simulated seedbank experiments under contrasting dry-humid and warm-cold conditions at the Agronomy Faculty experimental field (UBA)
• Assessment of how weed populations from different latitudes adapt to varied environmental scenarios

Technology readiness level

The models have been validated under field conditions and have demonstrated the capacity to adjust emergence timing predictions to the seasonality of temperate agroecological systems. Current readiness supports applied research collaborations and on-farm validation. Next-stage work focuses on broadening species coverage and testing population adaptation across diverse latitudes and climate regimes, moving toward broader commercial and advisory deployment in the Mercosur region and similar temperate production zones.