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Related Experiment Video

Updated: Nov 9, 2025

Protocols for Robust Herbicide Resistance Testing in Different Weed Species
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Protocols for Robust Herbicide Resistance Testing in Different Weed Species

Published on: July 2, 2015

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Modelling field emergence patterns in arable weeds.

L M Vleeshouwers1, M J Kropff1

  • 11 Wageningen University, Department of Plant Sciences, Group Crop and Weed Ecology, PO Box 430, 6700 AK, Wageningen, The Netherlands.

The New Phytologist
|April 17, 2021
PubMed
Summary
This summary is machine-generated.

A new model accurately predicts weed seedling emergence and density after soil cultivation. However, the model overestimates dormancy release, highlighting a key area for future research in weed management.

Keywords:
Chenopodium albumPolygonum persicariaSpergula arvensisdormancygerminationseed banksimulation modelweed emergence

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Area of Science:

  • Agricultural Science
  • Ecology
  • Computational Biology

Background:

  • Weed emergence timing and density significantly impact crop yields and management strategies.
  • Accurate simulation of weed emergence is crucial for developing effective control measures.
  • Soil cultivation is a common practice that influences weed seed dormancy and subsequent emergence.

Purpose of the Study:

  • To develop and evaluate a simulation model for predicting weed emergence patterns post-soil cultivation.
  • To identify key factors influencing weed seedling emergence, including dormancy, germination, and growth.
  • To assess the model's predictive accuracy for common weed species like Polygonum persicaria, Chenopodium album, and Spergula arvensis.

Main Methods:

  • A modular model was created to simulate dormancy release, germination, and pre-emergence growth.
  • Input variables included soil cultivation date, temperature, and penetration resistance.
  • Model parameterization and evaluation used field and laboratory data for three weed species.

Main Results:

  • The model accurately simulated germination and pre-emergence growth but overestimated dormancy release rates.
  • Seedling density and emergence timing were predicted accurately when dormancy data was experimentally derived.
  • Seedbed temperature showed correlations with emergence but lacked strong causal relationships for prediction.

Conclusions:

  • The developed model shows promise for predicting weed emergence, with potential for optimization.
  • Improving the simulation of seed dormancy release is critical for enhancing predictive accuracy.
  • Understanding the interplay between environmental factors and weed biology is key for effective weed management.