Modelling the persistence of weed populations

Jonathan Storkey (1) , Antoine Gardarin (2) , N. Colbach (3) , Helen Metcalfe (1) , Alice Milne (1)

1 Rothamsted Research
2 Agronomie
3 Agroécologie [Dijon]

One of the adaptations of weeds to highly disturbed environments is a persistent seedbank meaning there can often be a lag of several years in the response of a weed flora to a change in management. This buffering effect of the seedbank is a challenge to understanding weed dynamics through empirical field experiments that tend to be short lived. One solution is to simulate the response of weed populations to management in a computer model. Two approaches to modelling are discussed. The first draws on fundamental theory of plant ecological strategies to predict the shift in the distribution of weed functional traits under different management scenarios. Secondly, a detailed, process-based model of weed population dynamics is described with a worked case study example. This second approach also uses plant traits to parameterize the model and predict the function of emergent weed communities but has a more intensive requirement for parameterization. Models have a crucial role to play in predicting the persistence of weed populations in a field but care should be taken in investing too much confidence in predictions of absolute weed numbers. Rather, models are a powerful tool for testing the relative efficacy of contrasting weed management scenarios.

Mots clés: Functional traits; process-based model; weed management; parameterisation

Domaines: Botanique

Jonathan Storkey, Antoine Gardarin, N. Colbach, Helen Metcalfe, Alice Milne. Modelling the persistence of weed populations. Anil Shrestha; David R. Clements; Mahesh K. Upadhyaya. Persistence strategies of weeds, Wiley, 2022, 9781119525608. ⟨10.1002/9781119525622.ch9⟩. ⟨hal-03817813⟩