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Modelling the effects of cropping systems on weed dynamics: the trade-off between process analysis and decision support

Code: 9781801464826
Nathalie Colbach, AgroSup Dijon, INRAE, Université de Bourgogne, France

Chapter synopsis: Models are essential to synthesize knowledge on weeds and to design integrated weed-management strategies. These models must rank cropping systems as a function of weed infestation, and account for variability in effects to estimate probabilities of success or failure. Three case studies are presented: (1) an empirical static single-equation model that directly relates weed biomass to crop management, with few inputs and parameters, (2) a matrix-based multiannual model predicting a few key weed stages annually, from weed control options and a few parameters, (3) a mechanistic process-based multiannual model predicting detailed soil, crop and weed state variables daily, with an individual-based 3D canopy representation, requiring hundreds of inputs and parameters. The chapter concludes that models using a mechanistic representation of the cropping-system ´ environment interactions are best for quantifying effects and their variability, combined with a subsequent transformation with in silico experiments into empirical models of key cropping-system components.

DOI: 10.19103/AS.2021.0098.07
£25.00
Table of contents 1 Introduction 2 Comparing models: case studies 3 Limiting the modelled system: temporal, spatial and species scales 4 Modelling approaches: empirical versus mechanistic models 5 Modelling approaches: stochastic versus deterministic models 6 How to bridge the gap between process analysis and decision support 7 Conclusion and future trends 8 Where to look for further information 9 References

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