Hello guest
Your basket is empty
We provide two pathways to the content. Thematic (chapters that address certain themes, e.g. cultivation, regardless of crop or animal type) and Product (chapters that relate to a specific type of crop or animal). Choose the most applicable route to find the right collection for you. 
Can’t find what you are looking for? Contact us and let us help you build a custom-made collection. 
You are in: All categories > A-Z Chapters > M
Use the Contact form to discuss the best purchasing method for you... Start building your collection today!

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
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

Also in M

Our site uses cookies. For more information, see our cookie policy. Accept cookies and close
Reject cookies Manage settings