SUMMARYEnvironmental problems mean that herbicide applications must be drastically reduced and optimized. Models that quantify the effects of crop management techniques on weed dynamics are valuable tools for designing weed management strategies. Indeed, the techniques to be optimized are numerous and diverse, and their effects vary considerably with environmental conditions and the state of the weed flora. In the present study, a mechanistic weed dynamics model,AlomySys, was used to carry outin silicoexperiments in order to: (1) rank crop management components according to the resulting decrease in weed infestation, and (2) study the sensitivity of the major component effects to biophysical field state variables in order to identify indicators and thresholds that could serve for future decision-rules for farmers. The various results were compiled into rules for optimizing timing and other options (tillage tools, herbicide types) for the different crop management techniques. The rules were based on a series of biophysical field state variables, i.e. cumulated rainfall, thermal time, soil moisture and weed densities prior to the operation, in the previous and pre-previous crops. For instance, the first tillage should be delayed until the cumulated rainfall since harvest exceeds 50 mm and be carried out in moist conditions. Mouldboard ploughing is advised if the infestation of the previous crop exceeds 20 weeds/m2and particularly if this exceeds 0·3 times that of the pre-previous crop. Ploughing should occur when the cumulated rainfall since harvest reaches 100–200 mm. The effects of crop succession and long-term effects of management techniques have been studied in a companion paper (Colbachet al. 2012).
High-field state of the flux-line lattice in the unconventional superconductorCeCoIn5