mtk (version 1.0)

WWDM: The WWDM model

Description

The WWDM (Winter Wheat Dry Matter Model) is a very simple dynamic crop model with a daily time step. It has been developed at INRA (France) by David Makowski, Marie-Hélène Jeuffroy and Martine Guérif.

The behavior of the model is influenced by seven factors:

Eb:

Radiation use efficiency

Eimax:

Maximal ratio of intercepted to incident radiation

K:

Coefficient of extinction

Lmax:

Maximal value of the Leaf Area Index (LAI)

A:

Coefficient of LAI increase

B:

Coefficient of LAI decrease

TI:

Temperature threshold

Usage

• mtkWWDMEvaluator(listParameters=NULL)
• mtkNativeEvaluator(model="WWDM",information=NULL)
• mtkEvaluator(protocol = "R", site = "mtk", service = "WWDM", parametersList=NULL)

Parameters used to manage the simulation

year

Either NULL or a number between 1 and 14 to specify the number of years to simulate. A database with 14 yearly sequences of meteorological data are included in the environment (data frame wwdm.climates).

Details

1. The implementation of the WWDM model includes the object WWDM.factors on the input factors, the class mtkWWDMEvaluator to run the simulations, and the data frame wwdm.climates containing the climate data.
2. In mtk, there are a few ways to build an evaluator of the WWDM model, but we usually recommend the following class constructors: mtkWWDMEvaluator , mtkNativeEvaluator.

References

1. Makowski, D., Jeuffroy, M.-H., Guérif, M., 2004. Bayseian methods for updating crop model predictions, applications for predicting biomass and grain protein content. In: Bayseian Statistics and Quality Modelling in the Agro-Food Production Chain (van Boeakel et al. eds), pp. 57-68. Kluwer, Dordrecht.
2. Monod, H., Naud, C., Makowski, D., 2006. Uncertainty and sensitivity analysis for crop models. In: Working with Dynamic Crop Models (Wallach D., Makowski D. and Jones J. eds), pp. 55-100. Elsevier, Amsterdam.

See Also

help(WWDM.factors)

Examples

## Evaluation of the "WWDM" model

#	Example I: by using the class constructors: mtkWWDMEvaluator()

#	Generate the factors
		data(WWDM.factors)
		
# Build the workflow:
#   1) specify the design process
	designer <- mtkNativeDesigner("BasicMonteCarlo", information = list(size=50)  )

#   2) specify the evaluation process; 
	model <- mtkWWDMEvaluator(listParameters = list(year=3) )
  
#   3) specify the workflow with the processes defined previously

	exp <- mtkExpWorkflow( expFactors=WWDM.factors,
		    processesVector=c( design=designer, evaluate=model) )
# Run the workflow and report the results.
  run(exp)
  summary(exp)
  
# Personnalize the data reporting

  designData <-  extractData(exp,name="design")
  
  simulationData <- extractData(exp,name="evaluate")

  plot(designData$Eb, simulationData$Biomass, xlab="Eb",ylab="Biomass")
	
## Example II: by using the class constructor: mtkNativeEvaluator()

# Generate the input factors
	data(WWDM.factors)

# Build the workflow:
#   1) specify the design process
	designer <- mtkNativeDesigner("BasicMonteCarlo", information = list(size=20)  )

#   2) specify the evaluation process; 
	model <- mtkNativeEvaluator(model="WWDM", information=list(year=3)  )
  
#   3) specify the workflow with the processes defined previously

	exp <- mtkExpWorkflow(expFactors=WWDM.factors,
		    processesVector=c( design=designer, evaluate=model) )
        
# Run the workflow and report the results.
  run(exp)
  summary(exp)
  plot(exp)