The space dimension in the decision LP matrix

DTM Colli Esini

A great difference with AgriPoliS is that the MIP is spatially explicit. So it will be composed of the following activities:

  • n spacial explicit activities * m plot numbers
  • i spacial explicit investments * j_i suitable plots
  • z other spacial implicit activities
  • q other spacial implicit investments

The spacial explicitness has the disadvantage of a much larger matrix, especially in case of farmers with many plots, but it has the following advantages:

  • transport costs are associated directly with the individual pixels and already included in the Gross Margin;
  • pixel heterogeneity (e.g. different slopes) can be included straightforward, just changing the gross margins;
  • an explicit map of the region can be draw to show the different productions
  • binds on individual pixels can be easily inserted on the map (e.g. if the plot_4 is already employed for perennial crops, the matrix will not have the activity option “maize on plot_4” till the perennial crop investment will dead).

An example of matrix following such approach is given:

Resources Prod. activity 1
on plot1
Prod. activity 1
on plot2
Prod. activity 1
on plot3
Prod. activity 2
on plot1
Prod. activity 2
on plot2
Prod. activity 2
on plot3
Inv. activity 3
on plot3
Used recourses Available resources
Gross margin 300 295 305 60 58 62 140
Plot1 1 1 0 1
Plot2 1 1 0 1
Plot3 1 1 1 0 1
Other resources
Quantities 0 0 0 0 0 0 0

Gross margin already take into account that plot1 is closer than plot2 and that plot3 is even more close to the farm.
If the farmer decide to invest activity3 (a perennial crop) on plot3, the following years, “Prod. activity 1 on plot3” and “Prod. activity 2 on plot3” will obviously not be available.

This appoach let us fully differentiate between plots, e.g. on their elevation or even on their slope or exposure.

model/agentsbehaviour/space_mip.txt · Last modified: 2014/05/11 15:38 (external edit)
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