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I have a dataset of individual farms and I want to model the probability for a farm to select a certain type of irrigation (options: none, surface, sprinkler, drip, other).

As independent variables I have individual farm characteristics, such as soil quality and farmer experience. I read that it is also possible to include alternative-specific variables in the model (i.e. characteristics of the irrigation types). Does it make sense to include these if they do not vary over individuals? For example, the variable 'water efficiency' contributes to the irrigation decision, but is not different per individual.

In case it is useful to include these variables, which R package is suitable for this? From my understanding, the nnet package does not distinguish between different types of variables. Also, the mlogit makes the distinction but gives the error system is computationally singular because I have quadratic variables inside the model (at least, I think that this is the reason).

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  • $\begingroup$ Do you mean that, given a particular water efficiency, the farm will always select a particular type of irrigation, or that the water efficiency is literally the same for every farm? $\endgroup$ – Dave Jun 11 at 17:51
  • $\begingroup$ @Dave, the water efficiency is dependent on the irrigation type and this is the same for every farm, i.e. a drip system has efficiency X, a surface system has efficiency Y, etc. And this does not vary across farms. The reason why I think these variables can still be valuable is because a farm with less rain, for example, may attach higher importance to higher efficiency than a farm which has a lot of rain. $\endgroup$ – Charlotte Jun 14 at 5:40

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