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@INPROCEEDINGS{russ2010ida,
     author = {Ru{\ss}, Georg and Brenning, Alexander},
     editor = {R. Cohen, Paul and M. Adams, Niall and Berthold, Michael R.},
      title = {Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture},
  booktitle = {Proceedings of IDA2010},
     series = {LNCS},
     volume = {6065},
       year = {2010},
      pages = {184--195},
  publisher = {Springer},
    address = {Heidelberg},
       isbn = {978-3-642-13061-8},
        url = {http://www.springerlink.com/content/p63pn0561u18r34w/},
        doi = {10.1007/978-3-642-13062-5_18},
   abstract = {Precision Agriculture applies state-of-the-art GPS technology in connection with site-specific, sensor-based crop management. It can also be described as a data-driven approach to agriculture, which is strongly connected with a number of data mining problems. One of those is also an inherently important task in agriculture: yield prediction. Given a yield prediction model, which of the predictor variables are the important ones? 
In the past, a number of approaches have been proposed towards this problem. For yield prediction, a broad variety of regression models for non-spatial data can be adapted for spatial data using a novel spatial cross-validation technique. Since this procedure is at the core of variable importance assessment, it will be briefly introduced here. Given this spatial yield prediction model, a novel approach towards assessing a variable’s importance will be presented. It essentially consists of picking each of the predictor variables, one at a time, permutating its values in the test set and observing the deviation of the model’s RMSE. This article uses two real-world data sets from precision agriculture and evaluates the above procedure.}
}