I am using the 'krige.cv' function from the gstat toolbox in R. It does not take any 'newdata' as input for prediction ( as in universal kriging). How can I predict using the fitted model from this krige.cv function?
Well cross validation is a method to help test your models when you do not have enough data to separate into train and validation datasets. So by doing k-fold cross validation you can see how the performance of your model varies as the training/validation data partitions are changed.
If you are happy with the CV performance, then just implement using the krige function or gstat and predict (noting the quote from the vingette below)
Function krige is a simple wrapper method around gstat and predict for univariate kriging predic- tion and conditional simulation methods available in gstat. For multivariate prediction or simulation, or for other interpolation methods provided by gstat (such as inverse distance weighted interpolation or trend surface interpolation) use the functions gstat and predict directly.