A climatic data such as accumulated precipitation, mean temperature and precipitation were collected from the seedling date to time of harvesting for four years yield monitor data obtained from the same field. This is non spatial data because it is just a single value for the entire field in each year without georeferenced information. A variogram analysis had been carried out on each of year data where geostatistical parameter such as nugget, sill, and range were estimated. Does this climatic data has any influence of the outcome of my variogram analysis? How do I use the data to explain my result?
Seedling period (May to September 15)
Temp Precipitation Acc. Precipitation
Cropping year °C mm mm
2008 12.85 2.80 386.62
2009 13.12 1.17 162.10
2010 11.65 2.23 307.60
2011 13.07 1.94 267.60
Usually, because it's called spatial analysis, it's assumed that it's only done to physical space. Actually it might as well be called dimensional analysis because you are studying the continuity of a variable by means of a dimension which can be meters but can also be time (or any other).
In your particular example you could be doing XoY for physical space, and oZ for time. And for that reason that data could be important. The deciding factor, however, is another. It appears (if I have interpreted well) that your spatial data set, and the other one you are considering have different scales. One, let's imagine, is discretized in days, the other is one value per year. Is it a mean, a single sample, or a composition of other kind (like the sum of all days in a period, for instance)?
If both sets are compatible you should use them both (since they're real data) or, at very least, validate one through the other meaning that a spatial estimation model should resemble the information given by the other data set (this is sometimes called a blind test).
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