A patch-size index to assess machinery to match soil and crop spatial variability.

Abstract The increase of machinery size to gain work productivity gives concerns that spatial variability cannot be addressed sufficiently when using PF methods. Data from soil electrical conductivity sensors (EM38) and canopy light reflectance sensors (YARA N-sensor) from fields in Denmark were analysed. The geo-statistical analysis included a determination of semi-variograms and the main parameters from them. In order to directly evaluate the matching of soil or crop variability to VRA machinery size, a patch-size index was used. The index is identical with the mean correlation distance (MCD) calculated from semi-variogram data. The index values varied highly between fields as well as between data sources. It was surprising, that the values from the crop data were almost always smaller than those from the soil sensor. The common machinery size of the regions concerned (20 m and more working width) did not fit to the spatial resolution of the crop plant needs, but fitted better to spatial structures of soil parameters. The conclusion is that on some fields an existing potential for optimising inputs cannot be reached due to inappropriate machinery size. A decision tree based on variogram parameters is suggested to support farmers in matching machinery size to existing farm and field variability.