The concept of precision agriculture is straightforward at the scientific level but even basic goals are blurred at the level of everyday practice in the Hungarian crop production despite the fact that several elements of the new technology have already been applied. The industrial and the service sectors offer many products and services to the farmers but crop producers do not get enough support to choose between different alternatives. Agricultural higher education must deliver this support directly to the farmers and via the released young graduates. The price of agricultural land must be higher if well-organized data underpin the production potential of the fields. Accumulated database is a form of capital. It must be owned by the farmers but in a data-driven economy its sharing will generate value for both farmers and the society as a whole.

We present a methodological approach in which simple models were applied to predict yield by using only those yield data which spatially coincide with the soil data and the remaining yield data and the models were used to test different sampling and interpolation approaches commonly applied in precision agriculture. Three strategies for composite sample collection and three interpolation methods were compared. Multiple regression models were developed to predict yields. R2 values were used to select among the applied methods.