Abstract
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.
Highlights
The rapid development of sensor technology and ICT sector enabled emergence of new branches of economic activity and that is precision farming in the agricultural sector
Since the 1970’s salt and moisture content of soils have been monitored by contact measurement of electric conductivity (Rhoades et al, 1976) and later electromagnetic induction method were used at different depths (Rhoades and Corwin, 1981)
“Yield monitor data must be combined with mapping software and other spatial data layers in order to produce a thematic yield map showing variations in grain yield, moisture content and/or other yield related parameters sensed and recorded during yield monitoring
Summary
The rapid development of sensor technology and ICT sector enabled emergence of new branches of economic activity and that is precision farming in the agricultural sector. Research studies (Sudduth et al, 1995; Lund et al, 1999) have shown how mapping soil electrical conductivity can be a good surrogate measurement for spatially variable factors that are not easy to sense and map such as soil type and moisture content (Stafford, 2000). Researches have initially been fascinated by the technical possibilities to predict soil variables and crop growth stages by various proximal and remote sensing methods but farmers have been rather reluctant to adapt new technology at the beginning. They were and they are basically interested in yield and profit, and nowadays more and more in crop quality and profit. An important step in generating a good thematic map is deciding how the data will be interpolated” (Souza et al, 2016)
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