1. Objectives. A major topic of applied soil science is fertility research on agricultural land which aims to find relationships in site-soil-crop ecosystems and the steering soil factors of crop production, especially. Variable Rate Technology (VRT) of applications in Precision Farming (PF), e.g. seeding rate and yield-oriented fertilizing, demand for algorithms which use quantitative soil-yield functions. Within given climatic and agronomic boundary conditions, they have to predict soil-site specific target yields (TY) for crops by soil and land parameters. Vice versa within the extended causal chain of pedology (ECCP), data of crop yields may be an important indicator of Jennys organism (o) factor and help to predict and map the occurrence of soils. Both sides of the research medal can make profit from yield maps which are generated by yield flux sensors as a new standard equipment of harvestor combines.

2. Data sources and methods of evaluation. Relative local yield data of cereals (% of field means at ~10*10m pedocells; winter wheat YW, barley YB, oilseed rape (YR) and sugar beets (YS) were collected in the harvest years 1998 to 2005. The farms are located in the hilly, young morainic soilscapes of Holstein (North Germany, 8 degree C. mean temperature, 750mm annual precipitation), but also in various landscapes of total Germany with very different conditions (see preagro farms: poster Herbst&Lamp). First, technical and random variations of local yield data from several yield flux sensors are eliminated as "errors" (segment starts, narrow finishes, sensor/dGPS malfunctions). The corrected point-like data are calculated into percentages of yearly field means and interpolated by kriging. Detailed site parameter maps of slopes, aspects and local catchments are generated by raster GIS routines from 1m-isoline or RTK-GPS elevation data. Digital soil parameter maps stem from the old German Soil Ratings (texture classes and %-fertility-codes of rating zones: “Bodenzahlen”) or have been sensed along tramlines as soil electrical conductivity (EC) by the EM38-sonde. After calibration of soil data sources by a hydraulic 1.5m soil auger, special (AWC) and generic maps (e.g. soil types and texture substrates) are stored, managed and evaluated in the GIS (ArcView, Idrisi: total ~10,000 ha*a).

3. Results. (a) Special yield effects are detected, extracted and evaluated for different kinds of field border zones (10-30% yield reduction on headends, next to hedges/ forests/ ways etc.). (b) Frequency tables show the means and standard errors of yields within soil type zones (acc. to the German classification). Measures of contingency are generally low between crop yield statistics and soil taxa. (c ) Soil texture classes (substrates) show higher measures of contingency and correlation of yield statistics with the texture related AWC (available water capacity). For dry years and in precipitation-limited regions of Germany (<550mm) the soil parameter AWC may explain >50% of the yield variance, but under temperate-wet conditions (>650mm) the correlations may approximate zero (or even become negative). (d) Especially under the latter conditions, relief parameter show joint patterns and complex, year-specific correlations with the yield statistics of soilscapes. In dry years, land depressions tend to have higher yields, in dry years the hilltops, and middle slopes are rather stable zones. (e) German soil rating codes are related to texture classes and quite often perform poorly as actual crop response predictors (similar c).