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Precision farming needs management rules to apply spatially differentiated treatments in agricultural fields. Digital soil mapping (DSM) tools, for example apparent soil electrical conductivity, corrected to 25°C (EC 25 ), and digital elevation models, try to explain the spatial variation in soil type, soil properties (e.g. clay content), site and crop that are determined by landscape characteristics such as terrain, geology and geomorphology. We examined the use of EC 25 maps to delineate management zones, and identified the main factors affecting the spatial pattern of EC 25 at the regional scale in a study area in eastern Germany. Data of different types were compared: EC 25 maps for 11 fields, soil properties measured in the laboratory, terrain attributes, geological maps and the description of 75 soil profiles. We identified the factors that influence EC 25 in the presence of spatial autocorrelation and field-specific random effects with spatial linear mixed-effects models. The variation in EC 25 could be explained to a large degree (R 2 of up to 61%). Primarily, soil organic matter and CaCO 3 , and secondarily clay and the presence of gleyic horizons were significantly related to EC 25 . Terrain attributes, however, had no significant effect on EC 25 . The geological map unit showed a significant relationship to EC 25 , and it was possible to determine the most important soil properties affecting EC 25 by interpreting the geological maps. Including information on geology in precision agriculture could improve understanding of EC 25 maps. The EC 25 maps of fields should not be assumed to represent a map of clay content to form a basis for deriving management zones because other factors appeared to have a more important effect on EC 25 .

Abstract. The significance of biogenic silicon (BSi) pools as a key factor for the control of Si fluxes from terrestrial to aquatic ecosystems has been recognized for decades. However, while most research has been focused on phytogenic Si pools, knowledge of other BSi pools is still limited. We hypothesized that different BSi pools influence short-term changes in the water-soluble Si fraction in soils to different extents. To test our hypothesis we took plant (Calamagrostis epigejos, Phragmites australis) and soil samples in an artificial catchment in a post-mining landscape in the state of Brandenburg, Germany. We quantified phytogenic (phytoliths), protistic (diatom frustules and testate amoeba shells) and zoogenic (sponge spicules) Si pools as well as Tironextractable and water-soluble Si fractions in soils at the beginning (t 0 ) and after 10 years (t 10 ) of ecosystem development. As expected the results of Tiron extraction showed that there are no consistent changes in the amorphous Si pool at Chicken Creek (Hühnerwasser) as early as after 10 years. In contrast to t 0 we found increased water-soluble Si and BSi pools at t 10 ; thus we concluded that BSi pools are the main driver of short-term changes in water-soluble Si. However, because total BSi represents only small proportions of water-soluble Si at t 0 (< 2 %) and t 10 (2.8-4.3 %) we further concluded that smaller (< 5 µm) and/or fragile phytogenic Si structures have the biggest impact on short-term changes in water-soluble Si. In this context, extracted phytoliths (> 5 µm) only amounted to about 16 % of total Si contents of plant materials of C. epigejos and P. australis at t 10 ; thus about 84 % of small-scale and/or fragile phytogenic Si is not quantified by the used phytolith extraction method. Analyses of small-scale and fragile phytogenic Si structures are urgently needed in future work as they seem to represent the biggest and most reactive Si pool in soils. Thus they are the most important drivers of Si cycling in terrestrial biogeosystems.

Detailed information on soil textural heterogeneity is essential for land management and conservation. It is well known that in individual fields, measurement of the soil's apparent electrical conductivity (ECa) offers an opportunity to map the clay content of soils with free drainage under a humid climate. At the catchment scale, however, units of different land management and differing sampling dates add variation to ECa and constrain the mapping across field boundaries. We analyzed their influence and compared three approaches for applying electromagnetic induction (EMv) to clay‐content mapping at the landscape scale across the boundaries of individual fields and different sampling dates. In the study region, a separate calibration of the relation between clay and ECa for each field and sampling date (fieldwise calibration) yielded satisfactory clay‐content predictions only if the costly precondition of sufficient calibration points for each field was fulfilled. We propose a method (nearest‐neighbors ECa correction) for unifying ECa across boundaries based only on the ECa data themselves, and the assumption of continuity of textural properties at field boundaries, which was fulfilled in the landscape studied. Prediction is calibrated once for the entire landscape, which allows a reduced set of calibration points. The coefficient of determination for predicting clay content (here, including silt <4 μm) was improved from R2 = 0.66 (no correction for land use and sampling date) to R2 = 0.85 (n = 46). With the method developed, ECa offers a powerful and cheap method of clay‐content mapping in agricultural landscapes.

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