ALBERT

Description: Agricultural soil landscapes of hummocky ground moraines are characterized by 3D spatial patterns of soil types that result from profile modifications due to the combined effect of water and tillage erosion. We hypothesize that crops reflect such soil landscape patterns by increased or reduced plant and root growth. Root development may depend on the thickness and vertical sequence of soil horizons as well as on the structural development state of these horizons at different landscape positions. The hypotheses were tested using field data of the root density ( RD ) and the root lengths ( RL ) of winter wheat using the minirhizotron technique. We compared data from plots at the CarboZALF-D site (NE Germany) that are representing a non-eroded reference soil profile (Albic Luvisol) at a plateau position, a strongly eroded profile at steep slope (Calcaric Regosol), and a depositional profile at the footslope (Anocolluvic Regosol). At each of these plots, three Plexiglas access tubes were installed down to approx. 1.5 m soil depth. Root measurements were carried out during the growing season of winter wheat (September 2014–August 2015) on six dates. The root length density ( RLD ) and the root biomass density were derived from RD values assuming a mean specific root length of 100 m g −1 . Values of RD and RLD were highest for the Anocolluvic Regosol and lowest for the Calcaric Regosol. The maximum root penetration depth was lower in the Anocolluvic Regosol because of a relatively high and fluctuating water table at this landscape position. Results revealed positive relations between below-ground (root) and above-ground crop parameters ( i.e ., leaf area index, plant height, biomass, and yield) for the three soil types. Observed root densities and root lengths in soils at the three landscape positions corroborated the hypothesis that the root system was reflecting erosion-induced soil profile modifications. Soil landscape position dependent root growth should be considered when attempting to quantify landscape scale water and element balances as well as agricultural productivity.

Description: The determination of precipitation ( P ) is still a challenge, but central for quantifying soil water and element balances. Time series of mass changes (Δ M ) from high precision weighing lysimeter may be used to estimate P if deep drainage rates are determined independently. High temporal resolution, however, is accompanied by problems such as correlated data and noise. The objective was to analyze the temporal autocorrelation ( AC ) in Δ M time series and to identify temporal resolutions for determining uncorrelated P rates. Minute-based time series of Δ M are analyzed; the data have been recorded at the UMS Science Lysimeters that are located in Dedelow (northeast Germany) as part of the TERENO SoilCan lysimeter network. Periods in 2012 and 2013 were selected in which the wind speed was below 6 ms −1 . Data noise-correction was carried out by using a moving average before the Δ M values cumulated over 60, 30, and 10 min intervals were compared. On a monthly basis, the temporal AC lengths for Δ M were larger in spring (68 min), autumn (62 min), and winter (76 min) than in the summer (23 min). These AC lengths reflected mainly the effect of differences in P -rates and -duration between lower-intensity rainfall and shorter summer storms. The monthly sums of P based on the 60-min interval were up to 20% lower than those obtained by using the 10-min intervals. For P -values obtained by summing up the Δ M over periods shorter than the autocorrelation length, oscillated fluctuations in Δ M did not cancel out within an interval. The temporal autocorrelation in the highly-resolved lysimeter data limited the evaluation of Δ M time series. Compared to rain gauge data, the P -rates obtained from the weighing lysimeters were generally higher. However, this difference decreased when increasing the time interval for cumulating mass changes. Cumulated positive Δ M values based on time intervals larger than the AC length ( e.g ., 60 min) provided an optimal approximation of the quantity of P , but on the expense of a loss in temporal resolution limited by the AC lengths. Smoothing could reduce noise in the original lysimeter data; however, not the validity problems that are related to the temporal AC .