Ole Wendroth

Spatio-temporal patterns and covariance structures of soil water status in two Northeast-German field sites

Abstract Spatio-temporal patterns of soil moisture status highly affect the heterogeneity of soil water and solute transport and leaching of chemicals to the groundwater. In order to quantify and describe spatial variability of ecologically highly relevant spatial and temporal processes linked to soil moisture at the land surface, the spatio-temporal covariance structure and the reasons for its change in time need to be identified. Therefore, soil water pressure head was monitored in two surface horizons between April and November 1995 at two field sites with a shallow ground water table, a sandy loam and a heavy clay soil in north-east Germany. For the 10- and the 30-cm depth of the sandy loam soil and for the 30-cm depth of the heavy clay soil the variance of soil water pressure head h (on log basis) was large under wet conditions. With decreasing soil water pressure head the variance of log 10 (− h ) decreased to a critical value, for which a spatial correlation structure disappeared. With further drying, the variance of log 10 (− h ) increased again, and a spatial range of correlation existed. During drying, temporally stable variation patterns developed at both field sites. The change of variance of log 10 (− h ) which is probably associated with changing degree of heterogeneity of flow conditions validates the findings of Roth (1995) obtained from stochastic flow model calculations. In general, the temporal correlation length was better defined than that of spatial correlation. At both depths, correlation lengths of the sandy loam were larger than those of the heavy clay soil. Random spatial variation of log 10 (− h ) occurred under conditions when the hydraulic gradient was close to zero. With our experimental design we could identify (i) temporal persistence of spatial patterns and correlation ranges of soil water status that can be used for representativity studies, (ii) the within-site variation of land surface moisture status, and (iii) a basis for description of spatial processes of effective soil properties that are linked to soil water status at the land surface.

Review of heat and water movement in field soils

Coupled heat and water transport in soils has enjoyed extensive focus in soil physics and hydrology and yet, until recently, there has never been a satisfactory comparison of water vapor fluxes measured in the field with theory. At least two factors have led to this, first, most of the experimental work has been laboratory oriented with steady state boundary conditions imposed and second, there have been relatively few field experiments to test the existing theory. In this paper we review a new theoretical development which explains field observations of water vapor movement. The diurnal warming at the land surface leads to an expansion and contraction of the soil air as it warms and cools resulting in a convective (or ‘‘advective’’) transport of water vapor. This mechanism has important consequences for the transport of any vapor in the soil air near the landatmosphere interface. # 1998 Published by Elsevier Science B.V. All rights reserved.

Predicting yield of barley across a landscape: a state-space modeling approach

Spatial crop yield prediction is an enigma that needs to be solved to avoid ecological and economical risks in agricultural crop production, that can result from local fertilizer surplus or deficiency. Current approaches for site-specific fertilizer distribution are based on patterns of soil properties and yield maps obtained from previous years. The aim of this study was to evaluate the quality of crop yield prediction in an arable field using two sets of variables in autoregressive (AR) state-space models. One set included detailed soil information (texture, organic carbon content) and yield data from the previous year at a high spatial resolution. In the other set, remotely sensed soil and crop information (vegetation index, crop nitrogen status, land surface elevation) was assembled, which is available under farm conditions without intensive soil sampling campaigns. Soil and remotely sensed variables were evaluated in bi- and multivariate autoregressive state-space analysis to predict spring barley grain yield. Remotely sensed variables showed to be better predictors for spatial grain yield estimation than soil variables. Transition coefficients determined from state-space analysis were applied in AR-equations with soil and remotely sensed information, but yet given only the initial value of the spatial yield series. Both sets of variables elicited similar prediction quality.

Estimating unsaturated soil hydraulic properties from laboratory tension disc infiltrometer experiments

Four tension disc infiltration experiments were carried out on a loamy soil in the laboratory for the purpose of estimating the unsaturated soil hydraulic properties. Sixteen tensiometers were installed in pairs at the following coordinate (r, z) positions: (10, 2.5), (10, 5), (10, 10), (15, 5), (15, 10), (15, 15), (15, 20), and (15, 30), where r represents the distance from the axis of symmetry and z is the location below the soil surface. A time domain reflectometry (TDR) probe was used to measure water contents at a depth of 2 cm directly below the tension disc. The first three experiments involved supply pressure heads at the disc of 220, 210, 25, and 21 cm, with the experiment lasting for ;5 hours. The same supply pressure heads were also used for the fourth experiment, which lasted 6.25 days so as to reach steady state at each applied tension. The measured data were analyzed using Woodings (1968) analytical solution and by numerical inversion. The parameter estimation method combined a quasi three-dimensional numerical solution of the Richards equation with the Marquardt-Levenberg optimization scheme. The objective function for the parameter estimation analysis was defined using different combinations of the cumulative infiltrated volume, TDR readings, and tensiometer measurements. The estimated hydraulic properties were compared against results obtained with an evaporation experiment as analyzed with Winds (1968) method. Water contents in the retention curves were underestimated when both transient and quasi steady state experiments were analyzed by parameter estimation. Unsaturated hydraulic conductivities obtained by parameter estimation and using Woodings (1968) analysis corresponded well. Drying branches of the hydraulic conductivity function determined by parameter estimation also corresponded well with those obtained with the evaporation method.

Effect of sampling volume on the measurement of soil physical properties: simulation with x-ray tomography data

The dependence of macroscopic soil parameters on sampling volume is currently the object of renewed research focus. In this paper, x-ray computed tomography data related to cores obtained in two different locations in a field soil are used to simulate this dependence. Several integration methods are adopted, to mimic different measuring devices. Calculation results, relative to the volumetric water content, volumetric air content, gravimetric water content and dry bulk density, demonstrate that the size (up to 60×60×30 mm3), shape and positioning of sampling volumes influence significantly the measured values of soil parameters. In some cases, the instrumental dependence disappears within a range of sampling volumes, in agreement with a hypothesis underlying the so-called representative elementary volume concept. However, some parameters, like the soil bulk density, do not level off with increasing sampling volumes. These observations open new avenues for research on measurement processes in soils and other heterogeneous media.

A comparison of two methods to predict the landscape-scale variation of crop yield

Landscape-scale variation is a source of information that increasingly is being taken into consideration in agricultural and environmental studies. Models that encompass and interpret this variation in fields and across contrasting management practices have the potential to improve the landscape management of agroecosystems. Our objective was to compare the results of two approaches, analysis of covariance (ANCOVA) and state-space modeling, to determine the factors affecting grain yield in three crop rotations [pea (Pisum sativum L.)–wheat (Triticum aestivum L.)–barley (Hordeum vulgare L.), canola (Brassica napus L.)–wheat–barley, and wheat–wheat–barley] at two sites in Saskatchewan, Canada. Crop rotations were established in adjacent plots arranged in a randomized complete block with five replicates. Variables that were expected to affect resource availability and pest infestations in wheat (second rotation phase) or barley (third rotation phase) were measured. Each sampling point was classified according to landscape position as either a shoulder or footslope. Landscape position was considered as a cross-classified treatment along with crop rotation, and analyzed using ANCOVA procedures. State-space modeling was conducted on a single transect connecting sampling points across all of the rotations and replicates at each site. ANCOVA frequently indicated that grain yield and other measured variables differed between landscape position across all rotations, or in a specific crop rotation. For example, grain yield, soil water content, soil N availability during the growing season, and the incidence of common root rot were higher in the footslopes than the shoulders in all of the crop rotations at one of the sites. However, the landscape position effect for grain yield was never fully explained by the landscape position effects detected for the other variables (e.g., higher soil water content in the footslopes did not correspond with higher grain yields in footslope positions at both sites). State-space modeling indicated that most of the measured variables contributed to the prediction of landscape-scale variation for grain yield in the pea–wheat rotation; whereas only leaf and root disease incidences explained landscape-scale variation in the wheat–wheat rotation. The selective omission of data indicated that state-space modeling was accounting for the varied importance of the predictors across the landscape; i.e., localized response functions. The major reason that ANCOVA did not explain landscape-scale variation of grain yield across the different crop rotations may be because it was unable to account for highly localized variation. However, there is evidence from other studies that the ANCOVA approach is appropriate when the response functions explaining grain yield do not vary significantly within the study area. This situation is most likely to occur in studies with smaller experimental areas. Future research conducted at scales reflecting ‘real world’ field conditions (i.e., study units representative of producer’s fields) should consider the use of state-space modeling or alternative statistical techniques that are designed to address and predict the complex and dynamic nature of landscape-scale processes.

State-space prediction of field-scale soil water content time series in a sandy loam

Abstract The description of field soil water content time series can be affected by uncertainty due to measurement errors of the respective state variables, errors due to assumptions underlying the model, and errors in the determination of boundary conditions. In this study, a simple state-equation was applied for predicting field soil water contents at three different soil depths. The simple state-model yielded large deviations of predictions from the measured soil water content, especially for the upper soil depth. Apparently, the magnitude of the estimated evaporation rate was too high. The prediction result could significantly be improved when the calculated evaporation was reduced by a factor of 0.7. In order to account for uncertainty sources associated with this simple approach, the state-equation was combined with a stochastic technique, the so-called Kalman–Filter. Applying the Kalman–Filter, the prediction quality significantly increased, even when the erroneously high evaporation was assumed to be true. However, prediction uncertainty increased for the same time periods, for which it was shown earlier that spatial correlation of soil water status was either random or very short. When the Kalman–Filter was applied in a scenario to the surface layer only, simulated soil water content in layers 2 and 3 agreed to measurements and were highly improved compared to simulations when layer 1 was not filtered. Hence, application of lab determined soil hydraulic property functions in combination with state observations of upper soil horizon water content and with the Kalman–Filter provides a promising opportunity to describe and predict soil water contents for entire soil profiles even under the presence of uncertainty sources.

Identifying, understanding, and describing spatial processes in agricultural landscapes — four case studies

Abstract To evaluate the quality of the ecosystem and for making resources and land management decisions landscapes have to be assessed quantitatively. For a better understanding of landscape processes and their characterization, the analysis of the inherent variability is a major factor. Four case studies in which problems associated with landscape analysis are discussed. Spatial processes remain a main focus, as their analysis provides information on the relation between relevant state variables in agricultural landscapes. Variogram analysis showed that mineral soil nitrogen (Nmin) sampled in a field at different scales, domains, and times is an instationary spatial process. Spatial association of grain yield, soil index and remotely sensed vegetation index may not be identifiable from kriged contour maps as local coincidence may be obscured behind classified areas. Crop yield in subsequent years and remotely sensed information are not related if a unique response is assumed. An alternative data stratification procedure is described here for the identification of different response functions in agricultural ecosystems. Processes of crop yield and underlying variables are described in autoregressive state-space models. This technique incorporates both deterministic and stochastic relations between different variables and is based on relative changes in space.

Assessment of soil structure using X-ray computed tomography

Abstract Assessment of soil structure, characterized by complex morphological and functional properties, is difficult because most conventional soil physical investigations are destructive and variable in spatial resolution. The use of X-ray computed tomography, as a non-destructive technique, presents significant progress. It can be used to study soil structure at the millimetre scale, e.g. with a resolution of 0.25 mm in the horizontal direction and 1 mm in the vertical direction for the reported study. The measured Hounsfield Unit (HU) values characterize X-ray attenuation for each volume element of the soil core samples. From HU values, soil physical properties of soil cores or their subunits can be derived. They enable: (i) visual assessment of the soil structural condition through inspection of the X-ray CT images; (ii) 3D visualization of air-filled macropores; and (iii) calculation of the mean dry bulk density and standard deviation of voxel-related HU values for successive slices of soil cores. The degradation of structure of loamy and silty soils by tillage could be assessed by CT through quantification of decreased air-filled porosity, destroyed macropore connectivity, increased dry bulk density and decreased standard deviation of HU values in horizontal slices. Small-scale compactions near earthworm burrows could also be detected.

Describing water flow in macroporous field soils using the modified macro model

Abstract Preferential soil water movement was simulated using a modified version of the MACRO model. The study contributes to the analysis of the field water regime and the solute balance of two tile-drained no-till agricultural soils influenced by shallow groundwater tables. Previously measured soil matric potentials and groundwater levels as well as Bromide and dye tracer experiments indicated that flow and transport along preferential pathways might be quantitatively important at the heavy clay as well as at the sandy loam site. The objective of this study was to optimally describe the field measured matric potentials and groundwater level fluctuations by using a one- and a two-domain version of the MACRO model and by model calibration using available data. The original MACRO model was modified with respect to simulating effects of groundwater table fluctuations and tile drains in the one-dimensional flow model by including a sink term, based on the potential theory and an empirical approach, that approximately relates the local to the field water regime. We use an implicit finite difference discretization together with a Newton–Raphson iterative scheme for the numerical solution of the model. The coupled matrix and macropore flow equations are solved sequentially. Model parameters were calibrated using laboratory-measured soil hydraulic parameters and field-measured time series of matric potentials and water tables. Comparisons between the measured and simulated water table fluctuations indicate that the consideration of preferential flow in macropores improves the one-dimensional description of the water regime of the aggregated clayey soil. For the sandy loam soil, neither the modified MACRO nor a one-domain model could explain observed preferential flow patterns. The failure at the sandy loam site may possibly be caused by periodically occurring hydrophobic conditions at the soil surface or by the existence of fissures in a relatively low permeable subsoil horizon, effects which could not be considered in the model. Hydraulic parameters of the soil matrix and macropore system could satisfactorily be calibrated. However, the parameters of the exchange term describing water transfer between the inter- and intraaggregate pore system could not be clearly identified using the available field data.