Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Site-specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (ECa) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and ECa. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.

Exploring the spatial relations between soil properties and electro-magnetic induction (EMI) and the implications for management

The relations between soil electrical conductivity (ECa) and top- and sub-soil physical properties were examined for an arable field in England. The correlation coefficients between ECa and the soil particle size fractions were large and their cross variograms showed that the coregionalization was also strong. The coregionalization was stronger for the subsoil properties than for the topsoil, the reverse to the correlation coefficients. The relations between ECa and some soil properties, such as clay and water content, appear complex and emphasize that a map of ECa cannot substitute for sampling the soil.

Degradation of atrazine and isoproturon in surface and sub-surface soil materials undergoing different moisture and aeration conditions

The influence of different moisture and aeration conditions on the degradation of atrazine and isoproturon was investigated in environmental samples aseptically collected from surface and sub-surface zones of agricultural land. The materials were maintained at two moisture contents corresponding to just above field capacity or 90% of field capacity. Another two groups of samples were adjusted with water to above field capacity, and, at zero time, exposed to drying-rewetting cycles. Atrazine was more persistent (t(1/2) = 22-3S days) than isoproturon (t(1/2) = 5-17 days) in samples maintained at constant moisture conditions. The rate of degradation for both herbicides was higher in samples maintained at a moisture content of 90% of field capacity than in samples with higher moisture contents. The reduction in moisture content in samples undergoing desiccation from above field capacity to much lower than field capacity enhanced the degradation of isoproturon (t(1/2) = 9-12 days) but reduced the rate of atrazine degradation (t(1/2) = 23-35-days). This demonstrates the variability between different micro-organisms in their susceptibility to desiccation. Under anaerobic conditions generated in anaerobic jars, atrazine degraded much more rapidly than isoproturon in materials taken from three soil profiles (0-250 cm depth). It is suggested that some specific micro-organisms are able to survive and degrade herbicide under severe conditions of desiccation. (C) 2004 Society of Chemical Industry.

Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihood

It has been generally accepted that the method of moments (MoM) variogram, which has been widely applied in soil science, requires about 100 sites at an appropriate interval apart to describe the variation adequately. This sample size is often larger than can be afforded for soil surveys of agricultural fields or contaminated sites. Furthermore, it might be a much larger sample size than is needed where the scale of variation is large. A possible alternative in such situations is the residual maximum likelihood (REML) variogram because fewer data appear to be required. The REML method is parametric and is considered reliable where there is trend in the data because it is based on generalized increments that filter trend out and only the covariance parameters are estimated. Previous research has suggested that fewer data are needed to compute a reliable variogram using a maximum likelihood approach such as REML, however, the results can vary according to the nature of the spatial variation. There remain issues to examine: how many fewer data can be used, how should the sampling sites be distributed over the site of interest, and how do different degrees of spatial variation affect the data requirements? The soil of four field sites of different size, physiography, parent material and soil type was sampled intensively, and MoM and REML variograms were calculated for clay content. The data were then sub-sampled to give different sample sizes and distributions of sites and the variograms were computed again. The model parameters for the sets of variograms for each site were used for cross-validation. Predictions based on REML variograms were generally more accurate than those from MoM variograms with fewer than 100 sampling sites. A sample size of around 50 sites at an appropriate distance apart, possibly determined from variograms of ancillary data, appears adequate to compute REML variograms for kriging soil properties for precision agriculture and contaminated sites. (C) 2007 Elsevier B.V. All rights reserved.

Determining nugget: sill ratios of standardized variograms from aerial photographs to krige sparse soil data

Maps of kriged soil properties for precision agriculture are often based on a variogram estimated from too few data because the costs of sampling and analysis are often prohibitive. If the variogram has been computed by the usual method of moments, it is likely to be unstable when there are fewer than 100 data. The scale of variation in soil properties should be investigated prior to sampling by computing a variogram from ancillary data, such as an aerial photograph of the bare soil. If the sampling interval suggested by this is large in relation to the size of the field there will be too few data to estimate a reliable variogram for kriging. Standardized variograms from aerial photographs can be used with standardized soil data that are sparse, provided the data are spatially structured and the nugget:sill ratio is similar to that of a reliable variogram of the property. The problem remains of how to set this ratio in the absence of an accurate variogram. Several methods of estimating the nugget:sill ratio for selected soil properties are proposed and evaluated. Standardized variograms with nugget:sill ratios set by these methods are more similar to those computed from intensive soil data than are variograms computed from sparse soil data. The results of cross-validation and mapping show that the standardized variograms provide more accurate estimates, and preserve the main patterns of variation better than those computed from sparse data.

The analysis of ranked observations of soil structure using indicator geostatistics

Structure is an important physical feature of the soil that is associated with water movement, the soil atmosphere, microorganism activity and nutrient uptake. A soil without any obvious organisation of its components is known as apedal and this state can have marked effects on several soil processes. Accurate maps of topsoil and subsoil structure are desirable for a wide range of models that aim to predict erosion, solute transport, or flow of water through the soil. Also such maps would be useful to precision farmers when deciding how to apply nutrients and pesticides in a site-specific way, and to target subsoiling and soil structure stabilization procedures. Typically, soil structure is inferred from bulk density or penetrometer resistance measurements and more recently from soil resistivity and conductivity surveys. To measure the former is both time-consuming and costly, whereas observations made by the latter methods can be made automatically and swiftly using a vehicle-mounted penetrometer or resistivity and conductivity sensors. The results of each of these methods, however, are affected by other soil properties, in particular moisture content at the time of sampling, texture, and the presence of stones. Traditional methods of observing soil structure identify the type of ped and its degree of development. Methods of ranking such observations from good to poor for different soil textures have been developed. Indicator variograms can be computed for each category or rank of structure and these can be summed to give the sum of indicator variograms (SIV). Observations of the topsoil and subsoil structure were made at four field sites where the soil had developed on different parent materials. The observations were ranked by four methods and indicator and the sum of indicator variograms were computed and modelled for each method of ranking. The individual indicators were then kriged with the parameters of the appropriate indicator variogram model to map the probability of encountering soil with the structure represented by that indicator. The model parameters of the SIVs for each ranking system were used with the data to krige the soil structure classes, and the results are compared with those for the individual indicators. The relations between maps of soil structure and selected wavebands from aerial photographs are examined as basis for planning surveys of soil structure. (C) 2007 Elsevier B.V. All rights reserved.

Problems with determining the particle size distribution of chalk soil and some of their implications

Particle size distribution (psd) is one of the most important features of the soil because it affects many of its other properties, and it determines how soil should be managed. To understand the properties of chalk soil, psd analyses should be based on the original material (including carbonates), and not just the acid-resistant fraction. Laser-based methods rather than traditional sedimentation methods are being used increasingly to determine particle size to reduce the cost of analysis. We give an overview of both approaches and the problems associated with them for analyzing the psd of chalk soil. In particular, we show that it is not appropriate to use the widely adopted 8 pm boundary between the clay and silt size fractions for samples determined by laser to estimate proportions of these size fractions that are equivalent to those based on sedimentation. We present data from field and national-scale surveys of soil derived from chalk in England. Results from both types of survey showed that laser methods tend to over-estimate the clay-size fraction compared to sedimentation for the 8 mu m clay/silt boundary, and we suggest reasons for this. For soil derived from chalk, either the sedimentation methods need to be modified or it would be more appropriate to use a 4 pm threshold as an interim solution for laser methods. Correlations between the proportions of sand- and clay-sized fractions, and other properties such as organic matter and volumetric water content, were the opposite of what one would expect for soil dominated by silicate minerals. For water content, this appeared to be due to the predominance of porous, chalk fragments in the sand-sized fraction rather than quartz grains, and the abundance of fine (<2 mu m) calcite crystals rather than phyllosilicates in the clay-sized fraction. This was confirmed by scanning electron microscope (SEM) analyses. "Of all the rocks with which 1 am acquainted, there is none whose formation seems to tax the ingenuity of theorists so severely, as the chalk, in whatever respect we may think fit to consider it". Thomas Allan, FRS Edinburgh 1823, Transactions of the Royal Society of Edinburgh. (C) 2009 Natural Environment Research Council (NERC) Published by Elsevier B.V. All rights reserved.

Competition in tree row agroforestry systems. 3. Soil water distribution and dynamics

The purpose of this study was to test the hypothesis that soil water content would vary spatially with distance from a tree row and that the effect would differ according to tree species. A field study was conducted on a kaolinitic Oxisol in the sub-humid highlands of western Kenya to compare soil water distribution and dynamics in a maize monoculture with that under maize (Zea mays L.) intercropped with a 3-year-old tree row of Grevillea robusta A. Cunn. Ex R. Br. (grevillea) and hedgerow of Senna spectabilis DC. (senna). Soil water content was measured at weekly intervals during one cropping season using a neutron probe. Measurements were made from 20 cm to a depth of 225 cm at distances of 75, 150, 300 and 525 cm from the tree rows. The amount of water stored was greater under the sole maize crop than the agroforestry systems, especially the grevillea-maize system. Stored soil water in the grevillea-maize system increased with increasing distance from the tree row but in the senna-maize system, it decreased between 75 and 300 cm from the hedgerow. Soil water content increased least and more slowly early in the season in the grevillea-maize system, and drying was also evident as the frequency of rain declined. Soil water content at the end of the cropping season was similar to that at the start of the season in the grevillea-maize system, but about 50 and 80 mm greater in the senna-maize and sole maize systems, respectively. The seasonal water balance showed there was 140 mm, of drainage from the sole maize system. A similar amount was lost from the agroforestry systems (about 160 mm in the grevillea-maize system and 145 mm in the senna-maize system) through drainage or tree uptake. The possible benefits of reduced soil evaporation and crop transpiration close to a tree row were not evident in the grevillea-maize system, but appeared to greatly compensate for water uptake losses in the senna-maize system. Grevillea, managed as a tree row, reduced stored soil water to a greater extent than senna, managed as a hedgerow.

Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements - I. A universal approach to calculate thermal inertia

In this paper we pledge that physically based equations should be combined with remote sensing techniques to enable a more theoretically rigorous estimation of area-average soil heat flux, G. A standard physical equation (i.e. the analytical or exact method) for the estimation of G, in combination with a simple, but theoretically derived, equation for soil thermal inertia (F), provides the basis for a more transparent and readily interpretable method for the estimation of G; without the requirement for in situ instrumentation. Moreover, such an approach ensures a more universally applicable method than those derived from purely empirical studies (employing vegetation indices and albedo, for example). Hence, a new equation for the estimation of Gamma(for homogeneous soils) is discussed in this paper which only requires knowledge of soil type, which is readily obtainable from extant soil databases and surveys, in combination with a coarse estimate of moisture status. This approach can be used to obtain area-averaged estimates of Gamma(and thus G, as explained in paper II) which is important for large-scale energy balance studies that employ aircraft or satellite data. Furthermore, this method also relaxes the instrumental demand for studies at the plot and field scale (no requirement for in situ soil temperature sensors, soil heat flux plates and/or thermal conductivity sensors). In addition, this equation can be incorporated in soil-vegetation-atmosphere-transfer models that use the force restore method to update surface temperatures (such as the well-known ISBA model), to replace the thermal inertia coefficient.

Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements - II. Diurnal shape of soil heat flux

For vegetated surfaces, calculation of soil heat flux, G, with the Exact or Analytical method requires a harmonic analysis of below-canopy soil surface temperature, to obtain the shape of the diurnal course of G. When determining G with remote sensing methods, only composite (vegetation plus soil) radiometric brightness temperature is available. This paper presents a simple equation that relates the sum of the harmonic terms derived for the composite radiometric surface temperature to that of belowcanopy soil surface temperature. The thermal inertia, Gamma(,) for which a simple equation has been presented in a companion paper, paper I, is used to set the magnitude of G. To assess the success of the method proposed in this paper for the estimation of the diurnal shape of G, a comparison was made between 'remote' and in situ calculated values from described field sites. This indicated that the proposed method was suitable for the estimation of the shape of G for a variety of vegetation types and densities. The approach outlined in paper I, to obtain Gamma, was then combined with the estimated harmonic terms to predict estimates of G, which were compared to values predicted by empirical remote methods found in the literature. This indicated that the method proposed in the combination of papers I and II gave reliable estimates of G, which, in comparison to the other methods, resulted in more realistic predictions for vegetated surfaces. This set of equations can also be used for bare and sparsely vegetated soils, making it a universally applicable method. (C) 2007 Elsevier B.V. All rights reserved.

Effects of metals on life cycle parameters of the earthworm Eisenia fetida exposed to field-contaminated, metal-polluted soils

Two control and eight field-contaminated, metal-polluted soils were inoculated with Eisenia fetida (Savigny, 1826). Three, 7, 14, 21, 28 and 42 days after inoculation, earthworm survival, body weight, cocoon production and hatching rate were measured. Seventeen metals were analysed in E.fetida tissue, bulk soil and soil solution. Soil organic carbon content, texture, pH and cation exchange capacity were also measured. Cocoon production and hatching rate were more sensitive to adverse conditions than survival or weight change. Soil properties other than metal concentration impacted toxicity. The most toxic soils were organic-poor (1-10 g C kg(-1)), sandy soils (c. 74% sand), with intermediate metal concentrations (e.g. 7150-13, 100 mg Ph kg(-1), 2970-53,400 mg Zn kg(-1)). Significant relationships between soil properties and the life cycle parameters were determined. The best coefficients of correlation were generally found for texture, pH, Ag, Cd, Mg, Pb, Tl, and Zn both singularly and in multivariate regressions. Studies that use metal-amended artificial soils are not useful to predict toxicity of field multi-contaminated soils. (c) 2007 Elsevier Ltd. All rights reserved.

Uptake kinetics of metals by the earthworm Eisenia fetida exposed to field-contaminated soils

It is well known that earthworms can accumulate metals. However, most accumulation studies focus on Cd-, Cu-, Pb- or Zn-amended soils, additionally few studies consider accumulation kinetics. Here we model the accumulation kinetics of 18 elements by Eisenia fetida, exposed to 8 metal-contaminated and 2 uncontaminated soils. Tissue metal concentration was determined after 3, 7,14, 21, 28 and 42 days. Metal elimination rate was important in determining time to reach steady-state tissue metal concentration. Uptake flux to elimination rate ratios showed less variation and lower values for essential than for non-essential metals. In theory kinetic rate constants are dependent only on species and metal. Therefore it should be possible to predict steady-state tissue metal concentrations on the basis of very few measurements using the rate constants. However, our experiments show that it is difficult to extrapolate the accumulation kinetic constants derived using one soil to another. (C) 2009 Elsevier Ltd. All rights reserved.

Hydrological controls on soil redox dynamics in a peat-based, restored wetland

Increasing areas of altered wetland are being restored by re-flooding the soil. Evidence in the literature indicates that this practice can induce the redox-mediated release of soil nutrients, thereby increasing the risk of diffuse water pollution. However, for the sake of improving wedand management decisions, there is a need for more detailed studies of the underlying relationship between the hydrological and redox dynamics that explain this risk; this is particularly the case in agricultural peatlands that are commonly targeted for the creation of lowland wet grassland. A 12-month field study was conducted to evaluate the relationship between hydrological fluctuations and soil redox potential (Eh) in a nutrient-rich peat field (32 g N kg(-1) and 1100 mg P kg(-1) in the surface 0-30 cm soil) that had been restored as lowland wet grassland from intensive arable production. Field tensiometers were installed at the 30-, 60- and 90-cm soil depths, and Pt electrodes at the 10-, 30-, 60- and 90-cm depths, for daily logging of soil water tension and Eh, respectively. The values for soil water tension displayed a strong negative relationship (P < 0.001) with monthly dip well observations of water table height. Calculations of soil water potential from the logged tension values were used, therefore, to provide a detailed profile of field water level and, together with precipitation data, explained some of the variation in Eh. For example, during the summer, alternating periods of aerobism (Eh > 330 mV) in the surface, 0-10 cm layer of peat coincided with intense precipitation events. Redox potential throughout the 30-100 cm profile also fluctuated seasonally; indeed, at all depths Eh displayed a strong, negative relationship (P < 0.001) with water table height over the 12-month study period. However, Eh throughout the 30-100 cm profile remained relatively low (< 230 mV), indicating permanently reduced conditions that are associated with denitrification and reductive dissolution of Fe-bound P. The implications of these processes in the N- and P-rich peat for wetland plant diversity and water quality are discussed. (c) 2006 Elsevier B.V. All rights reserved.

Estimating the spatial scale of herbicide and soil interactions by nested sampling, hierarchical analysis of variance and residual maximum likelihood

An unbalanced nested sampling design was used to investigate the spatial scale of soil and herbicide interactions at the field scale. A hierarchical analysis of variance based on residual maximum likelihood (REML) was used to analyse the data and provide a first estimate of the variogram. Soil samples were taken at 108 locations at a range of separating distances in a 9 ha field to explore small and medium scale spatial variation. Soil organic matter content, pH, particle size distribution, microbial biomass and the degradation and sorption of the herbicide, isoproturon, were determined for each soil sample. A large proportion of the spatial variation in isoproturon degradation and sorption occurred at sampling intervals less than 60 m, however, the sampling design did not resolve the variation present at scales greater than this. A sampling interval of 20-25 m should ensure that the main spatial structures are identified for isoproturon degradation rate and sorption without too great a loss of information in this field.

Spatial analysis of the error in a model of soil nitrogen

Models of the dynamics of nitrogen in soil (soil-N) can be used to aid the fertilizer management of a crop. The predictions of soil-N models can be validated by comparison with observed data. Validation generally involves calculating non-spatial statistics of the observations and predictions, such as their means, their mean squared-difference, and their correlation. However, when the model predictions are spatially distributed across a landscape the model requires validation with spatial statistics. There are three reasons for this: (i) the model may be more or less successful at reproducing the variance of the observations at different spatial scales; (ii) the correlation of the predictions with the observations may be different at different spatial scales; (iii) the spatial pattern of model error may be informative. In this study we used a model, parameterized with spatially variable input information about the soil, to predict the mineral-N content of soil in an arable field, and compared the results with observed data. We validated the performance of the N model spatially with a linear mixed model of the observations and model predictions, estimated by residual maximum likelihood. This novel approach allowed us to describe the joint variation of the observations and predictions as: (i) independent random variation that occurred at a fine spatial scale; (ii) correlated random variation that occurred at a coarse spatial scale; (iii) systematic variation associated with a spatial trend. The linear mixed model revealed that, in general, the performance of the N model changed depending on the spatial scale of interest. At the scales associated with random variation, the N model underestimated the variance of the observations, and the predictions were correlated poorly with the observations. At the scale of the trend, the predictions and observations shared a common surface. The spatial pattern of the error of the N model suggested that the observations were affected by the local soil condition, but this was not accounted for by the N model. In summary, the N model would be well-suited to field-scale management of soil nitrogen, but suited poorly to management at finer spatial scales. This information was not apparent with a non-spatial validation. (c),2007 Elsevier B.V. All rights reserved.