Scale-dependent relationships between soil organic carbon and urease activity

Many soil properties and processes vary at different spatial scales. As a result, relationships between soil properties often depend on scale. In this paper we show this for two soil properties of biological importance, by means of a nested analysis of covariance. The variables were urease activity (UA) and soil organic carbon (SOC), sampled on an unbalanced nested design at three sites with different land uses (arable, forest and pasture). The objective of this study was to investigate the scale‐dependent relationships of UA and SOC at these three sites to exemplify the phenomenon of scale‐dependency in the covariation of biogeochemical variables. At each site the variables showed different scale dependencies, expressed in their correlations at different scales. At the pasture site, UA and SOC were uncorrelated at all scales in the sampling design (0.2 m, 1 m, 6 m and ≥15 m), and the overall product moment correlation was 0.10. A significant positive scale dependent correlation (0.65) was found at the 1‐m scale for the forested site. The soil properties were not spatially correlated at any of the other scales and the associated product moment correlation for this site was 0.14. Urease activity and soil organic C were found not to be correlated at the shorter scales in the arable site. However, significant positive correlation coefficients of 0.89 and 0.82 were obtained at the longer scales of 6 and ≥15‐m respectively for the arable site. The product moment correlation at this site was 0.65. At both the arable and forest site, we found that correlations at particular scales were stronger than the overall product moment correlation. This approach allowed us to identify significant relationships between urease activity and soil organic carbon and the scales at which these relationships occur and to draw conclusions about the spatial scales, which must be resolved in further studies of these variables in these contrasting environments. This study highlights the pervasive effect of scale in soil biogeochemistry and shows that scale‐dependence must not be disregarded by soil scientists in their investigations of biogeochemical processes.     

Inference about soil variability from the structure of the best wavelet packet basis

The plausibility of the assumption that soil variation can be treated as a realization of a random spatial process that is stationary in the variance can break down in various ways. It is possible to test the assumption using methods based on the wavelet transform. To date these approaches have been applied using the discrete wavelet transform. A drawback of this approach is that it uses a partition of the spatial frequencies represented in the data into intervals (scales) that are arbitrarily defined in advance and are not necessarily suitable for the representation of the variation of the data in question. A solution to this problem is to identify the best basis for the data from a wavelet packet library. An interesting question is whether the structure of this best basis is in itself informative about the plausibility of the stationarity assumption. In this paper, I show that this is indeed the case. The best basis for a stationary random variable from some packet library is the basis on the maximum dilation of the mother wavelet, which gives the finest resolution in the frequency domain. I propose the ratio of the entropy cost functional for this basis to that of the empirical best basis as a measure of evidence against the null hypothesis of stationarity in the variance. Critical values of this statistic may be obtained by Monte Carlo methods. I demonstrate the method using data on the clay content of soil on a transect in central England. The null hypothesis of stationarity in the variance may be rejected. Tests for the uniformity of variance can then be applied to wavelet packets in the best basis. The dominant local feature that is reflected in this behaviour is the unique pattern of variation in alluvium around a drainage channel that crosses the transect. This variation contrasts with that seen at most positions on the transect, variation that arises from a more or less regular pattern of boundaries between contrasting Jurassic strata.     

Estimating the local mean for Bayesian maximum entropy by generalized least squares and maximum likelihood, and an application to the spatial analysis of a censored soil variable

The Bayesian maximum entropy (BME) method is a valuable tool, with rigorous theoretical underpinnings, with which to predict with soft (imprecise) data. The methodology uses a general knowledge base to derive a joint prior distribution of the data and the prediction by the criterion of maximum entropy; the hard (precise) and soft data are then processed using this prior distribution to yield a posterior distribution that provides the BME prediction. The general knowledge base commonly consists of the mean and covariance functions, which may be extracted from the data. The common method for extracting the mean function from the data is a generalized least squares (GLS) approach. However, when the soft data take the form of intervals of plausible values, this method can result in errors in the BME predictions. This paper suggests a maximum likelihood (ML) method for fitting the local mean. The two methods are compared in terms of their predictions, firstly on simulated random fields and then on a case study to predict the depth of soil using some censored data. The results show that the ML method can result in more accurate BME predictions; the degree of improvement over the GLS method depends on the parameters of the spatial covariance model.     

Application of a novel method for soil aggregate stability measurement by laser granulometry with sonication

Aggregate stability is an important physical indicator of soil quality, and so methods are required to measure it rapidly and cost‐effectively so that sufficient data can be collected to detect change with adequate statistical power. The standard methods to measure water‐stable aggregates (WSA) in soil involve sieving, but these have limitations that could be overcome if the aggregates were measured with a laser granulometer (LG) instrument. We present a novel method in which a LG is used to make two measurements of the continuous size distribution (<2000 µm) of a sample of aggregates. The first measurement is made on the WSA after these have been added to circulating water (initial air‐dried aggregate size range 1000–2000 µm). The second measurement is made on the disaggregated material (DM) after the circulating aggregates have been disrupted with ultrasound (sonication). We then compute the difference between the mean weight diameters (MWD) of these two size distributions; we refer to this value as the disaggregation reduction (DR; µm). Soils with more stable aggregates, which are resistant to both slaking and mechanical breakdown by the hydrodynamic forces during circulation, have larger values of DR. We applied this method to six and ten sub‐samples, respectively, of soil aggregates (each ca. 0.3 g) from bulk soil material from two contrasting soil types from England, both under conventional tillage (CT). The mean DR values were, respectively, 178 and 30 µm, with coefficients of variation of 12.1 and 19% suggesting the DR value is reproducible for the small mass of soil used. We attribute the larger DR values to the greater abundance of micaceous clay minerals in one of the soils. The DR values computed for each Blackwater Drain (BD) sample after removal of organic matter (with hydrogen peroxide) were comparable to those subject to sonication suggesting that most of the aggregate structure is removed by sonication. We used aggregates (1000–2000 µm) from soil samples collected at 30 locations under CT (median soil organic carbon (SOC) = 1.4%) across two types of parent material in the Blackwater drain sub‐catchments of the Wensum catchment (Norfolk, UK). These soils had no coarse WSA, so we rescaled the size distributions to estimate DR for particle diameters <500 µm. Dithionite‐extractable iron concentration, plus a minor contribution from parent material class, accounted for 64% of the variation in rescaled DR highlighting the importance of crystalline iron oxyhydroxides for aggregate stability in this region where long‐term arable production has reduced top‐soil SOC concentrations. We discuss how this technique could be developed to monitor aggregate stability as a soil physical indicator.     

Cerebral infarction and meningoencephalitis following hot-iron disbudding of goat kids

CASE HISTORY: Twelve of 150 goat kids, 4–10 days old, died 3 days after disbudding with a hot iron. Another 18 kids had been ill the previous day but survived following antibiotic therapy. Five of the dead kids were necropsied.

PATHOLOGICAL FINDINGS: There was necrosis and haemorrhage of the skin, subcutaneous tissues and frontal bone at disbudding sites in all five kids examined post mortem. Beneath disbudding sites in 4/5 kids there were bilateral, dark red, often cavitated areas of necrosis extending deep into the frontal cortex of the brain. Histologically, these areas consisted of coagulation necrosis, haemorrhage, vascular thrombosis and suppurative inflammation. Numerous bacteria, predominantly large Grampositive rods, were present in the necrotic brain tissue. In the remaining kid, bilateral areas of yellow discolouration and flattening of gyri in frontal lobes corresponded histologically to extensive polioencephalomalacia. A mixed growth of aerobes and anaerobes was cultured from the brain of one kid with suppurative lesions.

CLINICAL RELEVANCE: Thermal disbudding of neonatal kids is widely practised in dairy goat herds and is considered the method of choice for disbudding in New Zealand. However, the skull of goat kids is much thinner than that of calves and the safety margin for thermal injury to the brain is markedly reduced. This report highlights the risks associated with the technique and its potential as a welfare issue.