What are the primary factors controlling the light fraction and particulate soil organic matter content of agricultural soils?

Particulate organic matter (POM) and light fraction organic matter (LFOM) are the fractions of soil organic matter (SOM) considered most active in terms of nutrient cycling and maintenance of soil structure. They respond quickly to changes in management and may offer insights into the long-term effect of management on SOM. However, the literature provides contradictory evidence regarding the factors which influence the amount of POM and LFOM, and there is little evidence to differentiate the relative importance of factors. Utilising data from over 150 experiments reported in the literature, we employed multiple regression to produce separate models quantifying the effect of management factors and environmental variables on POM, LFOM and total SOM; 29.3 % of the variance in the response variables was explained for POM, 28.3 % for LFOM, and 29.3 % for total SOM. Climate, organic amendments and inclusion of fallow periods were significant terms for all fractions. Climate had a larger influence on total SOM than POM or LFOM, whilst POM and LFOM were more strongly influenced by factors related to the recent history of organic matter addition; organic amendments and inclusion of fallows. Factors that were not significant variables for any of the fractions included tillage and application of N fertiliser, whilst soil texture was only a significant factor for SOM. General agreement between the total SOM, POM and LFOM models on the most important factors supports the idea that both POM and LFOM are good predictors of long-term changes to total SOM.     

Can soil moisture deficit be used to forecast when soils are at high risk of damage owing to grazing animals?

A potentially significant cause of damage to grassland soils is compaction of unsaturated soil and poaching of saturated or nearly saturated soil by animal hooves. Damage is caused when an applied stress is in excess of the bearing strength of the soil and results in a loss of soil structure, macroporosity and air or water conductivity. Severely damaged soils can cause reduced grassland productivity and make grazing management very difficult for the farmer. The actual amount of soil damage that can occur during grazing is dependent on the grass cover which acts as a protecting layer, the soil water content and the characteristics of the grazing animal (weight and hoof size). Assuming that the farmer is knowledgeable about the characteristics of the grazing animal and grass cover, it would be very useful for short‐term operational farm planning to be able to predict when soil water contents were likely to be in a critical range with respect to potential hoof damage. In this study soil moisture deficits (SMDs) which can be derived from meteorological forecasts are evaluated for predicting when soil water conditions are likely to lead to hoof damage. Two contrasting Irish grassland soils were analysed using a Hounsfield servo‐mechanical vertical testing machine to simulate static (285.4 N) and dynamic (571 N) hoof loads on the soil over a range of estimated SMDs (0, 5, 10 and 20 mm). The deficits were analysed with respect to the soil volumetric water content, compression (displacement) and change in dry bulk density. The SMDs imposed in the laboratory were similar to those under field conditions and thus the methods used in this study are applicable elsewhere. The change in dry bulk density following loading (0.2–0.7 g/cm³) was linearly related to SMD (R² ranged from 0.90 to 0.99), leading to the conclusion that a forecast of SMD can be used to predict when grassland soils are likely to be at risk of damage from grazing.     

LS-SVM data mining analysis: how does biochar influence soil net nitrogen mineralization in the field?

Purpose

Biochar has been considered as a stable-carbon source for improving soil quality and long-term sequestration of carbon. However, in view of ecological environmental feedback and the tightly coupled system of carbon-nitrogen cycling, further attention has shifted to the effect of biochar on soil net nitrogen mineralization (SNNM). Recently, ecological evaluations of biochar were mostly based on laboratory incubation or pot experiments, ignoring external and uncontrollable natural factors. Therefore, the essential characteristics of local environments were not accurately described.

Materials and methods

In this paper, a nonlinear stochastic model of SNNM based on least squares support vector machine (LS-SVM) was set up to study the effect of biochar on nitrogen cycling in a field experiment. In order to explore this effect in natural conditions, partial derivative (PaD) sensitivity analysis of LS-SVM was firstly proposed, evaluated by the data from a known equation, and then applied to open the “black-box” stochastic model of SNNM.

Results and discussion

Comparing with the sensitivity analysis of artificial neural networks (ANNs), the RD values of LS-SVM PaD1 algorithm were almost the same as those of ANNs PaD1 algorithm. However, the RSD values of LS-SVM PaD2 algorithm were closer to the given equation. In the SNNM model, RD values of LS-SVM PaD1 algorithm of initial nitrogen, time, and precipitation were 21, 15, and 14 %, and the biochar RD value was only 0.51 %, implying that biochar did not influence SNNM directly. However, the cumulative RSD of the PaD2 algorithm of biochar with the other factors was 15.05 %, the maximum of the interactions, implying that it could greatly enhance the tendency for SNNM by interacting with other factors.

Conclusions

PaD sensitivity analysis of LS-SVM was a stable and reliable data mining method. In the SNNM model, initial nitrogen, time, and precipitation were the main controlling factors of the SNNM model. Biochar did not directly influence SNNM; however, it could greatly enhance the tendency for SNNM by interactions with other factors by decreasing the inhibitory effect of initial nitrogen on SNNM and modifying soil condition to change the effect of other factors on SNNM.

     

Tree influence on soil biological activity: What can be inferred from the optical examination of humus profiles?

Humus forms may vary in different forest stands, but the local influence of trees upon soil microbial and faunal activities is still imperfectly known. Optical methods could help to discern processes of litter transformation and formation of organo-mineral assemblages, allowing a better diagnostic of tree influences upon humus-soil development. The microstratification of humus was studied under a beech (Fagus crenata), a mixed oak forest (Quercus crispula and Quercus serrata), and a cedar (Cryptomeria japonica) plantation. The three sites are located in Kyoto (Japan), and share similar environmental conditions. Litter decomposition rates and soil fauna were also investigated. At the beech site, which had the thickest O horizon, the main process was the gradual fragmentation of litter. This process, together with shallow root and weak fungal development, gave rise to a stable sandwich-like structure in the O horizon. In contrast, the oak site showed a two-step transformation of litter. Initially, litter decomposition was triggered by the activity of white rot fungi, and the discarded litter decayed much more slowly thereafter. The cedar site exhibited a sharp vertical delineation between upper thick Oe horizon developed since plantation time and a relict A horizon. The optical method thus demonstrated differences in soil biological activities and litter transformation patterns under the three sites.     

Biochar applications enhance the phytoextraction potential of Salix smithiana [Willd.] (willow) in heavily contaminated soil: potential for a sustainable remediation method?

Journal of Soils and Sediments - The combination of chemostabilization and phytoextraction provide an affordable and environmentally effective remediation technology for the heavy metals in...     

Review Article. What are the nature and formation conditions of hydroxy‐interlayered minerals (HIMs) in soil?

Primary minerals of the parent material undergo weathering during the formation of terrestrial soils to varying extent. As a result, secondary minerals develop, which comprise, among many others, hydroxy‐interlayered minerals (HIMs). These minerals have formed by interlayering of hydroxy‐metal complexes (especially of Al³⁺, also Mg²⁺, Fe^2+/3+) into micas, expansible 2:1 phyllosilicates and forming oligomers, or by weathering of primary chlorite. The degree of interlayer filling and the stability of these fillings affect several physico‐chemical soil properties, for instance the cation exchange capacity. Although many studies have been conducted on formation, occurrence, and properties of HIMs in soil during the last decades, several challenges still exist. These challenges include analytical identification and quantification of HIMs in soil, the nature of the interlayer filling and the identification of favorable conditions in soil for the formation of HIMs. In order to deepen the understanding of formation, properties, and fate of HIMs in soil, we critically reviewed the available literature. Based on the review, we recommend using a new structural model that enables quantification of hydroxy‐interlayered smectite in soil by X‐ray diffractometry, laboratory experiments on the formation and preservation of different types of interlayers and considering the temporal and spatial dimension of the formation of HIMs in soil in more detail.     

Linking Ni and Cr concentrations to soil mineralogy: does it help to assess metal contamination when the natural background is high?

Purpose

In soils from serpentinitic areas the natural background of Ni and Cr is so high that the assessment of contamination by comparing metal concentrations with some fixed thresholds may give unreliable results. We therefore sought a quantitative relation between serpentines and Ni and Cr concentrations in uncontaminated soils, evaluated if the approach may help in establishing a baseline, and discussed if additional anthropogenic inputs of Ni and Cr can be realistically individuated in these areas.

Materials and methods

We analysed the total, acid-extractable and exchangeable concentrations of Ni and the total and acid-extractable concentrations of Cr in 66 soil horizons, belonging to 19 poorly developed and uncontaminated Alpine soils. The soils had different amounts of serpentines, depending on the abundance of these minerals in the parent material. We calculated an index of abundance of serpentines in the clay fraction by XRD and related total metal contents to the mineralogical index. We then tested the regressions on potentially contaminated soils, developed on the alluvial plain of the same watershed.

Results and discussion

We found extremely high total concentrations of Ni (up to 1,887 mg kg^–1) and Cr (up to 2,218 mg kg^–1) in the uncontaminated soils, but only a small proportion was extractable. Total Ni and Cr contents were significantly related to serpentine abundance (r ²?=?0.86 and 0.74, respectively). The regressions indicated that even small amounts of serpentines induced metal contents above 200 mg kg^–1, and the 95% confidence limits were 75 and 111 mg kg^–1 of Ni and Cr, respectively. When the regressions were tested on the potentially contaminated soils, a good estimate was obtained for Cr, while the Ni concentration was overestimated, probably because of some leaching of this element.

Conclusions

The concentrations of Ni and Cr that can be expected in soils because of the presence of small amounts of serpentines are comparable to the amounts accumulated in the soil because of diffuse contamination and potentially contaminated soils had metal concentrations falling in the range expected from the presence of natural sources. Only in the case of very severe contamination events, the identification of anthropogenic sources adding to the natural background would be feasible.     

How much do soil and water contribute to the composition of meat? A case study: meat from three areas of Argentina

The main goal of this study was to propose a reliable method to verify the geographical origin of meat, establishing the influence of soil and water on its isotopic and elemental composition. Thus, beef meat, soil, and water samples were collected from three major cattle-producing regions of Argentina (Buenos Aires, Córdoba, and Entre Ríos). Multielemental composition was determined on these three matrices by inductively coupled plasma mass spectrometry (ICP-MS), δ(13)C and δ(15)N by isotope-ratio mass spectrometry (IRMS), and the (87)Sr/(86)Sr ratio by thermal ionization mass spectrometry (TIMS). Soil and drinking water samples could be characterized and clearly differentiated by combining the isotopic ratios and elements, demonstrating differences in geology and climatic conditions of three regions. Similarly, meat originating at each sampling area was characterized and differentiated using only five key variables (Rb, Ca/Sr, δ(13)C, δ(15)N, and (87)Sr/(86)Sr). Generalized procrustes analysis (GPA), using the three studied matrices (soil, water, and meat) shows consensus between them and clear differences between studied areas. Furthermore, canonical correlation analysis (CCA) demonstrates significant correlation between the chemical-isotopic profile of meat with those corresponding to both soil and water (r(2) = 0.93, p < 0.001; and r(2) = 0.83, p < 0.001, respectively). So far, there are clear coincidences between the meat fingerprint and those from soil/water where cattle grew, presenting a good method to establish beef provenance. To the authors' knowledge this is the first report linking the influence of soil and water all together on the composition of beef, presenting the basis for the authentication of Argentinean beef, which could be extended to meat from different provenances.