Phosphorus management for perennial crops in central Amazonian upland soils

The present contribution discusses the soil P status of central Amazonian upland soils, the effects of tree crops on soil P availability and the factors controlling soil P cycling in land use systems with tree crops. Soil fertility management has to target the prevalent P deficiency by adequate P fertilization, especially in southern and northern municipalities of central Amazônia where the largest areas with severe P deficiency are found. P fixation to clay minerals is not a major obstacle for P management in the highly weathered upland soils of the central Amazon due to their low Al- and Fe-oxide contents. Low total soil P amounts are mainly responsible for low P availability. Tree crops are found to be especially suitable for land use under low-P-input conditions. Their large P return to soil by litterfall and pruning improves soil P availability. Additionally, litter quality affects P release and soil P availability. Both aspects, quantity and quality effects, are strongly dependent on tree species. Phosphorus sorption does not seem to be reduced by different litter types confirming earlier results that P fixation is not a major problem in central Amazonian upland soils. In conclusion, biological approaches are more important than physical approaches to improve soil P availability in central Amazonian Oxisols. With large P cycling through soil microbial biomass and between plant and soil, a higher availability of added P can be maintained and P applications only need to replenish P exports by harvest. Low P additions will improve productivity also for long-term uptake by trees. This is of high importance in regions with poor infrastructure and the lack of financial resources.     

Simplified procedures for releasing and concentrating microorganisms from soil for transmission electron microscopy viewing as thin-sectioned and frozen-etched preparations.

A simplified procedure is presented for releasing and concentrating indigenous microbial cells from soil for viewing by transmission electron microscopy as thin sections or replicas of frozen-etched preparations. This procedure is compared with two others reported earlier, and their relative merits are discussed as concerns the choice of procedure for the cellular information desired from the soil. Freeze-etching showed that the cell types and size distributions for cells which have been released and concentrated from soil are in general agreement with those for cells in a crude soil slurry in which no attempt to release and concentrate cells was made. Microcolonies were present both in the crude slurry and in the discard soil debris centrifugation pellets from the cell release and concentration procedures. In contrast to the historic assumptions, these microcolonies, as well as some individual cells embedded in soil debris could not be broken up and (or) dislodged so that they would be washed from the soil. The relative numbers of these cells remaining with the soil debris, however, could not be quantitated in the present study.     

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO₂ with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg⁻¹ for mineral soils and a root mean square error of 50 g C kg⁻¹ for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.     

Development of a soil compaction indicator in life cycle assessment

Purpose

Integrating soil quality impacts in life cycle assessment (LCA) requires a global approach to assess impacts on soil quality that can be adapted to individual soil and climate contexts. We have developed a framework for quantifying indicators of impact on soil quality, valid for all soil and climate conditions, and considering both on-site and off-site agricultural soils. Herein, we present one of the framework’s impact indicators, which has not yet been quantified in detail in LCA studies: soil compaction.

Material and methods

The method includes guidelines and tools for estimating midpoint compaction impacts in topsoil and subsoil as a loss of soil pore volume (in cubic metre per functional unit). The life cycle inventory (LCI) and life cycle impact assessment are based on simulation modelling, using models simple enough for use by non-experts, general enough to be parameterised with available data at a global scale and already validated. Data must be as site specific and accurate as possible, but if measured data are missing, the method has a standardised framework of rules and recommendations for estimating or finding them. The main model used, COMPSOIL, predicts compaction due to agricultural traffic. Results are illustrated using a case study involving several crops in different soil and climate conditions: a representative pig feed produced in Brittany, France.

Results and discussion

Predicted compaction impacts result from the combination of site-specific soil, climate and management characteristics. The data necessary to the LCI are readily available from free soil and climate databases and research online. Results are consistent with compaction observed in the field. Within a soil type, predictions are most sensitive to initial bulk density and soil water content.

Conclusions

The method lays the foundation for possible improvement by refining estimates of initial soil conditions or adding models that are simple and robust enough to increase the method’s capacity and accuracy. The soil compaction indicator can be used in LCAs of bio-based materials and of waste management stages that consider composting. The framework includes other operational indicators (i.e. water erosion, soil organic matter change) to assess impact on soil quality. They complement other impact categories, providing increased ability to identify “impact swapping”.     

Distribution of Cryptostigmata and Mesostigmata (Acari) within the litter and soil layers of two subtropical forests

Differences in the litter and soil of northern temperate and tropical and subtropical forest soil and soil faunas are noted. Analyses of the distribution and abundance of Cryptostigmata and Mesostigmata mites in the litter and soil of two Australian subtropical forests are presented. The faunal associations in the litter and soil of these forests are different. It is suggested that the litter and soil habitats of subtropical and tropical forests should be regarded as separate but connected habitats in relation to their fauna. This contrasts with the assumption, for temperate forests, that litter and soil communities are portions of a single system.     

Identifying the microbial taxa that consistently respond to soil warming across time and space

Soil microbial communities are the key drivers of many terrestrial biogeochemical processes. However, we currently lack a generalizable understanding of how these soil communities will change in response to predicted increases in global temperatures and which microbial lineages will be most impacted. Here, using high‐throughput marker gene sequencing of soils collected from 18 sites throughout North America included in a 100‐day laboratory incubation experiment, we identified a core group of abundant and nearly ubiquitous soil microbes that shift in relative abundance with elevated soil temperatures. We then validated and narrowed our list of temperature‐sensitive microbes by comparing the results from this laboratory experiment with data compiled from 210 soils representing multiple, independent global field studies sampled across spatial gradients with a wide range in mean annual temperatures. Our results reveal predictable and consistent responses to temperature for a core group of 189 ubiquitous soil bacterial and archaeal taxa, with these taxa exhibiting similar temperature responses across a broad range of soil types. These microbial ‘bioindicators’ are useful for understanding how soil microbial communities respond to warming and to discriminate between the direct and indirect effects of soil warming on microbial communities. Those taxa that were found to be sensitive to temperature represented a wide range of lineages and the direction of the temperature responses were not predictable from phylogeny alone, indicating that temperature responses are difficult to predict from simply describing soil microbial communities at broad taxonomic or phylogenetic levels of resolution. Together, these results lay the foundation for a more predictive understanding of how soil microbial communities respond to soil warming and how warming may ultimately lead to changes in soil biogeochemical processes.     

Indicator based assessment of the soil compaction risk at arable sites using the model REPRO

Soil compaction impairs all essential soil functions, which are crucial for the lives of humans, animals, plants and soil organisms. In order to secure the various soil functions, soil compaction must be avoided. One successful method of preventing soil compaction could be based on the precautionary principle, and mathematical modelling might be used to support farmers or consultants when making decisions about husbandry operations. This paper presents a model which calculates an indicator and assesses the risk of soil compaction on arable land based on site-specific data including information on soil, weather and specific husbandry. The first step is to estimate the soil strength in response to soil stress for a topsoil (20 cm) and a subsoil (35 cm) layer. The estimations of these parameters take into account changes in soil moisture throughout the year. Soil strength compared with soil stress is used to calculate the indicator Soil Compaction Index (SCI) for each time the machinery passes over the soil. The results from the separate passes are then integrated for a comprehensive assessment of the risk of soil compaction at farm level. The model was validated in numerous trials. It was found that the calculated SCI was a good reflection of the actual change in soil structure. The model is already being applied on arable farms in Germany. As an example presented in this paper, the calculations for the subsoil at these farms result in low to medium compaction risks.     

Quick start guide to soil methods for ecologists

Increasingly biologists and ecologists are becoming aware of the vital importance of soil to processes observed aboveground and are incorporating soil analyses into their research. Because of the dynamic and heterogeneous nature of soil, proper incorporation of soil analysis into ecological studies requires knowledge and planning. Unfortunately, many ecologists may not be current (or trained at all) in soil science. We provide this review, based on our cumulative >60 years of work in soil science, to help familiarize researchers with essential information to appropriately incorporate soil analyses into ecological studies. Specifically, we provide a brief introduction into soils and then discuss issues related to soil sterilization, choosing a soil for a greenhouse project, sampling soils, and soil analyses.     

An Estimation Procedure for the Joint Distribution of Spatial Direction and Thickness of Flat Bodies Using Vertical Sections. Part II: An application in soil micromorphology

In soil micromorphology fissures are considered in vertical sections. To get information about the properties of the soil the joint distribution of spatial direction and width of these fissures is of interest. The fissures are mathematically generalized to flat bodies which are defined as stationary weighted surface processes with the weight “thickness”. In a typical point of the surface process suitable, joint parametric distributions of direction and thickness are assumed. The parameters have to be estimated from measurements on vertical sections which are taken from the soil. On these sections only a visible thickness and a visible angle can be observed. The joint distribution of these variables can be expressed by the joint distribution of spatial direction and thickness with the same parameters and in this indirect way the parameters can be estimated. The paper describes how to randomize the vertical section and how to measure the visible variables on the sections. The Chi-Square method is proposed for the parameter estimation. Further it is discussed how to derive good starting values for the numerical procedure. All this is demonstrated in a simulation study using the Bingham-Mardia distribution for the direction and the lognormal distribution for the thickness including a way to correlate the mean thickness and the direction. Finally an application in soil micromorphology is demonstrated for one soil horizon.     

一株产漆酶土壤真菌F-5的分离及土壤修复潜力

漆酶因可氧化许多种有机污染物,在土壤污染修复方面的应用潜力受到广泛重视。筛选具有较高漆酶活性的土壤真菌,可以为污染土壤修复提供生物资源。通过培养基中愈创木酚颜色反应,从土壤中筛选获得1株真菌菌株F-5。18S rRNA基因序列显示该菌株属于巨座壳科(Family Magnaporthaceae)。单因素试验和正交试验结果显示,蔗糖和蛋白胨分别是最有利于该菌产漆酶的碳源和氮源。在适当培养条件下,真菌F-5培养液酶活性可达4033U/L,表现出该菌具有较强的产漆酶能力。在多环芳烃(PAHs)污染土壤的生物修复中,真菌F-5可使土壤中苯并(a)芘、二苯并(a,h)蒽等高环、高毒性多环芳烃降解,并使土壤多环芳烃毒性当量大幅降低。因此,真菌F-5适合修复PAHs污染土壤。     

Direct DNA extraction for PCR-mediated assays of soil organisms.

By using the rDNA of a plant wilt pathogen (Verticillium dahliae) as the target sequence, a direct method for the extraction of DNA from soil samples which can be used for PCR-mediated diagnostics without a need for further DNA purification has been developed. The soil organisms are disrupted by grinding in liquid nitrogen with the natural abrasives in soil, and losses due to degradation and adsorption are largely eliminated by the addition of skim milk powder. The DNA from disrupted cells is extracted with sodium dodecyl sulfate-phenol and collected by ethanol precipitation. After suitable dilution, this DNA extract can be assayed directly by PCR amplification technologies. The method is rapid, cost efficient, and when combined with suitable internal controls can be applied to the detection and quantification of specific soil organisms or pathogens on a large-scale basis.     

污染土壤生态修复理论内涵的初步探讨

污染土壤修复的目的是转移或转化土壤中有毒有害污染物,消除或减弱污染物毒性,恢复或部分恢复土壤的生态服务功能.由于土壤污染大多属于复合污染,通常需要用多种方法联合修复污染土壤.用一种统一的方法涵盖多种修复方法,注重系统内在修复功能同外加修复功能的有机结合,以及土壤生态服务功能的全面恢复是污染土壤修复的发展趋势.本文据此提出了污染土壤生态修复的概念,并指出生态修复应该遵从生态学的3个原理和3个原则.在生态修复中,生物修复的作用十分重要,但不同方法之间的组合服从于工艺优化原则.人为强化、激活土壤系统的净化功能,并实现同外加净化功能的耦合,可使修复效率大大提高.生态因子调控是污染土壤修复的必要前提,是生态修复的基本特征,是强化修复效果的重要手段.生态修复应该具有协调性、高效性与稳定性特征.生态修复的最终目标是土壤生态功能的恢复,生态修复的基准应该根据土壤的生态功能建立.生态修复理论将在指导污染土壤修复的实践方面发挥重要作用.     

土壤-作物污染物迁移分配与食物安全的评价模型及其应用

人类对潜在有毒污染物的接触及其健康风险是当今环境科学与医学共同关心的热点问题,而污染土壤中毒污染物在土壤－植物系统迁移与转化是其中的关键基础问题,本文基于对现有资料的分析,提出计算污染物的环境控制标准的数学模型－污染生态模型和环境化学模型,模型计算结果表明,未污染条件下的土壤－植物间元素分配参数不符合污染土壤环境中的土壤－食用作物－人类间污染物分配的特点。因此,制定土壤环境控制标准必须考虑实施土壤的环境化学特点,本文建立的模型有其对污染物的土壤环境控制标准的计算结果,期望于对我国加强污染生态研究和食物安全评价有一定的参考意义。     

Calibration of Ellenberg indicator values for the Faroe Islands

Abstract. Elenberg's bio‐indication system for soil moisture (F), soil nitrogen (N) and soil reaction (R) was examined, based on 559 vegetation samples and environmental characteristics (vegetation cover, soil depth, soil moisture, chemical soil properties) from four Faroe islands. The original indicator values from central Europe were used for the calculation of weighted community indicator values of F, N and R. These were regressed with respect to environmental data, applying standard curvilinear regression and generalized linear modelling (GLM) and new predicted values of community indicator values were obtained from the best model. Faroe species optima values of 162 taxa for one or more of the three EUenberg scales were derived from fitting Huisman‐Olff‐Fresco (HOF) models of species abundance with respect to predicted community indicator values and are proposed as new EUenberg species indicator values to be used in the Faroe Islands. F was best correlated with a GLM model containing soil moisture, organic soil fraction, soil depth and total vegetation cover, R with a GLM model containing pH and calcium in % organic soil fraction, N with total phosphorus in % organic soil fraction. The calibrated species indicator scales are much truncated, as compared with the original values, resulting in significantly different overall distributions of the original and new species indicator values. The recalculated community indicator values are much better correlated to environmental measurements. Several species do not have clear optima, but linear or monotone relationships to the examined indicator scales. This probably indicates that the occurrence of some species in the Faroe Islands are either determined by factors other than moisture, pH or soil nutrient status or, given the young age and environmental instability of the islands, are governed by stochastic mechanisms. Extension of Ellenberg indicator values outside central Europe should always be carefully calibrated by means of adequate environmental data and adequate statistical models, such as HOF models, should be applied.     

Root site occupancy modelling of young New Zealand native plants: implications for soil reinforcement

Plants are widely used in soil conservation to control and prevent erosion on hillslopes and on riverbanks. Previous research has shown the mechanical root reinforcement on soil stability can be considerable. However, land and forest managers still require information and simple tools to enable them to determine how and when a species becomes effective in terms of soil stabilisation. This paper uses root length data from a trial of young New Zealand trees and shrubs to develop a simple model to account for the spatial occupancy of a planting site by roots, and by implication their potential strength contribution to soil reinforcement. It is developed by calculating root surface area in contact with the soil to obtain an effective radius of the root spread about the stem. The approach generates a set of coefficients that are unique to a species for a given site which can then be used in the generalised model to predict root site occupancy, which is taken as a proxy for when soil reinforcement is attained. This information can then be used to assess effectiveness of different species mixes in planting plans.     

Compression of soil around roots

Summary A simplified model is developed for soil compression around plant roots. The main assumptions are that the root volume is accommodated by loss of porosity in the surrounding soil; that there is a minimum soil porosity below which soil will not be compressed; and that the density decreases exponentially with distance from the roots surface with an exponent which is a constant multiple of the root diameter. These assumptions lead to simple, practical expressions for the soil porosity around roots and for the amounts of materials such as nutrients or organisms which lie within certain distances from a root surface.     

干旱区土壤盐渍化特征空间建模

当前,土壤盐渍化以及因灌溉引起的土壤次生盐渍化问题是我国干旱、半干旱区所面临的主要生态环境问题。在特征空间理论的支持下,以波谱分解技术为基础,以Landsat-TM、Landsat-ETM+多光谱遥感影像和野外调查数据为基础数据源,通过分析干旱区土壤盐渍化对地表生物物理特征的影响,探讨了表征盐渍化过程与地表生物物理特征之间的规律及定量关系,进而利用土壤盐渍化遥感监测中关键的3个指标——经过波谱分解技术获得的直接表征盐渍化的土壤盐渍化光谱、间接表征盐渍化的植被覆盖度和土壤水分含量协同构建了二维特征空间支持下的土壤盐渍化遥感监测模型VSSI(Vegetation fraction and Soil fraction Soil Index)、SVSI(Soil water contents and Vegetation fraction Soil Index)、SSSI(Soil water contents and Soil salinization fraction Soil Index)和三维特征空间支持下的土壤盐渍化遥感监测模型SVWSI和SDI。研究结果表明:基于三维特征空间建立的SVWSI(Soil salinization fraction-Vegetation fraction-Water contents Soil Index)和SDI(Soil Distance Index)模型对不同盐渍化程度土壤的敏感程度要高于基于传统二维特征空间建立的VSSI、SVSI和SSSI模型。其中,SVWSI和SDI模型与实测0—10 cm土壤盐分含量决定系数分别为R2=0.8325和R2=0.8646,这充分说明基于高维数特征空间所构建的土壤盐渍化遥感监测模型能更准确地反映盐渍化土壤地表盐量组合及其变化信息,且指标简单、易于获取,对于今后干旱区区域大尺度盐渍地信息提取以及动态监测研究具有重要意义。     

The preparation of microtome sections of unaltered soil for the study of soil organisms in situ

Summary A new technique for study of small soil organisms in situ in unaltered soil is described.The soil samples are cooled in a refrigerator at — 10°C to kill the animals. A small portion taken from a frozen soil sample, is slowly immersed in a solution of gelatin. When the specimen is infiltrated with gelatin and the whole cooled it is fixed in formalin to enable it to withstand treatment with hydro-fluoric acid for removal of sand grains. Subsequently the specimens are immersed in gelatin solution for a second time after which the specimens are affixed to wooden blocks which can be clamped in the microtome. Before sectioning, the embedded specimen affixed to the wooden block is hardened in methylalcohol after which it is possible to cut sections 7,5–10µ thick.The most satisfactory staining procedure proved to be the quadruple staining method of Johansen. By this method nematodes, fungi, bacteria and amoebae are easily distinguishable from the soil particles.     

石油烃和酚类物质在土中的生物降解与土壤酶活性

本文通过模拟实验,研究了不同条件下石油烃和酚类物质在土中的降解进程及其与土壤酶活性的关系,并在此基础上,对所述污染物的土地处理提出了若干建议。     

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, K_s. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.