Verwertung organischer Substanzen durch mikrobielle Bodenbiomasse als eine Funktion chemisch-thermodynamischer Parameter

Carbon use efficiency of organic substances by soil microbial biomass as a function of chemical and thermodynamical parameters A simple calculation determining carbon use efficiency by soil microorganisms based on chemical and thermodynamical parameters of organic matter is proposed. The use efficiency characterizes carbon fraction of organic matter which is incorporated into the cells of the soil microbial biomass. The proposed approach is based on the transition of the organic matter enthalpy into the microbial biomass enthalpy considering the formation enthalpy of the end products of respiration like CO₂, NH₄⁺ and H₂O. The combustion energy content of organic matter was used for the calculations. This combustion energy content can be determined by simple analytical means. It can be derived from data given in the literature and for various agricultural products. The comparison of organic matter use efficiency data calculated as shown above with literature data produced by diverse methods showed a satisfactory correlation. The calculated enthalpy of formation and maintenance of a C-unit of soil microbial biomass under in situ conditions amounted to —153,3 kJ · gC^—1. This value is compared with the maintenance coefficient used in microbiology. The use of the suggested approach for the calculation of carbon use efficiency based on the substance composition of the organic matter allows a uniform, standardized procedure which is not dependent on specific experimental conditions.     

Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data

The soil erosion model for Mediterranean regions (SEMMED) is presented and used to produce regional maps of simulated soil loss for two Mediterranean test sites: one in southern France and one in Sicily. The model demonstrates the integrated use of (1) multi-temporal Landsat Thematic Mapper (TM) images to account for vegetation properties, (2) a digital terrain model in a GIS to account for topographical properties and to assess the transport capacity of overland flow, (3) a digital soil map to assess the spatial distribution of soil properties, and (4) a limited amount of soil physical field data. The principle drawbacks of the model are that it does not account for soil particle detachment by overland flow nor for soil surface crusting. The model is most sensitive to the initial soil moisture storage capacity and the soil detachability index. The main advantages of SEMMED are that it simulates processes at a regional scale and, where possible, it uses available data sources such as remote sensing imagery, digital elevation models (DEM) and (digital) soil databases, which usually are not available for smaller catchment areas. Using SEMMED it is possible to produce regional maps of erosion assessments, which are of more practical use in land use planning and land management than simple extrapolations from small plot experiments.     

Some considerations on aggregate sample supports for soil inventory and monitoring

Soil monitoring and inventory require a sampling strategy. One component of this strategy is the support of the basic soil observation: the size and shape of the volume of material that is collected and then analysed to return a single soil datum. Many, but not all, soil sampling schemes use aggregate supports in which material from a set of more than one soil cores, arranged in a given configuration, is aggregated and thoroughly mixed prior to analysis. In this paper, it is shown how the spatial statistics of soil information, collected on an aggregate support, can be computed from the covariance function of the soil variable on a core support (treated as point support). This is done via what is called here the discrete regularization of the core‐support function. It is shown how discrete regularization can be used to compute the variance of soil sample means and to quantify the consistency of estimates made by sampling then re‐sampling a monitoring network, given uncertainty in the precision with which sample sites are relocated. These methods are illustrated using data on soil organic carbon content from a transect in central England. Two aggregate supports, both based on a 20 m 20 m square, are compared with core support. It is shown that both the precision and the consistency of data collected on an aggregate support are better than data on a core support. This has implications for the design of sampling schemes for soil inventory and monitoring.     

Relative stability, performance, and superiority of crop genotypes across environments

Assessments of the stability as well as the performance of plant genotypes across diverse environmental conditions are important to plant breeders and agronomists as tools for selecting superior cultivars for the target environments. In this study, the shortcomings of fitted response as an indicator of relative stability are discussed and use of a genotype-environment correlation coefficient, r _ge, as a measure of relative stability is proposed. Two other relevantindices are introduced: performance index, p _i , and superiority index, s _i . The latter is a compound index for stability and performance that provides a simple method for selecting superior genotypes for relevant environments. In application, a distinction is made between specific stability (over space and microclimate) and general stability (over space and time) depending on the format of the specified environmentalindex. A statistical significance test for relative stability is considered and three datasets are used to demonstrate the application of derived indices under varying environment combinations. Appraisal of the method and some currently appealing procedures applied to the same dataset reveal a general concordance under similar conditions. The introduced parameters prove to be simple, convenient tools for examining data from plant adaptation trials in the presence of genotype × environment interaction.     

Cokriging particle size fractions of the soil

It is often necessary to predict the distribution of mineral particles in soil between size fractions, given observations at sample sites. Because the contents in each fraction necessarily sum to 100%, these values constitute a composition, which we may assume is drawn from a random compositional variate. Elements of a D‐component composition are subject to non‐stochastic constraints; they are constrained to lie on a D– 1 dimensional simplex. This means we cannot treat them as realizations of unbounded random variables such as the multivariate Gaussian. For this reason, there are theoretical reasons not to use ordinary cokriging (or ordinary kriging) to map particle size distributions. Despite this, the compositional constraints on data on particle size fractions are not always accounted for by soil scientists. The additive log‐ratio (alr) transform can be used to transform data from a compositional variate into a form that can be treated as a realization of an unbounded random variable. Until now, while soil scientists have made use of the alr transform for the spatial prediction of particle size, there has been concern that the simple back‐transform of the optimal estimate of the alr‐transformed variables does not yield the optimal estimate of the composition. A numerical approximation to the conditional expectation of the composition has been proposed, but we are not aware of examples of its application and it has not been used in soil science. In this paper, we report two case studies in which we predicted clay, silt and sand contents of the soil at test sites by ordinary cokriging of the alr‐transformed data followed by both the direct (biased) back‐transform of the estimates and the unbiased back‐transform. We also computed estimates by ordinary cokriging of the untransformed data (which ignores the compositional constraints on the variables) for comparison. In one of our case studies, the benefit of using the alr transform was apparent, although there was no consistent advantage in using the unbiased back‐transform. In the other case study, there was no consistent advantage in using the alr transform, although the bias of the simple back‐transform was apparent. The differences between these case studies could be explained with respect to the distribution on the simplex of the particle size fractions at the two sites.     

Exploring soil databases: a self‐organizing map approach

A soil quality database (SQDB) is a collection of soil samples described by a given set of parameters, allowing farmers, scientists and other stakeholders to make informed decisions about practices, processes and policies for soil use and management. If each parameter is considered as a dimension of the space spanned by the SQDB, extracting information becomes a difficult task when the number of parameters is >3. A widely used approach to explore multidimensional data sets is the self‐organizing map (SOM) method, which is suitable for clustering, visualization and extraction of information from multidimensional data. We applied the SOM method as an exploratory technique to an unlabelled SQDB to extract knowledge – data patterns and data associations – from the data set (the time and location of each sample were unknown). The SQDB used in this study is a set of 1240 unlabelled samples within the Central Valley of Chile, covering ca 7500 km². The predominant soils are Andisols with a large organic matter content (7–12%), small bulk densities (0.6–1.0 g/cm³) and large water‐holding capacity. We identified three patterns: (i) isolated region within the map with close neurons (smooth transitions), (ii) two or more regions with predominantly large or small values and (iii) homogeneous map with small values with an isolated region of large values. These patterns show that the data set represented more than two groups that were not necessarily related. For pH, no important associations with other investigated parameters were observed. Previous studies carried out by the local agricultural research station showed that pH values below 5.5 constrain nutrient uptake. Thus, locations presenting pH<5,5 should be subject to seasonal monitoring to assess management practices that mitigate soil acidity. The component plane for organic matter indicates that ca. 50% of the soil samples had contents <8% related to soil series characteristics and management practices. As the k‐means is initialized by random partitions, the two‐step approach (clustering the map representing the input data) is less sensitive to variations in the input data (subsamples) than is the direct application of k‐means to the input data, but it also reduces the computational cost. The ability of SOMs to visualize multidimensional data sets helps gain an understanding of the data in the exploratory phase, such as the association and integration of physical, chemical and biological parameters.     

基于GIS的黑龙江省耕地集约利用水平的空间格局分析

利用现代信息手段研究一定尺度的耕地集约利用水平空间分布,为促进区域规模化耕地集约经营以及缩小区域耕地集约利用水平差距具有重要的作用.以黑龙江省64个县(市)为评价单元,采用熵值法和综合指数法定量计算各县(市)耕地集约利用水平,利用GIS手段和地学模型方法,分析和研究各县(市)耕地集约利用水平的空间分布.结果表明,64个县(市)耕地集约利用水平呈现4级分布,东南部耕地集约利用水平明显高于西北部,全局Moran指数,为0.23,各县(市)耕地集约利用水平趋向聚集,但聚集程度不强,耕地集约利用高值区的扩散效应不显著.     

Testing statistical hypotheses using standard error bars and confidence intervals

Abstract

When agricultural researchers construct figures or graphs displaying sample means from an experiment, a popular technique to display the relative variation is to include standard error bars. This technique can be very informative but misleading. Researchers will sometimes try to draw inference to the equality of their means by using these standard error bars. This paper explores the use of standard error bars in comparing population parameters and exhibits how conclusions drawn by this method will often be faulty. The use of confidence intervals to test hypotheses is also presented. Some simple mathematical derivations are presented, along with a small computer simulation study. If a researcher utilizes standard error bars in an attempt to test a hypothesis, he or she will be performing a test with an approximate type I error rate of α=0.16. In situations in which it is difficult to perform a test, but confidence intervals are available, an alternative for performing a=0.05 test is to evaluate 85% confidence intervals and reject the hypothesis that the parameters are equal if the intervals fail to overlap.     

Empirical Bayes analysis of variance component models for microarray data

A gene-by-gene mixed model analysis is a useful statistical method for assessing significance for microarray gene differential expression. While a large amount of data on thousands of genes are collected in a microarray experiment, the sample size for each gene is usually small, which could limit the statistical power of this analysis. In this report, we introduce an empirical Bayes (EB) approach for general variance component models applied to microarray data. Within a linear mixed model framework, the restricted maximum likelihood (REML) estimates of variance components of each gene are adjusted by integrating information on variance components estimated from all genes. The approach starts with a series of single-gene analyses. The estimated variance components from each gene are transformed to the “ANOVA components”. This transformation makes it possible to independently estimate the marginal distribution of each “ANOVA component.” The modes of the posterior distributions are estimated and inversely transformed to compute the posterior estimates of the variance components. The EB statistic is constructed by replacing the REML variance estimates with the EB variance estimates in the usual t statistic. The EB approach is illustrated with a real data example which compares the effects of five different genotypes of male flies on post-mating gene expression in female flies. In a simulation study, the ROC curves are applied to compare the EB statistic and two other statistics. The EB statistic was found to be the most powerful of the three. Though the null distribution of the EB statistic is unknown, a t distribution may be used to provide conservative control of the false positive rate.     

Simultaneous measurement of soil water and soil hardness using a modified time domain reflectometry probe and a conventional cone penetrometer

Soil strength and water content are important indices for assessing soil resistance to root growth and soil compaction both of which affect other soil properties. Therefore, simultaneous measurement of soil penetration resistance (PR) and soil water content can aid agricultural land management. We measured PR with a conventional cone penetrometer, followed immediately by determining water content using a modified TDR probe inserted into the penetrometer hole. From the results of a field feasibility test, soil water content was measured satisfactorily and correlated well with data obtained by the gravimetric method, except for those data from near the surface owing to soil disturbance when the cone penetrometer was extracted after the PR measurements. Field results demonstrate that PR and soil water content have three‐dimensional variability, with a markedly different distribution pattern between cultivated and subsoil layers at the field scale. Overall, the variability in the PR and soil water data is similar to that reported in previous studies. We conclude that our method produces results helpful to field management of soil and water because it is based on a simple and easy technique for the simultaneous measurement of soil water content and PR.     

Prediction of existing gully erosion in vineyard parcels of the NE Spain: a logistic modelling approach

This paper presents a study of determining factors and a method to predict the existence of gully erosion in vineyard parcels. The Alt Penedès-Anoia region (Catalonia, NE Spain), mainly dedicated to the cropping of vineyards for high quality wine production, was selected as the case study area. Single factors related to the existence of gully erosion were determined by means of statistical tests (Student's t-test and chi-square). The existence of gully erosion was predicted by means of a multivariate procedure. A stepwise selection of variables (relief, soil, land use/management characteristics) was performed, which allowed the identification of factors that present a significant relationship with the existence of gully erosion. The selected factors, slope degree and planar slope form, were considered as independent variables in a logistic regression of binary response. The model computes the probability that gullies exist in given vineyard parcels, and it can be implemented in a raster-based geographical information system (GIS). The assessment of the model in 52 parcels, different from the training data set, yielded an overall accuracy of 84.6%. The predictive model can be applied for areas with similar conditions, but the modelling approach can be applied in other different areas.     

Using regression models in design‐based estimation of spatial means of soil properties

The precision of design‐based sampling strategies can be increased by using regression models at the estimation stage. A general regression estimator is given that can be used for a wide variety of models and any well‐defined sampling design. It equals the π estimator plus an adjustment term that accounts for the differences between the π estimators for the spatial means of the auxiliary variables and the true spatial means of these variables. The regression estimator and ratio estimator follow from certain assumptions on the model and the sampling design. These are compared with the π estimator in two case studies. In one study a bivariate field of linearly related variables was simulated and repeatedly sampled by Simple Random Sampling without replacement and sample sizes 10, 25, 50, 100 and 200. For all sample sizes the ratio of the standard error of the simple regression estimator to that of the π estimator was approximately 55%. The bias of the simple regression estimator was negligibly small. The confidence interval estimators were valid for all sample sizes except for n = 10. Also the ratio estimator was approximately unbiased, and the confidence interval estimators were valid for all sample sizes, even for n = 10. This is remarkable because the ratio estimator assumes that the intercept of the regression line is 0 which was incorrect for the simulated field. On the other hand, only approximately 55% of the potential gain was achieved because the model was inappropriate. In a second study the spatial means of the Mean Highest Watertable of map units were estimated by Stratified Simple Random Sampling and the combined (multiple) regression estimator. The NAP elevation, the local elevation, the Easting and the Northing were used as auxiliary variables. For all map units except one the combined (multiple) regression estimator was more precise than the π estimator. The ratio of the standard errors varied from 0.36 to 1.04. The domain for which the regression estimator was less precise than the π estimator showed strong variation between strata. For this domain it was more efficient to group the strata into two groups and to fit simple models for these groups separately.     

伽玛射线测量径流泥沙含量算法中质量吸收系数优选及其对测量误差影响的分析

质量吸收系数是伽玛射线测量径流泥沙含量理论计算公式中由伽玛射线记数率确定泥沙含量的基本参数。该文研究了确定质量吸收系数的多点法和单点法。在多点法中，提出了确定质量吸收系数的回归方法，推导了由大量(多点)试验数据计算质量吸收系数的回归公式。采用大量实测数据由回归公式确定的质量吸收系数准确且精度较高，能在很大程度上消除随机误差的影响。而单点法通过几个特殊的取值点(单点)来估算质量吸收系数，具有参数确定快速、物理概念明确的优点。另外，该文从理论上分析了质量吸收系数变化对泥沙含量测量精度的影响，结果表明质量吸收系数的相对变化与泥沙含量的相对变化成正比例关系，且比例系数为1。从而确定了两种方法的有效性、优缺点和应用方法。     

基于表观热惯量的土壤水分监测

土壤水分含量是监测农业干旱的重要指标, 遥感法是大面积监测土壤水分时空特征的主要方法, 热惯量法是遥感方法监测土壤水分的主要研究手段之一。本文提出了一个改进的表观热惯量模型计算表观热惯量, 并通过地面验证试验对该模型的适用性进行了分析。在中国科学院栾城农业生态系统试验站, 通过严格的控制试验, 设计了10 个不同植被覆盖、不同土壤水分含量的试验小区, 针对表观热惯量的适用条件, 利用实测的地表温度、植被指数、反照率、太阳辐射等参数计算了不同植被覆盖不同土壤水分含量下的表观热惯量,并与土壤体积含水量进行了相关和回归分析。结果表明: 在植被覆盖度较低情况下[归一化植被指数(normalized difference vegetation index, NDVI)<0.35], 表观热惯量法具有较好的效果, 表观热惯量与土壤体积含水量之间的相关系数大于0.7, 通过了95%的显著性检验, 两者具有很高的相关性, 可以用热惯量法监测土壤水分状况; 在较高植被覆盖情况下(NDVI>0.35), 表观热惯量与土壤体积含水量之间没有相关性, 热惯量法监测土壤水分失效; NDVI 为0.35 可以作为热惯量法监测土壤水分状况是否可行的判断条件。     

Application of the acetylene reduction method in nitrogen fixation studies

The fixation of atmospheric nitrogen by organisms has usually' been measur'ed by the increase in t6tal nitrogen using the Kjeldahl method and, recently, by enriching with ¹⁵N isotope using a mass spectrometer after a suitable period of incubation. However, the Kjeldahl method is in accurate, laborious and not sensitive. The isotope UN method is 30 to 100 times more sensitive than the Kjeldahl method, but it is expensive and requires that the experiment is conducted only on a small scale, creating conditions quite different from the natural environment. The method also requires a mass spectrometer. More recently, DILWORTH (1) and SCHOLLHORN and BURRIS (2) observed that acetylene is reduced to ethylene by extracts from Clostridium pasteurianum. Ethylene production in the acetylene-reducing function of nitrogen-fixing microorganisms was determined by gas chromatographic techniques. The acetylene reduction method has been used in the detection of nitrogenase in biological systems (3,4,5,6). The method is reported to be simple, economical and a thousand times more sensitive than the liN method (6). This paper is a preliminary report on the use of the acetylene reduction method in studies on nitrogen fixation of various kinds of materials in the rice fields.     

Evaluation of a rapid microwave digestion method for determination of total organic carbon in soil

Precise measurement of soil organic carbon (SOC) is essential for constructing regional inventories, developing best agricultural management practices, and modeling purposes. Currently, the automated dry combustion method is considered standard, but the method is both costly and time-consuming. There is a need for a simple, easy to use and cost-effective method of organic C determination in soil. A simple method of total organic carbon (TOC) determination in soil that involved wet digestion of K₂Cr₂O₇-H₂SO₄-soil mixture in a commercial microwave oven followed by spectrophotomteric measurement of Cr (III) was evaluated. The method was compared with automated dry combustion and two other wet digestion methods. The method showed close agreement with dry combustion method (R² = 0.90; root mean square error = 0.70) and the TOC measured with the two methods did not differ for a range of soils drawn from lowland and upland land-uses and varying in pH (6.2–9.3), TOC (2.8–14 g kg^?1), and calcium carbonate content (0–6.7%). The recovery of the added organic C by the microwave method was 98.6 ± 4.2%. The results suggested that microwave-spectrophotometric method could be easily adopted in routine soil analysis as it is not only precise, rapid, and cost-effective but also produced small volume of reagent waste.     

改进的TOPSIS法在土地利用结构合理性评价中的应用——以兰州市为例

利用兰州市1997~2006年土地利用变化调查数据,采用改进的TOPSIS方法以及灰色关联度分析,对兰州市土地利用结构进行了综合评价。结果表明:1997~2006年期间兰州市土地利用结构合理度总体上较高,均在45%以上,在时间上呈波动上升趋势;不同土地利用类型的变化对土地利用结构的合理性影响不同,林地、牧草地、居民点及工矿用地与土地利用结构合理度的关联度最高;合理加快居民点及工矿用地的增长,保护林地,调整牧草地比例,加快牧草地的增长速度,对优化兰州市土地利用结构具有决定性的作用;改进的TOPSIS法,结果令人满意,适合于土地利用结构合理性评价的研究。     

Pasta Production: Complexity in Defining Processing Conditions for Reference Trials and Quality Assessment Methods

Pasta is a simple food made from water and durum wheat (Triticum turgidum subsp. durum ) semolina. As pasta increased in popularity, studies endeavored to analyze the attributes that contribute to high‐quality pasta. Despite being a simple food, the laboratory‐scale analysis of pasta quality is a complex process. Spaghetti is the most commonly studied form of pasta because its long, uniform shape lends itself well to analysis. Pasta production at a laboratory scale is not standardized, leading to large amounts of variation owing to differing equipment and protocols. Pasta production protocols vary in their target moisture content, mixing profile, extrusion die material, and spaghetti diameter. Once pasta is extruded, the equipment and protocols used to dry the pasta at the laboratory scale are far from consistent. Measurement of pasta quality parameters is also lacking in updated standardized methods, making it difficult to compare pasta quality data among laboratories. AACC International is actively working to develop an improved method for laboratory‐scale pasta production and analysis via the Pasta Products Analysis Committee. This review summarizes the equipment, protocols, and methods utilized in pasta quality studies.     

Use of GIS for Optimizing a Collecting Mission for a Rare Wild Pepper (<Emphasis Type="Italic">Capsicum flexuosum</Emphasis> Sendtn.) in Paraguay

This paper presents an effective method for prioritizing areas within a country for acquisition of germplasm of a crop gene pool for ex situ conservation. The method was applied to the rare wild pepper species, Capsicum flexuosum Sendtn., in south-east Paraguay. A model to prioritize areas for collecting germplasm was constructed by combining (1) a prediction of the species' geographic distribution based on the climate at previous collection points, (2) the distribution of forest margins (the species' natural habitat) and (3) areas accessible by road. The model was then tested in the field by visiting 20 sites having both high and low predicted probability of occurrence of C. flexuosum. Six new populations were found, representing a significant improvement over two previous collecting missions for the species in the same region, undertaken without the use of GIS targeting. Using the most optimistic analysis of model performance, C. flexuosum was found at five out of seven points predicted to harbour the species and not found at four of five points predicted not to harbour the species. The model was then improved by the use of higher resolution climate surfaces. It is recommended that future explorers use more recent and higher resolution satellite images to locate suitable habitats. The method is replicable for different species in different geographic regions and is offered as a means of optimizing efficiency in financially constrained, national plant genetic resources programs.     

Ermittlung des Spannungszustandes von Böden aus Werten des Eindringwiderstandes von Sonden

Evaluation of the soil consolidation state by using data from penetration resistance probes Penetration resistance data (EW) from handdriven equipment are easily obtainable because the equipment is simple, cheap, and easily carriable. Measurements are performed quickly without extensive destruction of the site. It is the only method to measure soil strength directly and in situ. Therefore, it is worthwhile to propose an interpretation of the results in order to obtain more conclusive statements on the structural soil state. The procedure applied in our study consists in assigning EW values to the principal stress σ_x and in using an auxiliary construction for the vertical component (σ_z ) as a function of penetration depth. The EW value obtained at the final soil depth is assumed to represent stresses at rest, i.e., K₀ = σ_x/σ_z = 1. Drawing a straight line from this point towards the origin of the coordinates EW and soil depth supplies values of the hydrostatic condition for each depth; e.g., values for σ_z are available for each depth. The coefficient for the equivalent stress at rest (K_0E) per depth can now be calculated simply by comparing the measured EW values with the assumed (hydrostatic) vertical depth function of EW. From a total volume of 29 sets of EW versus depth relations, means and standard deviation of K_0E are presented for arable and forest soils from central Europe. K_0E of forest soils tends to be close to 1 showing approximately normal compaction. In arable sites, K_0E > 2 prevail, indicating precompation. These results confirm the general feasibility of the approach to evaluate the compaction state of soil from EW data. Examples are given to show the K_0E characteristics for special cases of mechanical stress situations.