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.     

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.     

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.     

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.     

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.     

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.