土壤动物与土壤健康

土壤动物与土壤健康息息相关,土壤动物多样性和功能能够灵敏反映人类活动和气候变化引起的土壤扰动。同时,土壤动物还通过与生物和非生物组分间的相互作用对地上生态系统产生反馈作用。当前土壤动物在土壤健康评价体系中的应用相对较少,主要集中在土壤线虫、节肢动物和蚯蚓等类群,仍缺乏基于土壤动物的系统性评价指标。因此,本文围绕土壤动物在指示土壤健康方面的潜力,系统总结了现有基于土壤动物的土壤健康评价指标,强调未来应建立和完善土壤动物基因组信息数据库,挖掘土壤动物的功能性状,加强土壤食物网结构和生态功能的研究,建立集成土壤动物物种多样性、功能性状和土壤食物网的指标体系,从而促进土壤健康和生态系统的可持续发展。     

土壤质量与土壤可持续管理

土壤是农业生存之本,土壤质量是联系土壤管理与可持续农业的桥梁与纽带。介绍了土壤质量的概念及其发展,在客观地分析我国土壤质量现状的基础上,提出土壤管理必须建立在土壤质量的基础上并兼顾土壤的生产性、稳定性、持续性、生存性及社会的可接受性,才能实现农业的可持续发展。     

土壤保持与土壤管理

土壤保持是经常涉及到与风蚀、水蚀有关的主要的土地退化活动.土壤保持是一个较为广泛的问题.不仅关系到主要侵蚀活动,而且也关系到影响着土壤肥力总体保持的较为缓慢的、连续不断的隐伏过程.土壤紧实,土壤有机质、土壤养分含量和微生物活动能力减少,都是土壤保持专家应予关心的问题.土壤保持往往被认为是用以防止土地退化的一些技术,缺乏农学知识的土木工程师经常持己看法,而又使这种看法得以强化.对农民来说土壤保持会被认为是不可理解、不适合当地条件的不必要的花费.在国际土壤研究与管理委员会的观点看来.土壤保持是土壤管理实践能力维持方面的一部分,既无需破坏表土地上层.还要与耕作制度相结合及适宜生境的特性.土壤保持问题涉及国际土壤研究与管理委员会(IBSRAMS)的三个主要目标,首先是关于以供养农业为目的的热带土地的开垦.另一方面还涉及酸性带土壤管理和森林土壤的管理.     

西德土壤及土壤科学

我们应西德马普学会邀请,于1982年9月26日到10月24日,赴西德进行土壤学专业考察。考察计划分为两方面,(1)希望短期内较全面地了解西德的主要土壤类型和利用情况;(2)适当安排参观西德最著名的土壤研究机构和与著名的西德土壤学家接触,以便较深入地了解西德土壤学研究的现状和学术见解。     

苏联土壤力学中的土壤分类方法

苏联土壤力学中所指的‘土壤’,是包括一般所称的岩石。基于力学的观点,土壤力学中土壤分类所依据的条件,与土壤学中的土壤分类,很有些不同。‘岩石土类’是依据它的可压缩性及应压力的大小;‘石砂土类’是依据土壤中大小颗粒的百分比例;‘粘土类’是依据土壤的塑性系数。土壤的基本种类如下:     

土壤微生物与土壤营养关系研究进展

综述了土壤微生物的组成、主要作用,并提出了表征土壤肥力的微生物指标,阐述了土壤微生物与土壤肥力和土壤氮、磷、硫元素的关系,提出了今后土壤微生物和土壤营养关系的研究方向。     

土壤含水率与土壤碱度对土壤抗剪强度的影响

土壤含水率和土壤碱度是表征土壤物理化学性质的两个重要参数。通过室内三轴不固结不排水试验,研究了土壤含水率和土壤碱度对土壤抗剪强度的影响。试验处理采用5种土壤碱度(土壤可交换钠百分比ESP=0、5、10、20、40)和4种土壤质量含水率(0.05、0.10、0.20以及饱和含水率0.34)水平。试验结果显示,土壤黏聚力随着土壤含水率的增加基本上呈先增大后减小之趋势;当土壤含水率在0.10附近时黏聚力达到其最大值。土壤内摩擦角随着土壤含水率的增加而线性减小。土壤碱度对土壤黏聚力的影响机理较为复杂,其影响效果随土壤含水率的增加而减小;但土壤碱度对土壤内摩擦角的影响较小。土壤碱度对土壤抗剪强度的影响程度明显地小于土壤含水率对其的影响程度。     

香港土壤和土壤科学特点

1997年11月下旬,应香港大学邀请前往进行学术交流。     

允许土壤流失量与合理土壤流失量

 为确定防治农田土壤侵蚀的标准,20世纪中叶美国土壤学家首先提出了“允许土壤流失量”的概念,之后这一概念又延伸应用到流域和区域土壤侵蚀评价。“允许土壤流失量”的概念在全球,包括在中国得到了广泛的应用。随着土壤侵蚀及相关学科的发展,特别是全球变化研究的深入,可持续发展及健康河流理念的提出及水土保持实践中遇到的新挑战,“允许土壤流失量”的科学性和实用性方面存在的缺陷逐渐显现出来。本文在简要介绍“允许土壤流失量”由来的基础上,着重讨论“允许土壤流失量”存在的问题,并提出“合理土壤流失量”的概念及其确定的原则与方法。     

澳大利亚土壤及土壤科学研究近况

1986年4月14日至5月5日,我应澳大利亚联邦科学与工业研究组织的邀请,对澳大利亚进行了短期的访问考察。     

Alternative methods in the development of pedotransfer functions for soil hydraulic characteristics

Soil hydraulic properties are needed in the modeling of water flow and solute movement in the vadose zone. Pedotransfer functions (PTFs) have received the attention of many researchers for indirect determination of hydraulic properties from basic soil properties as an alternative to direct measurement. The objective of this study was to compare the performance of cascade forward network (CFN), multiple-linear regression (MLR), and seemingly unrelated regression (SUR) methods using prediction capabilities of point and parametric PTFs developed by these methods. The point PTFs estimated field capacity (FC), permanent wilting point (PWP), available water capacity (AWC), and saturated hydraulic conductivity (Ks) and the parametric PTFs estimated the van Genuchten retention parameters. A total of 180 soil samples was extracted from the UNSODA database and divided into two groups as 135 for the development and 45 for the validation of the PTFs. The model performances were evaluated with three statistical tools: the maximum error (ME), the model efficiency (EF), and the D index (D) using the observed and predicted values of a given parameter. Despite the fact that the differences among the three methods in prediction accuracies of the point and parametric PTFs were not statistically significant (p > 0.05) except θ_r and α (p < 0.05) based on the ANOVA test, overall MLR and SUR were somewhat better than CFN in prediction of the point PTFs, whereas CFN performed better than the other two methods in prediction of the parametric PTFs. The F.F values of FC and θ_r for CFN, MLR, and SUR methods were 0.705. 0.805, 0.795 and 0.356, −0.290, −0.290, respectively, which refer to the best and worst predictions. Properties (Ks, θ_r, α) having some difficulty in prediction were better predicted by CFN and SUR methods, where these methods predict all hydraulic properties from basic soil properties simultaneously rather than individually as in MLR. This suggests that multivariate analysis using such functional relationships between hydraulic properties and basic soil properties can be utilized in developing more accurate point and parametric PTFs with less time and effort.     

Performance of pedotransfer functions in predicting soil water characteristics of soils in Norway

Abstract

Pedotransfer functions (PTFs), predicting the soil water retention curve (SWRC) from basic soil physical properties, need to be validated on arable soils in Norway. In this study we compared the performance of PTFs developed by Riley (1996), Rawls and Brakensiek (1989), Vereecken et al. (1989), Wösten et al. (1999) and Schaap et al. (2001). We compared SWRCs calculated using textural composition, organic matter content (SOM) and bulk density as input to these PTFs to pairs of measured water content and matric potential. The measured SWRCs and PTF input data were from 540 soil horizons on agricultural land in Norway. We used various statistical indicators to evaluate the PTFs, including an integrated index by Donatelli et al. (2004). The Riley PTFs showed good overall performance. The soil specific version of Riley is preferred over the layer specific, as the latter may introduce a negative change in water content with increasing matric potential (h). Among the parameter PTFs, Wösten's continuous PTF showed the overall best performance, closely followed by Rawls&B and Vereecken. The ANN-based continuous PTF of Schaap showed poorer performance than its regression based counterparts. Systematic errors related to both particle size and SOM caused the class PTFs to perform poorly; these PTFs do not use SOM as input, and are therefore inappropriate for soils in Norway, being highly variable in SOM. The PTF performance showed little difference between soil groups. Water contents in the dry range of the SWRC were generally better predicted than water contents in the wet range. Pedotransfer functions that included both SOM and measured bulk density as input, i.e. Wösten, Vereecken and Rawls&B, performed best in the wet range.     

Soil water-retention prediction from pedotransfer functions for some Indian soils

This paper discusses the development of pedotransfer functions (PTFs) and uses a multiple nonlinear regression technique to validate point and parametric PTFs for the estimation of a water-retention curve from basic soil properties such as particle-size distribution, bulk density and organic matter content. One hundred soil samples were collected at different depths from different locations in the Pavanje river basin that lies within the southern coastal region of Karnataka, India. Prediction accuracies were evaluated using the coefficient of determination (R ²), root mean square error (RMSE) and mean error (ME) between measured and predicted values. Overall, both point and parametric methods predicted water contents at selected water potentials with considerable accuracy. However, prediction of the soil water-retention curve using PTFs by point estimation method was relatively more successful (best case R ² = 0.983) for the sampled soils. F-tests were also conducted for all cases. For one regression equation, the p-value was zero and for other equation, values were close to zero. Critical comparative analysis was carried out on the performances of the point and parametric methods. Use of the developed PTFs is suggested for the prediction of a water-retention curve for loamy sand and sandy loam soils in this area of the coastal region of southern India.     

Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity

Modeling water flow and solute transport in vadose zone requires knowledge of soil hydraulic properties, which are water retention and hydraulic conductivity curves. As an alternative to direct measurement, indirect determination of these functions from basic soil properties using pedotransfer functions (PTFs) has attracted the attention of researchers in a variety of fields such as soil scientists, hydrologists, and agricultural and environmental engineers. In this study, PTFs for point and parametric (van Genuchten's parameters) estimation of soil hydraulic parameters from basic soil properties such as particle-size distribution, bulk density, and three different pore sizes were developed and validated using artificial neural network (ANN) and multiple-linear regression methods and the predictive capabilities of the two methods was compared using some evaluation criteria. Total of 195 soil samples was divided into two groups as 130 for the development and 65 for the validation of PTFs. Although the differences between the two methods were not statistically significant (p > 0.05), regression predicted point and parametric variables of soil hydraulic parameters better than ANN. Both methods had lower accuracy in parametric predictions than in point predictions. Accuracy of the predictions was evaluated by the coefficient of determination (R²) and the root mean square error (RMSE) between the measured and predicted parameter values. The R² and RMSE varied from 0.637 to 0.979 and from 0.013 to 0.938 for regression, and varied from 0.444 to 0.952 and from 0.020 to 3.511 for ANN, respectively. Even though regression performs insignificantly better than ANN in this case, ANN produces promising results and its advantages can be utilized by developing or using new algorithms in future studies.     

Pedo-transfer functions for estimating the hydraulic properties of paddy soils in subtropical central China

Pedo-transfer functions (PTFs) have been widely used to estimate soil hydraulic properties in the simulation of catchment eco-hydrological processes. However, the accuracy of existing PTFs is usually inadequate for use. To develop PTFs for local use, soil columns were collected from a double rice-cropped agricultural catchment in subtropical central China. The PTFs for saturated soil hydraulic conductivity (K_s) and parameters (θ_s, α, and n) of the van Genuchten model for the soil water retention curve (SWRC) were obtained based on soil’s basic properties, and compared with models developed by Li et al. in 2007 and Wösten et al. in 1999, respectively. Our results indicated that K_s in the range of 0.04–1087 cm d^?1 and θ_s in the range of 0.34–0.51 cm³ cm^?3 were both well estimated with the R²_adj of 0.72 and 0.87, respectively, but α (0.04–0.65 cm^?1) and n (1.05–1.21) were relatively poorly predicted with the respective R²_adj of 0.38 and 0.55, despite the use of more input parameters. Our local derived PTFs outperformed the other two existing models. However, if the local PTFs for paddy soils are not available, the Wösten et al. 1999 model can be proposed as a useful alternative. Therefore, this study can improve our understanding of the development and application of PTFs for predicting paddy soil hydraulic properties in China.     

青海三江源地区土壤容重转换函数的建立与比较

Bulk density (BD) is an important soil physical property and has significant effect on soil water conservation function. Indirect methods, which are called pedotransfer functions (PTFs), have replaced direct measurement and can acquire the missing data of BD during routine soil surveys. In this study, multiple linear regression (MLR) and artificial neuron network (ANN) methods were used to develop PTFs for predicting BD from soil organic carbon (OC), texture and depth in the Three-River Headwater region of Qinghai Province, China. The performances of the developed PTFs were compared with 14 published PTFs using four indexes, the mean error (ME), standard deviation error (SDE), root mean squared error (RMSE) and coefficient of determination (R²). Results showed that the performances of published PTFs developed using exponential regression were better than those developed using linear regression from OC. Alexander (1980)-B, Alexander (1980)-A and Manrique and Jones (1991)-B PTFs, which had good predictions, could be applied for the soils in the study area. The PTFs developed using MLR (MLR-PTFs) and ANN (ANN-PTFs) had better soil BD predictions than most of published PTFs. The ANN-PTFs had better performances than the MLR-PTFs and their performances could be improved when soil texture and depth were added as predictor variables. The idea of developing PTFs or predicting soil BD in the study area could provide reference for other areas and the results could lay foundation for the estimation of soil water retention and carbon pool.     

Applicability of site-specific pedotransfer functions and rosetta model for the estimation of dynamic soil hydraulic properties under different vegetation covers

Background, Aims, and Scope  During the last decades, different methods have been developed to determine soil hydraulic properties in the field and laboratory. These methodologies are frequently time-consuming and/or expensive. An indirect method, named Pedotransfer Functions (PTFs), was developed to predict soil hydraulic properties using other easily measurable soil (physical and chemical) parameters. This work evaluates the use of the PTFs included in the Rosetta model (Schaap et al. 2001) and compares them with PTFs obtained specifically for soils under two different vegetation covers. Methods  Rosetta software includes two basic types of pedotransfer functions (Class PTF and Continuous PTF), allowing the estimation of van Genuchten water retention parameters using limited (textural classes only) or more extensive (texture, bulk density and one or two water retention measurements) input data. We obtained water retention curves from undisturbed samples using the ‘sand box’ method for potentials between saturation and 20 kPa, and the pressure membrane method for potentials between 100 and 1500 kPa. Physical properties of sampled soils were used as input variables for the Rosetta model and to determine site-specific PTFs. Results  The Rosetta model accurately predicts water content at field capacity, but clearly underestimates it at saturation. Poor agreement between observed and estimated values in terms of root mean square error were obtained for the Rosetta model in comparison with specific PTFs. Discrepancies between both methods are comparable to results obtained by other authors. Conclusions  Site-specific PTFs predicted the van Genuchten parameters better than Rosetta model. Pedotransfers functions have been a useful tool to solve the water retention capacity for soils located in the southern Pyrenees, where the fine particle size and organic matter content are higher. The Rosetta model showed good predictions for the curve parameters, even though the uncertainty of the data predicted was higher than for the site-specific PTFs. Recommendations and Perspectives  The Rosetta model accurately predicts the retention curve parameters when the use is related with wide soil types; nevertheless, if we want to obtain good predictors using a homogenous soil database, specific PTFs are required. ESS-Submission Editor: Prof. Zhihong Xu, PhD (zhihong.xu@griffith.edu.au)     

Pedotransfer Functions for the Estimation of Saturated Hydraulic Conductivity for Some Indian Sandy Soils

Determination of the saturated hydraulic conductivity (k_s) is needed in many studies and applications related to irrigation, drainage, water movement and solute transport in the soil. Although many advances are made for direct measurements of k_s, they are usually time consuming and costly. Some attempts have been made to indirectly predict the saturated hydraulic conductivity from the more easily or readily available basic soil properties. The objective of this study was to develop and validate Pedotransfer Functions (PTFs) for estimation of saturated hydraulic conductivity using multiple non-linear regression technique. One hundred and one soil samples were collected from agricultural and forest soils at different depths, at different locations in the Pavanje River basin that lies in the southern coastal region of Karnataka, India. Saturated hydraulic conductivity was measured, by variable falling head method through Permeameter in the laboratory. Prediction accuracies were evaluated using coefficient of determination, root mean square error, mean error, geometric mean error ratio and geometric standard deviation of the error ratio between measured and predicted values. The results show that, the PTFs for the estimation of saturated hydraulic conductivity could be used appropriately for the soils with loamy sand and sandy loam textures falling in this area of the coastal region of southern India.     

Evaluation of pedotransfer functions predicting hydraulic properties of soils and deeper sediments

Eight pedotransfer functions (PTF) originally calibrated to soil data are used for evaluation of hydraulic properties of soils and deeper sediments. Only PTFs are considered which had shown good results in previous investigations. Two data sets were used for this purpose: a data set of measured pressure heads vs. water contents of 347 soil horizons (802 measured pairs) from Bavaria (Southern Germany) and a data set of 39 undisturbed samples of tertiary sediments from deeper ground (down to 100 m depth) in the molasse basin north of the Alps, containing 840 measured water contents vs. pressure head and unsaturated hydraulic conductivity. A statistical analysis of the PTFs shows that their performance is quite similar with respect to predicting soil water contents. Less satisfactory results were obtained when the PTFs were applied to prediction of water content of sediments from deeper ground. The predicted unsaturated hydraulic conductivities show about the same uncertainty as for soils in a previous study. Systematic deviations of predicted values indicate that an adaptation of the PTFs to the specific conditions of deeper ground should be possible in order to improve predictions.