Identification of soil quality indicators for assessing the effect of different tillage practices through a soil quality index in a semi-arid environment

Tillage is known to potentially affect soil quality in various ways. In this study, a soil quality index (SQI) was developed by quantifying several soil attributes either sensitive or insensitive to physical disturbance, using factor analysis as a dimension reduction technique, in order to discriminate different tillage systems. Soil properties including physical (MWD), chemical (pH, organic C, total N, available P and POM contents) and microbial (MBC, MBN, PCM, PNM and three enzymes) parameters were measured to establish a minimum data set (MDS) for the assessment of overall SQI. The soil attributes were determined on samples (0–20 cm depth) collected under moldboard (MP) and disk (DP) plows as conventional tillage (CT), and rotary (RP) and chisel (CP) plows as reduced tillage (RT) systems with a similar plant C input rate and cover crop over a period of six years (2005–2011) in a semi-arid calcareous soil (Calcixerepts) from Central Iran. Results indicated a clear difference in soil quality among the tillage systems with a significant increase of SQI under RT over time, particularly under CP practices. Although RT improved most soil microbial attributes, not all attributes contributed to SQI because of their close interrelationship. The final SQI consisted only of geometric mean of microbial activity (GMA, the square root of the product of PCM and PNM) and geometric mean of enzyme activity (GME, the cube root of the product of enzyme activities). Soil GME and GMA were found to be as key indicators contributing 55% and 36% to SQI, respectively. Therefore, the GME and GMA were the most important indicators effectively discriminating tillage systems, and could be used to monitor the enhancement of soil quality under RT in this semiarid environment. The influence of tillage year on SQI was greater than that of tillage practices. In conclusion, RT systems were characterized by a higher value of SQI, suggesting a good recovery of soil capacity and functions after abandoning CT in the studied area. Smallholder farmers should therefore be aware of the potential for high soil quality in future as a result of continuing RT systems, especially with surface tillage using CP practices.     

农田土壤水分预测模型的研究进展及应用

本文对国内外土壤湿度的监测,预报发展情况进行了回顾,总结了国内外土壤水分监测方法及其原理,介绍了国内外土壤湿度预测模型的研究进展及其应用情况,讨论了传统的土壤水分平衡方程统计模型法,从土壤水分运动方程出发的物理学模型法,利用气象要素与土壤水相关关系为基础的统计预报法、及新兴的人工神经元网络和遥感数据估测法的优缺点,并对土壤水分研究工作所面临的问题进行了必要的阐述。     

Explore the influence of soil quality on crop yield using statistically-derived pedological indicators

The aim of this paper is (1) to find and discuss the best multivariate statistical method in exploring the soil productivity function in an East-Hungarian region; (2) to evaluate and interpret the edaphic indicators and Hungarian soil quality index (HSQI); and (3) to identify the main determinant factors and indicators in this region. Soil pH, carbonate content, soluble and exchangeable Na⁺, clay, humus, available phosphorus and potassium content were analyzed. Topographical position and HSQI were evaluated as well. Yield data (maize, winter wheat, sunflower) of 10 years were standardized using calculated relative yield of each crop. Having simple indicators, stepwise linear regressions for mean relative yield were inadequate for choice uncorrelated indicators which have significant influence on yields. The variables were analyzed using principal component analysis (PCA) with Varimax rotation. According to the eigenvalues greater than 1, the PCA yielded three principal components (PCs) explaining a total of 89.471% of the variance for the entire data set. These factors could be well interpreted as derived complex indicators. Having the three PCs, a stepwise linear regression process (PCR) was conducted with dependent variables mean relative yield. The explained variance for mean relative yield was as high as adjusted R² = 0.771 (p < 0.001). The three PC factors together explained the mean relative yield better than the simple indicators and the HSQI. So, the variables can effectively explain the yield and the variability together with other variables as linear combinations. Consequently, PCR is a successful method to reveal the site specific relationship between soil properties and yields and to revision the HSQI at local level.     

旱区流域水土环境质量的综合定量评价模型

现有流域水土环境质量的评价方法大多根据评价区评价指标量化值与评价等级标准建立评价模型.评价区不同,评价模型也不相同,计算工作量较大.本文根据给定的水土环境质量评价等级标准,采用随机技术模拟生成足够数量的评价指标序列,应用人工神经网络模型,以评价指标生成序列和其所属的评价等级值建立一种通用的评价模型,其特点是不需要构造评价指标集和评价等级值间的函数关系和计算权重值,减少了建立模型的工作量.以西北地区水资源开发利用程度最高的石羊河流域进行实例研究,表明该模型可操作性强,可用于流域水土环境质量评价.     

A minimum data set and soil quality index to quantify the effect of land use conversion on soil quality and degradation in native rangelands of upland arid and semiarid regions

Conversion of native rangelands to croplands potentially influences soil functions and quality. The aim of the current study was to assess soil quality (SQ) after rangeland conversion and degradation for more than 40 years using an indexing framework and integrated approach. Fifteen soil attributes were measured at two sampling depths (0–20 and 20–40 cm) of paired native undisturbed and adjacent cultivated rangelands at three rangeland sites. The soil organic carbon (OC), electrical conductivity (EC) and arylsulphatase (ARY) activity were found to be the key indicators of the minimum data set and these indicators greatly affected the computed soil quality index (SQI), particularly in the soil surface. The contribution of OC, EC and ARY to the overall SQI was 77, 13 and 10%, respectively. Although rangeland conversion reduced other soil attributes (including aggregate stability, available water capacity, cation exchange capacity, microbial biomass, microbial activity and the activities of urease and invertase enzymes), in particular at the 0–20 cm depth, these variables did not contribute to the estimated SQI values because of their high correlation with OC contents (i.e., strong interdependency). Cultivated rangelands were characterized by a low soil OC content, EC and ARY activity, and consequently a low SQI. A significant decline in SQI value (29–47%) was observed as a result of rangeland conversion to croplands, depending on soil depth considered and scoring function used to compute the SQI. Overall, converting native rangelands to croplands decreased SQ to 52–64% of their potential capacity using a non-linear scoring method. In summary, soil OC, EC and ARY are the most important indicators, which can be used to monitor and asses the degradation of rangeland SQ after conversion to croplands in these arid and semiarid upland environments. This finding is of especial importance because the assessment of SQ allows the successful and straightforward discrimination between rangeland and cropland ecosystems or to quantify land use conversion effects on SQ. It is concluded that the rate of soil changes can be assessed and compared more accurately in the studies of land use conversions in native rangeland ecosystems using the current indexing framework due to its simplicity and quantitative flexibility.     

应用土壤质量退化指数计算松嫩盐碱草地土壤营养位

土壤质量退化指数是一种定量评价土壤质量的方法,以某一土壤类型为基准,计算其他土壤类型与基准土壤类型之间各土壤属性的差异,进而反应土壤质量退化或改善的程度.采用1999年的数据应用该方法评价土壤营养状况,计算土壤营养位,分析松嫩盐碱草地植被种群分布格局与土壤营养的关系,从而探讨应用土壤质量退化指数计算土壤营养位的可行性.应用土壤质量退化指数评价土壤营养位得到的结果与主成分分析法的基本一致,并且方法更为简便,更利于土壤营养位分析在生产实践中应用.     

黄土丘陵区土壤质量评价指标研究

针对黄土丘陵区侵蚀土壤最主要的功能--生产力和抗侵蚀能力,运用敏感性分析、主成分分析和判别分析法,对10种土地利用类型、208个样点的32项土壤属性指标进行了筛选.结果表明,在黄土丘陵区,土壤速效磷含量、抗冲性、渗透系数、活性有机碳、有机质、脲酶作为土壤质量评价的高度敏感指标,是土壤质量恢复与调控的主要目标.土壤生物指标属于高度敏感和中度敏感指标.黄土丘陵区侵蚀土壤的29项理化及生物属性指标可以被归纳为5个土壤质量因子:有机质因子、质地因子、磷因子、孔隙因子和微结构因子.5个因子中,孔隙因子在不同土地利用方式之间差异不显著,其余4个质量因子在不同土地利用方式之间差异极显著.黄土丘陵区侵蚀土壤质量评价指标为有机质、渗透系数、抗冲性、CEC、蔗糖酶、团聚体平均重量直径、速效磷、微团聚体平均重量直径.其中,有机质、渗透系数、抗冲性是表征黄土丘陵区侵蚀土壤质量的关键指标.     

Soil textural class plays a major role in evaluating the effects of land use on soil quality indicators

Inappropriate land use that negatively affects ecological processes and soil quality is generally considered to be the primary cause of soil degradation in tropical agroecosystems. We hypothesized that in addition to land use, soil textural class also has an important effect on ecological processes and soil quality. To test our hypothesis, effects of land use change on soil organic fractions as well as microbial and biochemical indicators were quantified for clayey and sandy-clay-loam soils within the native Cerrado biome, pasture (Brachiaria brizantha) and sugarcane (Saccharum officinarum) agroecosystems in southwestern Brazil (Minas Gerais state). Labile carbon, humic substances, soil microbial respiration (SMR), microbial biomass carbon (MBC), metabolic quotient (qCO₂), hydrolysis of fluorescein diacetate (FDA), beta-glucosidase, urease, phosphatase and arylsulphatase activities were measured for each sample. Labile carbon concentrations were not affected by land use but were lower in sandy-clay-loam soil than clayey soil. Humic substances were at the highest concentrations in the native Cerrado and the lowest in sugarcane agroecosystems. Sandy-clay-loam soil had lower humic acid concentrations than clayey soil. Soil microbial indicators (SMR, MBC and FDA) showed lower values in pasture and sugarcane agroecosystems than in the native Cerrado. FDA was a more sensitive microbial indicator than SMR and MBC for detecting land use and textural class differences. The qCO₂ indices were greater in sugarcane systems than in either pasture or native Cerrado systems. The activity of exocellular hydrolytic enzymes, such as beta-glucosidase, urease, phosphatase and arylsulphatase, was smaller in sugarcane and pasture agroecosystems than in native Cerrado ecosystems. Within the same land use, the activity of these enzymes was always greater in clayey soil than in sandy-clay-loam soil, indicating a higher impact of land uses on enzyme activities in clayey soils. Results for the measured indicators support the hypothesis that soil textural class plays a major role in assessing differences between land use systems in the Brazilian Cerrado biome.     

Development of diatom-based tools for assessing stream water quality in south-eastern Australia: assessment of environmental transfer functions

Models were developed of relationships among water quality, geospatial and species-level diatom data for 465 samples collected from stream sites across south-eastern Australia. Transfer functions were derived from weighted averaging and artificial neural network approaches. Analysis of spatial variations in species assemblages was used to divide the sites into two groups according to site elevation. The strongest predictive models for the upland group associated diatom assemblages with conductivity, longitude, altitude, and to a lesser extent pH, NO _x and TKN. The strongest predictors for the lowland group were longitude and conductivity, but artificial neural network models performed well for NO _x and temperature. The importance of the geospatial variables suggests that there may be a capacity to develop diatom sub-regions within which robust models for other water quality variables important to management can be generated. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users. Handling editor: R. Norriss     

Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set

Little information is available to assess the dynamic changes in wetland soil quality in coastal regions, though it is essential for wetland conservation and management. Soil samples were collected in Suaeda salsa wetlands (SWs), Tamarix chinensis wetlands (TWs), Suaeda salsa–Tamarix chinensis wetlands (STWs), freshwater Phragmites australis wetlands (FPWs) and saltwater Phragmites australis wetlands (SPWs) in three sampling periods (i.e., summer and autumn of 2007 and spring of 2008). According to the flooding characteristics of these wetlands, the study area could be grouped into three sub-regions: short-term flooding region (STFR), seasonal flooding region (SFR) and tidal flooding region (TFR). Soil quality was evaluated using the soil quality index (SQI), which was calculated using the selected minimum data set (MDS) based on principal components analysis (PCA). Our results showed that soil salt content (SSC), total carbon (TC), magnesium (Mg), nitrate nitrogen (NO₃⁻-N) and total sulfur (TS) consisted of a MDS among 13 soil properties. The SQI values varied from 0.18 to 0.66 for all soil samples, of which the highest and lowest SQI values were observed in TFR. The average SQI values were significantly higher in summer (0.50 ± 0.13) than in spring (0.37 ± 0.13) and autumn (0.36 ± 0.11) in the whole study area (p < 0.05). The average SQI values followed the order STFR (0.44 ± 0.12) > TFR (0.41 ± 0.15) > SFR (0.35 ± 0.09) although no significant differences were observed among the three regions (p > 0.05). SPWs and SWs soils showed higher SQI values (0.50 ± 0.10 and 0.47 ± 0.15, respectively) than TWs (0.30 ± 0.08) soils (p < 0.05). The SSC was the dominant factor of soil quality with its proportion of 34.1% contributing to the SQI values, followed by TC (24.5%) and Mg (24.1%). Correlation analysis also showed that SQI values were significantly negatively correlated with SSC. SSC might be a characteristic indicator of wetland soil quality assessment in coastal regions. The findings of this study showed that the SQI based on MDS is a powerful tool for wetland soil quality assessment.     

海南岛土壤质量的指标与量化表达研究

在海南岛SOTER数据库基础上,结合联合国有关原则与规定,选择表征土壤质量的若干指标项目组成体系,在GIS环境中对图斑的土壤质量进行评价,并将评价等级指数与空间数据库相连,根据用户需求将土壤质量的评价结构输出,结果得到当地有关专家的认可,建议通过当地实践结果,对模型进行了必要的修订（适当修改评价指标及量化标准）,使之更趋完善。     

不同森林恢复类型对南方红壤侵蚀区土壤质量的影响

为了探讨主要森林类型对土壤质量的影响 ,对南方红壤侵蚀区 4种主要森林恢复类型下土壤物理、化学、生物学性状进行了比较研究 ,结果表明 :不同的森林恢复类型导致了土壤物理、化学和生物学性质的显著差异。 4种森林类型的土壤质量均比长期干扰下对照的土壤质量高。人工林土壤质量又相对比天然次生林土壤质量低。其土壤质量综合指数分别为 :天然次生林(0 .95 )、油茶林 (0 .6 8)、杉木林 (0 .5 5 )、湿地松林 (0 .36 )、对照 (0 .0 4 )。自然恢复在恢复初期是提高土壤质量的有效途径。导致人工林和对照土壤质量相对较低的主要因素是 :周期性的森林抚育打破土壤物理结构、凋落物质量较低、凋落物量较低、微生物生物量较低、微生物功能较差和土壤养分流失严重。在土壤质量指标选择方面 ,土壤微生物生物量结合微生物功能多样性是反映土壤生物学活性和土壤质量的较好指标。     

A novel method for measuring the thermal conductivity of organisms

Based on the theories of tissue optics and artificial neural network, the relationship between the optical properties and biological parameters was studied, and a new experimental method was derived. The properties of the organism were obtained indirectly by a black-box model derived by self-study of the artificial neural network between optical parameters and thermo-physical properties without using the heat transfer equation. In this method, the energy of light in diffuse radiation, diffuse transmission and collimated transmission was absorbed by a dual-integrating sphere experimental system of a spectrometer, and the spectrogram of the energy was obtained. Combining these spectral data of the energy, the diffuse-reflecting power, the diffuse transmissivity and the collimated transmissivity were calculated. The calculated results were taken as the input parameters of a black-box model. The experimental results show that there are apparent differences between the spectrogram of the energy on the diffuse radiation, the diffuse transmission and the collimated transmission of different matters, while there is a little difference in the same matter. Each spectrogram has its own characteristic. The values of the four thermal properties including the density, the constant pressure specific heat, the thermal diffusivity and the viscosity were calculated using the black-box model. Compared with the real values the calculated one has an average relative error between −5% and 5%. The conductivity of the tongue is 0.68 W/(m K) that calculated from the value of the density, the constant pressure specific heat and the thermal diffusivity. The results also show that there is a little difference on the conductivities in the longitudinal cross-section and the transverse section, but the effect of temperature on the conductivity of the tongue is not apparent. The difference implies the anisotropy of the properties of the organism, which cannot be easily obtained by a conventional experimental method.     

黄土丘陵区小流域土地利用和植被恢复对土壤质量的影响

土壤质量的维护和提高是全球生物圈可持续发展的重要因素之一．对黄土丘陵小流域持续利用25年后的荒草地、山杏林地、农地、油松林地、灌木林地和撂荒地土壤性状的研究结果表明,不同土地利用方式和植被恢复类型对土壤质量有很大影响;植被恢复重建和农地撂荒将增加土壤有机质含量,提高土壤质量;粗放的农业耕作措施将降低土壤质量并引起土壤退化;灌丛有明显的肥力岛屿作用;撂荒在一定程度上可以培肥土壤。随着“西部大开发”、“退耕还林还草”和生态重建工程的开展,在半干旱黄土丘陵沟壑区,建植灌木、种植牧草、农地撂荒和自然恢复是较好的生态重建和植被恢复方式。     

科尔沁沙地不同土地利用和管理方式对土壤质量性状的影响

研究了科尔沁沙地退化草地开垦后,在14年不同的土地利用和管理方式下土壤物理、化学和生物学性状的特征。结果表明,农林(林草)复合利用模式在土壤粒级组成、孔隙分布、持水性能、有机质和N、P养分、酶活性等方面表现出较好的质量性状特征。有机无机配施、精细管理的灌溉农田次之,而粗放管理的旱作农田。土壤质量性状严重恶化,逐步向流沙方向演变,与科尔沁原生景观草地土壤相比,管理良好的利用系统,土壤物理性状、P含量和一些酶活性指标提高,但有机质和全氮含量明显低,表明已退化生态系统C、N库的恢复和重建需要较长的时间。研究结果表明,不同的土地利用方式和管理措施显著影响土壤质量变化的程度和方向;调整土地利用结构,基本农田实行精细管理,旱作农田退耕还草还林是保护土地资源。实现区域生态恢复和重建的根本选择。     

Artificial Neural Network Method for Predicting the Specificity of GalNAc-transferase

The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R₄, R₃, R₂, R₁, R₀, R₁, R₂, R₃, and R₄, where the acceptor at R₀ is either Ser or Thr to which the reducing monosaccharide is anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a neural network method—Kohonen's self-organization model is proposed in this paper. Three hundred five oligopeptides are chosen for the training site, with another 30 oligopeptides for the test set. Because of its high correct prediction rate (26/30=86.7%) and stronger fault-tolerant ability, it is expected that the neural network method can be used as a technique for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. It might also be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.     

Applying undertaker cost functions to model quality assessment

Undertaker is a program designed to help predict protein structure using alignments to proteins of known structure and fragment assembly. The program generates conformations and uses cost functions to select the best structures from among the generated conformations. This paper describes the use of Undertaker's cost functions for model quality assessment. We achieve an accuracy that is similar to other methods, without using consensus‐based techniques. Adding consensus‐based features further improves our approach substantially. We report several correlation measures, including a new weighted version of Kendall's τ (τ₃) and show model quality assessment results superior to previously published results on all correlation measures when using only models with no missing atoms. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

报告基因技术及其在土壤质量监测中的应用

报告基因包括lux、gfp、lacZ、inaZ、luc、cat、xylE及uidA等,其中土壤质量监测中最常用的报告基因有lux、gfp、lacZ和inaZ 4种.从不同角度比较了土壤质量监测中最常用的上述4种报告基因,简要阐述了应用于土壤质量监测中的微生物监测报告基因的定义、类别、性质及特点,展示了报告基因表达系统的构建方式与检测方法,总结了报告基因技术在监测土壤重金属、污染物质、营养物质等化学物质以及在土壤微生物及其活动、土壤根际微生物与土壤中原生动物、植物间的相互作用等方面的应用.讨论了目前报告基因技术应用的局限性及未来研究方向和重点.     

运用BP人工神经网络预测长江中下游梨黑星病发病的研究

提出了运用人工神经网络技术进行梨黑星病预测的新思路,并以梨黑星病发病的主要影响因素,即上年7月的降水量和上年8月的降水量作为训练样本模式提供给网络,按照误差逆传播网络的学习规则对网络进行训练,经过计算机2844次学习后,网络达到预先给定的收敛标准,使网络具备了预测梨树黑星病流行趋势和流行强度的功能。检验结果表明,该方法性能良好,预测准确率高,可望成为果树病早害预测预报的有效辅助手段。     

A neural-network technique to learn concepts from electroencephalograms

A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms (EEGs). A desired concept is represented as a neuronal computational model consisting of the input, hidden, and output neurons. In this model the hidden neurons learn independently to classify the EEG segments presented by spectral and statistical features. This technique has been applied to the EEG data recorded from 65 sleeping healthy newborns in order to learn a brain maturation concept of newborns aged between 35 and 51 weeks. The 39,399 and 19,670 segments from these data have been used for learning and testing the concept, respectively. As a result, the concept has correctly classified 80.1% of the testing segments or 87.7% of the 65 records.