景观复杂地区土壤有机质变异的多尺度特征——以河南省登封市土壤为例

为研究景观复杂地区土壤有机质变异的尺度特征,以河南省登封市为研究区,探讨了该市土壤有机质在不同尺度、不同景观类型区的变异特征。结果表明:(1)2 km采样间距的大尺度土壤有机质平均含量和变异系数分别为20 g kg-1和44.8%;200、300、300和500 m间距的小尺度4个景观类型区:紫色土区、中山石英砂岩区、山前洪积扇区和低山丘陵洪积物母质区,有机质平均值和变异系数分别为15.72、15.53、26.00、18.98 g kg-1和29.9%、26.21%、16.04%、35.41%。(2)不同尺度、不同景观类型区土壤有机质的半方差函数,在块金值、基台值和变程等参数上有较大差异。(3)大尺度土壤有机质与高程因子、坡向因子呈极显著和显著相关性,与坡度相关性不明显;除了低山丘陵洪积物母质区与高程因子极显著相关外,小尺度土壤有机质在紫色土区、中山石英砂岩区和山前洪积扇区均和高程、坡度、坡向无显著相关性。     

土壤有机质空间变异性及其驱动因素研究进展

土壤有机质是表征土壤质量的主要指标,在土壤肥力、环境保护和全球碳循环中发挥着重要作用。土壤有机质的主要特征之一是具有高度的空间异质性,这是土壤形成过程中自然因子和人为活动综合作用的结果。本文对土壤有机质空间变异性的研究方法进行了回顾,从非生物和生物两方面阐述了驱动土壤有机质空间变异的主要因素,分析了研究土壤有机质空间变异性尺度效应的重要性及相关成果。在此基础上,结合以往研究的特点和不足,从深层土壤有机质的空间变异性、森林土壤有机质的空间变异性、土壤有机质空间变异性驱动因素间的交互作用以及较大尺度下土壤有机质空间变异尺度效应四个方面,对未来的研究方向提出了合理化建议。     

黑土区田块尺度土壤有机质含量遥感反演模型

为了对田块尺度土壤有机质进行空间反演并提高模型精度和稳定性,该文以黑龙江省黑土带41.3 hm~2田块为例,获取2016年5月中下旬两期(受限于拍摄周期和天气原因而选择不同卫星影像,2016年5月17日Landsat 8影像和5月25日Sentinel-2A影像)裸土时期遥感影像和4 m分辨率DEM数据;分析单期影像与土壤有机质(soil organic matter,SOM)的关系,两期影像所包含的土壤含水量变化信息与地形因素对SOM预测模型精度的影响,建立基于BP神经网络的SOM遥感反演模型。结果表明:该田块内SOM含量差异较大;利用单期影像预测SOM时,基于红波段和785~899 nm波段建立的预测模型精度(建模均方根误差RMSE 1.033,检验RMSE 1.079)和稳定性(建模决定系数R2 0.677,检验R20.644)较高;两期影像时,基于红波段和1 570~1 650 nm波段建立的预测模型精度(建模RMSE 0.855,检验RMSE 0.898)和稳定性(建模R2 0.792,检验R2 0.797)显著提高;在两期影像模型基础上,加入地形因子作为输入量,模型精度(建模RMSE 0.492,检验RMSE 0.499)和稳定性(建模R2 0.917,检验R2 0.928)进一步提高。研究成果可为土壤碳库估算和农田精准施肥提供理论与技术支持。     

县域尺度土壤有机质与土地利用动态耦合关系定位对比研究——以渭北旱塬区长武县为例

以陕西省长武县为研究区域,根据大规模采样数据,结合1983年和2008年土地利用空间数据,运用定位对比监测方法,分析了25年来不同土地利用变化的幅度、空间分布特征和方向,并对这种变化影响下的土壤有机质含量演化特征进行了研究。结果表明:该区土地利用方式在这一时期变化显著,旱地、草地、非农用地面积大幅减少,林地、果园面积大幅增加。土地利用方式的转变已经成为土壤有机质含量变化的驱动因素,其中,旱地转变为林地和果园,土壤有机质含量变化明显,分别增加了6.6 g/kg和5.11 g/kg,而林地转变为旱地和草地则无明显增量。土壤有机质含量的变化不仅改变了土壤结构,有效地控制了水土流失的发生,而且为该区域的土地利用方式朝着健康的方向发展,提供了参考价值。     

县域尺度农田深层土壤有机质的估算及空间变异特征

县域是实施农业绿色发展的基本单元,农田土壤中不仅耕层的有机质含量会对土壤肥力产生影响,深层有机质的作用也不可忽略,精确估算基于县域尺度农田深层有机质含量具有重要意义。该研究选定位于山西省运城市的永济市农田为研究区,采用多点混合取样法,获取了8个样地剖面的18层数据,共144个混合土样的有机质含量数据,建立了表层(0～20 cm)有机质含量估算深层有机质含量的模型,并进行深层有机质含量的估算。基于半变异函数、空间自相关理论分析了0～30、30～60、60～90、90～120、120～150和150～180 cm土层有机质含量的空间相关性和聚集特征,并进行了相关性检验,采用克里格插值方法对研究区农田各土层的有机质含量进行了预测。结果表明:1)土壤有机质含量随深度的增加呈负指数递减(R2=0.80,P0.01),各土层的有机质含量变异系数介于35.89%～47.84%之间,处于中等变异程度。2)通过建立的估算模型可以通过表层有机质含量估算出任意深度的有机质含量,且拟合精度R2达到了0.90(P0.01)。3)指数模型是反映该区域有机质含量空间结构特征的最佳模型(R20.80,RSS0.001),各土层的有机质含量均表现出了中等程度结构性特征,和空间正相关性特征(Moran’s I=0.26,P0.01),并存在显著的空间聚集特征和异常值现象。4)克里格插值可以较好地进行研究区各层有机质含量的预测,预测精度较高,稳定性较好,为县域尺度深层有机质的估算,调整农艺措施、提高土壤肥力、达到土壤减肥增效、绿色增产增效提供依据。     

伊犁新垦区土壤有机质的克里金插值和条件模拟多尺度分析

克里金插值和条件模拟作为两种常用的空间变异近似和逼近方法,能否真实刻画要素的多尺度结构关系到相应结论的正确性.本文采用尺度方差分析方法,结合经典的半方差函数和Moran's I,以伊犁新垦区土壤有机质为例,对普通克里金插值(OK)和序贯高斯模拟(SGS)的结果进行多尺度分析.结果显示新垦区土壤有机质具有比较强的空间自相关性,10km、35 km和60 km左右为土壤有机质的特征尺度.在小尺度上,SGS对真实的空间数据尺度结构刻画都出现一定的偏差.在大尺度上,两者能够比较真实地反映数据的多尺度结构,可有效刻画35 km和60 km的特征尺度.     

消减残差自相关性的县域土壤有机质整合模型预测研究

提升土壤属性空间预测精度对实现农田精准施肥和保护生态环境具有重要意义。利用河北省滦平县采集的1 773个样点耕地表层(0～20cm)土壤有机质(SOM)及其地理环境数据,通过逐步回归分析方法筛选出最优环境变量;基于其中1 426个农田样点分别建立多元线性回归(Multiple Linear Regression,MLR)、随机森林(Random Forest,RF)、贝叶斯正则化神经网络(Bayesian regularization neural network,BRNNBP)以及与普通克里格(OK)整合模型(MLR-OK、RF-OK和BRNNBP-OK)预测SOM空间分布,以其余347个样点数据为测试集检验分析不同模型预测精度,并对模型残差进行半方差函数和空间自相关分析以评价模型拟合效果。结果表明,研究区耕地表层土壤SOM处在8.62～35.64 g·kg^-1变化区间,变异系数为20.26%,属中等程度空间变异;SOM高值区主要分布在东北及东南海拔较高地区,低值区多分布在西南及中部河谷地区;海拔、坡度和年均温度与SOM关系密切(P<0.001);整合模型...     

气候变化与土壤有机质的关系

在宾地质历史中,气候变化原速率是史无前例的,它对地球生物圈产生了巨大的影响。土壤有机质中的碳是地球碳库的重要组成部分,它参与全球碳循环。土壤有机质分解而产生的ＣＯ２和ＣＨ４是重要的温室气体。土壤有机质对气候模型的反应较敏感,其总量取决于生物量生产与分解的平均状态,以及土壤储存有机质的能力。就全球规模来说,土壤有机质沿着降水增加和温度下降的梯度而增加。温度是支配凋落物分解速率的重要环境因素,它甚至能     

Spatial prediction of soil organic matter in the presence of different external trends with REML-EBLUP

The objective of our study was to compare the performance of the empirical best linear unbiased predictor (E-BLUP) with residual maximum likelihood (REML) with that of regression kriging (RK) for predicting soil organic matter (SOM) with the presence of different external drifts. The study was conducted on a 933 km² area in Pinggu district of Beijing. Terrain attributes (elevation, slope and topographic wetness index) calculated from DEM were used as external drift variable. The root mean squared errors (RMSE) and the mean squared deviation ratio (MSDR) were used to assess the accuracy of prediction and the goodness of fit of the theoretical estimate of error respectively. RK resulted in both the most and least accurate predictions. REML-EBLUP provided more correct residual variogram models than RK for each trend model. Our results have shown that when the value of adjusted R² is greater than 0.45, there is litter difference in the ability to increase the accuracy between REML-EBLUP and RK; and when the value is less than 0.45, the performance of REML-EBLUP is significantly better than RK. It also suggested that topographical data can further improve the accuracy of the spatial predictions of SOM by using RK and REML-EBLUP.     

Managing soil organic matter – implications for soil structure on organic farms

Abstract. This paper reviews current understanding of soil structure, the role of soil organic matter (SOM) in soil structure and evidence for or against better soil physical condition under organic farming. It also includes new data from farm case studies in the UK. Young SOM is especially important for soil structural development, improving ephemeral stability through fungal hyphae, extracellular polysaccharides, etc. Thus, to achieve aggregate stability and the advantages that this conveys, frequent input of fresh organic matter is required. Practices that add organic material are routinely a feature of organically farmed soils and the literature generally shows that, comparing like with like, organic farms had at least as good and sometimes better soil structure than conventionally managed farms. Our case studies confirmed this. In the reviewed papers, SOM was generally larger on the biodynamic/organic farms because of the organic additions and/or leys in the rotation. We can therefore hypothesize that, because it is especially the light fraction of SOM that is involved in soil structural development, soil structure will improve in a soil to which fresh organic residues are added regularly. Thus, we argue it is not the farming system per se that is important in promoting better physical condition, but the amount and quality of organic matter returned to a soil.     

复杂地貌类型区耕地土壤有机质空间插值方法研究

选择适宜区域特征的高效点面拓展模型对揭示土壤属性空间分布规律具有重要意义。根据不同地貌类型特征在福建省各地级市选取了9个典型县(市、区),利用2008年采集的29 320个样点数据系统评价不同空间插值方法对耕地土壤有机质含量推算精度的影响。结果表明,福建省各典型县耕地土壤有机质平均含量为27.83 g kg-1,变异系数为37.87%,半方差函数以指数模型拟合效果最佳;全省平原或盆地、丘陵和山地分布面积相当的地区(如闽侯县、建瓯市、永定县、涵江区、福鼎市)采用泛克里格法插值的精度较高,山地为主的地区(如大田县)采用简单克里格法插值的精度较高,而以平原、台地为主的地区(如南安市、同安区、漳浦县)采用普通克里格或张力样条函数法插值的精度较高。     

Effect of organic matter on changes in soil zinc fractions found in wetland soils

Abstract

Zinc fractions occurring in five wetland soils as a function of organic matter application and soil redox potential were studied under laboratory conditions. The results indicate that a large portion of native or added Zn is bound to the soil mineral component. Exchangeable and organic complexed Zn and Zn bound to amorphous and crystalline sesquioxides were found to be in dynamic equilibrium. Exchangeable and complexed Zn were positively correlated with both native and/or added organic matter, while Zn bound to the amorphous and crystalline sesquioxides were negatively correlated with added organic matter. As soil redox potential decreased, the amount of exchangeable and organic complexed Zn decreased, while Zn bound to the amorphous and crystalline sesquioxides increased. Zinc fractions examined varied, depending upon soil cation exchange capacity, clay and organic carbon content.     

土壤有机质对有效磷及水提取磷含量的影响

为了解土壤有机质对有效磷与总磷、水提取磷与有效磷关系的影响,采用田间调查的方法,对洛阳市郊主要农田4种土壤耕作层土壤进行取样调查,并采用常规土壤测试方法,分别测定土样有机质、金磷、有效磷和水提取磷含量.根据有机质含量(高于或低于16.0 g·kg-1)把土样分成两组,对两组土样的总磷与有效磷、有效磷与水提取磷分别做相关关系分析.结果表明:与低有机质土样组相比,高有机质土样组有效磷与总磷相关方程的斜率较大,水提取磷与有效磷相关方程的斜率较小,这一趋势在以Olsen法和Mehlich-3法测定有效磷时均相同.表明在缺磷环境中,有机质增加条件下,虽然磷的生物有效性降低,但有助于在土壤溶液中维持相对较高的磷酸根浓度,这可能是由于磷的周转加快;随着土壤磷含量增加,有机质增加有助于更快提高磷的生物有效性,但土壤溶液中维持相对较低的磷酸根浓度,这可能有助于减轻农田磷的渗漏流失.     

有机质对土壤光谱特性的影响研究

为了探明土壤有机质的光谱特征及其影响作用,从而为有机质土壤铁氧化物的定量反演提供理论依据。利用去有机质前后土壤的光谱数据,研究了有机质对土壤反射率、土壤线参数、土壤铁氧化物定量反演的影响。研究结果表明,去除有机质后,能明显提高土壤反射率,变化最明显的为可见光橙黄光波段,即570～630 nm。相关性分析也显示橙黄光波段反射率的相对变化量或差值与有机质去除量之间的相关系数要比其他波段高,相关系数最大值在600 nm。因此,建议采用570～630 nm的光谱数据进行有机质的反演;土壤线斜率在去有机质后明显降低,截距显著增大,二者变化量与有机质去除量呈极显著相关关系,可用土壤线参数预测有机质含量。有机质对铁氧化物的反演具有明显影响,特别是有机质大于20 g kg-1的土壤,在进行反演时应考虑有机质对反演精度的影响,需采取有效地技术手段消减其影响作用,才能达到较好的效果。     

耕作对土壤有机物和土壤团聚体稳定性的影响

Agricultural sustainability relates directly to maintaining or enhancing soil quality. Soil quality studies in Canada during the 1980‘s showed that loss of soil organic matter (SOM) and soil aggregate stability was standard features of non-sustainable land management in agroecosystems. In this study total soil organic carbon (SOC), particulate organic matter (POM), POM-C as a percentage of total SOC, and aggregate stability were determined for three cultivated fields and three adjacent grassland fields to assess the impact of conventional agricultural management on soil quality. POM was investigated using solid-state ^13C nuclear magnetic resonance (NMR) to determine any qualitative differences that may be attributed to cultivation. Results show a highly significant loss in total SOC, POM and aggregate stability in the cultivated fields as compared to the grassland fields and a significant loss of POM-C as a percentage of total SOC.Integrated results of the NMR spectra of the POM show a loss in carbohydrate-C and an increase in aromatic-C in the cultivated fields, which translates to a loss of biological lability in the organic matter. Conventional cultivation decreased the quantity and quality of SOM and caused a loss in aggregate stability resulting in an overall decline in soil quality.     

Invertebrate control of soil organic matter stability

 The control of soil organic matter (SOM) stability by soil invertebrates is evaluated in terms of their impact on the inherent recalcitrance, accessibility to microorganisms, and interaction with stabilizing substances of organic compounds. Present knowledge on internal (ingestion and associated transformations) and external (defecation, constructions) control mechanisms of soil invertebrates is also reviewed. Soil animals contribute to the stabilization and destabilization of SOM by simultaneously affecting chemical, physical, and microbial processes over several orders of magnitude. A very important aspect of this is that invertebrates at higher trophic levels create feedback mechanisms that modify the spatio-temporal framework in which the micro-food web affects SOM stability. Quantification of non-trophic and indirect effects is thus essential in order to understand the long-term effects of soil biota on SOM turnover. It is hypothesized that the activities of invertebrates which lead to an increase in SOM stability partly evolved as an adaptation to the need for increasing the suitability of their soil habitat. Several gaps in knowledge are identified: food selection and associated changes in C pools, differential effects on SOM turnover, specific associations with microorganisms, effects on dissolution and desorption reactions, humus-forming and humus-degrading processes in gut and faeces, and the modification of invertebrate effects by environmental variables. Future studies must not be confined merely to a mechanistic analysis of invertebrate control of SOM stability, but also pay considerable attention to the functional and evolutionary aspects of animal diversity in soil. This alone will allow an integration of biological expertise in order to develop new strategies of soil management which can be applied under a variety of environmental conditions. Received: 6 April 1999     

大别山区江子河流域土壤有机质的空间变异

土壤有机质(Soil organic matter,SOM)是土壤的重要组成部分,是反映土壤肥力的重要指标,是全球碳循环的重要源和汇,目前已成为土壤学和环境学的研究热点之一[1]。土壤空间异质性是系统的某种属性在空间上的复杂性和变异性,是土壤重     

Predicting soil N mineralization: Relevance of organic matter fractions and soil properties

Distinct extractable organic matter (EOM) fractions have been used to assess the capacity of soils to supply nitrogen (N). However, substantial uncertainty exists on their role in the N cycle and their functional dependency on soil properties. We therefore examined the variation in mineralizable N and its relationship with EOM fractions, soil physical and chemical properties across 98 agricultural soils with contrasting inherent properties and management histories. Mineralizable N was determined by aerobic incubation at 20 °C and optimum moisture content for 20 weeks. We used multivariate statistical modelling to account for multi-collinearity, an issue generally overlooked in studies evaluating the predictive value of EOM fractions. Mineralization of N was primarily related to the size of OM pools and fractions present; they explained 78% of the variation in mineralizable N whereas other soil variables could explain maximally 8%. Both total and extractable OM expressed the same soil characteristic from a mineralization perspective; they were positively related to mineralizable N and explained a similar percentage of the variation in mineralizable N. Inclusion of mineralizable N in fertilizer recommendation systems should be based on at least one OM variable. The most appropriate EOM fraction can only be identified when the underlying mechanisms are known; regression techniques are not suitable for this purpose. Combination of single EOM fractions is not likely to improve the prediction of mineralizable N due to high multi-collinearity. Inclusion of texture-related soil variables or variables reflecting soil organic matter quality may be neglected due to their limited power to improve the prediction of mineralizable N.