An approach to computing topographic wetness index based on maximum downslope gradient

As an important topographic attribute widely-used in precision agriculture, topographic wetness index (TWI) is designed to quantify the effect of local topography on hydrological processes and for modeling the spatial distribution of soil moisture and surface saturation. This index is formulated as TWI = ln(a/tanβ), where a is the upslope contributing area per unit contour length (or Specific Catchment Area, SCA) and tanβ is the local slope gradient for estimating a hydraulic gradient. The computation of both a and tanβ need to reflect impacts of local terrain on local drainage. Many of the existing flow direction algorithms for computing a use global parameters, which lead to unrealistic partitioning of flow. β is often approximated by slope gradient around the pixel. In fact, the downslope gradient of the pixel is a better approximation of β. This paper examines how TWI is impacted by a multiple flow routing algorithm adaptive to local terrain and the employment of maximum downslope gradient as β. The adaptive multiple flow routing algorithm partitions flow by altering the flow partition parameter based on local maximum downslope gradient. The proposed approach for computing TWI is quantitatively evaluated using four types of artificial terrains constructed as DEMs with a series of resolutions (1, 5, 10, 20, and 30 m), respectively. The result shows that the error of TWI computed using the proposed approach is generally lower than that of TWI by the widely used approach. The new approach was applied to a low-relief agricultural catchment (about 60 km²) in the Nenjiang watershed, Northeastern China. The results of this application show that the distribution of TWI by the proposed approach reflects local terrain conditions better.     

Imaging the electrical conductivity of the soil profile and its relationships to soil water patterns and drainage characteristics

Soil water content (θ) measurement is vital for accurate irrigation scheduling. Electromagnetic induction surveys can be used to map spatial variability of θ when other soil properties are uniform. However, depth-specific θ variations, essential for precision irrigation management, have been less investigated using this method. A quasi-2-dimensional inversion model, capable of inverting apparent soil electrical conductivity (EC_a) data to calculate estimates of true electrical conductivity (σ) down the entire soil profile, was developed using EC_a data collected by a multi-coil Dualem-421S sensor. The optimal relationships between σ and volumetric water content (θv) were established using all coil arrays of the Dualem-421S, a damping factor of 0.04, an initial model of 35 mSm^?1, and with ten iterations (R²?=?0.70, bias?=?0.00 cm³cm^?3, RMSE?=?0.04 cm³cm^?3). These relationships were then used to derive soil profile images of these properties, and as expected, θv and σ follow similar trends down the soil profile. The derived soil profile images for θv have potential use for irrigation scheduling to two EC_a-derived soil management zones under a variable rate irrigation system at this case study site. They reflect the intrinsic soil differences that occur between texture, texture transitions and drainage characteristics. The method can also be used to guide placement of soil moisture sensors for in-season monitoring of spatio-temporal variations of θv. This soil imaging method showed good potential for predicting 2D depth profiles of soil texture, moisture and drainage characteristics, and supporting soil, plant and irrigation management.

     

基于最小限制水分范围评价不同耕作方式对土壤有机碳的影响

以2009年吉林省德惠市中层黑土上进行了8a的田间定位试验小区土壤为研究对象,对免耕和秋翻两种耕作方式及玉米-大豆轮作和玉米连作两种种植方式下耕层有机碳进行分析,分别采用加权平均和分层两种方法计算最小限制水分范围(LLWR),用其评价不同耕作方式对土壤有机碳的影响.结果表明,免耕在玉米-大豆轮作和玉米连作下0-5 cm土壤有机碳含量分别比秋翻增加了15.2％和11.5％ (P＜0.05).采用加权平均法计算的LLWR值为0.148-0.166 cm3/cm3,不同耕作方式下玉米-大豆轮作的LLWR高于玉米连作且在两种种植方式下均表现出免耕小于秋翻的特点;利用分层法计算得到的LLWR值介于0.130-0.173 cm3/cm3之间,玉米-大豆轮作和玉米连作下免耕0-5 cm LLWR均显著小于秋翻,而5-30 cm LLWR数值免耕大于秋翻(P＞0.05);玉米-大豆轮作下0-30 cm各层LLWR均高于玉米连作.由于LLWR可以评价不同耕作方式对土壤有机碳的影响,因此采用加权平均法计算的LLWR可以客观的反映不同耕作处理尤其是种植方式对土壤有机碳的影响;而采用分层法计算的LLWR则更清晰的刻画了土壤表层与亚表层固碳能力的差异.     

Using apparent soil electrical conductivity (EC<Subscript>a</Subscript>) to characterize vineyard soils of high clay content

The adoption of precision viticulture requires a detailed knowledge of variation in soil chemical, physical and profile properties. This study evaluates the usefulness of apparent electrical conductivity (EC_a) data within a GIS framework to identify variations in soil chemical and physical properties and moisture content. The work was conducted in a vineyard located in the Carneros Region (Napa Valley, California). The soil was sampled using 44 boreholes to quantify chemical and physical characteristics and 9 open pits to verify the borehole observations. Moisture content was determined using time domain reflectometry (TDR). To characterize soil EC_a, three campaigns were undertaken using a soil electrical conductivity meter (EM38). Linear regressions between soil EC_a and soil properties were determined. Boreholes and TDR data were interpolated by kriging to characterize the spatial distribution of soil variables. The resulting maps were compared to the results obtained using the best EC_a linear regressions. Using EC_a measurements, soil properties like extractable Na⁺ and Mg²⁺, clay and sand content were well estimated, while best estimates were obtained for extractable Na⁺ (r ²  = 0.770) and clay content (r ²  = 0.621). The best estimates for soil moisture content corresponded to moisture in the deeper soil horizons (r ²  = 0.449). The methods described above provided maps of soil properties estimated by EC_a in a GIS framework, and could save time and resources during vineyard establishment and management.     

基于深度强化学习的耕作层土壤水分、温度预测

【目的】利用土壤近表面空气温湿度与土壤内部参数的关联关系对耕作层土壤水分、温度进行精准预测,为实现精细化农业种植管理提供服务。【方法】针对土壤耕作层水分、温度预测在训练集获取与模型验证等方面的实际需求,设计了基于嵌入式系统及窄带物联网(Narrow band internet of things,NB-IoT)无线通信技术的物联网数据采集系统。在此基础上基于深度Q学习(Deep Q network,DQN)算法探索了一种模型组合策略,以长短期记忆(Long short-term memory,LSTM)、门限循环单元(Gated recurrent unit,GRU)与双向长短期记忆网络(Bidirectional long short-term memory,Bi-LSTM)为基础模型进行加权组合,获得了DQN-L-G-B组合预测模型。【结果】数据采集系统实现了对等间隔时间序列环境数据的长时间稳定可靠采集,可以为基于深度学习的土壤水分、温度时间序列预测工作提供准确的训练集与验证集数据。相对于LSTM、Bi-LSTM、GRU、L-G-B等模型,DQN-L-G-B组合模型在2种土壤类型(...     

秸秆覆盖条件下土壤水分蒸发的动力学模型

通过室内模拟试验,建立了不同秸秆覆盖量下的土壤水分蒸发动力学模型,并对不同水分条件下秸秆覆盖量与土壤水分蒸发的关系进行了研究。结果表明,土壤水分累计蒸发量W与时间t的关系为W=a·tb,当初始含水量较高时,秸秆覆盖的保水效果十分明显,而且秸秆覆盖量越多,保水效果越好;当初始含水量较低时,秸秆覆盖的保水效果更加显著,但其保水效果与秸秆覆盖量的多少关系不大。     

基于物联网和LSTM的柑橘园土壤含水量和电导率预测模型

目的 构建柑橘果园环境信息物联网实时采集系统,建立基于物联网和长短期记忆(LSTM)的柑橘园土壤含量和电导率预测模型,为果园灌溉施肥管理、效果预测评估提供参考依据。方法 利用土壤温度、含水量、电导率三合一传感器,在柑橘果园中设置5个节点和1个气象站,通过ZigBee短距离无线通信和GPRS远距离无线传输,将果园气象数据和土壤墒情数据传输至远程服务器。利用LSTM模型建立气象数据与土壤含水量和电导率的预测模型,计算均方根误差(RMSE)和决定系数(R²)以进行性能评估。结果 物联网系统能够实现远程传输柑橘果园环境数据,建立了基于LSTM和广义回归神经网络(GRNN)的土壤含水量和电导率预测模型,模型在5个节点的数据集的训练结果分别为：LSTM模型训练的土壤含水量和电导率的RMSE范围分别为6.74~8.65和6.68~8.50,GRNN模型训练的土壤含水量和电导率的RMSE范围分别为7.01~14.70和7.60~13.70。利用生成的LSTM模型和气象数据进行拟合,将土壤含水量和电导率的预测值与实测值进行回归分析,LSTM模型拟合的土壤含水量和电导率的R²范围分别为0.760~0.906和0.648~0.850,GRNN模型拟合的土壤含水量和电导率的R²范围分别为0.126~0.369和0.132~0.268,说明LSTM模型的性能表现较好。结论 建立了柑橘果园环境的物联网信息传输系统,构建的基于LSTM的果园土壤含水量和电导率预测模型具有较高的精度,可用于指导柑橘果园的灌溉施肥管理。     

微生物强化对石油污染土壤的修复特性研究

通过实验室模拟修复研究了接种量为10³~10⁸ cfu·g^-1的降解菌群在土壤中生长的湿度条件和存活状况、对土著菌群的影响作用以及对石油烃的去除效果。结果表明,从石油污染土壤中筛选出的石油烃降解菌群主要由变形菌门（Proteobacteria,99.75%）-γ-变形菌纲（Gamma-proteobacteria,99.49%）-假单胞菌目（Pseudomonadales,99.36%）-莫拉氏菌科（Moraxellaceae,87.33%）-不动杆菌属（Acinetobacter,87.32%）和假单胞菌科（Pseudomonadaceae,12.04%）-假单胞菌属（Pseudomonas,12.00%）组成。利用筛选的降解菌群在土壤湿度为5.4%、接种量为10⁸ cfu·g^-1土的条件下对污染土壤修复60 d,石油烃去除率为10.61%;在土壤湿度为15.0%、接种量为10⁷ cfu·g^-1土时对石油烃去除率为18.67%。在5.4%和15.0%湿度下接种7 d,土壤中变形菌门相对丰度由28.22%增加至57.98%~66.35%,不动杆菌属相对丰度由0.04%增加至25.86%~30.25%,假单胞菌属由初始时的0.26%增加至5.03%~30.87%,说明在不同湿度条件下,接种的降解菌均能迅速生长为土壤中的优势菌;接种60 d时,其仍保持存活状态。研究表明,降解菌群的接种改变了土壤菌群结构,使土壤菌群的alpha多样性明显降低。土壤污染物的去除不仅依靠某种优势菌的特定降解功能,还需要土壤菌群的协同代谢作用。     

秸秆和植被覆盖对江苏滨海盐土土壤盐分变化的影响

研究秸秆覆盖和植被（田菁）覆盖条件下滨海盐土土壤水盐的动态变化规律,为沿海滩涂盐碱地脱盐改良提供依据。本研究以含盐量6.98 g·kg^-1的滨海盐土为研究对象,设置秸秆覆盖和种植田菁2种覆盖处理,以裸地为对照,研究不同覆盖处理对滨海盐土土壤含水量和土壤盐分动态变化的影响。结果表明：秸秆覆盖下的土壤含水量（27.58%）显著高于田菁覆盖（26.70%）和裸地（26.61%）,后两者差异未达显著水平。处理1年后0~20、20~40 cm土层的秸秆覆盖、田菁覆盖和裸地不同处理间土壤含盐量的差异均达显著或极显著水平;秸秆覆盖处理的脱盐率为田菁覆盖处理的2倍。回归分析表明裸地和田菁覆盖下土壤盐分含量与累积降雨量的关系可用二次多项式拟合,田菁覆盖下的淋洗方程（EC_a/EC_i与D_w/D_s间的关系）可用三次多项式拟合;而秸秆覆盖条件下土壤盐分含量与累积降雨量的关系、淋洗方程均表现为指数函数关系y=ae^bx（P<0.01）。秸秆覆盖条件下滨海滩涂0~40 cm土层脱盐80%需要386.8 mm的累积降雨量。结果表明秸秆和植被覆盖技术在江苏滨海盐土可获得较好的脱盐效果,具有较好的应用前景。     

虉草用于干旱地区土壤镉和铜污染修复的潜力

利用三因素三水平正交设计,采用盆栽土培法研究了重金属镉(Cd)和铜(Cu)以及水分胁迫对虉草(Phalaris arundinacea L.)生物量、生理指标及积累特性的影响,评估虉草用于干旱地区土壤Cd和Cu污染修复的潜力。结果表明:20%田间持水量胁迫下,虉草失绿、皱缩和萎蔫,生物量最低,MDA和脯氨酸含量显著增加,其余处理组与对照组差异不显著,显示虉草对Cd、Cu和水分胁迫有一定耐性;对虉草生物量和MDA含量影响程度依次为土壤水分含量Cd浓度Cu浓度,对脯氨酸含量的影响程度依次为土壤水分含量Cu浓度Cd浓度。Cd和Cu在虉草组织中的积累表现为地下部地上部;虉草Cd富集系数大于1,Cu富集系数基本大于1,Cd富集系数Cu富集系数,Cd和Cu转移系数均小于1。这表明虉草可用于适度干旱地区Cd和Cu污染土壤的植物固定,通过Cd和Cu富集系数的比较,其更宜用于Cd污染土壤。     

基于土壤优化光谱参数估测太湖地区土壤全氮含量

为明确太湖地区土壤全氮的高光谱特征,构建定量分析模型,以江苏省无锡市滨湖区为研究区域,选取地理位置跨度大、土壤质地相似的93个样品,进行土壤风干样品全氮含量测定和光谱数据采集,对光谱反射率进行一阶微分,运用相关系数峰谷值法筛选敏感波长,将敏感波长两两结合进行土壤调节光谱指数（MSASI）运算。将两两结合后敏感波段分别采用多元线性回归分析、人工神经网络分析和偏最小二乘法构建土壤全氮含量的定量高光谱分析模型。结果表明,研究区内土壤全氮含量与光谱反射率呈正相关,敏感波段包括420~444 nm和480~537 nm。基于土壤调节光谱指数的多元线性回归分析对敏感波段诊断的效果最佳（R²=0.98、RMSE=0.04）,其精度高、可靠性强,是筛选出的最佳土壤全氮含量估测模型。偏最小二乘法模型（R²=0.70、RMSE=0.13）次之,而人工神经网络模型（R²=0.69、RMSE=0.15）精度最低。该研究结果为太湖地区土壤全氮水平的高光谱快速估测提供了方法借鉴,可为土壤养分精准管理提供技术参考。     

基于高光谱的土壤含水率预测模型

为定量分析土壤含水率与反射光谱特征之间关系,以便为土壤含水率速测提供理论依据。以安徽省阜阳市临泉县为研究区,以区域典型土壤类型—砂姜黑土为研究对象,将土壤样本分别过10和20目的尼龙筛,并设置土壤含水率梯度实验。采用9点移动平滑法结合一阶微分、反射率对数及其一阶微分三种数学变换方法对光谱曲线进行预处理,分析不同目数、不同含水率下的光谱特性差异;拟合分析变换后的样本光谱数据与含水率相关性,提取特征波段,建立土壤含水率多元线性回归预测模型。结果表明：对4种不同土地利用类型的砂姜黑土样本的反射率数据进行对数一阶微分变换后,土壤含水率和光谱数据的相关性明显提升,根据数学变换后提取的特征波段建立的多元线性回归预测模型的预测精度最好;光谱反射率与水分含量呈负相关关系;土壤光谱法反演水分含量时,基于过10目筛的土壤样本建立的预测模型拟合精度要优于过20目筛的土壤样本,R²最高为0.928。研究结果可以为精准农业管理提供极为关键的参数支撑。     

Estimation of the Biological Methods of Assessing Soil N-Supplying Capacity in Calcareous Soil

Although many biological methods are used to determine soil nitrogen supplying capacity, there are certain differences in the results for different types of soils and various ways of measurement due to the complexity of soil N conformation, the high variance of soil and microorganism, and the difference of environment. Therefore, it is not clear about which biologic incubation method is better for calcareous soil. In this study, pot experiments were performed by using 25 different calcareous surface soil samples on the Loess Plateau and taking the N uptake of wheat and corn with leaching soil initial nitrate and without leaching in pot experiments as the control to investigate the difference of eight biological incubation methods for reflecting soil nitrogen supply capacity. The eight biological methods are waterlogged incubation, aerobic incubation for 2 weeks and for 4 weeks, dry-wet alternation aerobic incubation for 2 weeks, long-term alternate leaching aerobic incubation (and N mineralization potential, N^o), short-term leaching aerobic incubation, microbial biomass carbon (B_c), and microbial biomass nitrogen (B_N) method, respectively. Among these methods, the dry-wet alternation aerobic incubation and aerobic incubation for 4 weeks were the modification of the method of aerobic incubation for 2 weeks according to the actual farmland moisture. The results showed that the correlation coefficients between these methods and crop uptake N w with leaching soil initial nitrate were 0.530, 0.700, 0.777, 0.768, 0.764 (and 0.790, N₀), 0.650, 0.555, and 0.465, respectively (r_0.05 = 0.369, r_0.01 = 0.505). While without leaching soil initial nitrate, their coefficients were 0.351, 0.963, 0.962, 0.959, 0.825 (and 0.812, N₀), 0.963, 0.289, and 0.095, respectively (r_0.05 = 0.369, r_0.01=0.505). In conclusion, excluding the soil initial nitrate, the correlation coefficients between the eight methods and crop uptake N were, from high to low, N₀, aerobic incubation for 4 weeks, dry-wet alternation aerobic incubation for 2 weeks, and long-term alternate leaching aerobic incubation, while including the soil initial nitrate the correlation coefficients between them increased significantly and the values were all beyond 0.950 for these four methods, including aerobic incubation for 2 weeks and for 4 weeks, dry-wet alternation aerobic incubation for 2 weeks and short-term leaching aerobic incubation. The waterlogged incubation method, B_C and B_N in the calcareous soil, had lower correlation coefficient with crop uptake nitrogen compared with other methods. Thus, dry-wet alternation aerobic incubation for 2 weeks was a better index for evaluating calcareous soil N supply capacity due to some other methods having disadvantages and not suitable for the actual farmland characteristics.     

沟垄集雨种植模式下灌溉量对关中地区冬小麦产量和水分利用效率的影响

为探讨在灌溉区沟垄集雨模式对冬小麦产量和水分利用效率及土壤水热特征的影响设置田间试验。试验设置沟垄集雨种植(R)和传统平作(F)2种种植方式,每种种植方式下设置0 m3/hm~2(N)、400m3/hm~2(L)、1 200m3/hm~2(M)和2 000m3/hm~2(H)4种灌水量。结果表明:同一灌溉量下沟垄集雨种植0~10cm土层的土壤温度在小麦生长前期较平作高,同时沟垄集雨种植模式0~100cm土层的平均含水量在拔节期、开花期较平作种植高,其具有较好的蓄水保墒作用。各处理的总耗水量随灌溉量增加而增加,表现为RHFHRMFMRLFLRNFN。沟垄集雨种植能促进小麦对土壤水分的利用,显著提高小麦产量,在小麦全生育季不灌溉、灌溉量为400、1 200和2 000m~3/hm~2的情况下,沟垄集雨种植处理产量分别较平作相同灌溉量处理高41.52%、70.00%、27.54%和14.35%。同时在8个处理中RM、RL处理的水分利用效率最高。因此,通过发挥沟垄集雨种植蓄水保墒以及改善水分分配的作用能够在灌溉农区小麦生产上达到节水增产增效的效果。     

长江下游“玉米-花菜”轮作模式下旱地降雨产流过程及氮磷输出特征研究

农田氮(N)、磷(P)的流失是水体富营养化的主要原因,然而农田径流N、P浓度与流失量受到前期土壤含水率(AMC)、降雨量、翻耕等多种因素影响,尚缺乏田间监测方法。本研究以位于长江下游的崇明岛旱地为研究对象,建立自动取样方法,在天然降雨条件下对农田径流量和水质进行高频率取样与连续监测,分析"玉米-花菜"轮作模式下降雨产流过程及N、P输出特征,采用径流曲线数(CN)表征土壤的持水能力,研究10、30、50 cm深处土壤AMC(AMC_(10)、AMC_(30)、AMC_(50))对CN的影响,并利用电导率(EC)来反映径流离子浓度的变化趋势以及降雨的稀释效应。结果表明:农田径流峰值滞后于降雨峰值,CN值取值范围为37～88,且与AMC_(10)线性关系最好(y=293.40x-39.41,R~2=0.790 5,P0.01),与AMC_(50)无显著相关关系;秸秆还田后的翻耕导致总磷(TP)和溶解性总磷(DTP)浓度升高,夏季翻耕显著增加了P浓度,但N的浓度没有增加;径流N浓度随着径流速度的增大而减小,但P浓度相反。EC与径流速度呈现出完全相反的变化趋势,降雨量增加了径流流量并产生了稀释效应,导致EC下降,且EC可以反映总氮(TN)、NO_3~--N的浓度变化,EC的变化可充分反映N的输出特征,而P的输出与N输出不同,当径流速度较大且EC较低时,P的浓度可能较高;NO_3~--N、DTP分别是农田N、P输出的主要形式,径流流量和养分浓度差异影响N、P的输出负荷,但养分浓度差异对NH4_~+-N、TP、DTP输出负荷的影响要大于径流流量对其的影响。     

井窖风场特征的CFD模拟与验证

为明确井窖内部空气受外界气流的影响及井窖维持适宜水热条件的机制,通过建立未移栽作物条件下井窖的三维数值模拟模型,利用CFD计算流体力学软件分析在1.50、2.70和3.90m/s近地表风速条件下,对井窖内部空气流速和流向进行数值模拟计算,并进行试验验证。模拟试验结果表明:近地表风速设为1.50、2.70和3.90m/s时,模拟的井窖纵切面平均空气流速分别为0.25、0.48和0.72m/s,井窖内的平均空气流速较近地表下降81.54%~83.34%;近地表风向由右向左时,井窖内纵切面的空气呈逆时针流动。验证试验结果表明:井窖内空气流速与地表风速成线性相关,模拟方程为y=0.220 9 x-0.176(R2=0.925 3);近地表风速为2.70m/s时,井窖内实测空气流速为0.42m/s,模拟值为0.48m/s。井窖内部空气受外界大气扰动较弱,减少热量和水分向外散失,有利于保持烟苗生长适宜的水热条件。     

基于梯度扩散薄膜技术评估稻田土壤中镉的生物有效性

为了给重金属污染农田土壤的安全性评估提供方法学指导,以稻田土壤-水稻体系为对象,通过比较化学提取法(土壤溶液法和0.01 mol·L~(-1)Ca Cl2提取法)和梯度扩散薄膜技术(DGT)所提取的有效态镉(Cd)含量与水稻谷粒中Cd含量(0.06~2.16 mg·kg~(-1))的相关关系,阐述DGT是否能更准确评估Cd的生物有效性及其作用机理。结果表明,土壤Cd全量与谷粒中Cd含量、几种有效态Cd提取量均未显示出显著相关性,不能真实反映Cd的生物有效性。DGT提取的Cd含量与谷粒Cd含量的相关性系数(R_(线性)~2=0.89和R_(曲线)~2=0.94)高于土壤溶液法(R_(线性)~2=0.87和R_(曲线)~2=0.92)和0.01 mol·L~(-1)Ca Cl2提取法(R_(线性)~2=0.80和R_(曲线)~2=0.83),R值分析表明DGT技术模拟了根部吸收土壤Cd过程中土壤固-液释放补给动态过程。因此,与传统化学提取法相比,DGT技术能更好地预测Cd污染土壤(0.31~10.64 mg·kg~(-1))中Cd的生物有效性。     

黄土高原半干旱区尾菜高量埋压抑制土壤氮淋溶的研究

为明确尾菜高量埋压带来的土壤氮淋溶风险,本研究设计了不同尾菜埋压厚度和表层覆土厚度的组合试验,分析不同土层水分和无机氮(NH₄⁺-N和NO₃^--N)时空变化特征。结果表明：埋压尾菜厚度0.2～0.6 m、表层覆土厚度0.1～0.3 m时,试验前10 d,表层土壤水分快速增加,较对照提高了40%～110%,尾菜向深层土壤补水深度最大为1.6 m;试验开始土壤无机氮以NH₄⁺-N增加为主,下移深度仅为0.6 m,试验第83天时,NO₃^--N快速积累,最大下移深度为0.8 m,土壤无机氮主要集中于耕作层,尾菜层上、下0.1 m土壤无机氮含量是当地高产玉米农田的1.0～3.5倍。当尾菜埋压厚度达到3.0 m、表层覆0.4 m黄土时,尾菜向深层土壤补水深度为5.0 m,NH₄⁺-N下移深度为1.5 m,试验第194天时NO₃^--N增加不...     

不同水分处理对华中地区稻田土壤酶活性的影响

为探究不同水分处理对土壤碳转化相关酶活性的影响,以湖北咸宁和湖南长沙的稻田土为研究对象,通过室内培养试验,研究了4种土壤含水率,即40%、70%、100%和200%田间持水量(WHC)下土壤过氧化物酶、酚氧化酶活性以及酚类物质和可溶性有机碳(DOC)含量的变化。结果表明:咸宁土壤的酶活性高于长沙土壤,两种土壤酚氧化酶活性和过氧化物酶活性均随水分含量的增加而增加,土壤酚类物质含量随培养时间总体呈动态平衡,在40%WHC下最高,而在200%WHC下最低;土壤DOC含量在较高(100%WHC、200%WHC)含水率下较高,而在较低(40%WHC、70%WHC)含水率下较低。酚氧化酶活性在培养前期较低,在培养的第5~15 d较高,咸宁水稻土最高值为260.76nmol·g^-1·h^-1,长沙水稻土为107.10 nmol·g^-1·h^-1。较高水分处理(200%WHC)下,过氧化物酶活性在培养后期活性较高,最高值达到929.66 nmol·g^-1·h^-1。相关性分析表明,DOC含量与过氧化物酶活性、酚氧化酶活性、含水率呈极显著正相关,结果表明有机质含量高的土壤酚氧化酶活性、过氧化物酶活性、酚类物质含量均显著高于有机质低的土壤。较高含水率(100%WHC、200%WHC)可显著提高土壤的酚氧化酶活性和过氧化物酶活性,且在较高含水率条件下土壤酚类物质含量和DOC含量均较高。     

生物质炭对砖红壤性质与养分及硝化作用的影响

土壤性质制约着作为氮转化重要环节的硝化作用,生物质炭显著影响与硝化作用密切相关的土壤性质,可能对硝化作用产生影响。本文利用培养试验研究生物质炭对砖红壤性质及硝化作用的影响。试验设生物质炭+淹水、生物质炭+75%田间持水量、空白+淹水及空白+75%田间持水量4个处理。研究表明,生物质炭能显著提高土壤pH值和有机碳含量以及N、P、K养分元素的有效性。水分条件差异影响生物质炭的作用效果,淹水更利于提高砖红壤的pH值,但显著降低了磷的有效性。添加生物质炭后,土壤硝化作用进程加速,硝化彻底。在利用生物质炭改良砖红壤时,应根据土壤改良目的调整土壤水分,以防硝态氮淋失风险和氨挥发的可能。