麦田土壤水分时空变异特性及CA-Markov模型模拟预报

为揭示农田土壤水分时空变异特征,精准预测土壤墒情,该研究以河北省太行山山前平原井灌区典型麦田为例,在监测土壤水分的基础上,采用时间稳定性指数法、空间自相关性评价法研究土壤水分时空分布规律,构建了适用于模拟预报田间水分时空变化的CA-Markov 模型,并将该模型的模拟预报效果与HYDRUS 模型进行比较。结果表明：随着土层深度的增加,土壤水分等值线由密变疏,变异系数逐渐减小。随着小麦生育期的推移,前期监测的土壤水分稳定性高于后期;在土壤较湿润的情况下,土壤水分空间相关性较强,土壤水分全局Moran''s I 指数随小麦生育期的推移呈现先增大后变小的规律。CA-Markov 模型模拟预报的各土壤相对湿度等级面积误差的平均值为1.61%,比HYDRUS 模型模拟预报的面积误差平均值（10.86%）小9.25个百分点; CA-Markov 模型对研究区4月下旬、5月上旬的土壤水分干旱等级预测的空间分布Kappa 系数分别为 89.31%、91.46%。该模型可综合考虑麦田墒情的时空变化及随机特性,模拟预测土壤墒情的精度较高、效果良好,可以作为麦田水分管理的重要工具。     

基于HYDRUS-1D模型的荒漠绿洲水盐运移模拟与评估

针对河套灌区引水量逐年减少、荒漠绿洲农业生态环境恶化等问题,该研究以沙丘-荒地-海子系统为研究对象,通过2a定点观测,利用HYDRUS-1D模型对沙丘、沙丘-荒地交界和荒地进行了不同时期水盐动态模拟,揭示了荒漠绿洲水盐运移特征,评价了荒漠绿洲农业生态状况。结果表明:1)沙丘、沙丘-荒地交界和荒地土壤水分和盐分率定与验证精度平均相对误差分别为-1.30%～1.52%和-0.43%～5.71%,均方根误差为0.01～0.03 cm~3/cm~3和0.06～0.23dS/m,决定系数为0.82～0.93和0.76～0.82,回归系数为0.98～1.01和0.98～1.03,模拟精度较高;2)在生育期,沙丘、沙丘-荒地交界和荒地1 m土体各层水分呈现下降趋势,土壤处于亏水状态,荒地腾发量是沙丘的2倍,沙丘-荒地交界的介于二者之间,荒地地下水补给量为沙丘的3～5倍;秋浇后,沙丘水分补给较少,仍亏水67～103 mm,沙丘-荒地交界和荒地水分补给较多,可以维持土壤水分平衡;3)沙丘、沙丘-荒地交界和荒地全年处于略积盐状态,积盐率分别为47%～59%和3%～6%,荒地在秋浇后处于轻微脱盐状态,脱盐率为0.7%～5.0%。研究发现,降雨对维持荒漠绿洲农业生态平衡的作用较小,秋浇灌溉对改善灌区荒漠绿洲农业生态发挥关键作用。研究结果可为河套灌区荒漠绿洲农业生态治理提供参考。     

黄淮海北片模拟增加和降低温度对麦田地温、水分变化和水分利用效率的影响

采用模拟增降温法研究了温度变化对麦田地温、土壤水分变化和水分利用效率的影响。结果表明:模拟增温麦田、降温麦田在全生育期5 cm平均地温分别较常规麦田平均升高0.85℃和降低2.57℃,其中模拟增温麦田的增温效应以冬季>秋季>春季,模拟降温麦田的降温效应以春季>秋季>冬季;模拟增温麦田、降温麦田在全生育期5 cm土层的平均容积含水率分别较常规麦田降低3.76%、提高4.84%。对模拟增温麦田、降温麦田和常规麦田0-200 cm土层水分变化研究表明,模拟增温麦田和常规麦田(除成熟期外)各生育期的贮水量均低于模拟降温麦田;全生育期0-200 cm土层土壤耗水量模拟增温麦田大于模拟降温麦田和常规麦田,其中0-100 cm土层耗水量占0-200 cm土层耗水量的比例模拟增温麦田(55.72%)和常规麦田(55.14%)均低于模拟降温麦田(63.45%),而100-200 cm土层耗水量占0-200 cm土层耗水量的比例模拟增温麦田(44.28%)和常规麦田(44.86%)麦田均高于模拟降温麦田(36.55%);水分利用效率模拟增温麦田低于常规麦田和模拟降温麦田。研究结果可为气候变暖对小麦生产系统的影响提供理论依据和技术支撑。     

基于NDVI分区的全国草地生态系统覆盖区土壤水分遥感反演

为了建立土壤水分遥感反演模型,反演2016年全国草地生态系统土壤水分并分析其时空变化特征,通过结合表观热惯量(ATI)和温度植被干旱指数(TVDI)的混合模型反演2016年全国草地生态系统的土壤水分,并在实测数据与反演结果精度验证的基础上确定归一化植被指数(NDVI)阈值。结果表明:(1)NDVI≤0.2的像元区域,采用ATI模型反演精度较高; NDVI≥0.78的像元上,基于增强型植被指数(EVI)的TVDI反演精度较高; 0.2相似文献   

滨海土壤盐分动态集成预报模型及其应用

本文提出了以GM(1,1)模型拟合发展趋势、傅里叶变换提取周期分量和AR(p)模型模拟随机过程的集成预报模型,并用于黄河三角洲人工草场群落土壤盐分的定量研究中.经理论和应用检验证明该模型能以较高的精度模拟或预报土壤盐分在较长时期内的动态变化过程.方法简单,计算工作量小,并优于传统的单一预报模型.     

集合卡尔曼滤波数据同化方法改进土壤水分模拟效果

陆面过程模型是连续模拟土壤水分的有效工具,然而输入数据及模型结构本身的不确定性会导致模拟误差在模型运行过程中不断积累。数据同化技术可以考虑模型不确定性,实时修正模型状态变量,进而提高土壤水分的模拟精度。本研究构建集合卡尔曼滤波(En KF,ensemble Kalman filter)数据同化方法,将其集成到水文强化陆面过程模型HELP(hydrologically-enhanced land process)中,对模型中土壤水分及表面温度等状态变量进行优化。模型选取山东位山生态水文观测站2006年的数据进行验证,采用未经同化的模型率定结果作为基准值。结果表明,数据同化后表层、根层、深层土壤水分模拟结果相比基准值均有提高,土壤含水量均方根误差减小30%~50%,证明采用数据同化方法能够有效提高土壤水分的模拟结果。     

不同统计模型在冬小麦产量预报中的预报能力评估——以江苏麦区为例

在多类冬小麦单产统计预报模型中筛选出预报能力强的模型,并对优选出的模型进行加权集成,以此提高产量预报准确率,对保障粮食安全具有重要意义。利用1993—2018年江苏省69个基本气象观测站逐日气象资料和冬小麦产量数据及生育期资料,在5种气象产量分离方法(线性分离、差值百分率、5年滑动平均、3年滑动平均、二次曲线)的基础上,采用准确率、标准差、相关系数、泰勒图等检验法,评估分析了丰歉相似年法、关键气象因子法、气候适宜度法与集成预报法在江苏省冬小麦单产预报中的模拟效果。结果表明:1)对于同一种预报方法,不同的产量分离法对预报精度影响较大,二次曲线分离法要好于其他4种方法;丰歉相似年预报方法中加权法的预报精度高于大概率法。1993—2013年丰歉相似年法、关键气象因子法、气候适宜度法平均准确率分别为89.67%、94.86%和94.96%。2)集成预报法近5年预报准确率在96.33%以上,高于丰歉加权模型、关键气象因子二次曲线分离模型、气候适宜度二次曲线分离模型等单个最优模型,在一定程度上可以弥补单一预报方法预报结果稳定性差的不足。3)起报时间越接近成熟期,预报因子信息越全面,则预报模型准确率越高。研究结果可为江苏省冬小麦采用合理的单产预报模型提供科学依据,同时模型筛选思路也可供外省借鉴。     

灌木柠条锦鸡儿根-黄土状盐渍土界面相互作用力学特性

为了进一步探讨西宁盆地黄土区灌木植物根系固土护坡力学效应,以区内优势灌木植物柠条锦鸡儿为研究对象,开展不同土体密度、含水量、含盐量和根径单因素变化条件下的单根垂直拉拔试验,探讨和分析了上述因素对灌木植物根-土界面相互作用力学特性的影响和作用机理。结果表明:在垂直埋深为25 cm及其他因素恒定的情况下,随着土体密度由1.20 g/cm³增长到1.60 g/cm³,单根最大抗拔出力由(28.60±2.83)N增长至(114.33±7.17)N,呈指数函数增长趋势; 随着土体含水量由6.00%增长到22.00%,单根最大抗拔出力由(152.80±10.07)N降低至(31.50±5.53)N,呈指数函数降低的变化趋势; 随着土体含盐量由0.59%增长至2.00%,单根最大抗拔出力呈现出先增大后减小的变化趋势,在含盐量为1.00%时,单根最大抗拔出力达到峰值(61.20±0.94)N,且二者关系符合二次函数关系; 在根径为2.74～9.57 mm范围内,随着单根平均根径的逐渐增大,单根最大抗拔出力由25.20 N线性增长至97.10 N。在此基础上,提出了垂直单根最大抗拔出力计算模型,并指出计算垂直单根最大抗拔出力所需的根-土界面综合黏聚力和综合摩擦角可通过根-土界面直剪试验获得。     

时间序列分析模型在山东省粮食总产量预测中的应用

对比传统时间序列分析模型(线性回归、二次滑动平均、一次平滑、二次指数平滑和三次指数平滑等)与ARIMA模型在山东省粮食总产量中的拟合精度,并应用ARIMA(2,1,12)模型预测了未来3年内山东省粮食总产量.结果表明,在山东省粮食总产量拟合中,ARIMA(2,1,12)模型得到的粮食总产量拟合值与观测值的相对误差处于±10%和±5%范围内的分别为73.333%和53.333%,回归方程的决定系数为0.959,优于传统时间序列分析模型;利用ARIMA(2,1,12)模型预测未来3年内山东省粮食总产量,粮食总产量有逐年上升的趋势,且增长率逐年上升.     

半干旱黄土丘陵区不同植被类型的土壤水分特征及其稳定性

[目的]研究5种不同植被类型土壤水分动态特征,为区域内优化配植模式提供理论依据。[方法]以甘肃省定西市半干旱黄土丘陵区5种典型的植被类型为研究对象,使用土壤水分速测仪对2017,2018年0—200 cm土层土壤含水量进行观测,并对不同植被类型土壤水分特征及其稳定性进行分析。[结果]①不同植被类型土壤水分呈现出:山杏×侧柏混交林地(11.24%)退耕苜蓿草地(10.97%)自然荒地(10.66%)退耕林地(9.99%)柠条林地(9.55%)。②5种植被类型在0—200 cm土层依据有序聚类法可分为动态变化层(0—40 cm),利用层(40—100 cm)和弱利用层(100—200 cm)3个层次。在动态变化层(0—40 cm),土壤水分呈现出:自然荒地(14.23%)最高,柠条林地(11.04%)最低;在利用层(40—100 cm):退耕苜蓿草地(12.16%)最高,柠条林地(9.15%)最低;在弱利用层(100—200 cm):自然荒地(10.81%)最高,退耕林地(8.61%)最低。③不同植被类型土壤水分土层稳定性(变异系数)呈现出:退耕林地(19.9%)山杏×侧柏混交林地(21.3%)自然荒地(21.9%)退耕苜蓿草地(22.6%)柠条林地(23.6%)。④不同植被类型土壤水分时间稳定性(变异系数)呈现出:自然荒地(25%)退耕林地(25.2%)山杏×侧柏混交林地(26.5%)柠条林地(27.1%)退耕苜蓿草地(31.9%)。[结论]柠条林地土壤水分含量最低,在土层和时间上不稳定,山杏×侧柏混交林地较其他植被类型土壤水分含量更高,更具有稳定性。     

Effect of tillage treatments upon soil tests for soil acidity,soil phosphorus and soil potassium at three soil depths

Abstract

Soil samples were obtained at 0–3, 3–6, 6–9 and 0–9 inch depths from experimental plots receiving five tillage treatments. Each of two samplers composited approximately six one‐inch cores from each plot. Soil samples were analyzed for acidity, P and K using routine analysis procedures in the University of Illinois Soil Testing Laboratory.

Few significant differences were attributed to sampler and it was concluded that samplers using similar sampling techniques were obtaining soil samples from the same population.

No significant differences in soil acidity at different depths were observed. The different tillage methods did significantly affect soil P at the 0–3 inch depth, but had no significant effect on soil P at deeper depths. Different tillage methods also significantly affected soil K values at different depths.     

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。     

Usability of soil survey soil texture data for soil health indicator scoring

Soil textural information is an important component underlying other soil health indicators. Soil texture analysis is a common procedure, but it can be labor intensive and expensive. Soil texture data typically are available from the Soil Survey Geographic (SSURGO) database, which may be an option for determining soil health texture groups (SHTG). The SSURGO database provides soil texture information in the soil map unit (SMU) name, taxonomic class category (family), and detailed values (≤ 2 mm soil fraction) of percent sand, silt and clay by soil horizon. The objective of this study was to examine the possibility of using SSURGO data for SHTG at the 147-ha Cornell University Willsboro Research Farm in New York state as an alternative for soil texture data determined manually on collected soil core samples. Comparative results revealed that representative values for soil texture from the SSURGO database generally matched measured mean values for all SMUs.     

Relationship between soil respiration and soil moisture

Microbial activity is affected by changes in the availability of soil moisture. We examined the relationship between microbial activity and water potential in a silt loam soil during four successive drying and rewetting cycles. Microbial activity was inferred from the rate of CO₂ accumulating in a sealed flask containing the soil sample and the CO₂ respired was measured using gas chromatography. Thermocouple hygrometry was used to monitor the water potential by burying a thermocouple in the soil sample in the flask. Initial treatment by drying on pressure plates brought samples of the test soil to six different water potentials in the range -0.005 to -1.5MPa. Water potential and soil respiration were simultaneously measured while these six soil samples slowly dried by evaporation and were remoistened four times. The results were consistent with a log-linear relationship between water potential and microbial activity as long as activity was not limited by substrate availability. This relationship appeared to hold for the range of water potentials from ?0.01 to ?8.5 MPa. Even at ?0.01 MPa (wet soil) a decrease in water potential from ?0.01 to ?0.02 MPa caused a 10% decrease in microbial activity. Rewetting the soil caused a large and rapid increase in the respiration rate. There was up to a 40-fold increase in microbial activity for a short period when the change in water potential following rewetting was greater than 5 MPa. Differences in microbial activity between the wetter and drier soil treatments following rewetting to the original water potentials are discussed in terms of the availability of energy substrate.     

Volumetric soil measures for routine soil testing

Abstract

The design, dimensiors and materials for constructing volumetric soil measures for routine soil testing use are presented. Scoop calibration techniques are also described. Reproducibility of results obtained under routine laboratory, conditions are shown. The measures include volumes of 1.0‐, 2.5‐, 5.0‐ and 10‐ cm³ respectively.     

A soil core for routine soil sampling

Abstract

The design, materials and dimensions for constructing a coring device for sampling soil fauna and flora at different depths is described.     

Why soil erosion models over-predict small soil losses and under-predict large soil losses

Evaluation of various soil erosion models with large data sets have consistently shown that these models tend to over-predict soil erosion for small measured values, and under-predict soil erosion for larger measured values. This trend appears to be consistent regardless of whether the soil erosion value of interest is for individual storms, annual totals, or average annual soil losses, and regardless of whether the model is empirical or physically based. The hypothesis presented herein is that this phenomenon is not necessarily associated with bias in model predictions as a function of treatment, but rather with limitations in representing the random component of the measured data within treatments (i.e., between replicates) with a deterministic model. A simple example is presented, showing how even a `perfect' deterministic soil erosion model exhibits bias relative to small and large measured erosion rates. The concept is further tested and verified on a set of 3007 measured soil erosion data pairs from storms on natural rainfall and run-off plots using the best possible, unbiased, real-world model, i.e., the physical model represented by replicated plots. The results of this study indicate that the commonly observed bias, in erosion prediction models relative to over-prediction of small and under-prediction of large measured erosion rates on individual data points, is normal and expected if the model is accurately predicting erosion rates as a function of environmental conditions, i.e., treatments.     

Relationships between soil respiration and soil moisture

The interaction of soil microbes with their physical environment affects their abilities to respire, grow and divide. One of these environmental factors is the amount of moisture in the soil. The work we published almost 25 years ago showed that microbial respiration was linearly related to soil-water content and log-linearly related to water potential. The paper arose out of collaboration between two young researchers from different areas of soil science, physics and microbiology. The project was driven by not only our curiosity but also the freedom to operate without the constraints common to the current system of science management. The citation history shows three peaks, 1989, 1999 and from 2002 to the present day. Interestingly, the annual citation rate is as high as it has ever been. The initial peak is due to the application of the work to studies on microbial processes. The second peak is associated with the rise of simulation modelling and the third with the relevance of the findings to climate change research. In this article, our paper is re-evaluated in the light of subsequent studies that allow the principle of separation of variables to be tested. This re-evaluation lends further credence to the linear relationship proposed between soil respiration and water content. A scaled relationship for respiration and water content is presented. Lastly, further research is suggested and more recent work on the physics of gas transport discussed briefly.     

Tillage effects on soil degradation, soil resilience, soil quality, and sustainability

Soil degradation, decrease in soil's actual and potential productivity owing to land misuse, is a major threat to agricultural sustainability and environmental quality. The problem is particularly severe in the tropics and sub-tropics as a result of high demographic pressure, shortage of prime agricultural land, harsh environments, and resource poor farmers who presumably cannot afford science based recommended inputs. Tillage methods and soil surface management affect sustainable use of soil resources through their influence on soil stability, soil resilience, and soil quality. Soil stability refers to the susceptibility of soil to change under natural or anthropogenic perturbations. In comparison, soil resilience refers to soil's ability to restore its life support processes after being stressed. The term soil quality refers to the soil's capacity to perform its three principal functions e.g. economic productivity, environment regulation, and aesthetic and cultural values. There is a need to develop precise objective and quantitative indices of assessing these attributes of the soil. These indices can only be developed from the data obtained from well designed and properly implemented long-term soil management experiments conducted on major soils in principal ecoregions.     

黑土区土壤侵蚀厚度对土地生产力的影响及其评价

为了研究黑土区土壤侵蚀厚度对土地生产力的影响,采用盆栽试验,人为剥离黑土表层0、5、10、15、20、25和30 cm土壤以模拟侵蚀厚度不同的耕层土壤,分析土壤侵蚀厚度对土壤理化性质、大豆生物性状和水分利用效率等指标的影响。并对TOPSIS(technique for order preference by similarity to ideal solution)模型进行改进,用于评价侵蚀厚度不同的土壤的土地生产力。结果表明:土壤全N、碱解N、全P、速效P、有机质含量和土壤田间持水率均随侵蚀厚度的增加而递减,土壤容重随侵蚀厚度的增加而递增。土壤侵蚀厚度对大豆生长有显著影响,随着侵蚀厚度的增加,大豆减产率呈"S型"曲线递增,产量、耗水量呈"Z型"曲线递减,水分利用效率呈指数曲线关系递减。改进的TOPSIS模型对不同侵蚀厚度下土地生产力的评价结果较为理想,计算的土地生产力指数随土壤侵蚀厚度的变化呈"Z型"曲线,与大豆产量的变化趋势相同,且二者呈指数函数关系,决定系数达0.996,均方根误差为0.65。研究结果可为黑土区土壤侵蚀防治提供理论依据。