社会信任、合作能力与农户参与小农水供给行为——基于黄河灌区五省数据的验证

在政府主管的小农水供给模式无法完全满足农业分户经营的需要的现实背景下,农户参与供给是实现小农水供需平衡的有效方式。本文利用黄河灌区宁夏、内蒙古、陕西、河南和山东五个省份1558户微观农户数据,引入农户社会信任和合作能力因子,考虑农户农业收入水平和所处省份差异,采用多群组结构方程模型,探究农户参与小农水供给的行为。首先,运用信度和效度检验对农户参与小农水供给行为量表进行了分析。在此基础上,运用多群组结构方程模型,以农户农业收入水平和所处省份为调节变量,分析农户社会信任和合作能力对其参与小农水供给行为的影响。研究结果表明:农户的社会信任和合作能力显著正向影响农户参与小农水供给的意愿,其中农户的合作能力影响程度较大,农户的社会信任影响程度次之;农户的社会信任、合作能力和参与供给意愿对其参与小农水供给行为有显著正向影响,且影响程度最大的是农户的参与供给意愿,其次是农户的合作能力,最后为农户的社会信任;农户的社会信任对其合作能力有显著正向影响。多群组分析表明,农户的农业收入水平及其所处省份显著调节农户参与小农水的供给行为。其中,农业收入作为调节变量结果显示,农业收入高的农户参与小农水供给的意愿更强烈,参与供给行为水平更高;产生参与供给意愿后,农业收入中等偏上和中等偏下的农户更容易将意愿转化为行为。省份作为调节变量的结果显示,陕西、河南和山东农户的社会信任和合作能力对其参与小农水供给意愿的影响较大,河南和山东农户更容易将参与供给意愿转化为行为;宁夏和内蒙古农户的合作能力对其参与供给行为影响程度较大。根据以上结果,政府应根据不同地区农户的收入水平、社会信任和合作能力等实际情况,充分调动本地的社会资源,引导农户认识到参与小农水供给的重要性和迫切性,提高农户参与小农水供给的意愿和行为水平,促进小农水供需平衡的实现。     

Ion Composition Elucidation (ICE): an Investigative Tool for Characterization and Identification of Compounds of Regulatory Importance

Ion composition elucidation (ICE) often leads to identification of compounds and provides high-quality evidence for tracking compounds to their sources. Mass spectra for most organic compounds are not found in mass spectral libraries used to tentatively identify analytes. In addition, multiple matches are common. Ion Composition Elucidation provides the numbers of atoms of each element in the ions in the mass spectrum, greatly limiting the number of possible compounds that could produce the mass spectrum. Review of chemical and commercial literature then limits the number of possible compounds to one or a few that can be purchased to confirm tentative compound identifications by comparison of mass spectra and chromatographic retention times. Ion composition elucidation is conceptually simple relative to other analytical techniques and more easily explained to a judge or jury. It is based on sums of the exact masses of atoms and their isotopic abundances. Several applications of ICE are demonstrated for ultra-trace-level compounds in an extract of the e.uent from a tertiary sewage treatment plant including: (1) measurement of five values to determine an ion's composition and to generate evidence for the compound's identity, (2) rejection of incorrect library matches, (3) rapid screening for a target compound in an extract, and (4) a strategy for tracking unidentified compounds to their sources.     

Estimation of Methyl tert -Butyl Ether Plume Length Using the Domenico Analytical Model

Methyl tert -butyl ether (MTBE) plume is controlled by many factors, primarily by groundwater flow velocity, dispersion, natural attenuation. This study employed an analytical model introduced by Domemico (1987, J. Hydrol 91 , 49-58.) to describe the MTBE concentration distribution horizontal pattern and estimated the MTBE plume length. The model was applied to 90 leaking underground storage tank cases in Los Angeles, CA, U.S.A. The analytical model was calibrated with field data for each ease using a Microsoft Excel spreadsheet program. Methyl tert -butyl ether concentrations in one source monitoring well and one to two downgradient centerline monitoring wells were used for each case study. When the centerline well is not available, the closest off-centerline wells were projected to the centerline using an ellipse trigonometry method. The model parameter values for longitudinal dispersivity, groundwater velocity, and degradation rate constant were calibrated using the field data and then used to estimate the maximum distance between source well and the plume edge. This study demonstrates that the Domenico model can be applied to MTBE plume investigation when adequate field data are available. The correlation coefficients calculated based on the results of the 90 case studies indicate that MTBE plume length has a poor correlation with MTBE concentration at the source well, and a moderate negative correlation with the degradation rate constant ( m 0.65) and u / v ratio (0.64). Furthermore, MTBE plume length has a poor correlation with the longitudinal dispersivity ( m 0.4), hydraulic gradient ( m 0.1), and groundwater velocity (0.17).     

A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions

The evaluation of biophysical models is usually carried out by estimating the agreement between measured and simulated data and, more rarely, by using indices for other aspects, like model complexity and overparameterization. In spite of the importance of model robustness, especially for large area applications, no proposals for its quantification are available. In this paper, we would like to open a discussion on this issue, proposing a first approach for a quantification of robustness based on the variability of model error to variability of explored conditions ratio. We used modelling efficiency (EF) for quantifying error in model predictions and a normalized agrometeorological index (SAM) based on cumulated rainfall and reference evapotranspiration to characterize the conditions of application. Population standard deviations of EF and SAM were used to quantify their variability. The indicator was tested for models estimating meteorological variables and crop state variables. The values provided by the robustness indicator (I_R) were discussed according to the models’ features and to the typology and number of processes simulated. I_R increased with the number of processes simulated and, within the same typology of model, with the degree of overparameterization. No correlation were found between I_R and two of the most used indices of model error (RRMSE, EF). This supports its inclusion in integrated systems for model evaluation.     

Sanitary felling of Norway spruce due to spruce bark beetles in Slovenia: A model and projections for various climate change scenarios

A model is presented to predict sanitary felling of Norway spruce (Picea abies) due to spruce bark beetles (Ips typographus, Pityogenes chalcographus) in Slovenia according to different climate change scenarios. The model incorporates 21 variables that are directly or indirectly related to the dependent variable, and that can be arranged into five groups: climate, forest, landscape, topography, and soil. The soil properties are represented by 8 variables, 4 variables define the topography, 4 describe the climate, 4 define the landscape, and one additional variable provides the quantity of Norway spruce present in the model cell. The model was developed using the M5′ model tree. The basic spatial unit of the model is 1 km², and the time resolution is 1 year. The model evaluation was performed by three different measures: (1) the correlation coefficient (51.9%), (2) the Theil's inequality coefficient (0.49) and (3) the modelling efficiency (0.32). Validation of the model was carried out by 10-fold cross-validation. The model tree consists of 28 linear models, and model was calculated for three different climate change scenarios extending over a period until 2100, in 10-year intervals. The model is valid for the entire area of Slovenia; however, climate change projections were made only for the Maribor region (596 km²). The model assumes that relationships among the incorporated factors will remain unchanged under climate change, and the influence of humans was not taken into account. The structure of the model reveals the great importance of landscape variables, which proved to be positively correlated with the dependent variable. Variables that describe the water regime in the model cell were also highly correlated with the dependent variable, with evapotranspiration and parent material being of particular importance. The results of the model support the hypothesis that bark beetles do greater damage to Norway spruce artificially planted out of its native range in Slovenia, i.e., lowlands and soils rich in N, P, and K. The model calculation for climate change scenarios in the Maribor region shows an increase in sanitary felling of Norway spruce due to spruce bark beetles, for all scenarios. The model provides a path towards better understanding of the complex ecological interactions involved in bark beetle outbreaks. Potential application of the results in forest management and planning is discussed.     

基于24Model的不安全动作内在致因研究

为实现有效的不安全行为纠错,提升自我行为控制效果,基于24Model构建不安全动作的内在致因分析模型及分析程序,包含5项能力要素、15个维度因子和具有完整逻辑程序的4阶段步骤,并采用随机森林(RF)方法以煤矿安全专业人员为研究对象开展了不安全动作内在致因实证研究.结果 表明:对目标不安全动作影响最大的内在致因能力要素为...     

Diffusive wave models for operational forecasting of channel routing at continental scale

Operational forecast models require robust, computationally efficient, and reliable algorithms. We desire accurate forecasts within the limits of the uncertainties in channel geometry and roughness because the output from these algorithms leads to flood warnings and a variety of water management decisions. The current operational Water Model uses the Muskingum-Cunge method, which does not account for key hydraulic conditions such as flow hysteresis and backwater effects, limiting its ability in situations with pronounced backwater effects. This situation most commonly occurs in low-gradient rivers, near confluences and channel constrictions, coastal regions where the combined actions of tides, storm surges, and wind can cause adverse flow. These situations necessitate a more rigorous flow routing approach such as dynamic or diffusive wave approximation to simulate flow hydraulics accurately. Avoiding the dynamic wave routing due to its extreme computational cost, this work presents two diffusive wave approaches to simulate flow routing in a complex river network. This study reports a comparison of two different diffusive wave models that both use a finite difference solution solved using an implicit Crank–Nicolson (CN) scheme with second-order accuracy in both time and space. The first model applies the CN scheme over three spatial nodes and is referred to as Crank–Nicolson over Space (CNS). The second model uses the CN scheme over three temporal nodes and is referred to as Crank–Nicolson over Time (CNT). Both models can properly account for complex cross-section geometry and variable computational points spacing along the channel length. The models were tested in different watersheds representing a mixture of steep and flat topographies. Comparing model outputs against observations of discharges and water levels indicated that the models accurately predict the peak discharge, peak water level, and flooding duration. Both models are accurate and computationally stable over a broad range of hydraulic regimes. The CNS model is dependent on the Courant criteria, making it less computational efficient where short channel segments are present. The CNT model does not suffer from that constraint and is, thus, highly computationally efficient and could be more useful for operational forecast models.     

Dynamic modelling of aquatic exposure and pelagic food chain transfer of cyclic volatile methyl siloxanes in the Inner Oslofjord

The marine fate and pelagic food chain transfer of three cyclic volatile methyl siloxanes (cVMS: D4, D5 and D6) was explored in the Inner Oslofjord, Norway, using two dynamic models (the Oslofjord POP Model and the aquatic component of ACC-HUMAN). Predicted concentrations of D4, D5, and D6 in the water column were all less than current analytical detection limits, as was the predicted concentration of D4 in sediment (in agreement with measured data). The concentrations predicted for D5 and D6 in sediment were also in broad agreement with measured concentrations from the Inner Oslofjord. Volatilisation was predicted to be the most important loss mechanism for D5 and D6, whereas hydrolysis was predicted to dominate for D4. Concentrations of all three compounds in sediment are controlled by burial below the active mixed sediment layer. The marine food web model in ACC-HUMAN predicted “trophic dilution” of lipid-normalised cVMS concentrations between zooplankton and herring (Culpea harengus) and between herring and cod (Gadus morhua), principally due to a combination of in-fish metabolism and reduced gut absorption efficiency (as a consequence of high K_OW). Predicted D5 concentrations in herring and cod agree well with measured data from the inner fjord, particularly when measured concentrations in zooplankton were used to set the initial dissolved-phase aqueous concentrations. Predicted concentrations of D4 and D6 in fish were over- and under-estimated by the model – possibly due to extrapolation of the metabolism rate constant from D5.     

三工河流域油料作物的农户种植意愿影响因素分析——基于Logistic模型和240户农户微观调查数据

论文以新疆三工河流域27个行政村(连队)240户农户的调查数据为基础,通过建立农户种植油料作物意愿和各自变量的Logistic回归计量模型,从家庭特征因素、 资源禀赋因素、 家庭经济因素和其他因素四大方面分析了农户对油料作物种植意愿的影响因素。研究表明:家庭总人口、 对农业技术服务的态度与农户的种植意愿呈正相关关系,且对农业技术服务的态度标准化系数更大,影响更显著;常年从事农业劳动人数、 户主文化水平和地块位置与之呈反相关关系,且户主文化水平的标准化系数更大,影响更显著。但是反映土地投入、 种地收入以及人均收入没有通过检验,不能得出这几个变量与农户种植油料作物意愿之间的关系。文章最后探讨了兵地差异对农户种植意愿的影响及研究展望。