Multiscale effects of management, environmental conditions, and land use on nitrate leaching in dairy farms

Nitrate leaching in intensive grassland- and silage maize-based dairy farming systems on sandy soil is a main environmental concern. Here, statistical relationships are presented between management practices and environmental conditions and nitrate concentration in shallow groundwater (0.8 m depth) at farm, field, and point scales in The Netherlands, based on data collected in a participatory approach over a 7-yr period at one experimental and eight pilot commercial dairy farms on sandy soil. Farm milk production ranged from 10 to 24 Mg ha(-1). Soil and hydrological characteristics were derived from surveys and weather conditions from meteorological stations. Statistical analyses were performed with multiple regression models. Mean nitrate concentration at farm scale decreased from 79 mg L(-1) in 1999 to 63 in 2006, with average nitrate concentration in groundwater decreasing under grassland but increasing under maize land over the monitoring period. The effects of management practices on nitrate concentration varied with spatial scale. At farm scale, nitrogen surplus, grazing intensity, and the relative areas of grassland and maize land significantly contributed to explaining the variance in nitrate concentration in groundwater. Mean nitrate concentration was negatively correlated to the concentration of dissolved organic carbon in the shallow groundwater. At field scale, management practices and soil, hydrological, and climatic conditions significantly contributed to explaining the variance in nitrate concentration in groundwater under grassland and maize land. We conclude that, on these intensive dairy farms, additional measures are needed to comply with the European Union water quality standard in groundwater of 50 mg nitrate L(-1). The most promising measures are omitting fertilization of catch crops and reducing fertilization levels of first-year maize in the rotation.     

CONSIDERATIONS OF SCALE IN WATER QUALITY MONITORING AND DATA ANALYSIS1

ABSTRACT: An assumption of scale is inherent in any environmental monitoring exercise. The temporal or spatial scale of interest defines the statistical model which would be most appropriate for a given system and thus affects both sampling design and data analysis. Two monitoring objectives which are strongly tied to scale are the estimation of average conditions and the evaluation of trends. For both of these objectives, the time or spatial scale of interest strongly influences whether a given set of observations should be regarded as independent or serially correlated and affects the importance of serial correlation in choosing statistical methods. In particular serial correlation has a much different effect on the estimation of long-term means than it does on the estimation of specific-period means. For estimating trends, a distinction between serial correlation and trend is scale dependent. An explicit consideration of scale in monitoring system design and data analysis is, therefore, most important for producing meaningful statistical information.     

Effects of soil variability and weather conditions on pesticide leaching--a farm-level evaluation

In line with European regulations, Dutch law imposes an environmental threshold of 0.1 microg L(-1) on pesticide concentrations in ground water. During registration, the risk of exceeding this threshold is assessed through simulations for one or a few standard scenarios that do not reflect spatial variability under field conditions. The introduction of precision agriculture, where soil variability is actively managed, can increase control over pesticide leaching. This study presents a step-wise evaluation of the effects of soil variability and weather conditions on pesticide leaching. The evaluation was conducted on a 100-ha arable farm and aimed at identifying opportunities for precision management. As a first step, a relative risk assessment identified pesticides presenting a relatively high risk to the environment. Second, the effect of weather conditions was analyzed through 20 years of simulations for three distinct soil profiles. Results were summarized in cumulative probability plots to provide a probabilistic characterization of historical weather data. The year matching 90% probability (1981) served as a reference to simulate pesticide leaching from 612 soil profiles. After interpolation, areas where concentrations exceeded the environmental threshold were identified. Out of a total of 19 pesticides, isoproturon [N-dimethyl-N'-(4-(1-methylethyl)phenyl)urea], metribuzin [4-amino-6-tert-butyl-3-(methylthio)-as-triazin-5(4H)-one], and bentazon [2,1,3-benzothiadiazin-4(3H)-one, 3-isopropyl-, 2,2-dioxide] showed the highest risk for leaching. Leaching was strongly affected by soil variability at the within-field, field, and farm levels. Opportunities for precision management were apparent, but depended on the scale level at which environmental thresholds were implemented. When legislation is formulated in this issue, the presented step-wise evaluation can serve as a basis for identification and precision management of high-risk pesticides.     

Remotely Sensed Data for Ecosystem Analyses: Combining Hierarchy Theory and Scene Models

Remotely sensed data have been used extensively for environmental monitoring and modeling at a number of spatial scales; however, a limited range of satellite imaging systems often constrained the scales of these analyses. A wider variety of data sets is now available, allowing image data to be selected to match the scale of environmental structure(s) or process(es) being examined. A framework is presented for use by environmental scientists and managers, enabling their spatial data collection needs to be linked to a suitable form of remotely sensed data. A six-step approach is used, combining image spatial analysis and scaling tools, within the context of hierarchy theory. The main steps involved are: (1) identification of information requirements for the monitoring or management problem; (2) development of ideal image dimensions (scene model), (3) exploratory analysis of existing remotely sensed data using scaling techniques, (4) selection and evaluation of suitable remotely sensed data based on the scene model, (5) selection of suitable spatial analytic techniques to meet information requirements, and (6) cost–benefit analysis. Results from a case study show that the framework provided an objective mechanism to identify relevant aspects of the monitoring problem and environmental characteristics for selecting remotely sensed data and analysis techniques.     

EVALUATION OF HYDROLOGIC MODELING TECHNIQUES FOR FOREST LAND MANAGEMENT PLANNING1

ABSTRACT: A growing concern for environmental quality paralleled with increasing demands on our forest resources has prompted the Washington State Department of Natural Resources to evaluate simulation modeling as a technique for analyzing management decisions in terms of their environmental effects. The evaluation focused on a system of integrated models developed at the University of Washington which simulate processes and activities within the forest ecosystem. A major part of the system is a hydrologic model which predicts changes in discharge, stream temperature, and concentrations of suspended sediment and dissolved oxygen based on information generated by other models representing intensive management practices. The evaluation consisted of applying the system to a 72,000 acre tract of forest land, validating the models with two years of discharge and water quality data from a 93,000 acre watershed, and determining the pertinence of hydrologic modeling for management purposes. Results show several potential uses of hydrologic modeling for forest management planning, especially for analyzing the effects of timber harvesting strategies on water quality.     

Modelling soil series data to facilitate targeted habitat restoration: a polytomous logistic regression approach

Habitat restoration at a landscape scale is becoming increasingly important in environmental management. In this context, geographical information systems are well suited as they can store and integrate many of the abiotic and biotic criteria used to assess the ecological worth of a site. However, this capacity can be limited by the availability or suitability of spatial data sets. A classic example of the latter case is the National Soil Map of England and Wales, which groups soils of a varied nature into associations. Consequently the national soil map has proved to be a poor predictor of habitat suitability. Using polytomous logistic regression we put forward a method for separating soil associations into their constituent soils within the Chilterns Natural Area. This approach used soil association, aspect, slope and relative height as variables for this analysis. Whilst the model's performance is likely to have been limited by the accuracy of the soil association data set, a predictive accuracy of between 47 and 65% is sufficient to facilitate better targeting of habitat restoration when combined with other abiotic factors such as climate and topography.     

Agricultural Production Monitoring in the Sahel Using Remote Sensing: Present Possibilities and Research Needs

The main objective is to review the current status of research related to the monitoring of agricultural production in the Sahel (west Africa). The Sahel suffers from frequent shortages of food. It is therefore important to have a tool to monitor environmental variables, and thus crop production, during the agricultural season. Satellite remote sensing can contribute significantly to such a system by collecting information on crops and on environmental variables at a sub-continental geographical scale and with a high temporal frequency. One part of the problem is to estimate crop acreage. The technique of area-sampling frame has to adapted to the Sahelian landscape, which is dominated by traditional farming systems. The second part is to estimate crop yields. Three main approaches exist: statistical, semi-deterministic or deterministic. The use of vegetation indices is discussed as well as techniques to derive biophysical variables from remotely-sensed data. Finally, the integration of these remote-sensing techniques with crop-growth models is discussed and some research needs are identified. It is argued that the quantitative assessment of agricultural production in the Sahel should be based on the integration of remotely-sensed data with semi-deterministic agrometeorological models. This approach will allow a regionalization of the production estimates.     

面向管理决策的标准化流域水环境模型评估验证技术框架研究

不规范的流域水环境模型应用增加了决策风险。从过程管理的角度来看,我国尚未针对流域水环境模型的评估与验证建立标准化的技术流程,模型标准化应用水平较低。在总结已有研究成果以及先进管理经验的基础上,本文构建了标准化的流域水环境模型评估验证技术框架,提出了对应用于流域水环境管理决策的模型开展评估验证的基本原则、工作流程和技术要求,并通过案例研究验证了技术框架的可行性。技术框架引入了结构合理性评估、参数识别与灵敏度分析、模拟效果评估、不确定性分析等模型评估验证的关键技术,结合不同的模型类型、决策功能等特征给出了原则性的技术要求和应用建议。研究成果充分考虑了我国的环境管理需求,与现阶段环境模拟技术要求、环境监测能力和数据条件相适应,在理论探讨和技术实现层面具备明确的可行性,将促进我国流域水环境模型的规范化、标准化和本地化应用。     

Index models to evaluate the risk of phosphorus and nitrogen loss at catchment scales

This paper investigates index models as a tool to estimate the risk of N and P source strengths and loss at the catchment scale. The index models assist managers in improving the focus of remediation actions that reduce nutrient delivery to waterbodies. N and P source risk factors (e.g. soil nutrient concentrations) and transport risk factors (e.g. distance-to-streams) are used to determine the overall risk of nutrient loss for a case study in the Tuross River catchment of coastal southeast Australia. In the development of the N index model for Tuross, particulate N was considered important based on the observed event water quality data. In contrast to previous N index models, erosion and contributing distance were therefore included in the Tuross River catchment N index. Event-based water quality monitoring, and soil information, or in data-poor catchments conceptual understanding, are essential to represent catchment-scale processes. The techniques have high applicability in other catchments, and are complementary to other modelling techniques such as process-based semi-distributed modelling. Index models generally provide much more detailed spatial resolution than fully- or semi-distributed conceptual modelling approaches. Semi-distributed models can be used to quantify nutrient loads and provide overall direction to set the broad focus of management. Index models can then be used to refine on-the-ground investigations and investment priorities. In this way semi-distributed models can be combined with index models to provide a set of powerful tools to influence management decisions and outcomes.     

Factors influencing scaling up of agroforestry-based spatial land-use integration for soil fertility management in Arsamma Watershed,Southwestern Ethiopian Highlands

This study was set to examine factors influencing agroforestry upscaling, inter-plot natural fertilizer transfer and inter-plot income flow in Arsamma watershed. A semi-structured questionnaire was used to gather necessary information. Contingency table, chi-square, Phi and Cramer's V were used to analyze the data. Access to seedlings was the most important determinant of agroforestry upscaling; and farmers' production orientation, farm size and wealth status ranked, respectively, second, third and fourth. Inter-plot natural fertilizer transfer was primarily influenced by participation in agricultural extension. Wealth status and livestock size ranked, respectively, second and third in influencing inter-plot natural fertilizer transfer. The study indicated a geographic concept of spatial land-use integration for soil fertility management and key factors influencing agroforestry-based land-use integration. Agroforestry-centered diversified small-scale agricultural commercialization, tree-crop-livestock integration, agricultural extension services promotion and multi-purpose tree species supply are the way out to upscale agroforestry and agroforestry-based spatial land-use integration.     

土壤石油污染环境容量的评估与模拟研究

文章基于对某滨海油区环境和生态进行调研分析的结果,选择一般参数法、综合估值法和数值模拟法三种方法对土壤石油污染的环境容量进行评估和模拟研究。研究分析了污染物在土壤中的时空分布,预测分析污染物迁移趋势和范围,综合探讨了研究区土壤石油污染的环境容量,可为制订有关环境标准和加强土壤石油烃排放管理提供技术依据和参考。     

Spatial Analysis of Soil Salinity and Soil Structural Stability in a Semiarid Region of New South Wales,Australia

Salt-affected soils are a major threat to agriculture especially in the semiarid regions of the world. The effective management of these soils requires adequate understanding of not only how water and, hence, solutes are transported within the soil, but also how soil salinity and sodicity spatially interact to determine soil structural breakdown. For sustainable agricultural production, information on quantitative soil quality, such as salinity, is required for effective land management and environmental planning. In this study, quantitative methods for mapping indicators of soil structural stability, namely salinity and sodicity, were developed to assess the effect of these primary indicators on soil structural breakdown. The current levels of soil salinity, as measured by electrical conductivity (EC) of the soil/water suspension, soil sodicity, represented by exchangeable sodium percentage (ESP), and aggregate stability, were assessed. Remote sensing, geographical information system (GIS), and geostatistical techniques-primarily regression-kriging and indicator-kriging-were used to spatially predict the soil sodicity and salinity. The patterns of salinity (EC) and sodicity (ESP > 5%) were identified. The effect of land use on these soil quality indicators was found to be minimal. Co-spatial patterns were elucidated between sodic soils (defined by ESP > 5%) and highly probable mechanically dispersive soils predicted from indicator-kriging of ASWAT scores. It was established that the incorporation of EC with ESP into an objective index, called electrolyte stability index (ESI = ESP/EC), gave a good indication of soil dispersion, although the threshold ESI value below which effective structural breakdown might occur is 0.025, which is twice as small as the expected 0.05. The discrepancies between ESI and ASWAT scores suggest that other soil factors than salinity and sodicity are affecting soil structural breakdown. This calls for further investigation. The study provides valuable information in the form of risk zones of soil structural breakdown for land management. These zones, with a probability of mechanical soil dispersion of >0.70, require immediate management attention with greater monitoring and amelioration techniques, particularly gypsum or lime application and/or altered cultivation techniques.     

Evaluation of Three Watershed‐Scale Pesticide Environmental Transport and Fate Models1

Abstract: The U.S. Environmental Protection Agency (USEPA) Office of Pesticide Programs (OPP) has completed an evaluation of three watershed‐scale simulation models for potential use in Food Quality Protection Act pesticide drinking water exposure assessments. The evaluation may also guide OPP in identifying computer simulation tools that can be used in performing aquatic ecological exposure assessments. Models selected for evaluation were the Soil Water Assessment Tool (SWAT), the Nonpoint Source Model (NPSM), a modified version of the Hydrologic Simulation Program‐Fortran (HSPF), and the Pesticide Root Zone Model‐Riverine Water Quality (PRZM‐RIVWQ) model. Simulated concentrations of the pesticides atrazine, metolachlor, and trifluralin in surface water were compared with field data monitored in the Sugar Creek watershed of Indiana’s White River basin by the National Water Quality Assessment (NAWQA) program. The evaluation not only provided USEPA with experience in using watershed models for estimating pesticide concentration in flowing water but also led to the development of improved statistical techniques for assessing model accuracy. Further, it demonstrated the difficulty of representing spatially and temporally variable soil, weather, and pesticide applications with relatively infrequent, spatially fixed, point estimates. It also demonstrated the value of using monitoring and modeling as mutually supporting tools and pointed to the need to design monitoring programs that support modeling.     

SUBWATERSHED SPATIAL ANALYSIS TOOL: DISCRETIZATION OF A DISTRIBUTED HYDROLOGIC MODEL BY STATISTICAL CRITERIA1

ABSTRACT: Complex hydrologic models, designed for simulating larger watersheds, require a huge amount of input data. Most of these models use spatially distributed data as inputs. Spatial data can be aggregated or disaggregated for use as input to a model, which can impact model outputs. Although, it is efficient to minimize data redundancy by aggregating the spatial data, upscaling reduces the detail/resolution of input information and increases model uncertainty. On the other hand, a large number of model inputs with high degrees of disaggregation take more computer time and space to process. Hence, a balance between striving for a maximum level of aggregation and a minimum level of information loss has to be achieved. This study presents a definition of an appropriate level of discretization, derived by establishing a relationship between a model's efficiency and the number of subwater‐sheds modeled. An entropy based statistical approach/tool called Subwatershed Spatial Analysis Tool (SUSAT) was developed to find an objective choice of an appropriate level of discretization. The new approach/tool was applied to three watersheds, each representing different hydrologic conditions, using a hydrologic model. Coefficients of efficiency and entropy estimated at different levels of discretization were used to validate the success of the new approach.     

Grassland Management Activity Data: Current Sources and Future Needs

Estimates of potential and actual C sequestration require areal information about various types of management activities. Forest surveys, land use data, and agricultural statistics contribute information enabling calculation of the impacts of current and historical land management on C sequestration in biomass (in forests) or in soil (in agricultural systems). Unfortunately little information exists on the distribution of various management activities that can impact soil C content in grassland systems. Limited information of this type restricts our ability to carry out bottom-up estimates of the current C balance of grasslands or to assess the potential for grasslands to act as C sinks with changes in management. Here we review currently available information about grassland management, how that information could be related to information about the impacts of management on soil C stocks, information that may be available in the future, and needs that remain to be filled before in-depth assessments may be carried out. We also evaluate constraints induced by variability in information sources within and between countries.It is readily apparent that activity data for grassland management is collected less frequently and on a coarser scale than data for forest or agricultural inventories and that grassland activity data cannot be directly translated into IPCC-type factors as is done for IPCC inventories of agricultural soils. However, those management data that are available can serve to delineate broad-scale differences in management activities within regions in which soil C is likely to change in response to changes in management. This, coupled with the distinct possibility of more intensive surveys planned in the future, may enable more accurate assessments of grassland C dynamics with higher resolution both spatially and in the number management activities.     

Land cover mapping of wetland areas in an agricultural landscape using SAR and Landsat imagery

Saline wetlands in the Monegros Desert, NE Spain, are situated in an agricultural landscape which is undergoing significant changes. Agricultural intensification in recent decades and current installation of new irrigation systems threaten these valuable habitats, set to be included in the Natura2000 network. Their preservation and successful management depend on the information available regarding the transformation of surrounding areas. When soil and vegetation maps at adequate scale are not available, remote sensing is an alternative means to obtain needed data. We have used SAR data, taking advantage of the soil surface characteristics perceived in SAR images. The objective of this work is to explore the capability of multitemporal SAR data to characterize the land covers of these wetlands and their environment. We have developed specific contextual classifications which take into account the statistical properties of the radar distribution. Moreover, we tested the contribution of radar in Landsat classification.     

Assessment of surface water quality of selected estuaries of Malaysia: multivariate statistical techniques

Multivariate statistical techniques such as cluster analysis (CA), factor analysis (FA) were used for the evaluation of spatial variations and the interpretation of a large complex water quality data set of two selected estuaries of Malaysia. The two locations of interest with 10 sites in each location were Kuala Juru (Juru estuary) and Bukit Tambun (Jejawi estuary). Cluster analysis showed that some sites in both locations have similar sources of pollution from point or non-point sources whereas FA yielded four factors which are responsible for water quality variations explaining more than 80% of the total variance of the data set and allowed to group the selected water quality. Correlation analysis of the data showed that some parameters have strong association with other parameters and they share a common origin source. This study illustrates the usefulness of multivariate statistical analysis for evaluation and interpretation of complex data sets to get better information about the pollution sources/factors and understanding the behavior of the parameters in water quality for effective river water quality management.     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.