Thesis (M. Environmental Science (Biodiversity and Conservation Biology))--North-West University, Potchefstroom Campus, 2007. | Finding a common language in describing and interpreting multivariate data associated with rehabilitation and disturbance ecology, has became a major challenge. The main objective of this study is to find and evaluate mathematical models to describe ecosystem change based on selected indicators of change. Existing data from a previous rehabilitation project on Hendrina Power Station (Mpumalanga, South Africa) was used as a database for this study and this study aims to report on the development of models concentrating on radar graphs and a model based on matrix mathematics. The main groups of organisms selected for the construction of models, were vegetation, soil mesofauna and ant species. The datasets were limited to some indicative species and their mean abundances were determined. The grids that were used were randomly chosen and the models were constructed. Radar graphs were constructed to model the suite of species identified, through a sensitivity analysis, to indicate possible rehabilitation success over time and was applied to the different rehabilitation ages. The surface areas under the radar graphs were determined and compared for the different rehabilitation ages in the same year of survey. Correlation graphs were drawn between the surface area and the rehabilitation ages. These graphs did not indicate much relevance in indicating rehabilitation success, but the radar graphs proved to be good indicators of change in abundance of the selected species over time. The vegetation species, Eragrostis curvula, was the only species that showed a strong significant positive relationship with rehabilitation age and could be considered a good rehabilitation species and indicator of rehabilitation success. After the evaluation of this model, Eragrostis curvula, and two additional ant species, Tetramorium setigerum and Lepisiota laevis, were added. These species that were added, showed an increase in abundance over time, as found in a previous study. These radar graphs also did not indicate much relevance and it can be concluded that the radar graphs can only be used for a visual representation of the changes in abundance of the relevant species over time. This study also refers to a matrix model. This model focused on the interactions between the different variables selected. The percentage carbon in the soil were also added to the list of species. Model fitting graphs were constructed and correlations were drawn between the species that had significant values in the interaction table. This model could be useful for future studies, but more data and replication is necessary, over a longer period of time. This will serve to eliminate possible shortcomings of the model. | Masters

The last decade has seen an increasing interest in indicators of supply-chain performance. A large number of various performance indicators have been used to characterize supply chains, ranging from highly qualitative indicators like customer or employee satisfaction to quantitative indicators like return on investments. This large number of different performance indicators, and the lack of consensus on what determines performance of supply chains, complicates the selection of performance measures. Furthermore, combining these indicators into one measurement system proves to be difficult. Efforts as well as progress have been made in this area but supply-chain performance measurement received little or no attention in the field of food and agribusiness. This paper provides a literature review on existing performance indicators and models, and discusses their usefulness in agri-food supply chains. Furthermore, based on this overview, a conceptual framework is developed for further research in this area.

Water resources managers and conservation biologists need reliable, quantitative, and directly comparable methods for assessing the biological integrity of the world's aquatic ecosystems. Large‐scale assessments are constrained by the lack of consistency in the indicators used to assess biological integrity and our current inability to translate between indicators. In theory, assessments based on estimates of taxonomic completeness, i.e., the proportion of expected taxa that were observed (observed/expected, O/E) are directly comparable to one another and should therefore allow regionally and globally consistent summaries of the biological integrity of freshwater ecosystems. However, we know little about the true comparability of O/E assessments derived from different data sets or how well O/E assessments perform relative to other indicators in use. I compared the performance (precision, bias, and sensitivity to stressors) of O/E assessments based on five different data sets with the performance of the indicators previously applied to these data (three multimetric indices, a biotic index, and a hybrid method used by the state of Maine). Analyses were based on data collected from U.S. stream ecosystems in North Carolina, the Mid‐Atlantic Highlands, Maine, and Ohio. O/E assessments resulted in very similar estimates of mean regional conditions compared with most other indicators once these indicators' values were standardized relative to reference‐site means. However, other indicators tended to be biased estimators of O/E, a consequence of differences in their response to natural environmental gradients and sensitivity to stressors. These results imply that, in some cases, it may be possible to compare assessments derived from different indicators by standardizing their values (a statistical approach to data harmonization). In situations where it is difficult to standardize or otherwise harmonize two or more indicators, O/E values can easily be derived from existing raw sample data. With some caveats, O/E should provide more directly comparable assessments of biological integrity across regions than is possible by harmonizing values of a mix of indicators.

We use data from a survey of several hundred lakes in the northeastern United States by the U.S. Environmental Protection Agency to illustrate an approach to identifying promising indicators of lake condition. We construct a hypothetical gold standard of water quality from the first principal component of 16 chemical variables measured in the lakes, and examine its associations with 71 candidate indicators based on measurements of human activity, birds, fish and zooplankton in the lakes or their watersheds. Nonparametric summaries of these associations - based on rank correlations and receiver-operating-characteristic curves - suggest that variables summarizing the extent of human disturbance are generally the strongest indicators. To the extent that our water-quality variable is a useful proxy for ecological condition, our results suggest that easily-obtained measures of human activity are at least as predictive as many of the harder-to-measure biological indicators that have been proposed.

An important goal of vulnerability assessment is to create an index of overall vulnerability from a suite of indicators. Constructing a vulnerability index raises several problems in the aggregation of these indicators, including the decision of assigning weights to them. The purpose of this paper is to demonstrate a method of aggregating vulnerability indicators that results in a composite index of vulnerability, but that avoids the problems associated with assigning weights. The investigators apply a technique based on Pareto ranking to a complex, developed socioeconomic landscape exposed to storm surges associated with hurricanes. Indicators of social vulnerability to this hazard are developed and a principal components analysis is performed on proxies for these indicators. Overall social vulnerability is calculated by applying Pareto ranking to these principal components. The paper concludes that it is possible to construct an effective index of vulnerability without weighting the individual vulnerability indicators.

Environmental indicators were developed mainly to improve information flows from scientists to policy-makers. This article discusses the importance of timely environmental data and investigates the influence of indicator-based reporting on the data timeliness of environmental reports by international organizations. Timeliness of information contributes to the quality and appeal of the reports, and to their role as early warning tools, and increases their usability by decision-makers in short-term decision cycles. The results of an analysis of 11 international reports by the European Environmental Agency (EEA) and the Organization for Economic Co-Operation and Development (OECD) show a considerable time lag of three years on average, with only minor development towards more timely reporting. The results suggest that the introduction of environmental indicators has not improved the timeliness of reporting. In order to overcome these problems, the article recommends some methods for improving timeliness. These include better choice of indicators in smaller sets, use of preliminary data and outlooks, development of new indicators, publishing on the internet and more effective use of internet databases to avoid intermediate levels in data collection.

The aim of the research was to determinate a linear discriminant function and neural network that could be applied for classification of farms. The result of classification was forecasting financial situation of them, based on set of many variables, which including financial indicators. Models were built separately for each year, but they were verified in the rest. The additional aims were to determine the set of indicators with large forecasting ability, and to compare two classification methods - linear discriminant function and neural network

The paper presents a field experiment model to investigate the competition between the crop and weeds in agrophytocenosis. Indicators were proposed to be used to evaluate the effects of these interactions. The indicators are based on the relative differences in the number of individuals, amount of biomass and the value of biometric characters of the plants, thus allowing us to define and to compare the reactions of the crop to weed infestation, reactions of respective weed species and weed communities to the competition from the crop and to evaluate the competition between the crop and weeds in a given agrophytocenosis. The method of defining respective indicators of competitive effects makes it possible to compare these effects in different species, at different development stages and for different plant organs

The notion of multifunctionality increasingly gained attention during the nineties in discussions about agricultural policy changes and the future of agriculture, both at national and international levels, particularly within the framework of OECD works and in the WTO multilateral negotiations on agricultural trade. Since the term Multifunctionality of Agriculture (MFA) has rapidly emerged into common use in environmental, agricultural and international trade discussions, it covers a wide range of different perceptions in research literature today. The task that produced this deliverable is principally based on the work performed within the Multagri project, which analysed the existing literature on multifunctionality. Section 2 presents the main developments of the MFA different concepts, as reviewed in the Multagri project and how this conceptual organisation can improve the links between sustainability and multifunctionality. The Multagri project might help to explore to what extent multifunctionality is a concept that can make sustainable development more operational. The workpackage 3 of the Multagri project provided indicators to describe the three main function of agriculture (economic, environmental and social): the section 3 summarizes these developments. Section 4 assesses the relevance of these indicators for the SEAMLESS project in three steps: first, a list of indicators by category and domain is devised; second a selection grid is designed to help selecting indicators that are relevant for SEAMLESS; third a discussion of the accurate geographical level is outlined for each indicator. Sections 5 and 6 discuss the accuracy of MFA indicators for the SEAMLESS project.

"This report introduces new estimates of food insecurity based on food acquisition data collected directly from households as part of national household expenditure surveys (HESs) conducted in 12 Sub-Saharan African countries.The report has three objectives: (1) to explore the extent and location of food insecurity across and within the countries; (2) to investigate the scientific merit of using the food data collected in HESs to measure food insecurity; and (3) to compare food insecurity estimates generated using HES data with those reported by FAO and explore the reasons for differences between the two.The overall purpose is to investigate how the data collected in HESs can be used to improve the accuracy accuracy of FAO’s estimates, which are being used to monitor the MDG hunger goal.The study is based on both diet quantity and diet quality indicators of food insecurity. The two main indicators of focus are the share of people consuming insufficient dietary energy, or the prevalence of “food energy deficiency,” and the share of households with low diet diversity.The study finds these to be valid indicators of food insecurity and to be reasonably reliably measured.They are also comparable across the study countries despite differing methods of data collection." -- from Text | Non-PR | IFPRI1 | FCND