‘On–off–on’ fluorescent indicators for pH windows are obtained with ternary systems in which the three separated components are Cu²⁺, a tetraaza ligand and the fluorophore Coumarin 343: protonation of Coumarin 343, its coordination to Cu²⁺ and its displacement from Cu²⁺ by OH⁻ give an inverse bell-shaped variation of emission with pH.

An increasing variety of evaluation methods is being proposed to address the question of the environmental impacts of agriculture. This paper compares and analyses 12 indicator-based approaches to assessing environmental impact at the farm level, in order to propose a set of guidelines for the evaluation or development of such methods. These methods take into account a number of environmental objectives (e.g. soil erosion, water quality). A set of indicators is used to quantify the degree to which these objectives are attained. A total of 26 objectives were taken into account by one or several of the methods. A great diversity in breadth of analysis exists: the number of objectives considered per method varies from 2 to 13. Indicator-based methods for environmental evaluation at the farm level should take into account a range of objectives covering both local and global effects. Indicators based on the environmental effects of farmer practices are preferable to indicators based on farmer practices as the link with the objective is direct and the choice of means is left to the farmer. Indicators based on farmer practices cost less in data collection but do not allow an actual evaluation of environmental impact. Indicators allowing expression of impacts both per unit surface and per unit product are preferable. Indicators producing output in the form of values are preferred to indicators producing scores. If possible, science-based threshold values should be defined for indicators. The method should be validated with respect to (a) the appropriateness of its set of objectives relative to its purpose and (b) its indicators.

This article provides an overview of concepts, models, measures, and dimensions of forest sustainability assessments. The methods described include models that are fully developed and have actually been applied to various application contexts, as well as two normative models, graph theory and cognitive mapping, that appear to have potential for more in-depth analysis of the interactions of sustainability indicators. The former is based on the principles of multi-criteria analysis (MCA), an approach that is capable of accommodating the amount of complexity and uncertainty inherent in the concept and practice of forest sustainability. The other methods make use of “softer” analysis that are suited particularly for the assessment and measurement of qualitative indicators of sustainability. The MCA models are capable of determining relative importance of indicators and estimating sustainability index values cased on the combined impacts of indicators. Measures of relative importance can be used to prioritise indicators or filter out those that are relatively unimportant. ‘Softer’ analysis methods such as cognitive mapping and graph theory can help examine the interactions between and among indicators and help identify critical domains, critical paths, and tactically and strategically important indicators, based on the strength of their connectivity with other indicators, either directly or indirectly. These measures of connectivity can provide useful insights that can help managers identify indicators in need of attention, monitoring or even mitigation when necessary. Concepts such as the use of sustainability thresholds and qualitative flags also discussed.

The objective of this paper was to describe a method that enables a comparison of egg production systems for their contribution to sustainable development of egg production, using data from the Netherlands for demonstration purposes. One conventional system, the battery cage system, and two animal-friendly systems, the deep-litter and the aviary systems, illustrate this method. The method is based on a three-phase framework that identifies relevant issues regarding sustainable development (Phase 1), translates issues into sustainability indicators (Phase 2), and assesses the contribution of sustainability indicators to sustainable development (Phase 3). Phases 1 and 2 are based on analysis of literature and consultation with experts. Phase 3 is based on graphical and numerical comparisons. A graphical comparison presents the relative deviation between actual and target values for each sustainability indicator. A numerical comparison combines the relative deviation for each sustainability indicator into an overall contribution of an egg production system to sustainable development. Sustainability indicators selected include economic performance, ammonia emission, energy use, hen welfare, farmer welfare, and egg quality. Based on equal importance of these indicators, the battery cage system shows the least negative contribution to sustainable development. The aviary system is considered a better animal-friendly alternative for a battery cage system when compared with a deep-litter system. The aviary system especially needs to improve economic performance and farmer welfare. The conclusion can only be tentative, however, because methodological aspects such as selection and weighting of sustainability indicators will need careful attention and further research.

This article provides an overview of concepts, models, measures, and dimensions of forest sustainability assessments. The methods described include models that are fully developed and have actually been applied to various application contexts, as well as two normative models, graph theory and cognitive mapping, that appear to have potential for more in-depth analysis of the interactions of sustainability indicators. The former is based on the principles of multi-criteria analysis (MCA), an approach that is capable of accommodating the amount of complexity and uncertainty inherent in the concept and practice of forest sustainability. The other methods make use of softer analysis that are suited particularly for the assessment and measurement of qualitative indicators of sustainability. The MCA models are capable of determining relative importance of indicators and estimating sustainability index values cased on the combined impacts of indicators. Measures of relative importance can be used to prioritise indicators or filter out those that are relatively unimportant. Softer analysis methods such as cognitive mapping and graph theory can help examine the interactions between and among indicators and help identify critical domains, critical paths, and tactically and strategically important indicators, based on the strength of their connectivity with other indicators, either directly or indirectly. These measures of connectivity can provide useful insights that can help managers identify indicators in need of attention, monitoring or even mitigation when necessary. Concepts such as the use of sustainability thresholds and qualitative flags also discussed | G.A. Mendoza, R. Prabhu, 'Multidimensional measurements of approaches to forest sustainability assessments: an overview of models, approaches, and issues', Kluwer Academic Publishers, Dordrecht, Netherlands, 2002

The Third Assessment Report of the Intergovernmental Panel on Climate Change represented a consensus among the world's leading climate scientists that rapid climate changes were occurring on a global scale. In particular, the marked warming that had occurred over the past half century was, they concluded, substantially caused by the build-up of greenhouse gases in the troposphere as a result of anthropogenic activities. Globally, 1998 was the warmest year of the warmest decade of the warmest century of at least the last millennium. Such fluctuations, the IPCC suggested, were already capable of being associated with changes in a diverse set of physical and biological indicators in many parts of the world. Indicators of climate change are primarily used to simplify a complex reality and to communicate, more succinctly, critical information regarding climatic trends. They also provide an essential early warning system by making available information that may point to an environmental problem which is capable of being ameliorated before it becomes critical. In establishing indicators, a distinction can be made between primary indicators, based on analysis of directly observed meteorological data, and secondary indicators, based on the responses of the living world to climate changes which provoke a response in living organisms. | research

The article analyses the competitiveness of the Slovak wine industry on the domestic market, as well as on the markets of the EU member states and CEFTA. The analysis in based on the indicators of comparative advantages (DCR and PCR) and the coefficients of nominal protection for tradable outputs and inputs (NPCO and NPCl). These indicators were calculated based on the PAM method (Policy Analysis Matrix). Competitiveness of the Slovak wine sector on CEFTA and EU markets was assessed on the basis of agri-food trade and import/export prices. The RCA (Revealed Comparative Advantage) index was then used to calculate competitiveness in mathematical terms

The work analyses financial position of 60 agricultural enterprises, of which a half farm in better soil and natural conditions and another half in worse conditions. The evaluation of financial standing is based on the indicators of profitability, liquidity, term of debt collection and debt repayment, indebtedness, times interest, stock financing with long-term capital, some of cash-flow indicators

The level of development of various districts of Madhya Pradesh was estimated with the help of composite index based on optimum combination of socio-economic indicators. All the forty five districts of the State have been included in the study. The data for the year 1994-95 on forty seven socio-economic indicators have been used. The level of development was separately estimated for agricultural, industial, infrastructural and socio-economic fields. Out of forty seven indicators included in the study, twenty three indicators are directly connected with agricultural development, six indicators depict the progress of development in industrial sector and the rest eighteen indicators present the level of development in infrastructural service sector. The district of Raisen was found to rank first and that of Sidhi was the last in the level of socio-economic development. Wide disparities in the level of development were observed in different districts. Positive significant association was found between the levels of agricultural, industrial and socio-economic developments. Infrastutural facilities are influencing the growth and progress of agricultural, industrial and socio-economic development in the positive direction. For bringing about uniform regional development, model districts have been idebtified and potential targets for various indicators have been estimated for low developed districts. These districts require improvement of various dimensions in most of the indicators for enhancing the level of over all socio-economic development.

Reliable indicators of species richness (e.g., particular species), if they can be found, offer potentially significant benefits for management planning. Few efficient and statistically valid methods for identifying potential indicators of species richness currently exist. We used Bayesian‐based Poisson modeling to explore whether species richness of butterflies in the Great Basin could be modeled as a function of the occurrence (presence or absence) of certain species of butterflies. We used an extensive data set on the occurrence of butterflies of the Toquima Range (Nevada, USA) to build the models. Poisson models based on the occurrence of five and four indicator species explained 88% and 77% of the deviance of observed species richness of resident and montane‐resident butterfly assemblages, respectively. We then developed a test framework, including formally defined “rejection criteria,” for validating and refining the models. The sensitivity of the models to inventory intensity (number of years of data) and knowledge about the potential indicators was incorporated into this evaluation phase. We conducted a test of our models by using an existing set of data on butterflies in the neighboring Toiyabe Range. Predicted values of species richness were significantly rank correlated with the observed values. Thus, the models appear to have promise for predicting species richness based on the occurrence of certain taxa.