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Since environmental conservation has provoked much interest globally, indicators have been proposed for a wide range of different projects. The distinction between indicators is unclear. A project to clarify indicator issues was established by the European Union to link partners working with real data at a local level to highlight problems and discuss possibilities for rationalisation across systems. The major issues relate to indicator structure. They can have single or multiple components and be measured at different scales: local, community, regional, national and international. Many different frameworks exist and which may not be compatible. The quality of indicators across different systems, sectors and themes is reflected in the degree of difference in their definitions and calculation. Finally, the papers in this Special Issue are described. Appendix A gives a definition of an indicator.

Much progress has been achieved in defining indicators of impact and change at national and international levels. Concern has been raised about data quality, standardisation methods and aggregation issues. This paper addresses these issues for multidisciplinary studies at a project level, from where data are aggregated to supply national databases. Component indicators of different types are identified and problems of forming composites, weighting and aggregation are discussed. Initial suggestions for robust design and analysis to monitor change in a multidisciplinary way are given. Many system databases are not truly multidisciplinary and until these are available the necessary research to underpin good data quality may not be possible. Guidelines for the handling of indicators of the different types are given. Suggestions are provided for the inclusion of stakeholders at all project stages to identify issues and indicators, to liaise over project results and to encourage ownership of the data.

Participatory research has emerged as a powerful tool to identify agro-ecosystem indicators in developing countries. Indigenous knowledge, thus generated complements scientific information to the benefit of all stakeholders. This paper demonstrates the value of participating with farmers and hunters to identify indicators at a local level and how these supplement scientific information. Three examples are provided to demonstrate different degrees of participation and different indicator identification tools. The first shows participatory research to determine farmer constraints in Zambia and to explore the use of kraal manure and inorganic fertiliser in a traditional grassmound farming system. The second study concerns participatory research in rural areas of Bangladesh to explore a wide range of new technologies relating primarily to small-scale rice-based systems. The third study concerns participatory rapid rural appraisal to investigate biodiversity in a forest and a grassland area in Uganda. Participatory processes generate traditional knowledge that is broader and more descriptive than scientific information. Such knowledge can also be used to plan future research. The role of the interactive farmer–researcher process is discussed. It is concluded that participatory research has many benefits provided it is managed tactfully and farmers are encouraged to feel that they own the research process.

The value of environmental indicators largely depends upon the spatial and temporal scale that they represent. Environmental indicators are dependent upon data availability and also upon the scale for which statements are required. As these may not match, changes in scales may be necessary. In this paper a geostatistical approach to analyse quantitative environmental indicators has been used. Scales, defined in terms of resolution and procedures, are presented to translate data from one scale to another: upscaling to change from high resolution data towards a low resolution, and downscaling for the inverse process. The study is illustrated with three environmental indicators. The first concerns heavy metals in the environment, where the zinc content is used as the indicator. Initially, data were present at a 1 km 2 resolution, and were downscaled to 1 m 2 resolution. High resolution data collected later showed a reasonable correspondence with the downscaled data. Available covariates were also used. The second example is from the Rothamsted’s long-term experiments. Changes in scale are illustrated by simulating reduced data sets from the full dataset on grass cuts. A simple regression model related the yield from the second cut to that of the first cut in the cropping season. Reducing data availability (upscaling) resulted in poor estimates of the regression coefficients. The final example is on nitrate surpluses on Danish farms. Data at the field level are upscaled to the farm level, and the dispersion variance indicates differences between different farms. Geostatistical methods were useful to define, change and determine the most appropriate scales for environmental variables in space and in time.

Integrated pest management is promoted in coffee plantations to control pests and disease in a manner less harmful to the environment than the use of pesticides alone. The rate of adoption of these practices is variable, possibly influenced by different social, economic, environmental and institutional factors. This was explored by fitting standard non-linear curves to uptake data for each of four chosen integrated pest management (IPM) recommendations for control of coffee berry borer, Hypothenemus hampei (Ferrari), in Colombian coffee. Logistic curves were shown to be the most efficient for all four recommendations. Logistic regression analysis was then used to determine the impact of different factors upon the uptake of the recommendations singly and in combination. Comparisons are made between the results of the analyses to confirm the choice of the single or combined datasets and the reliability of the models. The results showed that different factors affected the adoption processes at different stages in time for the different recommendations, both when used singly and when in combination. A link was demonstrated between level of education, wealth of the farmer and choice of recommendation, poorer farmers choosing recommendations that did not require a large financial outlay or which required a high level of technological skill. Suggestions for more controlled studies of adoption are presented.

As the result of a recent BBSRC-funded workshop between soil scientists, modellers, statisticians and others to discuss issues relating to the derivation of complex environmental models, a set of modelling guidelines is presented and the required associated research areas are discussed.

SUMMARYMajor changes to the global environment and their impact upon statistical requirements are presented. The need for extensive yet accurate datasets is stressed and the difficulties of achieving appropriate sampling strategies are described. Examples are presented of environmental studies in use in diverse field such as global warming, sustainability, agroforestry and intercropping, and local community studies. The history of the use of environmental statistics is highlighted as is the need for modern methods to handle large, multivariate datasets likely to be subject to both spatial and temporal variability.