Gift | Summary (En, Nl, Sv)

Approaches to assess the effects of management practices on agroecosystem functions are needed. This paper describes a computer program designed to assess the relative sustainability of management practices using agronomic and environmental data. The program, entitled AgroEcosystem Performance Assessment Tool (AEPAT), utilizes performance-based index methodology to derive a relative ranking of agroecosystem performance among management practices for functions and indicators included in the procedure. The program is organized into eight major steps: Introduction, Input Files, Assign Indicators to Functions, Describe Indicators, Assign Weights, Select Output File, Calculate Agroecosystem Performance Scores, and Save Agroecosystem Project. Help windows as well as a tutorial are provided within the program to assist users through each step. Users must keep in mind the assumptions and drawbacks inherent to performance-based indices while using AEPAT. Additionally, the program requires data on many indicators to provide useful information on agroecosystem performance. Therefore, its use is intended primarily for clientele conducting long-term agroecosystem experiments.

Approaches to assess the effects of management practices on agroecosystem functions are needed. This paper describes a computer program designed to assess the relative sustainability of management practices using agronomic and environmental data. The program, entitled AgroEcosystem Performance Assessment Tool (AEPAT), utilizes performance-based index methodology to derive a relative ranking of agroecosystem performance among management practices for functions and indicators included in the procedure. The program is organized into eight major steps: Introduction, Input Files, Assign Indicators to Functions, Describe Indicators, Assign Weights, Select Output File, Calculate Agroecosystem Performance Scores, and Save Agroecosystem Project. Help windows as well as a tutorial are provided within the program to assist users through each step. Users must keep in mind the assumptions and drawbacks inherent to performance-based indices while using AEPAT. Additionally, the program requires data on many indicators to provide useful information on agroecosystem performance. Therefore, its use is intended primarily for clientele conducting long-term agroecosystem experiments.

There is an increasing call for tools that provide insight into the complex nature of agricultural systems and that deal with a broad range of sustainability issues related to policy intervention, technological innovations, and changes in environmental conditions (e.g., climate change). Sustainability indicators are useful, but only if their number is limited and the interactions between indicators are taken into consideration. In this context, we propose a methodology for an integrated analysis of tradeoffs between economic and environmental indicators. The analysis to quantify these relationships should be based on a multi-disciplinary approach and as such requires the usage of bio-physical as well as econometric-process simulation models. The communication between these very different models is based on explicit definitions of spatial and temporal scales and model integration software. The methodology is based on spatially explicit econometric simulation models linked to spatially referenced bio-physical simulation models to simulate land use and input use decisions. The methodology has been applied for the potato-pasture production system in the Ecuadorian Andes. Results of the analysis are presented in the form of tradeoff curves between different indicators, but also as maps, and risks diagrams. Besides an analysis of the current status, the approach allows for the analysis of alternative scenarios showing the effect of those scenarios on the position and slope of the tradeoff curve.

Sustainable forest management ideally involves five elements: 1) establishing a clear set of values, goals and objectives and, 2) planning actions that are most likely to meet desired goals and objectives, 3) implementing appropriate management activities, 4) monitoring the outcomes to check on predictions, effectiveness, and assumptions, and 5) evaluating and adjusting management depending on the outcome of monitoring. Within this framework, indicators are used to determine whether the outcome of management has met the intended goals. In this paper we provide general guidance for developing an integrated and logical monitoring system, define and differentiate between "evaluative" and "prescriptive" indicators, provide more specific advice on choosing evaluative indicators (including a comparison of types of ecological indicators), and provide specific advice on defining prescriptive indicators. Our guidelines for developing an indicator and monitoring framework are based on three principles. The first principle is to develop a logical framework, including 1) establishing clear values and goals before setting indicators and objectives, and 2) linking prescriptive and evaluative indicators directly to plan objectives, and to each other. The second principal is to use the framework to learn adaptively by: 1) designing management activities to address specific questions, 2) learning about thresholds, and 3) testing assumptions. The third principal is to create a formal plan for learning.

This article reports the methodology and results of a data envelopment analysis (DEA) that attempts to identify areas in the country where there is maximum potential for improving the forest and rangeland condition, based on 12 indicator variables. This analysis differs from previous DEA studies in that the primary variables are measures of human activity and indicators of forest and rangeland condition in place of the traditional economic inputs (costs) and outputs. It also involves a different (ecological) production process than traditional DEA analyses, and a statistical preanalysis is developed and applied to homogenize the large and highly diverse landscape where this ecological production process takes place for the purposes of the DEA. It is concluded that, based on this analysis, there are opportunities to improve the forest and rangeland condition without reducing the amount of human activity, but not over large areas, only for some indicators, and typically not for a large number of indicators in the same place. This means that large-scale improvements in environmental condition across many indicators may often not come about without a reduction in human activity.

The authors discuss the use of imputed data in regression analysis, in particular the use of highly disaggregated welfare indicators (from so-called "poverty maps"). They show that such indicators can be used both as explanatory variables on the right-hand side and as the phenomenon to explain on the left-hand side. The authors try out practical ways of adjusting standard errors of the regression coefficients to reflect the error introduced by using imputed, rather than actual, welfare indicators. These are illustrated by regression experiments based on data from Ecuador. For regressions with imputed variables on the left-hand side, the authors argue that essentially the same aggregate relationships would be found with either actual or imputed variables. They address the methodological question of how to interpret aggregate relationships found in such regressions.

Objective: To evaluate the quality of web-based information on the treatment of eating disorders and to investigate potential indicators of content quality. Method: Two search engines were queried to obtain 15 commonly accessed websites about eating disorders. Two reviewers evaluated the characteristics, quality of content, and accountability of the sites. Intercorrelations between variables were calculated. Results: The overall quality of the sites was poor based on the outcome measures used. All quality of content measures correlated with a measure of accountability (Silberg, W.M., Lundberg, G.D., & Mussachio, R.A., 1993). Discussion: There is a lack of quality information on the treatment of eating disorders on the web. Although accountability criteria may be useful indicators of content quality, there is a need to investigate whether these can be usefully applied to other mental health areas.

The Little Green Data Book 2004 is based on the World Development Indicators 2004, and represents a succinct collection of information. It is a collaboration between the Development Economics Data Development Group, and the Environment Department of the World Bank. Under the headings of agriculture, forests, biodiversity, energy, emissions and pollution, water and sanitation, and 'greener' national accounts, it presents key indicators of the environment, and its relationship to people, for more than 200 countries. Regional data include only low- and middle-income economies. Aggregates for regions and income groups are shown only if data are available for 66 percent of the economies in that group. The country composition of regions is based on the World Bank's analytical regions, and may differ from common geographic usage.