Using the Sustainability Monitoring and Assessment Routine (SMART) for the Systematic Analysis of Trade-Offs and Synergies between Sustainability Dimensions and Themes at Farm Level

When trying to optimize the sustainability performance of farms and farming systems, a consideration of trade-offs and synergies between different themes and dimensions is required. The aim of this paper is to perform a systematic analysis of trade-offs and synergies across all dimensions and themes. To achieve this aim we used the Sustainability Monitoring and Assessment Routine (SMART)-Farm Tool which operationalizes the Sustainability Assessment of Food and Agriculture Systems (SAFA) Guidelines by defining science-based indicator sets and assessment procedures. It identifies the degree of goal achievement with respect to the 58 themes defined in the SAFA Guidelines using an impact matrix that defines 327 indicators and 1769 relations between sustainability themes and indicators. We illustrate how the SMART-Farm Tool can be successfully applied to assess the sustainability performance of farms of different types and in different geographic regions. Our analysis revealed important synergies between themes within a sustainability dimension and across dimensions. We found major trade-offs within the environmental dimension and between the environmental and economic dimension. The trade-offs within the environmental dimension were even larger than the trade-offs with other dimensions. The study also underlines the importance of the governance dimension with regard to achieving a good level of performance in the other dimensions

Sustainability Monitoring and Assessment Routine: Results from pilot applications of the FAO SAFA Guidelines

There is currently no common understanding of how to measure sustainability in the food sector. To close this gap, the FAO has developed Guidelines for Sustainability Assessments of Food and Agriculture Systems (SAFA), which were published as a test version in June 2012. The Guidelines describe about 60 sustainability objectives, which are classified into 20 themes and four dimensions: Good governance, Environmental integrity, Economic resilience, Social well-being, as well as assessment procedures. This paper presents an approach for the sustainability assessments of enterprises in the food and agriculture sector in full compliance with the SAFA Guidelines. We developed an indicator-based tool (“SMART”), which is applicable at all food supply chain levels, and includes stakeholder and employee surveys. SMART consists of a pool of more than 430 indicators for processing and trade and 240 indicators for primary production. The tool has been tested in pilot applications in three enterprises and on 60 farms, in Europe and Mexico. The SAFA procedures of goal and scope definition, compliance and relevance checks, data collection, data analysis and reporting were all able to be applied to all enterprises and farms. An individual choice of suitable indicators for assessing the SAFA goals was necessary for each enterprise. The duration of the assessment increased with the size and complexity of the enterprise: from 4 hours for a family sized farm to 20 working days for an enterprise with more than 100 employees and a wide portfolio of products. The SAFA Guidelines provide an applicable but also resource-demanding framework for sustainability assessment. To decrease the diversity of statements about sustainability, we recommend a widespread uptake of the SAFA Guidelines. Our approach for operationalization of the SAFA Guidelines provides support for enterprises in applying the SAFA Guidelines in their specific context in a sound and efficient way

Accounting for uncertainty in multi-criteria sustainability assessments at the farm level: Improving the robustness of the SMART-Farm Tool

Many farm sustainability assessments use multi-criteria methods for aggregating indicators based on performance scores and importance weights. One of these is the SMART-Farm Tool, which measures the degree of goal achievement of farms across 327 indicators nested within 4 sustainability dimensions, 21 themes and 58 sub-themes of the FAO-SAFA Guidelines (Sustainability Assessment of Food and Agriculture). This study aims to improve the empirical foundation of the SMART-Farm Tool by (i) evaluating the uncertainty of indicator weights obtained via expert opinion and (ii) integrating this uncertainty into SMART assessment results. An adapted Delphi process was implemented, involving a group of 67 experts from 21 countries divided into thematic and regional sub-groups. Experts within each sub-group rated the importance of indicators of all 58 SAFA sub-themes, and self-assessed their own competence. Contrary to expectations, the uncertainty in expert opinions was relatively high for environmental indicator weights (although lowest for animal welfare), being comparable to social and governance indicators. Considerable uncertainty remained in indicator weights, even after two rounds of discussion and exchange of views. This is attributed to regional variation and inherent system complexity (i.e. experts having legitimate but diverging viewpoints based on contradictory evidence) rather than scientific ignorance (i.e. a lack of research evidence). Nevertheless, it is expected that the levels of uncertainty could be reduced by limiting the number of indicators to be evaluated and thus by allowing for more in-depth discussions among the experts. Monte Carlo Simulations were used to translate residual indicator weight uncertainty into SMART assessment results at the farm level for four example farms from both developed and developing countries. The resulting comparisons revealed several cases where substantial apparent differences between farms in sustainability scores for a specific sub-theme (up to 23%) were not statistically significant, while in other cases differences of 5% were significant. This emphasizes the general importance of considering uncertainty in multi-criteria assessment tools, with clear implications real-world applications, such as product certification, labelling and marketing. Finally, this study provides important methodological suggestions for implementing expert-based assessments in multi-criteria assessments efficiently

Documentation for LinTim 2023.12

LinTim is a scientific algorithm and dataset library that has been under development since 2007 and offers the possibility to carry out the various planning steps in public transportation. Although the name originally derives from "Line planning and Timetabling", the available functions have grown far beyond this scope. This is the documentation for version 2023.12. For more information, see https://www.lintim.net