Linking Ecosystem Services and the SDGs to Farm-Level Assessment Tools and Models

Abstract

A number of tools and models have been developed to assess farm-level sustainability. However, it is unclear how well they potentially incorporate ecosystem services (ES), or how they may contribute to attaining the United Nations Sustainable Development Goals (SDGs). Understanding how farm-level assessment tools and models converge on these new paradigms of sustainability is important for drawing comparison on sustainability performances of farming systems, conducting meta-analyses and upscaling local responses to global driving forces. In this study, a coverage analysis was performed for several farm-level sustainability assessment (SA) tools (SAFA, RISE, KSNL, DLG) and models (MODAM, MONICA, APSIM), in regard to their potential for incorporating ES and contribution to attaining the SDGs. Lists of agricultural-relevant CICES classes and SDG targets were compiled and matched against the indicators of the tools and models. The results showed that SAFA possessed the most comprehensive coverage of ES and SDGs, followed by RISE and KSNL. In comparison to models, SA tools were observed to have a higher degree of potential for covering ES and SDGs, which was attributed to larger and broader indicators sets. However, this study also suggested that, overall, current tools and models do not sufficiently articulate the concept of ecosystem services.

A process framework for integrating stressor-response functions into cumulative effects models

“…Given the onerous nature of developing CE models that account for multiple stressors, some pragmatic combination of the two approaches will often be used (MacPherson et al 2020).…”

“…For example, in a datalimited scenario, SR functions can be developed in workshops using expert opinion (MacMillan and Marshall, 2006). Here, experts must first determine the direction of the relationship (i.e., a positive vs. negative slope in the stressor-response space, informed by the endpoints of the relationship at the extremes of the stressor gradient), and then determine the functional shape of the relationship(e.g., linear, sigmoidal, asymptotic) by adding intermediate points to the curve (MacPherson et al 2020;French et al 2021). These semi-quantitative models can then serve as both starting points for quantitative modelling and management, and hypotheses that can be tested and refined through ongoing data collection and cycles of adaptive management (Fig.…”

“…Extrapolation of SR functions beyond the scale at which they were created requires transparency in assumptions and careful consideration of the potential for bias (MacPherson et al 2020).…”

“…More advanced non-linear modelling approaches (e.g, Generalized Additive Mixed Models; Hastie and Tibshirani 1986) also generate confidence intervals around model predictions, and methods are available for creating confidence intervals around optima and range limits of non-linear functions (e.g., Ashcroft et al 2016). Transparency is key in dealing with uncertainty (Schuwirth et al, 2019), and confidence in the SR function and underlying assumptions needs to be carefully documented, ideally in an open-access data base MacPherson et al 2020).…”

Sustainability assessment of short food supply chains (SFSC): developing and testing a rapid assessment tool in one African and three European city regions

“…Life cycle assessments (LCA) are especially widely used for assessing food supply chains of all kinds (Hagelaar and van der Vorst 2002 ; Roy et al 2009 ), including SFSC (Kulak et al 2015 ; Brunori et al 2016 ) or urban agriculture (Sanyé-Mengual et al 2015 ). Assessment tools for farms are widespread, but they usually do not assess impacts on soil quality, biodiversity or ecosystem services and neglect social, health or ethical issues of food production (Schader et al 2012 , 2014 ; MacPherson et al 2020 ). Social Life Cycle Assessment (S-LCA) tackling these social issues were evolved later than environmental LCA, but “the level of methodological development, application, and harmonisation of Social LCA is still in a preliminary stage” (Sala et al 2015 , p. 2).…”