A mathematical model was developed to analyze changes in the profitability of milk production, considering productivity of milk production, farm size, land availability and environmental footprint, national labor regulation, and the national subsidy policy in support of farmers.  The model was customized and parametrized to analyze different country contexts and determine the extent to which changes in trade policy towards sustainability would impact milk profitability. This analysis can be used as the starting point for the identification of complementary intervention options at the country level that simultaneously allow for the adoption of better labor standards, while reducing environmental impact, allowing to increase profitability at the same time.

Researchers, policy analysts and decision makers interested in quantifying, analyzing and interpreting the impacts of action and inaction across indicators that represent (i) various actors, (ii) dimensions of development, (iii) economic sectors, (iv) changes over time and (v) in space (i.e. using maps).

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A causal loop diagram (CLD) is a map of the system analysed, or, better, a way to explore and represent the interconnections between the key indicators in the analysed sector or system (Probst & Bassi, 2014). As indicated by John Sterman, “A causal diagram consists of variables connected by arrows denoting the causal influences among the variables. The important feedback loops are also identified in the diagram. Variables are related by causal links, shown by arrows. Link polarities describe the structure of the system. They do not describe the behavior of the variables. That is, they describe what would happen if there were a change. They do not describe what actually happens. Rather, it tells you what would happen if the variable were to change.” (Sterman, 2000)

As indicated by Sterman, CLDs include variables and arrows (called causal links), with the latter linking the variables together with a sign (either + or −) on each link, indicating a positive or negative causal relation (see Table 1). A causal link from variable A to variable B is positive if a change in A produces a change in B in the same direction. A causal link from variable A to variable B is negative if a change in A produces a change in B in the opposite direction. Circular causal relations between variables form causal, or feedback, loops. There are two types of feedback loops: reinforcing and balancing. The former can be found when an intervention in the system triggers other changes that amplify the effect of that intervention, thus reinforcing it (Forrester, 2002). The latter, balancing loops, tend towards a goal or equilibrium, balancing the forces in the system (Forrester, 2002).

By highlighting the drivers and impacts of the issue to be addressed and by mapping the causal relationships between the key indicators, CLDs support the identification of policy outcomes using a systemic approach (Probst & Bassi, 2014). CLDs can be used to create storylines corresponding to the implementation of policy interventions, by highlighting direct, indirect and induced policy outcomes across social, economic and environmental indicators. CLDs can also be used, when co-created with a multi-stakeholder approach, to generate a shared understanding on the main drivers of change of the system, stimulating the harmonization of intervention options across stakeholders.

Any party interested in better understanding the complexity of the socioeconomic and environmental systems relates to food systems, both in relation to local, national and trade dynamics. 

We have developed systems maps (also called Causal Loop Diagrams, CLDs) with various organizations, ranging from the UN system (e.g. for the TEEB AgriFood project) to local stakeholder groups. These maps can be used to anticipate the emergence of upcoming issues, and to design strategies to tackle existing challenges more effectively.

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