Community-Supported Agriculture: Systems Thinking in Action

Over the past 15 years or so, the disruption of societies and economies owing to natural disasters has increasingly raised global awareness. As supply chain risks have made the economic impact of ecological changes more visible, the corporate world has started paying more attention to these changes and has been looking for ways to protect its market base with environmentally friendly strategies. Food production and agriculture are on the front line in these sectors because many global uncertainties affect food supply chains and food consumption trends.

These uncertainties pose risks to agricultural productivity, which Sciabarrasi¹ lists under five main categories: production, marketing, financial, legal, and human resource risks. These concerns are on the production side so they are from the producers’ perspective. The production risks are attributable to several parameters, including uncontrollable adverse weather (e.g., drought, freeze, heavy rains), plant diseases and insects, and irrigation machinery problems. Marketing risks are related to dynamic relationships between wholesalers, buyers, and policymakers; these relations affect pricing, packaging, and distribution network design decisions. These risks interact with financial risks (i.e., because of cash flow problems and not being able to control costs to maintain sustained profit). In addition, a lack of financial literacy among farmers can result in inefficient investments that increase financial risks. There can also be legal and environmental risks emerging because of business contracts and agreements, tort liability, and environmental regulations.¹ Finally, risks to human resource management can be attributed to poor communication with farm workers and, in most cases, relations with family members.

SYSTEMS THINKING

There is also a growing concern among consumers. These concerns form risks on the demand side of the equation and can be categorized as financial risks (e.g., food affordability and access) and health concerns owing to increased consumer demands for healthier products. Community-supported agriculture (CSA) can be seen as a social response to these risks that is formed by participants on both the supply and demand sides. CSA systems evolved in the United States and have spread globally; however, their impact as an agro-economic system is not well studied. I propose a holistic thinking approach for looking at this community-designed and -driven system. In addition to considering CSA’s financial and cost-effectiveness, a systems thinking approach should be used to investigate the nonlinear dynamics among actors, with feedback loops and estimated delay on cause and effect relations.

Although the study by Basu et al. (p. 119) evaluates the health impact of CSA on low-income US adults, I would like to note the need for systems thinking and dynamics modeling as an important scientific methodology to assess the broader impact of agro-economic models. Basu et al. complement the literature by adding a perspective on how to evaluate the population health impact of these systems.

COST-EFFECTIVENESS OF CSA

Basu et al. present population-level effectiveness and the cost-effectiveness of a set of subsidized CSA interventions. They took a systems approach and developed an individual-based model and used a microsimulation technique for evaluating the impact of the interventions they considered. This method can be seen as a bottom-up approach² in systems science, in which individual-level attributes are characterized in a virtual population. The behaviors of individuals are simulated, and then population-level insights are provided for policy decisions or scenario analyses for intervention design. Over the past decade, this specific technique has become very popular in public health research, as more computationally powerful computers have become available. In addition, with this individual-based model approach, it has become more convenient to incorporate individual-level features into the modeling process.

Basu et al. present an individual-based model with a set of features for individuals in a specific population that includes demographics (i.e., age, sex, and race) and key health attributes, including a nutritional and biomedical profile using National Health and Nutrition Examination Survey data with a randomized trial data set. The model outcomes include risk of atherosclerotic cardiovascular disease events (e.g., myocardial infarction or stroke) and type 2 diabetes and related microvascular complications, including nephropathy, neuropathy and retinopathy, and mortality.

For an economic decision analysis that would provide support for policy decisions, Basu et al. compensated participants with $300 cash or a $300 subsidy to assess the effectiveness and cost-effectiveness of the CSA-based interventions. Empirical support is provided for the impact of CSA intervention via the randomized trial they used. The net effect of the cash intervention was to increase diet quality over baseline but to a lower extent than the CSA intervention. A comprehensive analysis of the health effects of the considered interventions can be found in the article with the cost-effectiveness evaluation, which was performed from a societal perspective. Basu et al. computed the disability-adjusted life years lost attributable to diseases with years of life lived with disease, and they presented final incremental cost-effectiveness ratio computations for comparative evaluations. Their results suggest that both cash transfer– and CSA-based interventions are cost-effective for improving diet in the lower income US population and that these programs have a broader health impact in addition to productivity gains in effective agriculture production.

As Basu et al. urge differentiating a health care system perspective from a societal perspective in the evaluation of these programs, it is important to recognize innovative financing strategies for agro-economic systems that are sustainable for the whole of society. I would like to propose a concept for a systems thinking approach, which I summarize with a concept map in Figure 1. In the concept map, the links do not indicate any linear and direct feedback in the system; rather, they represent the relationship between CSAs and the attributes of a sustainable community.³ These attributes include meeting social needs and enhancing and protecting the environment with efforts to promote economic success, and they are related to outcomes that are observed in society with financially well-designed and -executed CSAs. Although these links need thorough scientific investigation, the study of Basu et al. is an important contribution for assessing the societal value of CSA with health outcomes considered, and it puts systems thinking and modeling into action.

FIGURE 1—

Concept Map for the Relationship Between Sustainable Community Attributes and Community-Supported Agriculture