A Framework for Assessing Effects of the Food System (2015) / Chapter Skim
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7 A Framework for Assessing the Food System and Its Effects
7 A Framework for Assessing the Food System and Its Effects
Pages 243-282
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From page 243... ...
A robust framework for assessing the health, environmental, social, and economic effects of the food system should recognize the system's complexity while offering a tractable way forward. This chapter proposes such a framework, including key principles, important food system traits, and specific steps for developing an assessment.
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From page 244... ...
In Figure 7-1, the two upper quadrants illustrate principles associated with the desirable scope of an assessment: • Recognize Effects Across the Full Food System to highlight the connections among different food supply chain sectors and the important role of biophysical, social, economic, and institutional contexts. • Consider All Domains and Dimensions of Effects to ensure that the assessment captures the potential trade-offs across health, envi
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From page 245... ...
• Choose Appropriate Methods for Analysis and Synthesis, includ ing data, metrics, and analytical methods suited to systems analysis, while making explicit any assumptions needed for simplification. 1 Configurations are elements within the food system, such as policy interventions, technolo gies, market conditions, or organizational structure of different segments of the food system, that can be modified to achieve a particular goal or to explore how potential drivers (e.g., growth in demand for foods with particular traits)
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From page 246... ...
Principle 1: Recognize Effects Across the Full Food System The first key principle recognizes the food system as a supply chain that is managed by diverse actors with competing interests and goals. Positive and negative health, environmental, social, and economic effects occur all along the food supply chain, from the farm production and input supply sectors through the first line handlers; processing, manufacturing, wholesale, and logistics sectors; retail food and food service sectors; and finally consumption and waste disposal.
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From page 247... ...
Monitoring quantity characteristics of the food system also can capture depletion, degradation, or protection of natural resources upon which food production depends (e.g., soil) , as well as amounts of pollutants delivered from agricultural systems to the environment (e.g., nutrients, pesticides, greenhouse gases)
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From page 248... ...
Assessors who reject these judgments may reject the entire analysis. A useful evaluation framework provides factual and objective information that can be used by people with different judgments about the relative importance of these dimensions to develop a well-informed ranking of alternatives consistent with their own normative preferences (Nyborg, 2012)
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From page 249... ...
Although scope limitations will preclude any specific study from careful consideration of all effects and drivers, it is important for any study to acknowledge the potential role of relevant aspects not included. Principle 4: Choose Appropriate Methods for Analysis and Synthesis Assessments are ultimately no better than the data and methods they employ.
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From page 250... ...
This is particularly important when assessments are made in new areas where data or previous research results are lacking. ASSESSMENT STEPS With the four key principles in Figure 7-1 guiding the thinking behind an assessment, six specific steps emerge from the broader literature on assessment frameworks.
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At the same time, stakeholder engagement requires careful attention to representation of a broad diversity of stakeholder perspectives, and scientific assessments also may require a certain distance or buffer from the influence of powerful stakeholders in order to avoid conflicts of interest and create space for objective and independent decisions -- whether related to scoping, scenario development, or analysis activities. Additional comments pertaining to considerations for managing stakeholder participation are presented after the assessment steps.
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Inside those boundaries, the assessment seeks to describe the interactions and relationships among key actors along the relevant parts of the food supply chain as well as to show the impacts of changes on a range of health, environmental, social, and economic effects.
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The time horizon should match the research goals and system boundaries because, in effect, the time period is an additional boundary. Some studies may be narrow in scope, focusing on one or a few stages in the food supply chain or one domain of effects (e.g., health outcomes)
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Most assessments compare system performance to a baseline scenario and sometimes to one or more alternative scenarios. Alternative scenarios typically specify potential changes in a system to reflect an intervention, such as a new policy or a new technology.
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The relevant analytical methods divide importantly between two broad types of assessment scenarios: (1) a specific current food system configuration (e.g., a policy or a practice)
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indicator data that serve as indirect measures, and (3) simulation models that provide artificial data ("pseudo-data")
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All three kinds of metrics (directly measured data, indicator data, and pseudo-data coming out of simulation models) experience measurement error.
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Consider a multiple regression analysis that includes many factors that potentially affect lake water quality, including practice X A significance level set at 5 percent probability of Type I error would require strong evidence that the conservation practice was effective.
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Simulation models are best used with virtual versions of an experimental research design, like those used for laboratory experiments in the real world. The experimental treatments may take the form of scenarios,
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More sophisticated experiments compare probability distributions of simulated outcomes from different scenarios, which exemplify the distribution and resiliency dimensions of assessment. Simulation models can be particularly useful for assessing multiple outcome effects from scenarios describing possible conditions that cannot currently be observed (e.g., changed climate)
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. Dynamic programming models optimize over a fixed time horizon, although they can be adapted to a moving time horizon (Chen et al., 2014)
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. Similar biofuel policy analysis at the regional scale has linked an economic optimization model to the EPIC biophysical model to simulate water quality, soil quality, and climate effects from profit-maximizing farmers in the face of rising prices for energy biomass with other prices assumed to remain constant (e.g., Egbendewe-Mondzozo et al., 2011)
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. Once well-validated simulation models have been developed, they can be used to generate a large number of experimental replications with input data representing the full range of potential real conditions (Law and Kelton, 1991)
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Key criteria for determining whether a model is suitable are: it has passed scientific peer review, it has been well validated through testing in multiple settings, and it is well suited to the time horizon, spatial extent, and key component interactions of interest. Preexisting simulation models are best used in collaboration with knowledgeable modelers, because the models often need some adaptive programming to address new research questions.
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Especially when alternative scenarios are evaluated, assessors are often called on to identify which is "best" by one or more criteria. Yet when outcomes have multiple attributes and involve trade-offs, a definitive answer may not be possible.
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Different readers may be interested in different attributes; so, in principle, analysts need to include all of the attributes that any reader would judge relevant, potentially producing a table or radar diagram that provides so much information it is unwieldy
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The attribute levels can be combined into an index in many ways. One theoretical approach is to construct a social utility function that includes weights of each of the effects (or attributes)
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On the other hand, individuals who disagree with the weighting of attributes in an index may find the index invalid. An advantage of reporting individual attributes separately (as in the radar diagrams)
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. Assessment teams should be explicit about potential effects of narrowing the range of assessors' domains of expertise, which can include biases from their own professions or scientific disciplines.
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. Further guidance on the best practices to engage stakeholders can be found in several documents, including the Stakeholder Participation Working Group of the 2010 HIA (Health Impact Assessment)
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An illustrative, brief example on antibiotic resistance (see Box 7-7) is provided to demonstrate how the various steps of the framework might be applied.
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. The problem of antibiotic resistance provides an excellent example to moti vate and illustrate the framework presented in this report.
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For an assessment of AR, consideration of the entire supply chain is likely to be important -- including chemical manufacture of antibiotics as inputs, use for treatment or growth promotion (e.g., in animal husbandry, aquaculture, or fruit production) , use for medical purposes by food workers, and the potential exposure of consumers to resistant bacteria through food or environment (Marshall and Levy, 2011; Smith et al., 2005; Teuber, 2001; Wellington et al., 2013; Woolhouse and Ward, 2013)
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However, features of the topic (here, antibiotic resistance) will likely also provide important guidance.
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For example, attempts to estimate the relative amount of antibiotics used in human medicine and in the food system reach conclusions ranging from roughly comparable amounts in both contexts to much higher levels of use in the food system than in medicine (Phillips et al., 2004; Smith et al., 2005)
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In other instances, a systematic review of the literature for the relevant questions might be warranted rather than a full systemic analysis. The goal of the framework is to guide the evaluations and the decisionmaking processes in the area of food and agriculture.
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2010. Call of the wild: Antibiotic resistance genes in natural environments.
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2013. Antibiotic resistance threats in the United States.
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1991. Simulation modeling and analysis.
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2011. Best practices for stakeholder participation in health impact assessment.
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2013. The role of the natural environment in the emergence of antibiotic resistance in gram-negative bacteria.
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