Food System Sustainability Metrics: Policies, Quantification, and the Role of Complexity Sciences
Abstract
:1. Introduction
2. Sustainability Definition and Measuring Challenges
2.1. SD and Sustainability Definitions
2.2. Sustainability Frameworks, Indices, Indicators, and Metrics
2.3. Robustness, Resilience, and Sustainability
2.4. Quantifying Sustainability
- Under those ex ante and uncertain conditions, the sustainability criterion can only be met with some probability;
- Depending on whether this probability is associated with a notion of maintaining an aggregate wealth or welfare or with the maintenance of separate critical stocks and services above some minimum levels, we can speak, respectively, of a weak, or strong, notion of sustainability.
3. Sustainable Food System Policy Design and Control
3.1. Toward Sustainable Food Systems
- The properties of diversity and fluxes relate to the diversity and transparency attributes, allowing for timely and reliable access to information;
- The mechanism of building blocks (simple parts, or modules, that can be reused and articulated in different ways) is closely linked to the modularity attribute;
- The mechanism of internal models provides a way for the exploration of alternatives, leading to innovation;
- The previous properties and mechanisms and the additional properties of aggregation (facilitating the generation of meta-agents and model building) and nonlinearity (resulting in more complex interactions), together with the tagging mechanism (underlying the system’s organization), lead to the continuous search for fitness (or congruence) of the system’s complex adaptive process.
3.2. Food System Sustainability Quantification Developments
3.3. Policy Design and Control of Diverse, Complex, Multi-Scale, and Multi-Level SESs
3.4. Prediction, Intangibles, and Uncertainty
- Decision theory (use of “available information to make optimal decisions under uncertainty”);
- Threshold approach (“focusing attention on critical boundaries that have major consequences if crossed” and thus of the utmost relevance to sustainability);
- Scenario planning (based on “sets of plausible stories, supported with data and simulations, about how the future might unfold”);
- Resilience thinking (which “focuses on critical thresholds for system performance, [and] the capacity to adapt [and] transform”).
4. Complexity Sciences’ Contributions to SD
4.1. Articulating the Macro, Meso, and Micro Levels
- The micro concept to characterize individual agent interactions, with specific and fine-grain rules, providing detailed levels of description;
- The macro concept to address the higher-level overall aggregations of groups (of groups) of agents, using whole encompassing and coarse concepts, providing the more general and abstract level of description; and
- The meso concept to describe the intermediate individual and group of agents’ interactions, using linking concepts related to observed patterns, behavioral regularities, and shared rules, providing intermediate levels of description built through new lenses applied to the micro-level interactions and incorporated at the more abstract macro level.
4.2. The role of Multiple and Independent Modeling Frameworks
4.3. The Contribution of Complexity Sciences’ Theories and Tools
5. Case-Study: Sheep Grazing Policymaking and Management of a Rangeland System
5.1. Macro-Level Modeling of System Dynamics
- A strong, nonlinear relation between the sustainability and consumption pressure values (see Figure 9b);
- For the larger values of consumption pressure, the stability point is below the sustainability threshold value, which leads to dramatically low values of sustainability, even for short time horizons. On the other hand, for the smaller values of consumption pressure of sustainability, it keeps close to one even with long time horizons (see Figure 9c,d);
- The initial state of the system (with above the sustainability threshold value ) provides a “sustainability buffer” for the system for some (even if very limited) time (see Figure 9c,d).
5.2. Agent-Based Modeling of System Dynamics
- The rangeland of total area is modeled via atomic area units of area , in a bidimensional grid;
- Each atomic area unit is defined by its coordinates (i, j) in the grid and their crowns’, , and shoots’ biomass value , at time t;
- The rangeland field management is based on a sheep grazing patch rotation policy: (1) the rangeland is divided in equally sized patches, each one including atomic area units; (2) at each time the herd is allowed to graze in a single patch; (3) when the mean value of the shoots’ biomass is below a threshold value, , the herd is moved to the patch with the highest shoots’ biomass mean value;
- The herd is composed of sheep, with individual behavior;
- Each sheep has a nutritional requirement, , that it tries to meet by grazing in the current patch consuming shoot biomass until its nutritional requirement is met or the shoot biomass at its current location (an atomic unit area) is below a threshold value, ; in the latter case, the sheep moves randomly within the limits of the current patch.
6. Concluding Remarks
Author Contributions
Funding
Conflicts of Interest
References
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, grass Rangeland grazed by sheep and harvested for silage. | , crowns and roots. Rangeland crowns’ and roots’ available biomass. Measured in dry matter (DM) kilogram equivalents available per unit area and epoch. , shoots. Rangeland shoots’ available biomass. Measured in dry matter (DM) kilogram equivalents available per unit area and epoch. |
, sheep Herd to be raised. | , number of sheep. Herd, measured by the number of adult sheep in the herd. |
, silage | , silage reserve. Dry matter (DM) kilogram equivalents available from silage. |
, external funds. External funds (e.g., dry pellets) that may need to be acquired for herd nutritional needs. Modeled in the simplified system as an inexhaustible fund, thus having no associated variables. |
, dry matter (DM) equivalents Provision of the required dry matter equivalents in order to meet the herd’s feed requirements. | , grazing. Dry matter equivalents grazed by the herd. Measured in DM kilograms grazed per unit area and epoch. , harvesting. Dry matter equivalents harvested for silage. Measured in DM kilograms harvested per unit area and epoch. , silage consumption. Dry matter equivalents of silage consumed by the herd. Measured in DM kilograms consumed per epoch. , external feeding. Dry matter equivalents of food from external sources (e.g., pellets) consumed by the herd. Measured in DM kilograms consumed per epoch. |
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Matos, J.V.; Lopes, R.J. Food System Sustainability Metrics: Policies, Quantification, and the Role of Complexity Sciences. Sustainability 2021, 13, 12408. https://doi.org/10.3390/su132212408
Matos JV, Lopes RJ. Food System Sustainability Metrics: Policies, Quantification, and the Role of Complexity Sciences. Sustainability. 2021; 13(22):12408. https://doi.org/10.3390/su132212408
Chicago/Turabian StyleMatos, José V., and Rui J. Lopes. 2021. "Food System Sustainability Metrics: Policies, Quantification, and the Role of Complexity Sciences" Sustainability 13, no. 22: 12408. https://doi.org/10.3390/su132212408