Sustainable Diets, Population Growth & Regional Food Production: A Case Study of Waterloo Region, Ontario
Bibliographic record
Abstract
The industrialized food system poses significant human health challenges, while simultaneously \ncompromising planetary boundaries that we depend on for human flourishing. In 2019, the Canada Food \nGuide was updated to represent a more nutritious and environmentally sustainable diet, consistent with the \n2019 EAT-Lancet Report’s Planetary Health Diet recommendations surrounding the human and planetary \nhealth nexus. Both recommendations notably put less emphasis on meats and dairy, and more emphasis on \nplant-based protein and fresh vegetables and fruits. One way to encourage the transition to more nutritious \nfood consumption is to develop and enhance the regional food environment. The food environment \ndetermines in part what the population eats, and in turn, drives demand. ‘Food environments’ are created \nby social environments and are the physical, social, economic, cultural, and political factors that impact the \naccessibility, availability, and adequacy of food within a community or region (Rideout et al., 2015). They \nare often responsible for affecting how consumers make food decisions. COVID-19 exposed vulnerabilities \nin our industrialized just-in-time system, including challenges in food security and optimal nutrition as \nimport-dependent foods faced risks in supply due to labour and supply chain disruptions. Increased political \nattention on local and regional self-sufficiency at regional and national scales may offer a solution to \nenhance resilience within socio-ecological systems. An optimum nutritional environment (ONE) \nassessment bridges nutritional needs with environmental sustainability through regional planning. For this \nthesis, a case study foodshed analysis of Waterloo Region (WR), Ontario, was conducted in order to \nunderstand the potential for regional sufficiency in nutrient-dense food (according to the 2019 Canadian \nFood Guide guidelines). The nutritional requirements were then compared to the local production capacity \nfor the population in 2020 and the projected population in 2040 and 2060. The research objectives were (1) \nto estimate the quantity of locally grown vegetables, fruits, legumes, and whole grains needed to meet the \nRegion of Waterloo population’s optimal nutritional requirements in 2020, 2040, and 2060; (2) to estimate \nhow much of these healthy food requirements for the WR population could realistically be produced \nthrough regional agriculture by the year 2040 and 2060. \nThis study used Canadian databases to quantify and predict the opportunities and potential for WR \nto meet its growing population's nutritional needs within regional boundaries. The results show that \nconsumption and production levels in fruits, vegetables, whole grains, and plant-based protein are \ninsufficient in 2020, 2040 and 2060. There were changes in comparison to the 2006 and 2019 Canada Food \nGuide’s recommendations, specifically a reduction in starchy vegetables, wheat and oats, and an increase \nof tree nuts and meat alternatives. Agricultural land requirements that align with nutritional \nrecommendations could be met with a 4% conversion of current agricultural land in use in 2040 and 6% in \n2060. One possibility to meet these recommendations is converting land that is currently dedicated to soy \nand corn production. One limitation of the study is the exclusion of livestock and dairy, which contributes \nto a large proportion of land use. This study contributes to current foodshed analysis research, providing a \nreplicable case study methodology for other regions to identify the current status of local food provisioning \nand its relationship to nutritional needs, as well as to predict and plan for future scenarios with an enhanced \nfood environment. This research suggests that collaborative and simultaneous effort from various \nstakeholders is needed to support the transition to sustainable diets in Waterloo Region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".