The effect of climatic factors on nutrients in foods: evidence from a systematic map
Bibliographic record
Abstract
Abstract Climate change is projected to negatively affect human health and nutrition. There is a growing literature on the effects of climatic changes on food availability, quantity, and agricultural production, but impacts on the nutritional content of foods has not been widely studied. The aim of this paper is to systematically characterise empirical literature exploring the effects of climatic drivers on macronutrients and micronutrients in foods causing malnutrition globally. 69 peer-reviewed empirical articles (excluding experimental and modelling studies) analysing the effect of climatic drivers on nutrients in foods were retrieved from Web of Science TM , Scopus ® and PubMed ® databases (2013–2019). Publication frequency and trends, and existing evidence of the extent of nutrient change associated with variation in climate-related conditions were assessed. There is relatively limited literature on associations between climate and nutrients in foods. Where it exists, only crude proxies of climate (e.g. wet/dry season) are used, with limited interrogation of the potential causal mechanisms linking climate to nutrient content. 98% of the articles showed a change in nutrient content in relation to a seasonal or meteorological variable. Most analysed the association of nutrient changes between seasons over 1–2 years, rarely over longer periods of time. Preliminary descriptive estimates point to variation in nutrient content by meteorological variability, particularly in ocean and freshwater food sources. Robust assessment of potential climate impacts on nutrient content of foods would benefit from more precise estimation of specific causal pathways and variables that mediate climate impacts on food, going beyond seasonal or crude proxies. There is need for clear articulation of how climate change might impact nutrient content given mechanisms linking meteorological and seasonal variation with nutrients. This research highlights emerging evidence that climate change may have impacts beyond agricultural productivity by affecting food nutrient content, an understudied but potentially important pathway for climate impact on global food and nutrition security.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".