Organic agriculture enhances zinc concentrations in edible crop parts: A meta-analysis
Bibliographic record
Abstract
Zinc (Zn) and iron (Fe) are essential micronutrients for humans, and their deficiencies lead to widespread malnutrition and other related health problems. Organic agriculture is often promoted for its potential to enhance soil health and environmental sustainability, but its effects on Zn and Fe concentrations in crops have remained inconsistent. This meta-analysis aimed to compare Zn and Fe concentrations, and also evaluated crop yield, between organic and conventional agriculture systems. It also sought to identify environmental and agronomic factors that influence these outcomes. A total of 322 paired data points from 54 peer-reviewed publications on cereals, legumes, and vegetables were analyzed. The natural logarithm of the response ratio (lnRR) was employed as the effect size for Zn and Fe concentrations in edible crop parts and crop yield. The influences of crop type, soil properties (soil texture, soil organic carbon, soil pH) and climate factors (climate region, annual mean air temperature, annual precipitation) on the effect sizes were assessed using a mixed-effects model. Zinc concentrations in organically grown crops were 14.2 % (95 % CI: 9.7 – 19.0 %, p < 0.001) higher than those under conventional agriculture, with the effectiveness being more evident in vegetables. This increase corresponded to an average of 4.3 mg kg -1 higher Zn concentrations across crop types. Iron concentrations did not show an overall difference between the two systems, only under wet conditions (annual precipitation > 850 mm) where organically grown crops had 14.5 % (95 % CI: 3.57 – 26.66 %, p < 0.001) higher Fe concentration than conventionally grown crops. Despite these effects on micronutrients, organic agriculture was associated with a 24.7 % (95 % CI: −31.2 to −17.6 %, p < 0.001) reduction in crop yield, especially for cereals grown in arid regions. These findings underscore a critical trade-off between nutritional micronutrient concentration and crop productivity. This is the first meta-analysis comparing organic and conventional agriculture regarding their impacts on micronutrient availability in crops. The findings highlight the need for integrated agronomic strategies that optimize nutrient quality while maintaining productivity. Bioavailability was not assessed in the present study but is highlighted as an urgent research priority when examining how organic systems influence micronutrient bioavailability for human consumption. • Organic farming increases Zn in crops by 14.2 %, especially in vegetables. • No overall significant difference in Fe was observed between farming systems. • Organic systems show 24.7 % lower yield, mainly for cereals from arid regions. • Trade-off exists between micronutrient gain and yield loss in organic farming.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.030 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".