Integrated Soil and Crop Management in Organic Agriculture: A Logical Framework to Ensure Food Quality and Human Health?
Bibliographic record
Abstract
The environmental and biodiversity benefits of organic farming are widely recognized, but there is still controversy about the effects of organic production methods on the nutritional composition of food and human health. In the first part of this article therefore, we critically review the evidence that organic farming methods improve the nutritional quality of food crops. Moreover, we summarize our current understanding of how quality gains are linked to the implementation of the “innovations” introduced into conventional crop production during the intensification or “green revolution” of agriculture over the last 100 years. In the second part of the article, we critically review the evidence for the range of health benefits related to organic food consumption. Specifically, we describe and discuss the results from: (i) dietary intervention studies which have found that organic food consumption substantially reduces pesticide exposure in humans and affects feed intake, growth, hormone balances and immune system responsiveness in animal models; (ii) human cohort/epidemiological studies which have reported significant positive associations between organic food consumption and the lower incidence of a range of diseases including obesity, metabolic syndrome, cancer, hypospadias, pre-eclampsia, eczema and middle ear infections in infants; (iii) interactions and trade-offs between diet (e.g., whole-grain, fruit and vegetables and reduced red-meat consumption) and food types (organic versus conventional) concerning public health and future food security. The article also identifies knowledge gaps and highlights the need for (i) long-term, factorial field experiments to understand the relative effects of agronomic and pedoclimatic drivers on crop quality and safety, and (ii) clinical trials and additional human cohort studies to confirm the positive health outcomes linked to organic food consumption. The main conclusions from our review are that there is growing evidence that (i) agricultural intensification has resulted in a reduction in the nutritional quality of food and the sustainability of food production, and (ii) organic farming practices not only improve food quality and human health, but also food security. This is particularly true where current nutritional guidelines (increasing whole-grain, fruit and vegetable products, while reducing red-meat consumption) are implemented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".