The impact of sustainable organic agriculture on food security in sub-Saharan Africa: a mixed methods systematic review with meta-analysis
Bibliographic record
Abstract
This study aimed to undertake a comprehensive mixed methods systematic review to examine the effectiveness of sustainable organic agriculture (SOA) in improving food security in sub-Saharan Africa. Medline (Ovid), Embase, Web of Science, ProQuest, CINAHL, The Cochrane Library, and SCOPUS/ Elsevier were searched from January 2000 to July 2024. The impact of three types of SOA was evaluated: sustainable intensification practices (SIPs), climate-smart agriculture (CSA), and nutrition-sensitive agriculture (NSA). Weighted pooled mean differences (PMD) and 95% CIs were used to assess between-group differences using random effect models (I2 > 50%). Seventy-eight studies were eligible for inclusion. Compared to non-adopters of SOA, adopters experienced a 16.33% lower prevalence of food insecurity (95%CI: − 21.62, − 11.03, p < 0.001) and a 5.24% lower prevalence of coping strategies (95%CI − 9.06, − 1.41; p < 0.01). They recorded an additional 1019 kg/ha in cereal yield equivalent (95%CI: 397.05, 1641.39, p < 0.01), USD 155.60 in per capita farm food yield revenue (95%CI 89.40, 221.81; p < 0.001), and USD 57 in per capita income (95%CI 17, 97; p < 0.01). They also reported USD 14 higher per capita food consumption expenditure per year (95%CI 6, 21; p < 0.001), a 10.4% increase in food and nutrient intake (95%CI 6.5, 14.3, p < 0.001), 687.4 kcal higher calorie intake (95%CI 302.0, 1072.9; p < 0.001), a 0.90-point increase in dietary diversity score (95%CI 0.62, 1.19, p < 0.001), a 0.14 standard deviation improvement in children’s height-for-age z-scores (95%CI 0.053, 0.225, p < 0.001), and a 0.28-point increase in women’s BMI (95%CI 0.272, 0.292; p < 0.001). Overall, results were significantly consistent across all sub-Saharan regions as well as SIPs and CSA. SOA practices significantly improve food security among smallholder farming households.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".