A Geospatial Approach to Assessing the Impact of Agroecological Knowledge and Practice on Crop Health in a Smallholder Agricultural Context
Bibliographic record
Abstract
In the context of food insecurity in resource-poor settings, agroecology (AE) has emerged as an important approach promoted for improving crop productivity, yet few studies have demonstrated how a combination of agroecological methods can improve crop health and thereby crop productivity. Using a geospatial approach, this study investigated whether agroecological practices can improve crop health in smallholder contexts. We compared leaf area indexes (LAIs) of crops on AE and non-AE farms and prospectively predicted the impact of AE using vegetation indexes (VIs). We found that crops on AE farms produced higher average growing season LAIs for maize and pigeon peas (1.28 m2/m2) and maize and beans (1.29 m2/m2) farms compared to 0.97 m2/m2 and 0.80 m2/m2, respectively, for the same crops on the non-AE farms. The higher LAIs suggest that the combination of farming strategies practiced on the AE farms produced healthier crops on AE farms. Random forest regression prospective predictions generated statistically significant higher LAIs for maize and beans (R2 = 0.90, root mean square error [RMSE] = 0.32 m2/m2) and maize and pigeon peas (R2 = 0.88 m2/m2, RMSE = 0.42 m2/m2) on the AE farms, but predictions for the non-AE farms were not statistically significant. The findings demonstrate that combining AE strategies can potentially improve crop productivity to enhance household food security and income in smallholder contexts.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".