Integrated farming with intercropping increases food production while reducing environmental footprint
Bibliographic record
Abstract
Food security has been a significant issue for the livelihood of smallholder family farms in highly populated regions and countries. Industrialized farming in more developed countries has increased global food supply to meet the demand, but the excessive use of synthetic fertilizers and pesticides has negative environmental impacts. Finding sustainable ways to grow more food with a smaller environmental footprint is critical. We developed an integrated cropping system that incorporates four key components: 1) intensified cropping through relay planting or intercropping, 2) within-field strip rotation, 3) soil mulching with available means, such as crop straw, and 4) no-till or reduced tillage. Sixteen field experiments, conducted with a wide range of crop inputs over 12 consecutive years (2006 to 2017), showed that the integrated system with intercropping generates significant synergies-increasing annual crop yields by 15.6 to 49.9% and farm net returns by 39.2% and decreasing the environmental footprint by 17.3%-when compared with traditional monoculture cropping. We conclude that smallholder farmers can achieve the dual goals of growing more food and lowering the environmental footprint by adopting integrated farming systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".