Soil fertility and the yield response to the System of Rice Intensification
Bibliographic record
Abstract
Abstract The System of Rice Intensification (SRI) is a low-input rice (Oryza sativa L.) production system that differs from conventional systems in several ways: seedlings are transplanted earlier and are more widely spaced, organic fertilizer is often used in addition to mineral fertilizer, and soils are irrigated intermittently rather than flooded for long periods. The yield benefits of SRI compared to conventional systems can be substantial, and yet are regionally variable and have been the subject of considerable debate, due partly to a lack of mechanistic understanding. Here we show that soil properties may in part explain the variability in yield response to SRI. A meta-analysis of data from 72 field studies where SRI was compared with conventional systems indicates that yields increased significantly (P<0.0001) when SRI was implemented on highly weathered infertile soils rich in iron and aluminum oxides (Acrisols and Ferralsols), but there was no difference in yield between SRI and conventional systems in more fertile favorable soils for rice production (Gleysols, Luvisols and Fluvisols). The yield difference between SRI and conventional rice production therefore appears to be related in part to soil properties linked to weathering. This should help resolve the debate about the value of SRI and allow research to be targeted toward understanding the biological and chemical processes in soils under SRI management.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".