Suitability of Mulch and Ridge-furrow Techniques for Maize across the Precipitation Gradient on the Chinese Loess Plateau
Bibliographic record
Abstract
Mulch and ridge-furrow are effective techniques to improve water harvest, reduce evaporation and increase crop productivity in dry rainfed areas. We collected grain yield data of maize (Zea mays L.) across the precipitation gradient on the Loess Plateau under three treatments: (1) CK, flat plot without mulch; (2) HM, half-mulch flat plot, i.e. alternating mulched row and bare row without ridge-furrow; and (3) DRM, double ridges and the furrow fully mulched with plastic film. Maize grain yields were highest in the DRM treatment and lowest in the CK treatment. Mulch or ridge-furrow with mulch have increased maize grain yield significantly. The highest increase was found in low growing season precipitation regimes. Grain yields of the three treatments trended to converge in high growing season precipitation regimes. Regressions between grain yields and growing season precipitation for the three treatments showed that maize yields increased linearly with precipitation for CK; statistically significant quadratic models were found for HM and DRM treatments. The economic net incomes were calculated based on yields and inputs of capital and labor for the three treatments. Considering both water resource and economic outcome, we recommend that a precipitation range of 196-532 mm is most suitable for mulch and ridge-furrow techniques for maize on the Loess Plateau. Spatially, CK and HM treatment were most suitable for small parts of the southeast part of the plateau and DRM was suitable for most of (87%) the plateau.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".