Integrated Water and Nitrogen Management Practices to Enhance Yield and Environmental Goals in Rice–Ratoon Rice Systems
Bibliographic record
Abstract
Water and N management play a vital role in rice ( Oryza sativa L.) production; however, limited information is available on the options to increase rice yield in rice–ratoon rice systems, including an appropriate combination of water regime (W), N application rate (N AR ) and N application method (N AM ). To address this question, field experiments were conducted with two Ws [simplified alternate wetting and drying (SAWD) and continuous flooding (CF)], four N ARs (control, N 0 ; 180 kg N ha −1 , N 180 ; 255 kg N ha −1 , N 255 ; and 330 kg N ha −1 , N 330 ) and two N AMs [45% of fertilizer pre‐plant, 15% of fertilizer at tilling, 40% of fertilizer at bud (BTB) and 45% of fertilizer pre‐plant, 15% of fertilizer at boot, 40% of fertilizer during grain filling (BPG)]. On average, the grain yields of the main crop, the ratoon crop, and their total for SAWD were 3.7%, 6.8%, and 4.4% higher than for CF, respectively. The relationships between N AR and the main crop, ratoon crop, and total yields were well fitted by quadratic equations. The rice yields of the main crop, ratoon rice, and their total under BPG were equal to or slightly higher than those under BTB. The interactive effect of W×N AR was significant on the main rice crop yield and total rice yield, but W×N AM , N AR ×N AM and W×N AR ×N AM were all related to the soil‐based yield. The use of integrated water and N management practices could achieve high yields and reduce water and N inputs in rice‐ratoon rice systems. Core Ideas The relationships between N AR and the main crop, ratoon crop, and total yields were well fitted by quadratic equations. The interactive effect of W×N AR was significant on the main rice crop yield and total rice yield, but W×N AM , N AR ×N AM and W×N AR ×N AM were all related to the soil‐based yield. The use of the SAWD‐N 180 –BPG treatment could achieve high yields and reduce water and N inputs in rice‐ratoon rice 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".