Side-deep banding liquid fertilizer during transplanting for indica rice crops promoted root system development to enhance grain yield and improve nitrogen use efficiency
Bibliographic record
Abstract
Mechanical side-deep fertilization (MSDF) method during transplanting and the utilization of liquid nitrogen fertilizer both offer significant benefits to rice production. However, research integrating these two approaches remains unexplored. This study aims to evaluate the method of side-deep banding application of liquid fertilizer (D-LF), compare it to side-deep banding application of solid fertilizer (D-SF) and broadcasting of solid fertilizer (B-SF), all synchronized with mechanical transplanting, and explore the impacts of these methods on rice production. Field experiments using the completely random block design were conducted on four indica rice varieties at two locations in South China (Zhaoqing and Jiangmen) during the two rice seasons. Each experiment included a non-nitrogen fertilizer treatment, namely the control (C), and three nitrogen fertilizer application treatments: D-LF, D-SF, and B-SF. The results indicated that at the midterm tillering stage (MT), the D-LF treatment exhibited the best root characteristics, and the tillering number under D-LF increased by 10.36 %–26.54 %, compared to D-SF and B-SF. At the maturity stage (MS), D-LF resulted in a 7.37 %–24.02 % increase in grain yield, compared to D-SF and B-SF. Significant positive correlations were shown between root characteristics and grain yield by Pearson correlation analysis. Additionally, nitrogen use efficiency (NUE) under D-LF was significantly improved relative to D-SF and B-SF. Notably, compared to existing fertilizer application methods, D-LF enhances rice root traits at the early growth stages, improving early tillering capacity and increasing the number of effective panicles , which ultimately boosts grain yield. Moreover, this method effectively improves the NUE and economic profitability. This study is the first to combine the mechanical side-deep fertilization with liquid fertilizer and compare their impact on rice production with traditional methods, offering valuable insights for optimizing nitrogen fertilizer application strategies in rice cultivation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".