Strategies for enhancing yield and quality of forage-grain dual-purpose ratoon rice: role of first-season density and nitrogen management
Bibliographic record
Abstract
The “forage-grain dual-purpose” model helps ease land-use competition and supports high-yield, high-quality rice production. However, integrated strategies to simultaneously improve silage rice and ratoon rice yield and quality across both seasons require further systematic study. A two-year field study (2022–2023) was conducted using two widely cultivated indica hybrid rice cultivars, F You 498 (FY498) and Chuankangyou Simiao (CKYSM), in Southwest China. Treatments included three planting densities (D1: 16.7 × 10 4 ; D2: 20.8 × 10 4 ; D3: 27.8 × 10 4 hills ha −1 ) and three nitrogen levels (N1: 150; N2: 225; N3: 300 kg ha −1 ) in the first season. Results showed that: Silage yield increased significantly with higher density and nitrogen input. FY498 reached the highest yield under D3N3 (48.46-57.60 t ha −1 ), while CKYSM performed best under D2N3 (45.39-50.93 t ha −1 ). Elevated density and nitrogen levels increased acid detergent fiber and neutral detergent fiber contents and reduced starch, indicating a decline in overall silage quality, though crude protein improved. Within the studied parameters, nitrogen application had a more pronounced influence on silage quality compared to planting density. Relative feed value was highest under D1N1 or D1N2, meeting national Grade II silage standards. In the ratoon season, increased density and nitrogen enhanced aboveground biomass, SPAD values, panicle number, and actual yield. However, higher density reduced leaf area index, and excess nitrogen decreased seed setting rate and 1000-grain weight. Maximum actual yields were observed under D2N3 or D3N2: FY498 (7.71-9.61 t ha −1 ) and CKYSM (6.49-9.00 t ha −1 ). Nitrogen application improved milling quality to some extent, while higher density negatively affected it. Both factors reduced appearance quality and RVA characteristics. Nutritional and safety quality varied significantly across treatments and cultivars. FY498 had high protein and low cadmium content under D1N3; CKYSM showed high starch and low cadmium under D2N3, indicating superior overall performance. In summary, D1N1 produced better silage and rice quality but lower yield. For higher overall productivity and safety, FY498 with D3N3 and CKYSM with D2N3 were optimal, despite moderate declines in quality traits. This new cultivation method may provide a beneficial option to balance silage rice and ratoon rice yield and quality.
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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 | 0.000 |
| 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 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".