Balancing Yield and Environmental Impact: Nitrogen Management and Planting Density for Rice in Southwest China
Bibliographic record
Abstract
With growing concerns about global warming, it is crucial to adopt agronomic practices that enhance rice yields from paddy fields while reducing greenhouse gas (GHG) emissions for sustainable agriculture. An optimal nitrogen (N) fertilization rate and planting density are vital to ensure high rice yields, minimize GHG emissions, and understand emission behavior for better field management. We hypothesized that optimizing N application rates and planting density to improve nitrogen use efficiency (NUE) in rice cultivation would reduce resource losses and GHG emissions. To test this hypothesis, we implemented five treatments with a rice straw return cultural system: two planting densities (16 hills m−2 (traditional density, D1) and 20 hills m−2 (25% higher density, D2)) and three N application rates (no N fertilizer (N0), 180 kg N ha−1 (N1), and 144 kg N ha−1 (N2)). The control treatment (CK) was traditional planting density with no N fertilizer. The four new cropping modes were N1D1, N1D2, N2D1, and N2D2. We investigated the effects of N application rates and planting density on rice grain yield, NUE, and GHG emissions in multiple rice-growing seasons. The N1D2 treatment exhibited the highest grain yield over the three years, with a value of 10,452 kg ha−1, representing an increase of 12.2% compared to CK. Moreover, N uptake in N1D2 was the highest, averaging 39.2% (p < 0.05) higher than CK, and 8.5%, 3.5%, and 2.8% (p < 0.05) higher than N1D1, N2D1 and N2D2, respectively. N2D2 exhibited the highest NUE, with a value of 58.99 kg kg−1, surpassing all other treatments over the three years. GHG emissions, global warming potential (GWP), and greenhouse gas intensity (GHGI) in N2D2 were lower than in N1D1, N1D2, and N2D1. Additionally, reducing N application (comparing N1D1 to N2D1) and increasing plant density (comparing N1D1 to N1D2) improved N agronomic efficiency (NAE) and N partial productivity (PFPN). The negative correlation between the NAE and PFPN with GWP and GHG emissions further supports the potential for optimized N management and denser planting density to reduce environmental impact. These findings have important implications for sustainable rice cultivation practices in Southwest China and similar agroecosystems, emphasizing the need for integrated nutrient management strategies to achieve food security and climate change mitigation goals.
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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 | 0.000 | 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 | 0.000 |
| 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".