Study on Optimizing Density Planting and Fertilization Strategies to Increase Bean Yield
Bibliographic record
Abstract
Legumes play an important role in ensuring food security and promoting sustainable agricultural development. As key agronomic measures to increase legume yields, dense planting and fertilization management have attracted increasing attention for their optimal combination. Based on field trials of various legumes and recent research results, this study systematically explored the effects of different dense planting levels on plant morphology, population structure and yield composition, analyzed the regulatory mechanisms of nitrogen, phosphorus and potassium ratios, organic and inorganic fertilizer synergy and topdressing timing on nutrient absorption and nitrogen fixation efficiency, and further discussed the effects of the interactive effects of dense planting and fertilization on biomass accumulation and resource allocation. Through regional trials in Zhumadian, Henan and Qiqihar, Heilongjiang, this study clarified the yield potential and economic benefits of the "medium-high density + nitrogen reduction and potassium increase" and "medium density + controlled-release fertilizer" models, providing a theoretical basis and practical path for achieving regionalized precision management and green and high-yield legume cultivation, which will help improve China's legume self-sufficiency level and promote the green transformation of agriculture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".