Bibliographic record
Abstract
Soybeans are a critical crop for global food security and agricultural economies, making it essential to identify and optimize agronomic practices that enhance yield and sustainability. This review explores various strategies for improving soybean cultivation through advanced agronomic practices. We examine soil health management, including organic and inorganic fertilization, crop rotation, and sustainable practices from global case studies. Water management, including irrigation techniques and drought resistance, is discussed in the context of optimizing yield potential. The role of advanced crop management, such as planting optimization, weed control, and tillage practices, is evaluated for improving soybean productivity. Genetic improvement through breeding technologies, including marker-assisted selection and CRISPR, is explored to boost yield and disease resistance. Additionally, we assess the importance of sustainable agricultural practices like integrated pest management and precision agriculture in reducing environmental impact. The review concludes with a case study comparing agronomic practices in the United States and Argentina, illustrating the effectiveness of these strategies in boosting soybean yields. This study aims to provide a comprehensive review of current best practices and future directions for soybean cultivation, offering insights for enhancing productivity and sustainability in global 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".