Bibliographic record
Abstract
This study examines the role of Integrated Agronomic Management (IAM) in optimizing soybean ( Glycine max L.) yield and sustainability through a combination of strategic agricultural practices. Recognizing the dual importance of soybean as a major protein and oil source and as a soil-enhancing crop, IAM integrates chemical fertilizers, organic manures, microbial inoculants, efficient irrigation, and advanced planting techniques. Findings from multiple studies reveal that IAM approaches improve nutrient management, water-use efficiency, weed and pest control, and climate resilience in soybean cultivation. Key practices, such as combining organic amendments with inorganic fertilizers, adopting optimal row spacing and seeding rates, and utilizing targeted irrigation techniques, are shown to enhance soybean productivity while minimizing environmental impacts. Through case studies, this research highlights the economic and ecological benefits of IAM, including yield increases, improved soil health, and reduced greenhouse gas emissions, underscoring the potential of IAM to address global food security challenges sustainably. Future research should continue exploring IAM strategies that adapt to climate variability and optimize genetic selection for yield improvements in diverse ecological contexts.
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".