Bibliographic record
Abstract
The enhancement of yam productivity is of great importance for food security, economic stability, and nutritional supply in many regions.This study focuses on integrated agronomic practices to improve yam productivity, systematically analyzing the impact of soil management, crop management, environmental adaptation, technological innovation, and socio-economic factors on yam cultivation.The research provides an overview of the economic and nutritional value of yams and highlights the demand and challenges associated with increasing productivity.In terms of soil management and fertility optimization, the study explores soil quality assessment, the efficient application of organic and inorganic fertilizers, and the role of cover crops and crop rotation in promoting soil health.Crop management includes the selection of high-quality yam varieties, optimal plant density and spacing, and strategies for pest, disease, and weed management.Environmental and climate adaptation management examines climate-adaptive cultivation techniques and soil and water conservation measures.The section on technological innovation covers advances in precision agriculture, genomics, biotechnology, and the use of smart machinery.Regarding socio-economic factors, labor optimization, agricultural extension, market access, and economic incentives are discussed.Through case studies of yield-enhancement practices in different regions, this study analyzes strategies for increasing yield and extracts successful experiences and their potential for broader application.This research provides valuable recommendations for future studies and practical applications in the field, contributing to the global development of the yam industry.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".