A global bibliographic review of soil variability trends on arable land: An impetus to sustainable land management
Bibliographic record
Abstract
Crop production is significantly affected by soil properties under the influence of climate, management practices, and geographical location. Soil variability affects the development, quality, biochemical reactions, and heterogeneity of soil. The most recent research has focused on soil variability monitoring, highlighting the importance of soil testing. This review aimed at identifying global research trends and assessing soil testing in monitoring variability on arable land, based on the bibliographic method. Literature search in Scopus Database (2020-2023) yielded 8,898 documents, refined to 815 articles. VOSviewer 1.6.20 Software was used for analysing exported data. The results revealed a growing emphasis on monitoring soil variability, with key countries including India, United States of America (USA), China, Australia, Canada, United Kingdom, and Brazil. Funding mainly came from Asia, North America, and Europe. Common monitoring approaches included soil tests and remote sensing, focusing on organic carbon, nitrogen, phosphorus, potassium, microorganisms, and soil moisture. However, digital illiteracy and high costs were major hindrances to using remote sensing and modern soil testing tools. The study suggests that whereas soil variability monitoring is essential for sustainable land management, development of affordable soil testing equipment and improved digital education are needed for its enhanced adoption.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.039 | 0.065 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".