Assessing the Convergence of Cropland Ecological Balance: A Panel Data Analysis of 13 Major Agricultural Countries
Bibliographic record
Abstract
This study investigates the convergence hypothesis and stochastic dynamics of agricultural land use and ecological balance across 13 major agricultural countries from 1992 to 2022. The study's concentrated samples are Russia, the United States, the Netherlands, Brazil, Germany, China, France, Spain, Italy, Canada, Belgium, Indonesia, and India. The research uncovers notable variations in ecological balance by utilizing a comprehensive set of advanced panel unit root tests (Panel CIPS, CADF, Panel-LM, Panel-KPSS, and Bahmani-Oskooee et al.’s Panel KPSS Unit Root Test). The findings highlight significant improvements in Canada, contrasting with declines in the Netherlands, France, Germany, and the United States. The results indicate convergence in ecological balance among these countries, suggesting that agricultural practices are progressively aligning with sustainability objectives. The considered countries can determine and enact joint and collective policy actions addressing cropland sustainability. However, the univariate outcome also shows that the cropland ecological balance of Germany, China, France, Indonesia, and India does contain a unit root and stationary which means the presence of the constant-mean. The univariate actions from the mentioned governments will not promote persistent impact. Therefore, joint actions determined by the countries considered are recommended for the mentioned countries. However, the rest of the countries also enact local policies. The insights gained are critical for informing global sustainability strategies and aiding policymakers in developing effective measures to enhance agricultural practices and mitigate environmental impacts. This research provides a data-driven foundation for optimizing agricultural sustainability and supports international efforts to achieve long-term ecological stability.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".