Reducing land fragmentation to curb cropland abandonment: Evidence from rural China
Bibliographic record
Abstract
Abstract Reducing land fragmentation can theoretically curb cropland abandonment, thus ensuring food security. However, few studies have quantified this relationship using large‐scale survey data at the household level. This study adopts a two‐way fixed‐effects (TWFE) model to examine the effect of land fragmentation on cropland abandonment using nationally representative panel data from the China Rural Household Panel Survey (CRHPS). The panel data set contains 15,138 households across 29 provinces in 2017 and 2019. We find that land fragmentation has a significant and positive relationship with cropland abandonment. The mechanism analysis reveals that this relationship is due to high labor costs and difficulties in renting out the fragmented land. The heterogeneity analysis indicates that farmers with poor human capital and those living in non‐plain areas are at a higher risk of abandoning their cropland due to land fragmentation. Furthermore, the association between land fragmentation and cropland abandonment tends to vary across different land rent‐in scenarios. For instance, an increase in the number of plots in the case of land rent‐in is not necessarily associated with cropland abandonment. These findings are conducive to correcting the underestimation of the role of land fragmentation in cropland abandonment, and their implications may be extended to various countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| 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".