Examining the relationship between channel disintermediation and chemical fertilizer application: Empirical evidence from smallholder farmers in central China
Bibliographic record
Abstract
Abstract Digital advancements have reshaped the sales channels for industrial products like chemical fertilizers, allowing for the elimination of certain intermediaries. Utilizing survey data from 1374 rice‐growing smallholder farmers across counties in central China, this study explored the impact of disintermediation in chemical fertilizer sales channels on application rates among smallholder farmers. Benchmark regression analyses indicated that channel disintermediation significantly decreased chemical fertilizer application across various scenarios. Additionally, the alignment of contractual factors and technical services between chemical suppliers and farmers, facilitated by disintermediation, reduced chemical fertilizer application. Heterogeneity analysis revealed that the reduction in chemical fertilizer application due to channel disintermediation was more pronounced among farmers with higher digital literacy or lower production capacity. These findings highlighted the importance of encouraging chemical suppliers with direct‐to‐consumer channels to enhance their technical services and increase supervision over the quality of their products and services, thereby building trust among farmers and facilitating their adaptation to changes in the sales channel structure.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".