Factors Affecting Chinese Farmers' Decisions to Adopt a Water‐Saving Technology
Bibliographic record
Abstract
Chinese farm households (N = 240) were interviewed to understand some of the factors affecting their adoption of a water‐saving technology called the Ground Cover Rice Production System (GCRPS). A logit model was established on the basis of a survey to estimate the determinants of adoption and to simulate impacts of changes in these determinants on adoption potential. There are no significant influences of age and number of laborers on the probability of GCRPS adoption. Male farm managers had higher adoption probabilities than female farm managers. Large farms had higher adoption probability than small farms. Off‐farm occupation of farm managers had negative influences on adoption. Education had complex impacts on GCRPS adoption in China. The farm manager with middle school education had low probability in GCRPS adoption, whereas the farm manager with primary education and high education had high probability of adoption. Previous experience with GCRPS had a positive impact on adoption. Membership in extension service was an important driving factor of adoption. Farmers with high income showed a high probability to adopt GCRPS. Soil type was also an important determinant in GCRPS adoption, probability of GCRPS adoption was very low at red soil, but high at yellow and brown soil. Low reliability of irrigation water supply led to a high rate of adoption, whereas high reliability of water supply led to a low rate of adoption. Nous avons interrogés des ménages agricoles chinois (N = 240) pour comprendre certains des facteurs qui influencent l'adoption de la technologie d'économie de l'eau appelée système de culture du riz sous couvert (Ground Cover Rice Production System – GCRPS). Nous avons établi un modèle logit fondé sur une enquête pour déterminer les facteurs qui favorisent l'adoption de la technologie et pour simuler l'impact d'une modification de ces facteurs sur le potentiel d'adoption. L'âge et le nombre de travailleurs n'ont pas eu d'impact significatif sur la probabilité d'adoption de la technologie. Les probabilités d'adoption étaient plus élevées chez les gestionnaires de ferme masculins que chez les gestionnaires de ferme féminins, et aussi plus élevées dans les fermes de grande taille que dans celles de petite taille. L'occupation d'un emploi hors ferme a eu un impact négatif sur l'adoption de la technologie. La scolarité a eu un impact complexe sur l'adoption de la technologie en Chine. Les gestionnaires de ferme ayant reçu un enseignement intermédiaire présentaient une faible probabilité d'adoption, tandis que ceux ayant reçu un enseignement primaire et secondaire présentaient une probabilité d'adoption élevée. Les expériences antérieures avec le GCRPS ont eu un impact positif sur l'adoption. L'adhésion à un service de vulgarisation a été un important facteur de motivation. Les producteurs à revenus élevés ont montré une forte probabilité d'adoption. Le type de sol était aussi un facteur important: la probabilité d'adoption était très faible dans le cas du sol rouge, mais élevée dans le cas des sols jaune et brun. La fiabilité peu élevée de l'alimentation en eau d'irrigation a entraîné un fort taux d'adoption, tandis qu'une fiabilité très élevée a entraîné un faible taux d'adoption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".