Exploring Socio-Economic Factors Influencing the Adoption of Climate Smart Agriculture
Bibliographic record
Abstract
The suboptimal income of tidal rice farmers, who are increasingly affected by climate change, is a major challenge in improving their welfare.Climate Smart Agriculture (CSA) technology is expected to be a solution to increase farmer productivity and income.This study aims to analyze the level of adoption of CSA technology and examine the influence of socio-economic factors on its adoption, as well as its impact on farmer income and productivity.The research respondents were tidal rice farmers in Telang Makmur Village.To test the relationships between socio-economic variables, CSA adoption levels, and productivity, Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach was applied using SmartPLS 4.0 software.The results of the study indicate that socio-economic factors such as age, education level, family size, farming experience, cultivation area, and other income have a significant influence on the level of adoption of CSA technology.In addition, the use of CSA technology has been shown to significantly increase farmer income and productivity.Therefore, there should be increased socialization and training related to CSA technology for farmers, as well as the provision of subsidies and technological assistance to encourage wider 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".