Exploratory Analysis on the Management of Agricultural Knowledge and Information by Smallholder Farmers in Batticaloa District
Bibliographic record
Abstract
Extent of farmer knowledge and availability of reliable information at the level of farm are considered key driving forces in agricultural development as both support interchangeably to transform the livelihoods of rural poor into one with increased household income stability and food security. The farmers who do not have a free/subsidized and open/fair access to the vital agricultural information would, therefore, face severe difficulties in their attempt to achieve the goals of farming. The purpose of the study was to investigate the factors that trigger or hinder farmer access to agricultural knowledge and information. The smallholder agricultural farmers in the Batticaloa district in Sri Lanka was used as the case. A series of pre-tested structured questionnaire-based face-to-face interviews were carried out with a cross section of farmers (n=144) to gather data and both descriptive and quantitative techniques, including Factor and Logistics Regression analysis, were employed to analyse data. The results show that the level of acquisition of agricultural knowledge by farmers has a positive and negative relationship with the level of education and age of farmer, respectively. Amongst other factors, the farmers, in general, requested badly the valid and reliable agricultural information on a regular basis with regard to the market prices of inputs and outputs, credits/subsidies/loans etc. available and the measures of crop protection, i.e. pests and disease control. The inadequacy in information services and agricultural officers, limited infrastructure and insufficient funds/resources hinder farmer access to agricultural knowledge and information. The outcome of analysis, overall, implies the importance of developing appropriate packages to facilitate information sharing and knowledge mobilization and targeting so to specific farming communities in need with a right mix of market-based and regulatory incentives.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".