Knowledge and Adoption of Conservation Agriculture Technologies by the Farming Community in Different Agro-Climatic Zones of Tamilnadu State in India
Bibliographic record
Abstract
Tamilnadu State in India is one of the earlier beneficiaries of Green Revolution which contributed to multifold increase in agricultural productivity. Though the State has been progressive, it has also experienced the ill effects of over exploitation of natural resources through intensive agriculture. There is an urgent need to shift its focus from over exploitative intensive farming to more sustainable farming with optimal use of resources without causing imbalances. Conservation Agriculture (CA) offers potential solution which not only enhances the productivity but also maintains the environmental safety and ecological sustainability. With this at the backdrop, the present study was conducted during 2013-2014 in all the seven agro-climatic zones of Tamilnadu State in India covering 350 respondents to understand the knowledge and adoption levels of Conservation Agriculture among the farming community in the State. Three Conservation agricultural technologies namely, Minimum Tillage, Crop Rotation and Permanent Soil Cover were identified. Knowledge of the farmers about these technologies and their adoption by the farmers were studied. As regards the awareness and knowledge level of respondents majority of them do not have knowledge on minimum tillage (72.6%) and permanent soil cover (75.1%) but a vast majority is knowledgeable on crop rotation (71.1%). Farmer characteristics such as age, educational status and innovativeness of farmers played a significant impact on the knowledge levels of CA whereas number of years of experience in farming and land holding pattern did not have significant influence on the knowledge levels of farmers on CA. Among the knowledgeable farmers only 11.5% of farmers adopted minimum tillage, 27.6% of farmers adopted permanent soil cover and 78% adopted crop rotation. None of the farmers adopted CA as a whole comprising all the three components.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".