Impact assessment of rabi Onion variety Agrifound Light Red (AFLR) through OFTs in Sidhi District of Madhya Pradesh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sidhi district is situated in Kaymore plateau and satpura hills of Madhya Pradesh. Onion is one of the major vegetable crops grown in rabi season in district. Krishi Vigyan Kendra laid down On Farm Demonstration in the year 2012-13 with scientific package of practices, high yielding variety “Agrifound Light Red (AFLR)” and applying scientific practices in their cultivation. The OFTs were carried out in village “Chorgadhi” block Rampur Naikin of Sidhi district in supervision of KVK scientist. The productivity and economic returns of Onion in improved technologies were calculated and compared with the corresponding farmer’s practices (local check). The improved technology recorded higher yield of 285 q/ha respectively 192 q/ha. The average yield increase was observed 48.43 per cent. In spite of increase in yield of Onion, technology gap, extension gap and technology index existed. The improved technology gave higher gross return (285000 & 192000 Rs./ha), net return (245000 & 162000 Rs./ha) with higher benefit cost ratio (1.4.8 & 1.5) as compared to farmer’s practices. The increase in the yield was found to be due to the lack of good agriculture practices, lack of knowledge dissemination and lower socio economic condition. Under sustainable agricultural practices, with this study it is concluded that the OFTs programmes were effective in changing attitude, skill and knowledge of improved package and practices of HYV of Onion adoption.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it