Economic Analysis of Processing of Guava (Psydium guajava L.) in Uttar Pradesh State of India
Bibliographic record
Abstract
A study was conducted to analyse the economics of guava processing with the objectives of estimating benefit cost ratio, capacity utilization, cost of processing, price spread, marketing efficiency and constraints faced by guava processors in processing of guava in Allahabad district of Uttar Pradesh (U.P.) India. The processing of fresh guava was undertaken only by 10 units (processing firms) in the study area, so all the 10 units were evaluated for the present study. The processing units included cottage scale (03), small scale (05) and large scale (02). The study revealed that returns per rupee invested in cottage scale units, was calculated as 2.74 in making jelly and 2.99 in jam; for small scale units, it was 2.28 in jelly, 2.43 in jam and 1.89 in toffee; and for large scale units, the returns per rupee invested were 2.52 in the manufacture of jelly, 2.82 in jam and 2.01in toffee. As far as capacity utilization is concerned, the large scale units utilized 92.16% of installed capacity followed by cottage scale units with 86.73% and small scale with 79.89% capacity utilization, respectively. The cost of processing per quintal of guava was found to be Rs.176.60 (cottage scale), Rs.195.58 (small scale) and Rs.222.90 (large scale). Major problems faced by processors were non-availability of skilled labours, lack of capital, setting of guava products and degree of competition, etc. It could be concluded from the study that guava processing was an economically viable entrepreneurial activity and provided ample opportunities for employment generation particularly for the local youth, and also earning much required foreign exchange. This could be possible provided the government functionaries and concerned agencies take steps towards strengthening infrastructural support for a better marketing mechanism to be in place, and introduce export promotion schemes to boost the processing industry, given its competitive advantage. Efforts need to be made to encourage local unemployed youth to opt for guava processing as their livelihood source, by providing training and financial support to them.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".