Impact of Covid -19 on marketing of vegetables in the Amritsar district of Punjab
Bibliographic record
Abstract
Due to the lockdown, there were restrictions on the number of farmers allowed into the mandis and the quantity each farmer could sell on any day. This resulted in distressed sales by producers, ad customer also paid higher prices due to low availability of vegetables. The present study attempted to determine the impact of Covid-19 lockdown on marketing of vegetables. The primary data have been collected from 190 farmers, 10 wholesalers and 20 retailers for the period of 2021-22.The study found that all economic activities witnessed a negative growth rate during the April - June quarter of 2020, except for the agriculture and allied sector, which grew at 3.3 per cent. There was decline in the prices and arrivals of tomato and onion during lockdown. On the contrary, potato prices increased during lockdown as potato sowing was delayed by almost a month due to heavy rainfall in 2019. During lockdown, the decrease in marketing efficiency was maximum in marketing channel I (producer- wholesaler-retailer-consumer). It decreased to 0.28, 0.6 and 0.38 in lockdown year from 0.71, 0.77 and 0.83 in pre lockdown year for tomato, potato and onion, respectively. The overall major problems perceived by the vegetable grower were Covid-related restrictions (86.84 per cent), followed by costly labour (81.05 per cent), transportation problem (77.37 per cent) and fluctuation in prices (74.21 per cent), respectively. The study suggested that to increase the producer share in consumers’ rupee there should be direct delivery of food grains, vegetables, and fruits to consumers. It can be done through SHGs, FPOs and cooperative marketing. Additional funding should be allocated to agriculture in order to develop the infrastructure of the supply chain, which includes storage, warehousing, refrigerated transport, etc. This would assist farmers withstand shocks. Post-harvest management of tomato, onion and potato should be prioritized to promote vegetable production.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".