UNLOCKING INCLUSIVE COMPETITIVE VALUE CHAINS OF CHICKPEAS IN PUNJAB, PAKISTAN: A WALKING THE CHAIN APPROACH TO IDENTIFY BARRIERS, OPPORTUNITIES, AND OPTIONS
Bibliographic record
Abstract
Improved and inclusive agricultural value chains by linking all stakeholders’ particularly farmers with the market have remained a challenge in Pakistan. This study is aimed at identifying issues and obstacles that hinder the development of chickpeas’ value chain by using the lens of value sought by consumers. Primary data are collected from consumers, retailers, wholesalers, processors, and farmers to understand consumer value and identify opportunities and options to create and deliver greater consumer value along the chickpea chain. Results of focus group discussion of consumers depict that the important quality parameters are cleanliness, grading, and packaging. Moreover, results of the data analysis of farmers and other stakeholders reveal that mostly households stick to their traditional farm management practices and crop types. Value addition and storage of chickpeas at the farm level is very limited. Farmers are weakly connected with the market indicating the high potential of developing and upgrading chickpeas value chains in Pakistan. Value addition at the stakeholders’ level, such as processors and wholesalers existed but not purely consumer oriented. However, value addition at retailers’ level is consumer oriented. It indicates that information flow from consumers to value chain actors is not adequate. An important option can be to give small cleaning and grading machine to farmers and building their capacity to perform value added activities and adopt appropriate storage techniques at farm level which facilitate them to sell value added products at good price to processors, wholesalers or retailers which could lead to increase the farmers’ profitability and in turn motivate them to increase area under chickpea 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 | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".