Legal Barriers and Quality Compliance in the Business of Biofertilizers and Biopesticides in India
Bibliographic record
Abstract
Abstract Biofertilizers and biopesticides (together known as ‘biologicals’) hold the potential to increase farmers’ current agricultural productivity, while at the same time contributing to the soil’s ability to produce more in the future. However, the legal registration of microbial products and the operation of businesses dealing in biologicals face certain barriers, which ultimately affect the expansion and widespread use of these green products in Indian agriculture. By involving manufacturers, suppliers and traders of biologicals, as well as government officers dealing with biologicals in India, a study was conducted using participatory methods of semi-structured interviews, structured interviews and informal discussions. This article explores the participants’ perceptions and understanding of the barriers, obstacles and issues in the registration, licensing, proliferation and business operations surrounding the manufacturing, sale, trade, import, export, storage, use, and transport of microbial products. Numerous barriers to business and trade in microbial green products – biofertilizers and biopesticides – are identified. Nevertheless, certain weaknesses related to quality compliance and monitoring are also identified on the part of the manufacturers and suppliers of these biologicals, indicating that the government’s regulatory system must be more efficient and competent in handling these processes.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".