Comparative Advantage of Using Biofertilizers in Indian Agroecosystems: An Analysis from the Perspectives of Stakeholders
Bibliographic record
Abstract
The use of excessive chemical fertilizers and pesticides have decreased soil microbial life and upset the balance between soil microbes and plants, negatively impacting plant nutrition, production and soil health. Biofertilizers hold the potential to revive soil biology and increase farmers’ current agricultural productivity, while at the same time contributing to the soil’s ability to produce more in the future. This article is part of a larger Université de Montréal study conducted with the support of Mitacs and Earth Alive Clean Technologies. The responses of farmers using and not using biofertilizers, manufacturers or suppliers of biofertilizers, and research and development scientists are captured to build cases of how microbial products (biologicals) prove to be advantageous when applied in field crops. The agronomic advantage of biofertilizers compared to conventional chemical fertilizers is well proved biologically and in economic terms. The farmers interviewed stated their preference of biofertilizers over chemical fertilizers. However, production and distribution of biofertilizers are inadequate compared to the demand for them. Studies need to be pursued to understand reasons for the supply gaps and the slow growth of biofertilizers in the agriculture sector of India and methods of linking them to farmers’ preferences in order to advance protections of soil and plants in India.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".