Comparative Advantage of Using Biofertilizers in Indian Agroecosystems: An Analysis from the Perspectives of Stakeholders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it