Comparative Advantage of Using Bio-pesticides in Indian Agro-ecosystems
Bibliographic record
Abstract
The use of unsustainable levels of plant protection chemicals and fertilizershas resulted in a steady decline in soil quality and crop productivity the world over. To combat this decline, agricultural practices must evolve to meet the growing global demand for food without irreversibly damaging the world’s natural resources.Biopesticides have tremendous potential to bring sustainability to agriculture and environmental safety.This article is part of a larger study conducted in India by the authors at theUniversité de Montréal with the support of Mitacs and Earth Alive Clean Technologies. In this research, farmers, manufacturers or suppliers of biopesticides, and R&D scientistswere interviewed, and their responses demonstratethe advantages of applyingmicrobial biopesticidesto field crops. Participants reported a15-30% increase in yields and crop production after the application ofbiopesticides, with better quality and quantity of fruits, grains, and tubers with a longer shelf life. Moreover, while the risk of croploss is high (60-70%) with chemicallygrown crops, this risk is reduced to 33% on average when crops are grown using biopesticides. The risk of crop loss is thus considerably reducedby the use ofbiopesticides.Yet, despite their positive impact on the health of humans, soil,ecosystems, andfriendly invertebrates,biopesticides face significant challenges and competition vis-à-vis synthetic pesticides for a variety of reasons. The development of biopesticides must overcome the problems of improper formulations, short shelf life, delayed action, and high market costs, as well as a variety oflegal/registration issues.
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.
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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".