Carbon footprint reduction in Punjab agriculture: Analyzing impacts and strategies in major crop rotations
Bibliographic record
Abstract
• Resource Conservation Technologies cut carbon footprint by 16–194 % in crop rotations. • Crop residue management decreases carbon footprint by up to 98 % in paddy-wheat rotation. • Paddy cultivation emits 5 times more carbon than wheat and cotton in Punjab, India. • Direct Seeded Rice reduces methane emissions to zero compared to flooded cultivation. • Integrated Pest Management in cotton reduces pesticide-related emissions by up to 23 %. This research aimed to evaluate the carbon footprints in Punjab's agriculture, focusing on crop production and its mitigation strategies. Utilizing both primary and secondary data, the study sampled 120 farmers from two Punjab districts, Mansa and Sri Muktsar Sahib, through a multi-stage sampling technique. The sample was equally divided between farmers practicing paddy-wheat and cotton-wheat crop rotations. Secondary data, including emission factors for various agricultural inputs, were compiled from published sources. The study further categorized Resource Conservation Technology (RCT) adopters into specific scenarios for both crop sequences. The study reveals that emissions from paddy-wheat crop rotation (14,176 176 ± 3027 kg CO 2 eq/ha) are 3.5 times higher than that of cotton-wheat crop rotation. RCTs showed the significance reduction in carbon emissions in case of both crop rotations ranging from 0.5 to 194 % The findings also reveal that paddy cultivation is the principal carbon emitter, with emissions five times higher than wheat and cotton. The carbon footprint resulting from paddy residue burning was found to be 6997 ± 1660 kg CO 2 eq/ha, significantly reduced by 95 % to 161 - 311 kg CO 2 eq/ha in farmers adopting CRM techniques. Methane emissions from P PTR farms were significant (1747 ± 843 kg CO 2 eq/ha), constituting 16 % of total GHG emissions, whereas DSR farms showed no methane emissions. Adoption of C IPM +W CT leads to reduction in carbon emissions from insecticides by 23 % than C CT +W CT . The study advocates for financial incentives to motivate farmers towards adopting RCTs and ensuring timely availability of machinery for crop residue management and no-till practices for effective carbon footprint reduction. These findings highlight the critical need for integrated strategies involving technology adoption, efficient management practices, and policy interventions to achieve sustainable agricultural development and significantly reduce carbon emissions in Punjab's agriculture.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".