Optimizing Energy Consumption in Legume Production to Mitigate Greenhouse Gas Emissions: An Empirical Investigation
Bibliographic record
Abstract
Owing to the escalating utilization of agricultural inputs, energy consumption and the associated greenhouse gas (GHG) emissions have seen a significant surge.Consequently, the optimization of energy usage to mitigate environmental pollutants has emerged as a critical focus.This study employs the Data Envelopment Analysis (DEA) methodology to optimize energy consumption and reduce GHG emissions in agricultural production.Data was collected through randomized, face-to-face interviews with 200 agricultural producers in Chennai, India, in 2021.The results revealed that the total energy input for legume production was 2000 MJ/HA 2 for seed, 29950 MJ/HA 2 for fertilizer, and 1065 MJ/HA 2 for machinery.Among the consumption components, nitrogen fertilizer, electricity, diesel fuel, and irrigation water accounted for 35%, 27%, 18%, and 9% of the total, respectively.However, under optimal input consumption conditions, the total energy requirement was determined to be 31678 MJ/HA 2 .Accordingly, a saving of 2.36% in total energy consumption could be achieved without compromising yield.This research underscores the potential for energy optimization in agricultural practices, contributing significantly to GHG emission reduction efforts.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".