Assessing the carbon footprint of irrigated and dryland wheat with a life cycle approach in bojnourd
Bibliographic record
Abstract
This research is conducted with the purpose of studying greenhouse gas emissions by wheat production in Bojnourd using the life cycle approach. The basic information is collected from wheat farmers in the form of questionnaires in the crop year 2015–2016. The meteorology and crop data are gathered, respectively, from the Meteorological Organization and Ministry of Agriculture Jihad. The functional unit, research boundary and impact category are, respectively, considered to be “the production of 1 kg of wheat grains,” “the farm gate,” and “Global Warming Potential.” Data were prepared and analyzed in Excel and SimaPro software. The global warming index is calculated to be, respectively, 1.22 and 0.72 equivalent kilograms of carbon dioxide for the production of 1 kg of irrigated and dryland wheat. Based on the results, electricity, and machinery (53% and 22%, respectively, in the irrigated wheat) and machinery, diesel fuel, and chemical fertilizer application (44%, 4%, and 4%, respectively, in the dry wheat) have the highest share in greenhouse gas emissions. The results indicate that improving the management of the optimal use of inputs and the increase of the area under cultivation can have a significant role in the reduction of greenhouse gas emissions. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13134, 2019
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".