Corn production and processing into ethanol in Turkey: An analysis of the performance of irrigation systems at different altitudes on energy use and production costs
Bibliographic record
Abstract
This research aims to assess the energy input and output involved in corn production in the Elazig province of Turkey in different agricultural systems. This study is interested in energy resource allocation to analyze maize production systems since the value of production inputs and outputs is affected by economic crises as well as the country's politico-economic status. The amounts of energy used for production are still very challenging to change. The typical energy usage of the farms examined in this study is 3359,82 MJda−1, 3715,74 MJda−1, 5366,13 MJda−1 and 6456,24 MJda−1 according to the distance between the water sources and the farms. Of the total mean of energy, 31.06% is direct, 12, 26% is indirect, 14,38% in renewable energy and 42,30% in non-renewable energy. A kilogram of typical maize is thought to require 3,93 MJ of energy in total to produce. Energy used for irrigation makes up most of the input (37.20%), followed by sowing (22.57%) and fertilizer (16.65%). As a result of the benefit-cost ratio analysis is 0.29, the cost of corn production per decare is found to be $112.05/da in the area, with variable costs accounting for 83.17% of the total. The analysis of data reveals that the transformation of 100 kg of corn generates an energy expenditure of 2219.58 MJ and more than 50% of energy expenditure comes from the use of machines.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".