Optimization of Energy Consumption of Broiler Production Farms using Data Envelopment Analysis Approach
Bibliographic record
Abstract
This study applied a non-parametric method to analyze the efficiency of farmers, discriminate efficient farmers from inefficient ones and to identify wasteful uses of energy in order to optimize the energy inputs for broiler production. Data were collected from 44 broiler farms in six villages in Yazd province (Iran) by using a face-to-face questionnaire performed in January– February 2010 period. The data were collected from 44 broiler farms in six villages from Yazd province, Iran. Average capacity of surveyed farms was 18142 birds. Maximum, minimum and average meat production of farms was 2000, 3000 and 2601 kg (1000bird)-1, respectively. Total energy used in various operations during broiler production was 186885.87 MJ (1000bird)-1. We determined TE (Technical Efficiency), PTE (Pure Technical Efficiency) and SE (Scale Efficiency) of energy use in broiler farms using Data Envelopment Analysis (DEA). Two basic DEA models (CCR and BCC) were used to measure the TEs of the farmers based on five energy inputs and two outputs. The CCR and BCC models indicated 10 and 16 farmers were efficient, respectively. The average values of TE, PTE and SE of farmers were found to be 0.90, 0.93 and 0.96, respectively. The results also revealed that about 11% of the total input resources could be saved if the farmers follow the input package recommended by the DEA.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".