Energy Consumption in Onion and Potato Production within the Province of El Hajeb (Morocco): Towards Energy Use Efficiency in Commercialized Vegetable Production
Bibliographic record
Abstract
<p>Energy use efficiency is a key requirement for sustainability in agricultural production, but often overlooked. The aim of this study was to quantify the amount and efficiency of energy consumed in the production of onions and potatoes in El Hajeb province of Morocco. These estimates are of significant importance in informing contemporary policy discourse related to energy subsidy reform in Morocco, and more specifically within an ongoing national strategy for ‘modernizing’ the agricultural sector under the ‘Green Morocco Plan’. Data were collected through the administration of a direct questionnaire with 60 farmers and analyzed using PLANETE. Our results indicate that total energy consumption in onion production is 107483 MJ ha<sup>-1</sup> with butane (79.5%) as the main source of direct energy. Chemical fertilizers (61.53%) and water for irrigation (30%) were main sources of indirect energy. Energy indices related to energy efficiency ratios, energy profitability and energy productivity were estimated at 0.78, -0.22 and 0.54 kg MJ<sup>-1</sup>, respectively. Total energy consumption in potato production was estimated at 74,270 MJ ha<sup>-1</sup>, with direct energy consumption of 28,521 MJ ha<sup>-1</sup> stemming from butane (70%) and diesel (19.14%) as primary sources. Indirect energy consumption was estimated at 45749 MJ ha<sup>-1</sup> and generated principally through the use of fertilizers (60%). Energy indices (efficiency, profitability and productivity) were estimated at 1.54, 0.54, and 0.45 kg MJ<sup>-1</sup>, respectively. GHG emissions were found to be 3.47 t CO<sub>2eq</sub> ha<sup>-1</sup> in the production of onions and 3.63 t CO<sub>2eq</sub> ha<sup>-1</sup> for potatoes. We find that within the study area, increases in the size of production plots are not necessarily consistent with increases in energy use efficiency.</p>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".