Bibliographic record
Abstract
This paper aimed at assessing the economic and financial viability of solar-powered irrigation of tomato crop in Jordan Valley. Data were collected from 16 tomato farms that use solar-powered irrigation system. Another 16 farms with diesel-powered irrigation system was investigated for comparative reasons. Descriptive statistics, Cost Function Analysis (CFA), Life-cycle Cost Analysis (LCCA), Water Productivity (WP) and the financial indicators of Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PP) and Benefit to Cost Ratio (B/C) were the main economic and financial analytical tools used in this study. The results of the study revealed that costs of inputs, labor costs and equipment and maintenance costs have had a lower adverse impact on the total revenues level when using solar-powered irrigation system. The results also indicated the preference of the investigated financial indicators (NPV, IRR, PP and B/C ratio) when solar-powered irrigation is used compared to diesel-powered irrigation. The results also revealed a lower cost of life of the farm under the use of solar-powered irrigation. The governmental policies and programs should be directed toward the concepts of renewable energy in general and solar energy uses in agriculture in particular. Special agricultural extension plans in training and capacity building of farmers and extension workers on the use of solar energy in irrigation of agricultural crops should be developed. Cooperation in the fields of solar energy between the Ministry of Agriculture and related parties such as the Royal Scientific Society and the Ministry of Energy should be initiated to conduct specialized researches in the fields of solar energy use in agriculture.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".