Economic and Technical Evaluation of Different Irrigation Systems for Date Palm Farming System in the GCC Countries: Case of Oman
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Bibliographic record
Abstract
In the frame of the ICARDA project “Development of sustainable date palm production systems in the GCC countries of the Arabian Peninsula”, researchers succeeded to introduce one promising technology (subsurface drip irrigation - SDI) in the date palm farming system in the Gulf region, defined as the poorest in the word in terms of water resources. In the light of these challenges, the main objective of this study is to evaluate the effect of the irrigation water volumes on the date palm productivity and water use efficiency under several conventional and improved irrigations systems.Three intervention levels on SDI have been used: at the rate of 60% 40% and 20% of water requirement. Results of this experimental study showed that SDI under the three intervention/options uses water more efficient in comparison to BI. Indeed, a considerable quantity of water for about 3545.554, 5726.45, and 7565.473 m3/ha could be saved by using SDI at the rate of 20%, 40 and 60% of water requirements, respectively. Thus, the WUE indicator is for about 2.0, 2.7, and 4.7 kg/m3, respectively. These figures are much higher when are compared to BI system where WUE is around 1.3 kgm-3.The economic evaluation suggests that under BI system, the total return, total variable costs, water costs and net profit were 20211.36, 5857.81, 1224.29, and 13129.25 $ ha-1, respectively. From another hand, by using SDI at the rate of 60% of water requirements, we note a slight difference in net profit when using this irrigation system, which is about US$12825.02/ha. Economic findings suggest that using SDI method versus BI method have additional cost but is economical at the long term as the SDI found to sustain the date palm farming system in this region where arid conditions acts as natural constraints for expansive agriculture.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it