Agro-ecological Zoning for Crop Suitability using the AquaCrop Model in the Arid Regions of Khuzestan Province, Iran
Bibliographic record
Abstract
Climate and weather largely determine the amount and mode of human performance and function in each sector, especially agriculture. However, obtaining agricultural information based on regional climate data can be time-consuming and expensive. Zoning can facilitate access to this information on a wide scale. This issue is particularly essential in areas with water deficit conditions in Iran, and the predominantly arid status of Khuzestan province. Camelina (Camelina sativa L.) is a rich source of oil and omega-3 fatty acids. Its unique properties include low water requirement and tolerance to drought, pests, and diseases. The purpose of this study was to apply a simulation strategy using the AquaCrop modeling software to evaluate the effects of various environmental factors on camelina yield. Also, it was aimed to determine the best locations for cultivating this plant using agro-ecological zoning (AEZ) and introduce the plant to Khuzestan province. In this study, the AquaCrop model was used for simulation to estimate the yield potential of camelina plants. It employed weather data to demonstrate how Khuzestan’s water deficit condition can affect camelina growth. Before using the model to simulate different factors, the research procedure involved model calibration and verification for the camelina genotype in the province. The findings resulted in plant zoning. A zoning map for camelina cultivation was generated to reveal three zones in terms of camelina yield potential, i.e., very suitable, moderately suitable, and unsuitable. The very suitable zone had a long-term average simulated yield potential of over 1800 kg/ha, encompassing the cities of Omidiyeh and Baghmalek. The moderately suitable zone had a long-term average yield potential of 1700–1800 kg/ha and included the cities of Izeh, Dezful, and Shushtar. The unsuitable zone had a long-term average simulated potential yield between 1600–1700 kg/ha, and included the cities of Ahvaz, Behbahan, Khorramshahr, Dasht Azadegan, Ramshir, Ramhormoz, and Shush.
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 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 | 0.001 | 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".