Simulating the Response of Red Bean Yield to Deficit Irrigation and Plant Densities by Applying the Rbean Model
Bibliographic record
Abstract
The Rbean Model was simulated for deficit irrigation conditions and plant density by using experimental data collected over two consecutive years regarding the cultivation of red bean in the Badjgah region of Shiraz, Iran. A split plot arrangement was conducted in a complete randomized block design during two years. The foremost variable was four levels of irrigation including 60 (I4), 80 (I3), 100 (I2), and 120% (I1) of the potential evapotranspiration under surface irrigation. A second variable was the spacing of 5 cm (S1), 10 cm (S2) and 15 cm (S3) between the plants within each row, and the distance of 30 cm between the rows. The experiment layout was conducted in three replications. Results of the first and second growing seasons of red bean (phaseolus vulgaris L. cv. Akhtar) were used for the calibration and validation of the Rbean model, respectively. According to the NRMSE and the d value indices, the Rbean model presented a very good to fair estimation of actual evapotranspiration, soil water content, canopy cover, dry matter, crop yields and water productivity (WP). The advantage of this model is its simplicity and easy calibration in a range of climatic conditions for the cultivation of bean crop. The prediction of bean yield by this model can be used for a better irrigation management under different plant densities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".