An improved CROPR model for estimating cotton yield under soil aeration stress
Bibliographic record
Abstract
Accurate estimation of crop yield under aeration stress is crucial for field water table management. In this study, the CROPR crop model was improved in two aspects: (i) a new aeration factor, which was related to a drainage index, was proposed and used to represent the condition of soil aeration; and (ii) a multiplicative structure, instead of the original additive structure, was used in the calculation of dry matter accumulation to include the after-effect of aeration stress. Four-year lysimeter experiments on cotton (Gossypium hirsutum L.) growth under aeration stress were conducted from 2008 to 2011 to calibrate and validate both the original and improved CROPR. The results indicated that the improved CROPR performed better than the original CROPR and was suitable for simulating cotton yield under aeration stress. In the calibration, with the improved CROPR, the root-mean-squared error (RMSE) of seed cotton yield was 832.84 kg ha–1 with a normalised value (NRMSE) of 15.87%, whereas with the original CROPR, the RMSE was 973.03 kg ha–1 with an NRMSE of 18.55%. In the validation, with the improved CROPR, the RMSE of seed cotton yield was 686.22 kg ha–1 with an NRMSE of 14.87%; with the original CROPR, the RMSE was 1019.02 kg ha–1 with an NRMSE of 22.08%.
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 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".