Evaluation and Improvement of Crop Production Functions for Simulation Winter Wheat Yields with Two Types of Yield Response Factors
Bibliographic record
Abstract
Water is an important item of crop production and Irrigation water is limiting for crop production in arid and semi-arid areas. On the other hand, considering the population growth and increasing the water and food needs and also limited resources of water, the optimization of water consumption, especially in section of agriculture is important. For this purpose, in first step, the production function of expected products in any region should be obtained acceptably, because in this case, the models are totally dependent on the production function. Thus, finding the optimal production function has the lowest error in the estimation of risk-taking and decision-making power in the future will be important. So this study was conducted in Esmaeil Abad in Qazvin plain in Iran in the growing season of 2009-2012. Deficit Irrigations applied on different growth stages of winter wheat. The maximum evapotranspiration 641 mm and maximum attainable yield 5847 kg/ha was determined. After that the different production functions were studied and these methods have been tried to improve a new method with the least error. On the other hand yield response factor (Ky) per month was defined as either one of the standard values by FAO and the other using correction values by Najarchi et al. (2011). The result showed that the new model in this study is normalized with yield response factors of Najarchi et al. (2011) with 5% normal root mean square (NRMSE) has the lowest error. Therefore this technique for estimating water deficits of winter wheat in the Qazvin Plain was suggested.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".