Calibration and Simulation of the CERES-Sorghum and CERES-Maize Models for Crops in the Central-West Region of Paraná State
Bibliographic record
Abstract
Simulation models have been widely used to generate yield data by forecasting climate variables and changes in growing seasons. The aim of this study was to calibrate genetic coefficients and simulate growth, development and yield in maize and sorghum crops based on historical meteorological data for the municipality of Juranda (2007 to 2013), in the central-west region of Paraná State, Brazil. Treatments were established based on three planting dates in two growing seasons for a group of super early maturity maize hybrids (DKB 330 Pro), and two groups of sorghum hybrids, the first a super early variety (ADV 123) and the second with a normal cycle (1G282). The variables assessed were number of days from planting to flowering, leaf area index (LAI), and 1000 seed weight and yield. Statistical coefficients were used to evaluate calibration accuracy. The results demonstrated that the models were highly efficient at simulating crop cycles, yield and leaf area index, with agreement indices and modeling efficiency values above 0.90. The results indicated that the CERES-Maize and CERES-Sorghum models generated satisfactory and comparative simulations of maize and sorghum yield for the study area on different planting dates.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".