A Structured Procedure for Assessing How Crop Models Respond to Temperature
Bibliographic record
Abstract
Crop simulation models are widely used to analyze temperature effects on crop growth, development, and yield. Unfortunately, temperature responses of models often are not examined critically to ensure that a model is appropriate for a given research application. This paper describes a procedure for assessing how models respond to temperature. The procedure treats major processes in a balanced fashion but does not require access to source code. The results are easily interpretable by nonmodelers and readily documented and employed with different models. Sensitivity analyses are run using standardized conditions of nonlimiting water and N with regimes of constant mean temperatures from 3 to 40°C and daily range of 10°C. Daily model outputs define responses that are grouped in seven categories: crop mass (including economic yield), phenology, reproductive growth, canopy development, root growth, resource use efficiency, and water balance. To avoid interactions of duration of life cycle with growth, several responses are assessed before partitioning to reproductive growth reduces total aboveground biomass. Emphasis is on graphical analysis of individual variables vs. mean temperature, but cardinal temperatures and a response index are also estimated. When applied to the CSM‐CERES‐Sorghum and CSM‐CROPGRO‐Drybean models, the procedure readily identified differences in temperature adaptation of the two crops. Various examples were found where modeled responses appeared to differ from data from field or controlled‐environment studies. The proposed procedure will require adjustments for specific situation but provides a foundation for assessing modeled responses to temperature in a structured and reproducible fashion.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".