Evaluation of climatic variables as yield‐limiting factors for maize in Kansas
Bibliographic record
Abstract
ABSTRACT Kansas is situated from the lower Missouri Basin to the high plains lying along the eastern slope of the Rockies so that distinct climates across the state make crop production systems vulnerable to changes in climate. Based on climatic indices such as growing degree‐day (GDD), extreme degree‐day (EDD), and precipitation (P), this study assessed the sensitivity of maize (Zea mays L.) yield to a changing climate for seven diverse cropping areas from 1981 to 2013 across Kansas. Our results indicated that maize yield increased by 2.4 and 3.4% per annum 100 GDD increase under non‐irrigated (i.e. rainfed) and irrigated environments, respectively. Maize yield positive response to changes in GDD during the pre‐silking period was more significantly pronounced for irrigated environments than rainfed sites. Rainfed yields showed a significantly negative response to EDD (−3.0% per +10 EDD) compared with irrigated environments (−1.2% per +10 EDD). This EDD negative effect was more pronounced during post‐silking growth as compared with the pre‐silking period for both irrigated and non‐irrigated conditions. Yield sensitivity to a unit change of P (mm) was less than the sensitivity to a unit change of GDD (in °C days) and EDD (in °C days) indices. Nonetheless, maize productivity has a positive response to post‐silking P with a greater yield gain at rainfed sites compared with irrigated sites. Irrigation could partially mitigate the effect of extreme heat on maize yield potential. Because the frequency of extreme temperatures and P are predicted to increase in Kansas and the Great Plains region, this study might provide guidelines to farmers, crop consultants, and agronomists to manage maize production thereby providing the ability to mitigate or adapt to climate change impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".