Spatial Adaptabilities of Spring Maize to Variation of Climatic Conditions
Bibliographic record
Abstract
ABSTRACT Environmental conditions have important effects on maize (Zea mays L.) growth. To examine spatial variation in maize yield and aboveground biomass and to understand differences in the response of maize yield and aboveground biomass to climatic factors under various ecological conditions, we conducted experiments from 2007 to 2010 at 34 locations in seven provinces in the spring maize region of northern China between 35°11′ N lat and 48°08′ N lat. We used a most widely cultivated maize hybrid ZD958. The maize yield and aboveground biomass (presilking and postsilking) were found to be strongly influenced by locations. A nonlinear relationship existed between the maize yields and latitude. Maize yield was the greatest (12.19 Mg ha–1) at 39°08′ N lat, and the corresponding presilking and postsilking aboveground biomass at this location were 143.41 and 215.35 g per plant, respectively. Variations in the harvest index (HI) and 1000‐kernel weight were the main reasons for yield latitudinal trends. Among the climatic factors, air temperature had the best relationships with variations in maize yield, HI, and 1000‐kernel weight. With latitudes increasing northward, presilking aboveground biomass affected by growth duration length and accumulated solar radiation increased significantly. The aboveground biomass of postsilking stage that was affected by the maximum temperature, daily mean temperature, and growing degree days decreased significantly with latitudes increasing northward. However, there were no significant changes of total aboveground biomass with latitudes increasing northward.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".