Spatial Variation and Improving Measures of the Utilization Efficiency of Accumulated Temperature
Bibliographic record
Abstract
ABSTRACT Temperature, especially accumulated temperature, is very important for agricultural productivity. Fully using the heat resource is important for increasing yields of maize ( Zea mays L.). To understand spatial variation and improving measures of the utilization efficiency of accumulated temperature, we conducted experiments during the period of 2007 to 2012 at 52 locations in the north spring maize (NM) and Huanghuaihai summer maize (HM) regions of China. We found that the utilization efficiency of ≥10°C accumulated temperature for the whole year (hereafter, UE10y) and residual accumulated temperature for maize were highly correlated with latitude. The UE10y increased by 1.3 and 1.4% for 1° increases in latitude moving northward for NM and HM, respectively. In NM, the variation in residual accumulated temperature was large across the experiment locations, ranging from 76 to 1655°C degree days. To make full use of the heat resource, we studied the effects of delaying harvest, optimum sowing date, and planting‐adapted cultivars on the UE10y and maize yield. We found that, compared to conventional harvest time, delaying harvest significantly increased UE10y and maize yield. For different sowing dates, maize yield increased significantly with increasing UE10y in the northern area of NM but not in the southern area of NM. Regarding the use of different planting‐adapted cultivars, yield potentials of the longest maturity cultivars and cultivars adapted to mechanical grain harvest averaged 17.12 and 14.39 Mg ha –1 in NM, which were 27.9 and 7.5% higher than the yield potential of traditional cultivars, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".