Analysis of Climatic Potential Productivity and Wheat Production in Different Producing Areas of the Northern Hemisphere
Bibliographic record
Abstract
Abstract Cultivation patterns under water saving management are closely related to climate change and crop productivity. Survey data for wheat production in China, Canada, and the USA from 1965 to 2014 was analyzed through classification statistics, comparative analysis and weighting methods. The results showed that China, Canada and the USA wheat grain production accounts for 26.49% to 34.68% of global wheat production. Of these three countries, both the highest total yield and the highest average yield per area sown were in China post 1985. Since 2000, China has accounted for 10.70% to 11.12% of global wheat planted area, producing 15.73% to 17.45% of global wheat output. Achieving high wheat yield in China was mainly based on consuming more irrigation water. The per capita arable land in China, Canada and the USA was 0.10 ha, 1.31 ha and 0.70 ha, respectively. The per capita arable land in Hebei, China, Saskatchewan, Canada and Oklahoma, USA was 0.09 ha, 16.11 ha and 3.48 ha, respectively. Hengshui, China (HS) produces two crops a year. Outlook, Saskatchewan, Canada (OK) and Woodward, Oklahoma, USA (WW) produce one crop a year. Mean precipitation averages 109 mm, 210 mm and 291mm during the wheat growing season, respectively. Due to less per capita arable land in China, achieving high yield means higher water consumption, which creates a sharp contradiction between water scarcity and demand for grain in China. Consequently, high water use efficiency technologies in agriculture are more needed in China than in the other two countries. High demand for water at post 1985 yield levels cannot be met by rain-fed production in HS. Grain yields of 5975.6 kg/ha to 7031.2 kg/ha can be achieved with irrigation. Economic benefits mainly determine wheat production in the USA and Canada, whereas in China production is driven by food security and sustainable production goals. The conclusions of this paper could provide a reference for wheat production in different regions.
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 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".