Analysis of trends in world grain production
Bibliographic record
Abstract
Total production of cereals and pulses in the world for the period from 2009 to 2020 increased by 20.6%, amounting to 3086 million tons. The largest share in the structure of global grain production is occupied by the countries of Asia and America. The analysis showed that over the past decade, the world leadership in grain production volumes belongs to China (20%), the USA (14-15%), EU countries (12.8%), India (about 12%). Russia's share in world grain production for the period from 2009 to 2020 increased from 3.8 to 4.3%, which is a positive factor. It has been established that our country is one of the states with the highest specialization in wheat production (over 61%). In addition, countries with a high share of it include Canada (51%), France (48.6%), Germany (45.3%), Ukraine (42.9%) and the UK (41.8%). Global growth rate of grain production for the period from 2009 to 2020 amounted to 20.6%, while in Russia it reached 37.5%, in Africa – 26.8%, in Asia – 25.5%, in America – 25.1%, in Europe – 10.1%. It has been determined that the highest level of wheat yield is observed in Germany, Great Britain, France, Egypt, and China. To increase the economic efficiency of grain production, it is necessary to carry out regular economic analysis of production costs and monitoring of market prices, as well as increase the share of exports of food products with a high degree of processing of raw materials. This will ensure the growth of added value in the industry and the transition to expanded reproduction in agriculture on a highly intensive basis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".