The potential of Russia to increase its wheat production through cropland expansion and intensification
Bibliographic record
Abstract
Russia is a major player in the global wheat market, but extensive unused land resources and large yield gaps suggest that wheat production can be substantially increased. We combined time series of cultivated cropland, abandoned cropland and yield gap estimates to assess the potential production of wheat in European Russia. Current wheat production is constrained by volatile inter-annual precipitation patterns and low applications of nitrogen fertilizers. We demonstrate that modest increases in the crop productivity and the recultivation of the recently abandoned croplands could increase wheat production by 9–32 million tons under rainfed conditions. Increases in the wheat yields, particularly within the fertile black soil belt in southern European Russia, will contribute the major share of the prospective production increases. Frequently recurring droughts, likely exacerbated by future climate change, and adverse market conditions jeopardize the exploitation of the production potentials. Improved adaptation to the volatile climate conditions and substantial institutional and political reforms in the agricultural sector are necessary to leverage the agricultural production potential of Russia.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".