Bibliographic record
Abstract
Agribusinesses are investigating sustainable ways to meet the predicted increased demand for food production due to an increasing world population and higher living standards. Therefore, there is a strong need to increase agronomic output. This paper will review the current state of agricultural production of the main annual top-five staple grain crops grown around the world, their current yields and harvested area averages and trends. It concludes with a discussion of which changes are needed to increase the yield in lower yielding areas of the world. Finally, there is an assessment of what level of yield increases that could be attained provided the proposed changes are made and its predicted impact on food security by 2050.The current yield trends and trends for harvested area, when extrapolated out to 2050, indicate crop production will increase 106%. This includes an expansion of the total crop production area by 31%. This increase of cropping area can be achieved by increased utilization of available, uncropped land suitable for crop production, increased double cropping, and relay intercropping, allowing for multiple crops in a calendar year.In order to double crop production by 2050, it is necessary to focus on growing crops where the conditions make it possible, adopt the best sustainable crop production practices and implement them as intensively as possible everywhere, and consider improved crop production machine system options to reduce risk of soil compaction, which can reduce crop yields.With proposed changes across the world, it will be possible to exceed a doubling of food production by 2050 relative to 2005 levels, providing a reasonable high level of food security, absent wars and widespread natural disasters.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".