Limits to Growth in Global Crop Yields: Insights from Data Mining of the FAOSTAT Database from 1961 to 2023
Bibliographic record
Abstract
We conducted a comprehensive data mining analysis of the FAOSTAT database to assess historical trends and current limits in global crop yield development. The study included 157 major crops across 202 countries from 1961 to 2023, focusing on time series of yield (t/ha) and area harvested (ha). Weighted global average yields and annual maximum yields were calculated for each crop and classified into four categories of temporal evolution: never improved, still increasing, stagnating, and decreasing. Over the study period, total crop production rose by a factor of 3.9, primarily driven by a 2.54-fold increase in average yield, with harvested area contributing a smaller share. Analysis revealed that approximately 77% of global production volume remains in the "still increasing" category for average yield, although this share has declined from previous decades. In contrast, only about a quarter of production volume continues to experience increases in maximum yield, suggesting a growing number of crops nearing biophysical yield limits. Yield-area diagrams, categorized by a semi-quantitative "L-chart" approach, indicate that high yields are predominantly restricted to relatively small harvested areas, with over 90% of crops showing strong spatial limitations to yield scalability. These findings imply that opportunities for further global crop production expansion via yield improvement are increasingly constrained, and that recent output gains have largely depended on continued expansion of harvested area.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".