Productivity‐led Pathways to Sustainable Agricultural Growth: Six Decades of Progress
Bibliographic record
Abstract
Summary In recent decades, world agriculture has undergone a vast transformation. Between 1961 and 2020, global agricultural output increased nearly four‐fold while population grew 2.6 times, leading to a 53 per cent increase in output per capita. Real food prices declined, providing for more affordable and diverse diets. There was a pronounced and sustained shift in the location of production to the Global South (developing countries), which increased its share of global agricultural output from 44 to 73 per cent. Since the 1990s, increases in agricultural total factor productivity (TFP) has become the major driver of world agricultural output. However, insufficient productivity growth relative to demand has drawn more resources into agriculture. Globally, agricultural land area expanded 7.6 per cent between 1961 and 2020, although it contracted in the Global North (developed countries). In the EU, where agricultural output has remained relatively flat in recent decades, improvements in productivity reduced total inputs and environmental resources used by the sector. In contrast, in Canada‐United States, productivity growth enabled agricultural output to expand without increasing total inputs and environment resources. By the decade of the 2010s, however, the pace of output and productivity growth in world agriculture slowed, real food prices rose, the number of food insecure people increased, and pressure to expand the use of natural and environmental resources to produce food intensified.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".