Likely decline in the number of farms globally by the middle of the century
Bibliographic record
Abstract
Farm number and size are deemed important for a variety of social and environmental outcomes,including yields, input use efficiencies, biodiversity, crop diversity, climate change, and concentrationof power in food systems. Using a model incorporating theoretical drivers of the creationand consolidation of farms within countries, I historically reconstruct the number of farms onEarth over 1969-2013 and predict their future evolution. I show that under current developmenttrajectories the number of farms globally will likely decline from the current 616M (95% CIs495M-779M) in 2020 to 272M (95% CIs 200M-377M) by the end of the 21st century, with averagefarm size doubling. In some regions, Europe and Northern America, we will see a continueddecline from recent history, whereas in other regions, including Asia, Middle East & NorthAfrica, Oceania, and Latin America and the Caribbean, we will see a turning-point from farmcreation to widespread consolidation. The turning point also occurs for Sub-Saharan Africa, butmuch later in the century. This world in which significantly fewer large farms replace numeroussmaller ones carries major rewards and risks for the human species and the food systems whichsupport it.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".