An open-access dataset of crop production by farm size from agricultural censuses and surveys
Bibliographic record
Abstract
This dataset is a cross-country convenience sample of primary data measuring crop production and/or area by farm size for 55 countries that underlies the article entitled "How much of the world׳s food do smallholders produce?" (DOI: https://doi.org/10.1016/j.gfs.2018.05.002). The harmonized dataset is nationally representative with subnational resolution, sourced from agricultural censuses and household surveys. The dataset covers 154 crop species and 11 farm size classes, and is ontologically interoperable with other global agricultural datasets, such as the Food and Agricultural Organization׳s statistical database (FAOSTAT), and the World Census of Agriculture (WCA). The dataset includes estimates of the quantity of food, feed, processed agricultural commodities, seed, waste (post-harvest loss), or other uses; and potential human nutrition (i.e., kilocalories, fats, and proteins) generated by each farm size class. We explain the details of the dataset, the inclusion criteria used to assess each data source, the data harmonization procedures, and the spatial coverage. We detail assumptions underlying the construction of this dataset, including the use of aggregate field size as a proxy for farm size in some cases, and crop species omission biases resulting from converting local species names to harmonized names. We also provide bias estimates for commonly used methods for estimating food production by farm size: use of constant yields across farm size classes when crop production is not available, and relying on nationally representative household sample surveys that omitted non-family farms. Together this dataset represents the most complete empirically grounded estimate of how much food and nutrition smallholder farmers produce from crops.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.027 |
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".