Perennial Staple Crops: Yields, Distribution, and Nutrition in the Global Food System
Bibliographic record
Abstract
Staple crops, which have large amounts of carbohydrates, proteins, and/or fats, provide the bulk of calories in people's diets. Perennial plants, which can be productive for many years without the need for replanting, can produce staple foods and environmental benefits, but their agronomic and nutritional properties haven't been considered synthetically in comparison to annual staples. Here we offer a framework to classify perennial staple crops according to their nutritional categories and cultivation status. We assemble literature to report on the yield potential of 51 perennial staple crops, only 15 of which are well-characterized in existing global datasets. We show the extent and distribution of perennial staple crop production in relation to annual crop types, calculate the carbon stocks they hold, and analyze their nutritional content for three macronutrients and nine micronutrients. We found that most perennial staple crops are regional crops (not globally traded) that grow in the subtropics to tropics. At least one perennial staple crop in each of the five nutritional categories has yields over 2.5 t/ha, in some cases considerably higher, competitive with and in many cases exceeding those of nutritionally comparable annual staples. Perennial staple crops only comprise ~4.5% of total cropland. They hold a modest ~11.4 GtC above and below ground, less than one third of the anthropogenic carbon-equivalent emissions for the year 2018, but more than the ~9 GtC held by the same amount of annual cropland. If linear growth in land under perennial staple production continues to 2040, and replaces only annual cropland, an additional ~0.95 GtC could be sequestered. Many perennial crops also had competitive macronutrient density and yield (per unit area) compared to annual staples; moreover, specific perennial staples are abundant in specific micronutrients, indicating that they can be a nutrient-dense part of diets, unlike the most ubiquitous annual staple crops (corn, wheat, rice) that do not appear in the top 85th percentile for any of the nine micronutrients analyzed. Transition of land and diets to perennial staple crops, if judiciously managed, can provide win-win solutions for both food production and ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.003 | 0.001 |
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".