Closing Research Investment Gaps for a Global Food Transformation
Bibliographic record
Abstract
Recent calls for a global food transformation have centered on simultaneously improving human and environmental health, recognizing that food and nutrient diversity have declined over time while food systems have exacted a heavy climate and ecological toll. Grain legumes and coarse grain crops provide important human nutrition and environmental benefits, but the production and consumption of many of these crops remains relatively low compared to major commodities, such as maize, wheat, rice, and soy. Outstanding hurdles to scaling up these “minor commodity” crops include (among other things) their relatively lower yields, and lower farmer adoption, based partly on actual or perceived profitability and marketability. We hypothesize that these limitations are attributable in part to unequal funding for these crops' research and development (R&D) both on a national and global scale. In the United States, we show that investment patterns for a snapshot of USDA-funded research grants from 2008 to 2019 consistently favor major commodity crops, which received 3 to 4.5 times more funding and 3 to 5 times as many grants than the minor commodity crop groups. This current USDA funding allocation poses a barrier to food system transformations. Achieving nutritious diets for planetary health requires more public agricultural investment toward minor commodity crops and increased collaboration between public health, nutrition, agriculture, and environmental sectors.
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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 | 0.049 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".