Improving field legume nodulation by crushing nodules onto seeds: implications for small-scale farmers
Bibliographic record
Abstract
One billion people globally suffer from protein (amino acid) malnutrition. Grain legumes represent a solution. They recruit symbiotic rhizobia bacteria from soil into root nodules, where the rhizobia convert atmospheric nitrogen gas (N2) into ammonia (NH3) which serves as a building block for chlorophyll and protein. However, when a legume species is newly introduced to a region, yields can be low due to incompatible soil rhizobia. Millions of subsistence legume farmers can benefit from inoculation with exotic rhizobia bacteria, but many subsistence farmers especially in Africa do not benefit from commercial inoculants due to real-world constraints. Here, in a sequential series of indoor and outdoor experiments, we show that root nodules (rhizobia habitats) can be harvested and crushed onto legume seeds, ultimately improving nodulation and chlorophyll under field conditions. 16S rRNA metagenomic sequencing confirmed that nodule crushing onto seeds effectively transferred rhizobia to next-generation nodules. Therefore, nodule crushing represents a simple method to diffuse elite rhizobia strains. However, exotic rhizobia come with risks and limitations. Therefore, in addition to diffusing elite rhizobia, we propose that this simple, decentralized technology can also empower smallholders to improve indigenous strains or indigenize exotic strains by repeated nodule crushing from healthy plants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".