Corn microbial diversity and its relationship to yield
Bibliographic record
Abstract
This study aimed to identify possible relationships between corn (Zea mays L.) productivity and its endosphere microbial community. Any insights would be used to develop testable hypotheses at the farm level. Sap was collected from 14 fields in 2014 and 10 fields in 2017, with a yield range of 10.1 to 21.7 tonnes per hectare (t/ha). The microbial sap communities were analyzed using terminal restriction fragment length polymorphism (TRFLP) and identified using an internal pure culture reference database and BLAST. This technique is rapid and inexpensive and is suitable for use at the grower level. Diversity, richness, and normalized abundances of each bacterial population in corn sap samples were evaluated to link the microbiome of a specific field to its yield. A negative trend was observed (r = –0.60), with higher-yielding fields having lower terminal restriction fragment (TRF) richness. A partial least square regression analysis of TRF intensity and binary data from 2014 identified 10 TRFs (bacterial genera) that positively, or negatively, correlated with corn yields, when either absent or present at certain levels or ratios. Using these observations, a model was developed that accommodated criteria for each of the 10 microbes and assigned a score for each field out of 10. Data collected in 2014 showed that sites with higher model scores were highly correlated with larger yields (r = 0.83). This correlation was also seen when the 2017 data set was used (r = 0.87). We were able to conclude that a positive significant effect was seen with the model score and yield (adjusted R2 = 0.67, F[1,22] = 46.7, p < 0.001) when combining 2014 and 2017 data. The results of this study are being expanded to identify the key microbes in the corn sap community that potentially impact corn yield, regardless of corn variety, geographic factors, or edaphic factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".