Using a soil bacterial species balance index to estimate potato crop productivity
Bibliographic record
Abstract
The development of 'molecular-omic' tools and computing analysis platforms have greatly enhanced our ability to assess the impacts of agricultural practices and crop management protocols on soil microbial diversity. However, biotic factors are rarely factored into agricultural management models. Today it is possible to identify specific microbiomes and define biotic components that contribute to soil quality. We assessed the bacterial diversity of soils in 51 potato production plots. We describe a strategy for identifying a potato-crop-productivity bacterial species balance index based on amplicon sequence variants. We observed a significant impact of soil texture balances on potato yields; however, the Shannon and Chao1 richness indices and Pielou's evenness index poorly correlated with these yields. Nonetheless, we were able to estimate the portion of the total bacterial microbiome related to potato yield using an integrated species balances index derived from the elements of the bacterial microbiome that positively or negatively correlate with residual potato yields. This innovative strategy based on a microbiome selection procedure greatly enhances our ability to interpret the impact of agricultural practices and cropping system management choices on microbial diversity and potato yield. This strategy provides an additional tool that will aid growers and the broader agricultural sector in their decision-making processes concerning the soil quality and crop productivity.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".