Interactive Effects of Copper on Alfalfa Growth, Soil Copper, and Soil Bacteria
Bibliographic record
Abstract
Copper sulfate (CuSO4) foot baths are a management practice used by dairy farms in an effort to control hoof infections. As an unintended consequence, agricultural soils experience Cu accumulation when spent foot baths are disposed of in wastewater lagoons utilized for irrigation purposes. We investigated the effect of Cu applications (up to 1000 mg kg-1) to a Xeric Haplocalcid (Declo series) and a Typic Calciaquoll (Logan series) on alfalfa (Medicago sativa) growth and Cu concentration, soil total and diethylenetriaminepentaacetic acid (DTPA)-extractable Cu, and the soil bacterial community diversity using ribosomal intergenic spacer analysis (RISA). Copper application up to 250 mg kg-1 did not affect alfalfa growth; above 500 mg Cu kg-1 alfalfa did not grow. The 100 and 250 mg kg-1 Cu application rates increased alfalfa Cu content grown in Declo soil, while the 250 mg kg-1 Cu application rate increased alfalfa Cu content when grown in the Logan soil. Regardless of initial application rate, 48 to 80% of the added Cu was still plant-available at the end of the study. Comparing DTPA-extractable Cu to alfalfa Cu concentrations, 63 or 95 mg kg-1 of DTPA-extractable soil Cu for the Declo and Logan soils, respectively, would be detrimental in terms of cattle dietary Cu intake. For Declo soils, bacterial diversity remained relatively stable across all Cu application rates; Logan soils saw a peak in bacterial diversity at the 50 mg kg-1 Cu application rate. Cluster analysis revealed differences in the bacterial RISA profiles between the lower and higher Cu application rates. To prevent excessive alfalfa Cu accumulation and negative impacts on the soil bacterial community, it is suggested available soil Cu not exceed 63 mg kg-1 in agroecosystems associated with these soil series.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".