Temporal Effects of Biochar and Dairy Manure on Physicochemical Properties of Podzol: Case from a Silage-Corn Production Trial in Boreal Climate
Bibliographic record
Abstract
A field experiment was conducted to evaluate the effects of biochar and dairy manure (DM) on physicochemical properties of podzolic soils, as well as to establish the relationships between selected physicochemical properties and soil electrical conductivity (EC) in a silage-corn production system. Nutrient requirements of the crop were met through different nutrient sources considering soil nutrient status, nutrient availability from DM (DM, DM + biochar) and regional crop nutrient recommendations. Experimental treatments included control, inorganic nitrogen (IN), IN + biochar, IN + DM, and IN + DM + biochar. DM was applied at 30,000 L ha−1, whereas biochar was applied at 20 Mg ha−1 and mixed within the top 20 cm of the soil. Disturbed soil samples as well as time domain reflectometry (TDR) measurements were collected from treatment plots on four field days. Results showed no significant (p > 0.05) treatment effects on soil pH and cation exchange capacity (CEC) within each field day. However, significant temporal effects were recorded for pH, EC, apparent electrical conductivity (ECa) and electrical conductivity of the soil solution (ECw). Soil depth (0–10 cm and 10–20 cm) had no significant effect on treatments. Significant positive correlations were recorded for EC with soil organic carbon and CEC (ECa, ECw 0–10 cm, & 10–20 cm, p = 0.000). Correlation results show that ECa measurements as a proxy to investigate the variability of key soil properties over large areas, but further investigation between ECa data and soil properties should be carried out to address uncertainties associated in predicting these properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".