Soil carbon and nitrogen response to 25 annual cattle manure applications
Bibliographic record
Abstract
Abstract Improving manure management to benefit both agricultural production and the environment requires a thorough understanding of the long‐term effects of applied manure on soil properties. This paper examines the effect of 25 annual solid cattle manure applications on soil organic carbon (OC), total N (TN), and KCl‐extractable NO3‐N and NH4‐N under both non‐irrigated and irrigated conditions. After 25 annual manure applications, OC and TN contents increased significantly with the rate of manure application at the top two sampling depths (0–15 cm and 15–30 cm), and the increases were not affected by the irrigation treatment. The NO3 content increased at all sampling depths with greater increases observed under non‐irrigated conditions, while NH4 content was not affected by manure application rates or the irrigation treatment. The changes in OC and TN at the surface (0–15 cm) and 15–30 cm depth were dependent on the cumulative weight of manure added over the years. The relationships between cumulative manure OC added and soil OC content and between cumulative manure TN added and soil TN content were linear and not affected by the irrigation treatment. For every ton of manure OC added, soil OC increased by 0.181 g kg–1 in the topsoil (0–15 cm). Similarly, for every ton of manure TN added, surface soil TN increased by 0.192 g kg–1. The linear relationship between manure C added and soil C content suggests that the soil had a high capacity for short‐term C sequestration. However, the total amount of NO3‐N in the soil profile (0–150 cm) was affected by both the manure application rates and the irrigation treatment. A large amount of NO3 accumulated in the soil, especially under non‐irrigated conditions. The extremely high level of NO3 in the soil increases the potential risk of surface and groundwater pollution and losses to atmosphere as N2O.
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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 | 0.000 | 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".