Effect of Application of Liquid Swine Manure on Soil Organic Carbon and Enzyme Activities in Two Contrasting Saskatchewan Soils
Bibliographic record
Abstract
Repeated application of animal manure to agricultural fields as a source of plant nutrients has led to questions concerning the impact of this practice on soil organic carbon (C) and biochemical properties, specifically the activity of soil enzymes. There are also some environmental concerns of using livestock manure. The objectives of this study were to determine the effects of repeated applications of liquid swine manure (LSM) on total organic C (TOC), light fraction organic C (LFOC) and on the activity of the soil enzymes (arylsulfatase, alkaline phosphatase and urease) in two contrasting soil-climatic zones and cropping systems in Saskatchewan. Liquid hog manure was applied annually at 37,000 L ha-1 and 74,000 L ha-1rates for three years at Melfort (Mollic Cryoboralf) and for four years at Plenty (Typic Boroll) in Saskatchewan, Canada. Soil samples were collected to a depth of 0-15 cm in the spring of 2003 and analyzed for TOC, LFOC and enzyme activities. Annual application (37,000 L ha-1) and larger application made every two years (74,000 L ha-1) of LSM at Melfort increased LFOC, which was attributed to stimulation of plant growth and thus residue inputs, from the nutrients contained within the manure. Applications of LSM at 37,000 L ha-1 and 74,000 L ha-1 at the Plenty site increased both TOC and LFOC concentration when compared to the control. Soil at the Plenty site is a Typic Boroll of heavy clay texture, which aids in protecting soil organic matter (SOM) from decomposition by soil microorganisms. Melfort was the only site that responded to LSM applications in terms of increased enzyme activity, which may be a result of a shorter application history. The Melfort site also had significantly higher LFOC in the manure treatments compared to the other site and LFOC has been linked to enzyme activity. The results of this study indicate that it may take a long period of time for addition of LSM to produce measurable changes in TOC and LFOC, as the effect from LSM is mainly from the stimulation of plant growth rather than from direct additions of C, and the nutrients contained in the LSM may potentially enhance microbial decomposition. In soils that receive repeated applications of LSM, nutrient loading may contribute to reduced enzyme activity after a period of time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".