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Record W7018531777

Does pasture rejuvenation by sod-seeding with non-bloat legumes affect greenhouse gas emissions?

2024· dissertation· en· W7018531777 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2024
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPastureGreenhouse gasGrazingCarbon footprintLife-cycle assessmentForageLivestockClimate change
DOInot available

Abstract

fetched live from OpenAlex

Pasture rejuvenation through sod-seeding (i.e., the use a zero-till drill to place the seed directly into the soil) with non-bloat legumes, such as cicer milkvetch (Astragalus Cicer L.) or sainfoin (Onobrychis vicifolia Scop.), has emerged as a favored strategy for ranchers in the western Canadian prairies due to it being both time- and cost-effective. Meanwhile, this management also makes alterations to soil carbon and nitrogen cycling as well as to the diet of cattle grazing the pastures. Yet, our understanding on how these changes influence the greenhouse gas (GHG) budgets of the pastures remains limited. The goal of this research was to assess the impact of rejuvenating pastures by sod-seeding cicer milkvetch or sainfoin into a depleted meadow bromegrass-alfalfa mixed pasture on GHG emissions and their contribution to the total GHG footprint of the pasture system. To achieve this goal, field studies were conducted in east-central Saskatchewan, Canada to: (1) evaluate and compare GHG emissions between the rejuvenated pastures and the depleted pasture, and (2) quantify GHG emissions from dung and urine patches deposited by cattle grazing these pastures. Finally, the legume option resulting in the lowest GHG footprint was identified by integrating the GHG data with enteric methane (CH4) production by the cattle. \nThe results indicate pasture rejuvenation through sod-seeding had only a minimal impact on soil-derived GHG emissions relative to the control (depleted) pasture, likely due to the limited soil disturbance associated with this method. At the paddock-scale, annual GHG emissions averaged 10.11 Mg CO2-C ha-1, 2.54 kg CH4-C ha-1 (uptake), and 0.18 kg N2O-N ha-1, with no significant differences among pasture types. However, changes in grazing diet resulting from the pasture rejuvenation were found to affect urine patch N2O emissions, with the highest cumulative N2O emissions associated with beef cattle grazing on the depleted pastures sod-seeded with cicer milkvetch. Surprisingly, this did not reflect differences in the available N content of the urines, suggesting a potential link to the presence of secondary metabolites such as hippuric acid. Notably, the dung and urine patches yielded distinctly different N2O emission factors, averaging 0.03% and 0.26%, respectively. Within the pastures, landscape position emerged as a dominant regulatory factor for CO2 and CH4 emissions—with the lower slope positions exhibiting the highest CO2 emissions and the lowest CH4 uptake, likely due to denser vegetative cover in these areas due to an accumulation of soil moisture. Such landscape-scale patterns remained unaffected by dung/urine deposition. Partial C footprints for the pastures (based on non-CO2 emissions from the soil, cattle excreta, and enteric CH4) were developed and it was determined that there were no significant differences between the rejuvenated and depleted pastures. Averaged across pastures, the C footprint was 970 kg CO2eq ha-1 yr-1, with enteric CH4 being the largest contributor to the footprint. Insights gained from this study will be valuable for ranchers and policymakers in developing sustainable pasture management strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.160
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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