Effects of environmental factors and agronomic practices on greenhouse gas emissions
Bibliographic record
Abstract
Identifying environmental factors that enhance the efficacy of best management practices (BMPs) in mitigating cropland nitrous oxide (N2O) emissions is crucial for reducing emissions. The research reported in this thesis quantitatively assessed BMPs for their efficacy in reducing emissions by focusing on variations in soil conditions, season, or climate. Practices of water table and nutrient management were investigated through field studies on sandy loam and silty clay soil sites in southwestern Quebec, Canada. Three non-growing season practices including cover crops (CC), nitrification and urease inhibitors (NI + UI) and tillage were investigated using meta-analysis. In addition, the effects of changing climate on winter N2O emissions were evaluated using the Denitrification and Decomposition (DNDC) model.Long-term effects of controlled drainage with sub-irrigation (CDS) on crop yield and N2O emissions revealed that CDS could improve grain yield compared to regular free tile drainage (FD), depending on the growing season rainfall and its temporal distribution. CDS positively affected grain yield based on data collected over 12 years at one of the study sites. Lower yields under CDS were observed when excessive monthly rainfall (230 mm) occurred during the crop’s vegetative period. In three of six years, N2O fluxes under CDS treatments were greater by 49% than those under FD, but 45% lower in the remaining years, implying that – notwithstanding the quantity of growing season rainfall – CDS does not necessarily always produce greater fluxes than FD. N2O fluxes coincided more with fertilizer application. The effects of soil type (sandy loam vs. silty clay) on GHG emissions were examined by investigating three nitrogen fertilization rates (140, 180, 220 kg N ha-1) on yield-scaled N2O emissions. Grain yields increased with N fertilization rate in both soils. Yields were greater on the sandy loam than on the silty clay. Yield-scaled N2O emissions from the silty clay soil were lowest at the 180 kg N ha-1 fertilization rate, compared to 140 kg N ha-1 for the sandy loam. Under grain corn production, yield-scaled emissions from the poorly drained silty clay soil were five-fold greater than well-drained medium-textured sandy loam soil. A meta-analysis study focusing on over-winter cropland N2O emissions showed that the non-growing season emissions ratio to full-year N2O emissions ranged between 5% to 91%. No-till significantly reduced N2O emissions by 28% compared to conventional tillage, and this effect was more pronounced in drier climates. NI + UI also significantly reduced over-winter emissions by 23% compared to conventional fertilizers, and this effect was more evident in medium-textured soils than coarse soils. CC showed an overall reduction potential of 18%; however, this effect was not significant. Under the CC practice, N2O emissions were reduced in humid climates but increased in drier climates, while no-till and NI + UI practices effectively reduced over-winter emissions in dry and humid winter regions on all soil types. The DNDC model was used to simulate historical and future winter emissions over 30 years for intensive grain corn production in southwestern Quebec. A historical analysis showed that the greatest average winter N2O emissions occurred in warm and wet years. Future scenario [2038 - 2067] analysis showed a 10% rise in winter N2O emissions, associated with an increase of winter soil temperature of 1°C, soil moisture (WFPS) increase of 8%, and snow water equivalent decrease of -1 mm yr-1. These simulations highlighted the need to focus on mitigation measures for winter N2O emissions from agricultural soils.Farm practices’ effectiveness in mitigating GHGs varied substantially due to differences in soil types and climate patterns. These results will be useful to stakeholders and policymakers seeking to make decisions regarding promoting and adopting BMPs for climate change mitigation solutions
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".