Effects of environmental factors and agronomic practices on greenhouse gas emissions
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».