Nitrous Oxide Emissions in Southern Alberta Croplands in response to Nitrogen Rates, Fertigation and Moisture
Notice bibliographique
Résumé
Irrigated agriculture is an important source of global food supply due to its high production intensity; however, it is also a large user of water and nitrogen fertilizer, and therefore, a potential large contributor of N2O emissions. This study explores the viability of fertigation, a method of splitting N fertilizer by using existing irrigation equipment to apply in-crop applications of N added with irrigation water, as a means to reduce N2O emissions. This field study examined N fertilizer rates of 0, 60, 90 or 120 kg N ha-1 in wheat and canola crops applied once at seeding versus split N application using in-crop fertigation (i.e., fertilizer applied at two timings throughout the growing season: once at seeding at 30, 60 or 90 kg N ha-1 plus 30 kg N ha-1 through fertigation done at wheat tillering or canola 5-leaf growth stages in early June) in Southern Alberta during two experimental years (2015 and 2016). The cumulative emissions from weekly gas measurements revealed that N2O emissions, on a per-area and per-yield basis, were directly related to N fertilizer rates. When examining the effects of fertigation to split the N application, we found that canola was unaffected, however, at intermediate rates (60 and 90 kg N ha-1), fertigation effectively reduced N2O emissions by half in the wheat crop in 2016. These results suggest that lower N rates at crop seeding reduce the availability of N substrate in the soil early in the growing season when plant uptake is still low, thereby reducing the risk of N transformation to N2O. The use of fertigation to apply N later in the growing season, when plant N demand and uptake is relatively high, could lead to a better use of fertilizer compared to a one-time application in the early spring. These effects were amplified when high soil moisture in the early spring was coupled with higher seeding N fertilizer rates which led to even higher rates of N2O production. A laboratory incubation of the 2016 wheat treatments reinforced the principle that available N was associated with N2O production. An in-depth examination of the 90 and 120 kg ha-1 total N treatments showed that higher initial concentrations of nitrate in the incubation soils was highly correlated to the amount of N2O produced over a 32-day incubation. However, the cumulative N2O emissions in soil taken from the fertigation 90 kg N ha-1 rate treatment had contradictory results to those seen in the field, larger amounts of N2O were produced from fertigated compared to the unfertigated microcosms. This was a result of the higher concentration of N in the soil following the recent fertigation (applied 9 days prior to field sample collection). However, this difference in nitrate concentration was not detected at the higher 120 kg N ha-1 rate and may have been a result of the differences in the sizes of the plant nutrient sink; larger plants from the higher fertilizer rate plots were able to take up the additional N applied through fertigation. Moisture treatments that simulated a typical range of irrigated conditions were also imposed on the incubated soils but did not significantly affect the production of N2O. More extreme moisture fluctuations (higher moisture for prolonged periods simulating irrigation and early-season rainfalls) should be explored to determine the effect of intense irrigation regimes or weather patterns on N2O emissions.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».