Greenhouse gas emissions from drip irrigated tomato fields
Bibliographic record
Abstract
Irrigation practices change the soil moisture in agricultural fields, and in turn influence the emissions of greenhouse gases.A two year field study was conducted to assess the emissions of CO2, CH4 and N2O from surface drip and subsurface drip irrigated tomato fields in Southwestern Ontario, Canada.Gas fluxes were obtained through the static chamber method, taking samples every 15 minutes over a one hour time period.Soil moisture and soil temperature were measured once per chamber per hour and used to help explain the gas emissions.A peak mean N2O flux at 405 µg N2O-N m -2 h -1 was observed from the five chambers in the surface drip irrigation treatment on July 8 th , 2013, shortly following a rainfall event.Most N2O fluxes which occurred during the irrigated portion of the growing season were around 50 µg N2O-N m -2 h -1 in both treatments, water added here was less than some of the large rainfall where peaks occurred.Both the lowest CO2 mean treatment fluxes (12 mg CO2-C m -2 h - 1 ) and highest (12 mg CO2-C m -2 h -1 ) were observed in the surface drip irrigation plots.Seasonal emissions of CO2 were significantly greater in surface drip plots than subsurface drip plots in 2013, but not in 2012, and this is likely attributed to soil temperature differences.Methane fluxes were mainly negative, indicating that the soil is a sink, as opposed to a source for this gas.Generally, there was no iii significant difference in soil moisture between the types of drip system.Consequently, there were only a few days which showed significant differences between subsurface drip irrigation and surface drip irrigation treatments for the hourly collected gas fluxes throughout the two growing seasons.Overall, the use of subsurface drip irrigation or surface drip irrigation does not affect the emissions of greenhouse gases from the tomato fields in this study.iv Résume M.Sc.Kerri Pearl Edwards Génie des bioressources Les techniques d'irrigation affectent l'humidité du sol dans les terres agricoles, ce qui affecte les émissions de gaz à effets de serre.Une étude sur le terrain d'une durée de deux ans a été menée dans le sud-ouest de l'Ontario au Canada afin de déterminer les émissions de CO2, CH4 et N2O de champs de tomates irrigués au goutte-à-goutte de surface et au goutte-à-goutte souterrain.Les flux de gaz ont été obtenus en prenant des échantillons à chaque 15 minute pour une durée d'une heure, en utilisant la méthode de chambre statique.L'humidité et la température des sols ont été mesurés et utilisés afin d'expliquer les émissions de gaz.La moyenne de flux de N2O a atteint un sommet de 405µg N2O-N m -2 h -1 dans le cas de l'irrigation au goutte-à-goutte de surface, peu de temps après un épisode de pluie.La majorité des flux de N2O qui se sont produits pendant le moment de la saison utilisant de l'irrigation était d'environ 50 µg N2O-N m -2 h -1 dans les deux traitements.La plus faible (12mg CO2-C m -2 h -1 ) et la plus haute (123mg CO2-C m -2 h -1 ) moyenne des flux des traitements ont toutes les deux été observées dans les champs irrigués au goutte-à-goutte de surface.Les flux de CH4 étaient principalement négatifs ce qui indiquent que les sols sont des puits, plutôt qu'une source de ce gaz.Une différence significative entre les flux des traitements a été démontrée seulement pour un nombre limité v de jour parce que l'humidité des sols créée par les deux systèmes d'irrigation de goutte-à-goutte était similaire.Les émissions saisonnières de CO2 étaient significativement plus hautes dans le cas des champs irrigués au goutte-àgoutte de surface que les champs irrigués au goutte-à-goutte souterrain en 2013, mais pas en 2012, ce qui est probablement dû aux différences dans les températures des sols.Globalement, l'utilisation de l'irrigation au goutte-à-goutte de surface ou de l'irrigation au goutte-à-goutte souterraine n'a pas d'effet majeur sur les émissions de gaz des sols dans les champs de tomates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".