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Record W2604881663 · doi:10.2134/csa2017.62.0413

Measuring Nitrous Oxide Emissions from agriculture

2017· article· en· W2604881663 on OpenAlexaboutno aff
Tracy Hmielowski

Bibliographic record

VenueCSA News · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNitrous oxideGreenhouse gasEnvironmental scienceDenitrificationAgricultureFertilizerFossil fuelSoil waterReactive nitrogenGreenhouse effectGlobal warmingEnvironmental protectionClimate changeNitrogenAgronomyWaste managementEcologyChemistryEngineeringSoil science

Abstract

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Photo by Andrew VanderZaag. Carbon dioxide (CO2) emissions are usually the first thing to come to mind when talking about greenhouse gases, but there is a suite of greenhouse gases of concern. One of these is nitrous oxide (N2O). Nitrous oxide occurs naturally in the atmosphere, but levels are increasing due to human activity. This increase is concerning as N2O has more than 300 times the warming potential of CO2 and can remain in the atmosphere for over 100 years. The greatest sources of N2O from human activity include agriculture and fossil fuel combustion. Agricultural systems contribute N2O through fertilizer application, the breakdown of crop residue, and animal waste. Nitrous oxide is a by-product of soil microbial processes, and the production of N2O depends upon soil characteristics, weather, carbon sources, and other environmental factors. Because these factors can vary from site to site, it is challenging to develop broad strategies to reduce N2O emissions. Recent articles in the Journal of Environmental Quality (JEQ) and Soil Science Society of America Journal (SSSAJ) report on different aspects of N2O emissions. From improving methods to measure N2O flux to evaluating the impact of seasonal cycles, fertilizer inputs, and cover crops on emissions, these articles point out the need to have accurate measures of N2O emissions and an understanding of how management actions and environmental variables can influence N2O flux. The primary pathways for production of N2O in soils are denitrification and nitrifier-denitrification. These biological pathways are influenced by soil properties, including oxygen availability. “When there is nitrogen available as a substrate for microbial metabolism, rates of N2O production from these pathways generally increase as soil oxygen concentrations decrease,” says Jen Owens, a recent Ph.D. graduate of Lincoln University in New Zealand and co-author of the new SSSAJ article, “Nitrous Oxide Fluxes and Soil Oxygen Dynamics of Soil Treated with Cow Urine” (as of this writing, its’ in the First Look section of the SSSAJ site but will be available soon at https://doi.org/10.2136/sssaj2016.09.0277). Despite N2O being a potent greenhouse gas, there is relatively little data exploring how soil physical properties impact soil oxygen concentration and subsequently influence N2O emissions. Laboratory work has demonstrated that relative soil gas diffusivity explained N2O emissions (see Balaine et al. 2013 at https://doi.org/10.2136/sssaj2013.04.0141). Owens explains, “Relative soil gas diffusivity is a physical variable that describes the rate of gas diffusion in soil relative to that of free air. It gives an indication of how easily oxygen moves into and through the soil by telling us how much of the pore space is available for diffusion of gas.” Wanting to determine how well soil moisture, soil oxygen content, and soil gas diffusivity explained N2O emissions, Owens and colleagues conducted a field study. The study took place in 2014 in an ungrazed perennial ryegrass pasture at Lincoln University. The experimental site is poorly drained, which Owens points out is an important detail because “soil oxygen concentrations are more prone to decreasing when soil moisture increases in poorly drained soils, which encourages greater rates of N2O production.” The authors compared soil with a simulated urine patch, using urine collected from cows off site, to soil without urine inputs. Soil in the simulated urine patches were reported to have daily fluxes of N2O that were 16 times greater than non-urine soils. In these patches, Owens and colleagues observed an immediate increase in N2O emissions after urine was applied and also measured greater emissions when flooding occurred. This demonstrated how, in a poorly drained site, urine patches provide a source of nitrogen that is readily converted to N2O when soil oxygen concentration drops. The results also support the hypothesis that soil oxygen and soil gas diffusivity would be correlated with N2O emissions. Relative diffusivity of oxygen decreases as soil water content increases, and fluxes of N2O increase as relative diffusivity of oxygen decline. “I think a significant outcome of this study is showing that the relationship between N2O emission and relative gas diffusivity is as good, or better, than the relationship between N2O emissions and water-filled pore space,” Owens says. Being able to explain the drivers of N2O emissions in the field will improve the ability of researchers to monitor conditions and the effectiveness of mitigation strategies. The graph shows the daily average nitrous oxide (N2O) fluxes and the daily average relative soil gas diffusivity (DP/DO) from the urine treatment for each day of the experiment. According to the results from Balaine et al. (2013), the DP/DO value of ∼0.006, marked with the red dashed line, is where N2O emissions are expected to reach a maximum. As shown in the graph, as DP/DO falls below 0.006, there is a increase in N2O fluxes, with N2O fluxes and DP/DO dynamics mirrored about a DP/DO value of 0.006. Manure applied to agricultural fields as fertilizer is another source of N2O emissions. Farmers have some control over fertilizer applications, including the timing and type of manure applied. Understanding how these options influence emissions could lead farmers to change their management practices to reduce one source of greenhouse gas. In the temperate zone, fertilizer applied in the fall sits through the winter and is present during the spring thaw conditions, which favor N2O emissions. However, N2O is not the only pathway for nitrogen loss from agricultural fields. “Perhaps there are these trade-offs between trying to reduce nitrous oxide emissions and potentially ending up increasing nitrate leaching,” says Andrew VanderZaag, a researcher with Agriculture and Agri-Food Canada. Nitrate (NO3) can leach into waterways and cause eutrophication or provide an indirect pathway for N2O emissions. Farmers may also have access to different types of manure. For example, some farmers apply raw manure while others use digested manure from biodigesters. Manure biodigesters reduce methane emissions and produce energy, having environmental and monetary benefits for farmers. However, digested manure has less carbon and more mineral nitrogen, and it is unclear if this change also alters nitrogen loss. To determine how timing and type of manure would impact nitrogen loss throughout the year, VanderZaag and colleagues measured N2O emissions and NO3 leaching. The results of this study were published in JEQ in an article titled, “Field Nitrogen Losses Induced by Application Timing of Digestate from Dairy Manure Biogas Production” (https://doi.org/10.2134/jeq2016.04.0148). The experiment ran from the fall of 2011 through the spring of 2014 in an experimental field where tile drainage is collected from individual plots, providing detailed data on NO3 leaching. Nitrous oxide emissions were measured using soil respiration chambers. By collecting data throughout the year, the authors were able to compare growing and non-growing season patterns and look at how spring thaw events impacted nitrogen loss. They were also able to compare a dry year to a wet year. The study showed total nitrogen loss was 6–10 kg ha–1 greater for fall-applied manure compared with spring applications. When comparing raw and digested manure, the authors report that overall nitrogen loss was similar for both types of fertilizer. To determine what was driving the seasonal difference in N loss, the researchers looked at data for N2O and NO3 loss individually. They report that N2O emissions were similar for fall and spring applications, but the timing of N2O loss was different. When manure was applied in the fall, peak N2O flux occurred during the spring thaw. When manure was applied in spring, peak N2O flux occurred during tillage and planting. “There might be a tendency to just look at the emissions during the growing season, but as we saw here, emissions could just be shifting from one time of the year to another,” notes VanderZaag, making it important to do year-round monitoring when comparing mitigation strategies. Left: Measuring N20 emissions in the winter. Below: Isolated tile drains where leaching was sampled in the study by VanderZaag et al. Photos courtesy of Andrew VanderZaag. The data for NO3 leaching provided an explanation for the difference in total N loss. There was greater NO3 leaching from the fall application plots. This was observed over the winter and early spring and was more pronounced in the wet year of the study. When considering management implications of farmer choices, VanderZaag says, “The spring application certainly looks like a better management practice, if at all possible.” The use of cover crops to reduce soil erosion and improve soil quality is a growing practice. Research scientist Xiying Hao and postdoctoral researcher Ben Thomas, both with Agriculture and Agri-Food Canada in Lethbridge, Alberta, set out to determine how cover crops might affect N2O emissions. In their article published in the January–February 2017 issue of SSSAJ, titled, “Non-Legume Cover Crops Can Increase Non-Growing Season Nitrous Oxide Emissions,” (https://doi.org/10.2136/sssaj2016.08.0269) they compare N2O emissions of two cover crops. When considering the impacts of cover crops, the authors point out how winter conditions in this region are unique. Minimal snow accumulation combined with warm chinook wind events result in a soil surface that is exposed to freeze-thaw cycles throughout the non-growing season. Nitrous oxide emissions can increase during these thaw events, making it important to determine if, and how, cover crops alter emissions during the non-growing season. The authors conducted a two-year study to quantify how cover crops impact N2O emissions, soil NO3, and water-extractable organic carbon (WEOC). Two cover crops, fall rye (fibrous roots, survives the winter) and oilseed radish (large taproot, winter-kills), were used as well as control plots without cover crops. Nitrate was measured given that it is a substrate for denitrification, and WEOC was measured as a proxy for carbon that is available for microbes. Increased levels of soil NO3 or WEOC associated with cover crops could provide a pathway for increased N2O emissions. In general, N2O emissions corresponded with thawing events in the non-growing season, and winter emissions were greater than spring and fall. The greatest non-growing season N2O fluctuation recorded was associated with a thaw event where soil surface temps rose by 7.7 °C in 13 days. In the first year of the study, 2013–2014, there were no significant differences among treatments. In the second year, 2014–2015, there was a significant difference in emissions between the two cover crops. Oilseed radish had greater N2O emissions compared with fall rye. The authors also report that cover crops had greater N2O emissions than non-cover crop control plots. There were also differences in NO3 and WEOC associated with cover crops. Oilseed radish treatments had greater soil NO3 than fall rye, and fall rye treatments had greater levels of WEOC than oilseed radish. The interactions among weather conditions, carbon and nitrogen availability, microbial activity, and N2O production are complex, and the authors state in the paper that “there was an apparent alternating relationship between WEOC limitation and NO3 limitation controlling the over-winter N2O fluxes.” Over the course of this study, the availability of NO3 appeared more limiting on N2O emissions than WEOC. Thomas points out that while the cover crop treatments had greater emissions, the N loss was relatively small, and that the benefits of cover crops may outweigh this negative aspect. In this region, the use of cover crops will need to take into account site-specific practices and goals of individual farmers and fields.

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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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

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

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 head0.043
GPT teacher head0.226
Teacher spread0.183 · 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 designObservational
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".

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Citations3
Published2017
Admission routes1
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