Sylvia Toet

In the United Kingdom (UK), forests on peaty gley, peaty podsol and deep peat soils contain ca. 50 % of the total forest soil C stock (Vanguelova, 2015). Many such forests were planted in the 1970s and 80s and are due for harvest, raising the question: what is the greenhouse gas (GHG) balance after felling? Typically, planted forests in the wetter UK uplands contain a network of ditches and ridge-with-furrows resulting in a complex mosaic of microtopographical features. Measuring GHG exchange from such a complex landscape is challenging; methane (CH₄) and nitrous oxide (N₂O) fluxes can vary greatly in both space and time, and ditches have been highlighted as potentially important GHG sources although they are challenging to measure. We used a combination of flux measurement techniques to quantify GHG fluxes and identify the drivers from the key microtopographies (ridges, hollows, ditches) within an upland forest in northern England immediately after clear felling. We deployed manual flux chambers, a SkyLine2D automated chamber system and two eddy covariance towers to measure carbon dioxide (CO₂), CH₄, and N₂O for an intensive campaign of five weeks. We used remote sensing to estimate the proportions of microtopographies and upscaled fluxes from the chamber to the forest block scale. We investigated the contribution of brash to the GHG emissions of harvest through a litter addition experiment. Cumulative flux estimates based on the different techniques and the GHGs measured varied considerably. We found that CO₂ fluxes did not differ between microtopographies but the needle litter in harvesting residues increased CO₂ emissions by ca. 33 %. Soil moisture was an important driver of both CH₄ and N₂O fluxes. Ditches were the largest source of CH₄ fluxes, followed by hollows and then ridges. The opposite pattern was seen for N₂O fluxes, which were greatest from ridges and other drier areas within the landscape. Following heavy rainfall, emissions of all GHGs increased rapidly over the next 24 hours.

In the United Kingdom (UK), forests on peaty gley, peaty podsol and deep peat soils contain ca. 50 % of the total forest soil C stock (Vanguelova, 2015). Many such forests were planted in the 1970s and 80s and are due for harvest, raising the question: what is the greenhouse gas (GHG) balance after felling? Typically, planted forests in the wetter UK uplands contain a network of ditches and ridge-with-furrows resulting in a complex mosaic of microtopographical features. Measuring GHG exchange from such a complex landscape is challenging; methane (CH₄) and nitrous oxide (N₂O) fluxes can vary greatly in both space and time, and ditches have been highlighted as potentially important GHG sources although they are challenging to measure. We used a combination of flux measurement techniques to quantify GHG fluxes and identify the drivers from the key microtopographies (ridges, hollows, ditches) within an upland forest in northern England immediately after clear felling. We deployed manual flux chambers, a SkyLine2D automated chamber system and two eddy covariance towers to measure carbon dioxide (CO₂), CH₄, and N₂O for an intensive campaign of five weeks. We used remote sensing to estimate the proportions of microtopographies and upscaled fluxes from the chamber to the forest block scale. We investigated the contribution of brash to the GHG emissions of harvest through a litter addition experiment. Cumulative flux estimates based on the different techniques and the GHGs measured varied considerably. We found that CO₂ fluxes did not differ between microtopographies but the needle litter in harvesting residues increased CO₂ emissions by ca. 33 %. Soil moisture was an important driver of both CH₄ and N₂O fluxes. Ditches were the largest source of CH₄ fluxes, followed by hollows and then ridges. The opposite pattern was seen for N₂O fluxes, which were greatest from ridges and other drier areas within the landscape. Following heavy rainfall, emissions of all GHGs increased rapidly over the next 24 hours.

In the United Kingdom (UK), forests on peaty gley, peaty podsol and deep peat soils contain ca. 50 % of the total forest soil C stock (Vanguelova, 2015). Many such forests were planted in the 1970s and 80s and are due for harvest, raising the question: what is the greenhouse gas (GHG) balance after felling? Typically, planted forests in the wetter UK uplands contain a network of ditches and ridge-with-furrows resulting in a complex mosaic of microtopographical features. Measuring GHG exchange from such a complex landscape is challenging; methane (CH₄) and nitrous oxide (N₂O) fluxes can vary greatly in both space and time, and ditches have been highlighted as potentially important GHG sources although they are challenging to measure. We used a combination of flux measurement techniques to quantify GHG fluxes and identify the drivers from the key microtopographies (ridges, hollows, ditches) within an upland forest in northern England immediately after clear felling. We deployed manual flux chambers, a SkyLine2D automated chamber system and two eddy covariance towers to measure carbon dioxide (CO₂), CH₄, and N₂O for an intensive campaign of five weeks. We used remote sensing to estimate the proportions of microtopographies and upscaled fluxes from the chamber to the forest block scale. We investigated the contribution of brash to the GHG emissions of harvest through a litter addition experiment. Cumulative flux estimates based on the different techniques and the GHGs measured varied considerably. We found that CO₂ fluxes did not differ between microtopographies but the needle litter in harvesting residues increased CO₂ emissions by ca. 33 %. Soil moisture was an important driver of both CH₄ and N₂O fluxes. Ditches were the largest source of CH₄ fluxes, followed by hollows and then ridges. The opposite pattern was seen for N₂O fluxes, which were greatest from ridges and other drier areas within the landscape. Following heavy rainfall, emissions of all GHGs increased rapidly over the next 24 hours.

Empirical in-situ measurements of ecosystem carbon dioxide respiration ( R _eco ) in high-latitude ecosystems remain limited, yet they are essential for understanding how tundra carbon cycling responds to climate warming across different environmental contexts and for reducing uncertainties in upscaled carbon budgets and carbon–climate feedbacks. Here, we present the TundraFlux Database, which to date is the most comprehensive synthesis of tundra R _eco responses to experimental warming. The database compiles over 24 000 daily-aggregated in-situ R _eco measurements from control and warmed plots with open-top chambers at 64 Arctic and alpine tundra sites across 12 countries. By coupling R _eco measurements with extensive metadata on climate, vegetation, and soil characteristics, the TundraFlux Database enables the integration of field-scale ecological processes into large-scale models, offering new opportunities to refine global carbon budgets and test predictions of tundra ecosystem responses to warming. Open access to the TundraFlux Database will empower the research community to better quantify and predict how warming alters carbon cycling in Arctic and alpine tundra ecosystems. The data can be accessed on Zenodo (https://doi.org/10.5281/zenodo.17976235, Schwieger, 2026a).

In the United Kingdom (UK), forests on peaty gley, peaty podsol and deep peat soils contain ca. 50 % of the total forest soil C stock (Vanguelova, 2015). Many such forests were planted in the 1970s and 80s and are due for harvest, raising the question: what is the greenhouse gas (GHG) balance after felling? Typically, planted forests in the wetter UK uplands contain a network of ditches and ridge-with-furrows resulting in a complex mosaic of microtopographical features. Measuring GHG exchange from such a complex landscape is challenging; methane (CH4) and nitrous oxide (N2O) fluxes can vary greatly in both space and time, and ditches have been highlighted as potentially important GHG sources although they are challenging to measure. We used a combination of flux measurement techniques to quantify GHG fluxes and identify the drivers from the key microtopographies (ridges, hollows, ditches) within an upland forest in northern England immediately after clear felling. We deployed manual flux chambers, a SkyLine2D automated chamber system and two eddy covariance towers to measure carbon dioxide (CO2), CH4, and N2O for an intensive campaign of five weeks. We used remote sensing to estimate the proportions of microtopographies and upscaled fluxes from the chamber to the forest block scale. We investigated the contribution of brash to the GHG emissions of harvest through a litter addition experiment. Cumulative flux estimates based on the different techniques and the GHGs measured varied considerably. We found that CO2 fluxes did not differ between microtopographies but the needle litter in harvesting residues increased CO2 emissions by ca. 33 %. Soil moisture was an important driver of both CH4 and N2O fluxes. Ditches were the largest source of CH4 fluxes, followed by hollows and then ridges. The opposite pattern was seen for N2O fluxes, which were greatest from ridges and other drier areas within the landscape. Following heavy rainfall, emissions of all GHGs increased rapidly over the next 24 hours.

Empirical in-situ measurements of ecosystem carbon dioxide respiration (R_eco) in high-latitude ecosystems remain limited, yet they are essential for understanding how tundra carbon cycling responds to climate warming across different environmental contexts and for reducing uncertainties in upscaled carbon budgets and carbon–climate feedbacks. Here, we present the TundraFlux Database, which to date is the most comprehensive synthesis of tundra R_eco responses to experimental warming. The database compiles over 24,000 daily-aggregated in-situ R_eco measurements from control and plots warmed with open-top chambers at 64 Arctic and alpine tundra sites across 12 countries. By coupling R_eco measurements with extensive metadata on climate, vegetation, and soil characteristics, the TundraFlux Database enables the integration of field-scale ecological processes into large-scale models, offering new opportunities to refine global carbon budgets and test predictions of tundra ecosystem responses to warming. Open access to the TundraFlux Database empowers the research community to better quantify and predict how warming alters carbon cycling in Arctic and alpine tundra ecosystems.

Nitrous oxide (N2O) is a potent greenhouse gas (GHG) with a global warming potential 265 times that of carbon dioxide (CO2) over 100 years. Contributing approximately 70% of global anthropogenic N2O emissions, agriculture represents the largest area of uncertainty for GHG reporting and the most challenging sector for emissions reduction: global N2O emissions are increasing at double the rate estimated by the Intergovernmental Panel on Climate Change (IPCC). The largest source of agricultural N2O emissions is from application of inorganic-N fertilisers, the manufacture of which produces more than 1% of global CO2 emissions and consumes 1% of global energy output.However, typical crop N uptake efficiency (NupE) means approximately half the fertiliser doesn’t reach the target plant, causing further ecological problems, such as biodiversity loss from eutrophication and atmospheric deposition. The extent to which microbial immobilisation of fertiliser N contributes to the NupE value of ca. 60% is currently unknown. If N immobilisation is found to be a large contributor to reducing N available to crops, this offers new opportunities to better manage fertiliser N inputs. Critically, with a growing global population, it is vital that we can increase food crop yields, and more efficient use of water and nutrients could help close the 70% ‘yield gap’ between potential and actual crop yields. Finally, inorganic N is the largest single cost in gross margins for wheat production and prices are rising. Increased NupE therefore represents a key opportunity for farmers to increase their financial sustainability. We hypothesised that under the conventional management of three applications of inorganic N in the spring, crops do not have the ability to outcompete the fast-growing soil microbial community for N, and that by supplying N to the crop in a ‘little and often’ approach, we could increase NupE by reducing immobilisation, and consequentially reduce N2O emissions. We conducted a field study of a winter wheat crop on a northern UK farm to investigate this, which compared conventional N fertiliser management (220 kg N ha-1 over three applications) of ammonium nitrate, to a little and often approach (220 kg N ha-1 over six applications) and an untreated (0 kg N ha-1) control. We followed the crop until harvest, and continuously measured N2O emissions and net ecosystem exchange of CO2 using a skyline2D automated flux system and also measured C and N pools in soil, plants and microbial biomass to assess changes in N uptake and allocation.We will present data which shows the outcome of plant-microbe competition for N in our agricultural system, and discuss the implications of different N fertiliser management for yield, profitability and GHG mitigation.

The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a clear need to measure emissions and verify the efficacy of mitigation measures. To this end, reliable estimates are needed of the GHG balance at the national scale and over long time periods, but these estimates are difficult to make accurately. Because measurement techniques are generally restricted to relatively small spatial and temporal scales, there is a fundamental problem in translating these into long‐term estimates on a regional scale. The key challenge lies in spatial and temporal upscaling of short‐term, point observations to estimate large‐scale annual totals, and quantify the uncertainty associated with this upscaling. Here, we review some approaches to this problem and synthesize the work in the recent UK Greenhouse Gas Emissions and Feedbacks Program, which was designed to identify and address these challenges. Approaches to the scaling problem included: instrumentation developments which mean that near‐continuous data sets can be produced with larger spatial coverage; geostatistical methods which address the problem of extrapolating to larger domains, using spatial information in the data; more rigorous statistical methods which characterize the uncertainty in extrapolating to longer time scales; analytical approaches to estimating model aggregation error; enhanced estimates of C flux measurement error; and novel uses of remote sensing data to calibrate process models for generating probabilistic regional C flux estimates.

Nitrous oxide (N2O) is a potent greenhouse gas (GHG) with a global warming potential 298 times that of carbon dioxide (CO2). Measurements of soil N2O emissions typically use manual chambers, with samples taken at low temporal resolution over long durations (months), or at higher temporal resolution (multiple samples per day) over short durations. Automated GHG flux systems have allowed the measurement of high frequency (sub-daily) N2O fluxes over longer periods (weeks to months), revealing that emissions can vary diurnally by up to 400% in agricultural soils.Contributing approximately 70% of global anthropogenic N2O emissions, agriculture represents the largest area of uncertainty for GHG reporting and the most challenging sector for emissions reduction: global N2O emissions are increasing at double the rate estimated by the Intergovernmental Panel on Climate Change (IPCC). Improvements to agricultural GHG emission estimates have increased the accuracy of GHG reporting, but N2O emissions from agricultural soils still contribute 25% of the uncertainty of total GHG emissions across all sectors. Our project, diurnal variation in soil nitrous oxide emissions (DIVINE), combines field and laboratory experiments that exploit high-resolution, robotic and continuous N2O measurement technology, to investigate the drivers and mechanisms underpinning diurnal variation in N2O.We will present work from a field study investigating the effect of soil properties and nitrogen (N) fertiliser management on diurnal variation in N2O emissions from a wheat crop. We assess how N fertiliser application (rate and frequency) and soil gas diffusivity (determined by bulk density and rain events), affect the depth of N2O production and N2O transport in the soil, and resultant impacts on the peak timing and amplitude of diurnal N2O emissions across the crop life cycle and seasons.N2O emissions will be compared in paired transects with contrasting bulk density but similar soil texture and history, with three ammonium nitrate fertiliser scenarios. N2O is being measured continuously using SkyLine2D automated flux chamber technology. To resolve depth/gas transfer coefficients after N fertiliser and rain events, we will measure soil N2O concentration profiles across the rooting zone in discrete campaigns during the crop life cycle.Further, we will discuss how our data will be used to improve the accuracy of N2O emission factors by accounting for environmental and diurnal variation. Bayesian statistical modelling will be used to represent the spatial and temporal distribution of emissions following fertilisation, and the effects of known environmental factors (e.g. temperature, soil moisture, light intensity), as well as the residual effect explicable by the diurnal cycle.