Minor effects of no-till treatment on GHG emissions of boreal cultivated peat soil

doi:10.21203/rs.3.rs-2947147/v1

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Minor effects of no-till treatment on GHG emissions of boreal cultivated peat soil

https://doi.org/10.21203/rs.3.rs-2947147/v1

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published 23 Nov, 2023

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Greenhouse gas emissions of a spring cereal monoculture under conventional tillage and no-till treatments were measured in a peatland in Southwestern Finland for three years in 2018–2021. Nitrous oxide (N₂O), carbon dioxide (CO₂) and methane (CH₄) fluxes were measured with an opaque chamber technique approximately biweekly throughout the years. During the growing season, canopy net ecosystem exchange (NEE) was measured with a transparent chamber technique and hourly ecosystem respiration (ER) and gross photosynthesis (GP) were modelled with empiric models. On average, the annual emissions were 6.4 ± 2.4 Mg CO₂-C ha ^− 1 yr^− 1, 7.6 ± 3.5 kg N₂O -N ha^− 1 yr^− 1, and − 0.35 ± 0.42 kg CH₄-C ha^− 1 yr^− 1 for NEE, N₂O and CH₄, respectively. The effect of no-till management on the GHG balance was non-consistent through years and thus generally of minor significance. No-till reduced the annual CO₂ emissions by 24% in 2019 and N₂O emissions by 33% in 2020 compared to conventional tillage while there were no differences between the treatments in other years. Measured differences in ER occurred mostly during the winter periods, especially after ploughing. The results indicated that no-till may reduce CO₂ and N₂O emissions from cultivated peat soil, but it does not lead to large consistent reductions during the first years of NT management.

organic soil

tillage

N2O

CO2

CH4

As a large reservoir of organic carbon, peatlands have a key role in climate regulation (Nichols and Peteet 2019). Pristine peatlands act as a carbon sink but drainage for forestry or agriculture leads to peat oxidation and decomposition causing greenhouse gas (GHG) emissions (Joosten et al. 2016). Although the area of drained organic soils is a small fraction of cultivated soils, drained organic soils are a large GHG emission source in the global scale (Joosten et al. 2016) but especially in countries with high peatland coverage such as Finland where > 10% of cultivated soils are peatlands (Statistics Finland 2022). In drained peatlands, CO₂ emissions constitute the majority of the climate impact, followed by N₂O emissions (Tan et al. 2020) whereas CH₄ fluxes have a minor role as a source or sink of carbon (Maljanen et al. 2010; Kandel et al. 2018).

In terms of mitigating GHG emission, it is necessary to find means to reduce the climatic effect of drained peatlands. Rewetting should be prioritized (Humpenöder et al. 2020) but due to economic and social reasons also other types of climate-wise management practices are sought for. In no-till management (NT), seeds are directly sown to the stubble which reduces disturbance of the soil surface compared to conventional tillage (CT) with annual ploughing and harrowing. NT increases topsoil density and reduces porosity compared to CT (Elder and Lal 2008b) which might lead to reduced gas exchange (Maljanen et al. 2003b) and reduced emissions in organic soils. NT brings immediate savings in workload and fuel usage (West & Marland, 2002) and prevents erosion (Honkanen et al. 2021) but its role in carbon sequestration and reducing GHG emissions is less straightforward. It has been commonly found that NT increases the carbon content in the very topsoil and with limited potential also in the whole soil profile (Ogle et al. 2019; Cai et al. 2022). The effects of NT on the emissions of N₂O are similarly variable with some indication of benefits from long-term NT management (Van Kessel et al. 2013). Several meta-analyses have been performed on the effects of NT on GHG emissions in mineral soils (Ogle et al. 2012; Van Kessel et al. 2013; Shakoor et al. 2021) but such data compilations from organic soils are lacking as only a couple of studies on GHG emissions in NT have been performed on organic soils. Elder and Lal (2008b) reported lower N₂O emissions in NT but no significant difference in CO₂ emissions compared to CT in an organic soil. In the study of Regina and Alakukku (2010) there was a tendency of lower CO₂ emissions and higher N₂O emissions in NT compared to CT at several Finnish sites of which one was on organic soil.

The aim of this study was to quantify the climatic effects of tillage managements in an arable organic soil. A three-year experiment with CT and NT treatments was set up in Southwestern Finland for measuring carbon exchange and emissions of N₂O and CH₄. We hypothesized that reducing tillage would decrease losses of carbon in soil respiration due to less soil disturbance and increase N₂O emissions due to a dense topsoil layer.

Study site

The experimental field was located in Kuuma, Jokioinen in Southwestern Finland (60.22 ^oN, 24.78 ^oE, 110 m a.s.l.). The field has been drained and cultivated at least from the end of the 19th century and it has a subsurface drainage system. Prior to the beginning of this experiment, the field was in monoculture with spring cereals, conventionally cultivated and ploughed for several years.

[FIGURE 1 HERE]

The experimental site was placed between the subsurface drainage pipes so that each plot (8 x 18 m = 144 m²) had hydrologically as similar conditions as possible (Fig. 1). The experimental setup was a fully randomized plot experiment in four blocks. Originally, the field was set up with four treatments: half of the CT and NT plots were sown with a cover crop (Italian ryegrass, Lolium multiflorum). However, the ryegrass did not manage (mean yield 410 ± 300 kg dry matter ha^− 1) and as it did not cause any differences in the results between treatments, we decided to divide the plots into two treatments only (CT and NT) resulting in eight replicate plots per treatment in the data analysis. Our cover crop yields were clearly below the mean Italian ryegrass biomass of 2.2 Mg dry matter ha^− 1 observed in boreal climatic conditions (Känkänen 2019) or the < 1 Mg ha^− 1 biomass estimated necessary for carbon sequestration (Blanco-Canqui et al. (2020)). Despite not considering it in the statistical comparisons, the cover crop leaf area index was included in the models and calculations for GHG fluxes.

The whole field was ploughed in the spring 2017, and the experiment was set up on May 24th, 2018. The CT treatment was ploughed in spring 2018 (May 24th, 2018), spring 2019 (May 20th, 2019), autumn 2019 (Nov 18th, 2019) and autumn 2020 (Nov 30th, 2020). The sowing dates were June 14th, 2018, May 22nd, 2019 and June 12th, 2020.

The experimental field was growing oat (Avena sativa) in 2018, barley (Hordeum vulgare) in 2019 and oat in 2020. The sowing density was 500 seeds m^− 2 for both barley and oat. The plots were fertilized each year with Yara Mila Y3 fertilizer which contains 26% nitrogen (N), 3% phosphorus (P) and 8% potassium (K). The applied amount was 260 kg ha^− 1 each year which corresponds to N rate of about 60 kg per hectare. The fertiliser was given once a year at the time of sowing. To control weeds during the experiment, herbicides such as Premium Classic (12 kg ha^− 1), Starane (0.25 l ha^− 1), Refine Super SX (0.25 kg ha^− 1) and Primus (0.006 l ha^− 1) were applied annually as a one-time treatment according to the user manual.

Ploughing, sowing, plant protection, fertilizing and harvesting were done using typical farming machinery. Sowing was performed using VM300SK direct sowing machine (Vieskan Metalli Oy, Finland) in 2019–2020 and Väderstad 300-400C/S (Väderstad Ab, Sweden) in 2018. The CT plots were mouldboard ploughed to the depth of 20 cm. Plot yields were determined in the middle of the plots with an experimental combine harvester (Sampo Rosenlew SR2010) in Sep 18th, 2018, Sep 26th, 2019 and Sep 22th, 2020. Biomass outside of these areas was harvested with a field-scale combine harvester.

The peat layer was deep (over 2 m), and its organic carbon content was about 25% in the topsoil 0–20 cm, 29% in the 20–40 cm layer, and 35% in the 40–60 cm layer. The topsoil was sampled in May 2018 (except density and porosity), and its properties are presented in Table 1. Soil conductivity and acidity were determined using the ISO 11265 and ISO 10390 methods, respectively. Content of nutrients was analysed as in Vuorinen and Mäkitie (1955) and that of soil carbon and nitrogen using the dry combustion method (Leco TruMac CN Determinator, Leco Corp. St. Joseph, MI, USA). Soil core samples for dry bulk density and porosity (diameter 5 cm) were taken from the surface layer (0-17.5 cm) of each plot in Oct 2020 using the Kopec corer, and the samples were dried at 37°C.

Flux measurements

Closed opaque chambers were used to measure ecosystem respiration and N₂O and CH₄ fluxes. In each plot, a 60 cm x 60 cm steel collar was installed to the depth of 10–15 cm. An aluminium chamber (height 40 cm) mounted at the top of the collar was sealed with water in the groove of the upper edge of the collar. Steel extensions were used when the height of the crop exceeded the height of the chamber. The measurements were done during daytime between 10 am and 2 pm approximately biweekly, or monthly in the winter. The chambers were closed for 30 minutes, and four 20 ml gas samples were taken with a 60-ml plastic syringe to pre-vacuumed vials (Exetainer, Labco Limited, UK) in 10-minute intervals starting immediately after closing. Prior to sampling, the syringe was pumped a few times to mix the air in the chamber. The samples were analysed with a gas chromatograph (Agilent 7890 Agilent Technologies, Inc., Wilmington, DE, USA) equipped with flame ionizer and electron capture detectors, and a nickel catalyst for converting CO₂ to CH₄. The gas chromatograph had a 2 ml sample loop and a backflush system for separating water from the sample and flushing the precolumn between the runs. The precolumn and analytical columns consisted of 1.8 and 3 m long steel columns, respectively, packed with 80/100 mesh Hayesep Q (Supelco Inc., Bellefonte, PA, USA). Nitrogen was used as the carrier gas and a standard gas mixture of known concentration of CO₂, N₂O and CH₄ was used for a calibration curve with seven concentration points. An autosampler (222 XL Liquid handler, Gilson Medical Electronics, France) fed the samples to the loop of the gas chromatograph. A linear regression model was fitted to calculate gas concentrations and the ideal gas law was used to solve the flux rate for every enclosure. The criterion of R² > 0.9 for CO₂ was used as the acceptance limit for all gases.

A transparent chamber (60x60x60 cm) made of polycarbonate plexiglass was used to measure crop NEE approximately biweekly during the growing season. The chamber was equipped with a Vaisala GMP-343 probe and temperature and humidity sensor (Vaisala Oy, Vantaa, Finland) and two fans for mixing the air during the measurement. One or two layers of a white fabric shroud and one blackout curtain were used to control the amount of light entering the chamber (approximately 100, 50, 25, 0% of ambient radiation). The measurements were done in the same collars as the opaque chamber measurements. Four measurements with different amount of entering light were taken from each subplot in order to cover a large range of light conditions during one measurement round. Each measurement took one minute with a five second sampling rate. The chamber was flushed after each measurement to reconstitute ambient CO₂ and air humidity contents. A 10 second lag time was applied after closing and before starting the measurement to exclude the deadband when the flux was not yet stabilised. Clear sky conditions were preferred to avoid problems related with changing cloudiness and to achieve the widest possible range of available light. On the hottest summer days, freezer blocks were used to cool the chamber air. The temperature change inside the chamber was less than 1.5 degrees which was also used as a criterion for data filtering.

Due to the short chamber enclosure time, the change of gas concentration during the chamber enclosure was mostly linear. The primary measurement results as parts per million (ppm) unit were converted to g m^− 2 h^− 1 in accordance with the ideal gas law using the measured temperature inside the chamber.

If the snow cover was > 20 cm, a concentration snow gradient method Maljanen et al. (2003a) was used to determine the GHG fluxes. A probe made of a steel pipe (Ø 3 mm), with a three-way valve and a plastic syringe, was used to sample 15 ml of air just above the snow cover, in the bottom of the snow cover and at one depth in between in tree replicates per plot. The gas was stored in the pre-vacuumed vials and the concentrations were determined gas chromatographically as with the opaque chamber samples. The fluxes were calculated using the equation based on Fick’s law as in Maljanen et al. (2003a).

Measurements for bare soil respiration were made in 7/2019–5/2021. We installed one steel air ventilation pipe 27 cm in diameter and 30 cm in length to the depth of 5–10 cm in each of the 16 subplots. All green vegetation was removed within the chamber area. For the measurements, the cylinders were closed with a cover equipped with a CO₂ sensor (GMP-343; Vaisala Oyj, Vantaa, Finland) and a small fan. One measurement lasted for one minute with a five second sampling rate, and was taken about once in a week or two. The measurement frequency was raised to determine the effect of ploughing on soil respiration in autumn 2020 when the respiration was measured 0, 30, 90 and 210 minutes after ploughing as well as once per day in the next two days. The respiration chamber method showed higher values and more deviation compared to the large opaque chamber method particularly during the winter period in 2020–2021, and soil respiration was not modelled on an annual basis due to poor performance of the empiric soil respiration model. However, data from the period 7/2019–9/2020 were used to estimate the total soil respiration in the growing seasons of 2019 and 2020. The additional carbon loss caused by ploughing was also included in the estimate of total annual respiration in 2020 in 2019 when determining the NEE.

Ancillary measurements

Photosynthetically active radiation (PAR) was continuously measured at the edge of the field by LI-190 quantum PAR sensor (LI-COR, Lincoln, Nebraska, USA) with a one minute sampling rate. Soil temperature was measured at the depth of 10 cm (changed to depth of 5 cm in May 2020 to achieve better response of CO₂ to air temperature) in CT and NT plots (one plot per treatment) with Elcolog sensors (Elcoplast Oy, Tampere, Finland). The air temperature, precipitation and radiation data were taken from the weather station of Finnish Meteorological Institute (FMI) located about 10 kilometres from the site. Gaps in the measured PAR data were filled with global radiation data from FMI corrected using the ratio of global radiation and the measured PAR.

Leaf area index (LAI) was measured usually at the same time with the transparent chamber measurements with a portable LAI meter (SunScan; Delta-T Devices Ltd, Cambridge, United Kingdom). Because the measured LAI corresponds poorly to the actual photosynthetically active biomass in the late growing season due to ripening of cereals, we selected the highest measured LAI values for the beginning of August and assumed that LAI is zero in the end of August when the measured GP was negligible. LAI of the cover crop in the respective treatments was set to zero after ploughing in the CT plots and at mid-December in the NT plots. LAI values > 3 were set to 3 as they were assumed not to affect GP due to saturation of the reflectance (Aparicio et al. 2000).

Flux modelling

NEE (net ecosystem exchange) consists of GP (gross photosynthesis) and ER (ecosystem respiration) and thus GP was estimated for each NEE measurement by (Eq. 1),

$$GP=NEE-ER$$

where the full darkened transparent chamber measurement result (ER) is subtracted from the light-dependent flux (NEE) measured during the same day. ER and GP were modelled using nonlinear regression (fitnlm function in MATLAB 2019b) for all 16 plots and all years separately summing up to 96 models in total. Modelling was done for the period from May to April. Empiric models were used for ER as in (Lohila et al. 2003) and for GP as in (Kandel et al. 2013). Instead of the phytomass indices used in the above publications, we used LAI to describe the stage of the crop growth. Air temperature and PAR were assumed to be the same for all plots, whereas we used the measured LAI for each plot distinctly and the soil temperature from either CT or NT plots.

We used the following equation defined by Long et al. (1993) for GP to estimate empirical coefficients (A_max and k):

$$GP=\frac{{A}_{Max}*PAR}{k+PAR}*LAI{*T}_{Scale}$$

where PAR is the measured photosynthetically active radiation, LAI is the measured leaf area index, A_max is the asymptotic maximum, and k is a half-saturation value. T_Scale represents the temperature sensitivity of photosynthesis and follows the equation performed by (Raich, Rastetter et al. 1991):

$${\text{T}}_{Scale}=\frac{(T-{T}_{min})(T-{T}_{max})}{\left(T-{T}_{\text{m}\text{i}\text{n}}\right)\left(T-{T}_{max}\right)-{(T-{T}_{opt})}^{2}}$$

where T is the measured temperature, photosynthetically active minimum temperature T_min is -2 ˚C, maximum T_max is 40 ˚C and the optimum is 20 ˚C as in (Kandel et al. 2013).

ER consists of autotrophic (R_auto) i.e. plant respiration and heterotrophic (R_hetero) i.e. soil respiration (Lloyd and Taylor 1994):

$$ER= {R}_{hetero}+{R}_{auto}$$

$${R}_{hetero}={R0}_{s}*\text{e}\text{x}\text{p}\left({E0}_{s}\right(\frac{1}{56.02}-\frac{1}{{T}_{soil}+46.02}\left)\right)$$

$${R}_{auto}=LAI*{R0}_{p}*\text{e}\text{x}\text{p}\left({b}_{d}\right(\frac{1}{10+273}-\frac{1}{{T}_{air}+273})$$

, where T_soil is the measured soil temperature, LAI is the measured leaf area index, T_air is the measured air temperature, R0_s is soil respiration at the reference temperature 10 ˚C, R0_p is plant respiration at the reference temperature at 10 ˚C, E0_s is ecosystem sensitivity set to 308 and b_d was the temperature dependence of dark respiration set to 5000 as in (Lohila, Aurela et al. 2003).

The empirical coefficients (R0_p and b_d) were estimated with a nonlinear regression model similarly as in the case of GP. Hourly timeseries of GP and ER were predicted with the above equations using the modelled parameters and hourly timeseries of the field measurements. Missing values of LAI and soil temperature were acquired by interpolation. Gaps in soil temperature during sowing, harvesting or ploughing were filled with averaged air temperature using daily moving mean (correlation between soil and averaged temperature was R² = 0.8 in the full dataset). The NEE was calculated by subtracting the modelled GP from the modelled ER. ER was estimated using data from both opaque chambers and darkened transparent chambers. Annual fluxes were computed as integral of the hourly fluxes with a trapezoidal method (trapz function in MATLAB 2019b). Net ecosystem carbon balance consists of NEE and the amount of carbon removed in yield. The carbon content of the crop yield was assumed to be 45% (Jensen et al. 2005).

Hourly soil respiration was modelled for each plot for the period of May 22, 2019 – Sep 22, 2020 based on the soil respiration measurements and Eq. 4. Total effluxes were counted for growing seasons (between sowing and harvesting) of 2019 and 2020.

Data cleaning and analysis

The flux measurements were visually examined, and obvious outliers and data points from the beginning of the measurement were removed if the flux was clearly not yet stabilized. For transparent chamber measurements, the criteria R² > 0.9 for the fitted linear assumption of flux measurements would exclude a large amount of data, especially with small CO₂ change, leading to a biased dataset. Therefore we decided to add the criterion S_xy < 2.3 g m^− 2 h^− 1 as in Kutzbach et al. (2007) (S_xy is the standard deviation of the residuals and 2.3 g m^− 2 h^− 1 is the 95% percentile of values). Data points containing gaps or possibly erroneous content (due to changing cloudiness etc.) in the background values were marked for a later decision about removal.

In the modelling phase, fitted values were examined visually, and marked outliers and other clear outliers were removed to avoid distortion. In 2018, 34 out of 427 GP values and 21 of 366 ER values were removed. In 2019, 66 out of 537 GP values and 5 of 467 ER values were removed. In the dataset of 2020, 86 out of 568 GP values and 23 of 499 ER values were removed. Average correlations of models were 0.83, 0.78 and 0.92 for ER, and 0.86, 0.91 and 0.80 for GP in 2018, 2019 and 2020, respectively. Measured versus modelled values of GP and ER are shown by years in Fig. 2. All models for soil respiration were accepted although data were highly variable and the average R² of the models was 0.47 ± 0.24.

The coefficients for converting N₂O and CH₄ into CO₂ equivalents were 273 and 27, respectively (Forster et al. 2021).

The statistical comparisons of the daily or annual flux values or soil properties and yields between the treatments were made by using one-way ANOVA procedure in the MATLAB version R2019b. Normal distribution of the variables was investigated using histograms and homogeneity of the variances using the Levene test. The difference was considered statistically significant when P < 0.05.

Site variables

Annual mean temperature was 6.8 ⁰C in 2018 (May 2018–Apr 2019), 6.7 ⁰C in 2019 (May 2019–Apr 2020) and 6.0 ⁰C in 2020 (May 2020–Apr 2021). These values were thus higher than the long-term average of 5.2 ⁰C during the reference period 1991–2020 (Jokinen et al. 2021). Precipitation for the three consecutive years was 471, 788 and 596 mm, exhibiting both lower and higher values compared to the long-term average of 621 mm during the reference period. Typically, soil is frozen and covered by snow in this location during the winter but winter 2019–2020 was warm without a long period of snow cover and frost. Between sowing and harvest, the global radiation sum was 430, 610 and 480 kWh m^− 2 in 2018, 2019 and 2020, respectively.

The peat was highly decomposed, as indicated by the low carbon content, high ash content and von Post value of eight (Table 1). No significant differences in dry bulk density or porosity were observed between treatments in Oct 2020, after three growing seasons of differing soil management.

Crop yields did not differ significantly between treatments, but barley had higher yields compared to oats (Table 2). LAI was at its highest in the end of July, and the maximum peak in LAI values was around 9 in 2019 and 2020 but 4–5 in 2018.

[TABLE 1 HERE]

Carbon flux

No differences were observed in ER between the treatments before ploughing of the CT plots in May 2019 (Fig. 3). After that, there were seven occasions when ER differed statistically significantly between the treatments. CO₂ flux was higher in CT compared to NT in Jun 2019, and in Mar, Apr, May and Dec 2020 whereas it was lower in CT in Jul 2019.

[FIGURE 3 HERE]

The modelled ER values (Fig. 4d) were comparable to those based on the chamber measurements (Fig. 3). Both ER and NEE were higher in CT compared to NT during the late spring in 2019, 2020 and 2021, as well as in autumn 2019 and the following winter.

GP followed well the pattern observed for LAI and PAR (Figs. 4b,c,e). There were no significant differences in the modelled GP between the treatments except in autumns when there was still a weak sward of the cover crop in the NT plots while CT plots were tilled (Fig. 4e). However, this was the only time during the whole monitoring period with a notable effect by the cover crop. Its photosynthesis can be estimated to be approximately only 2.6% of the total GP, and thus the cover crop was not considered a treatment group in the statistical analysis.

Temporal variation in NEE followed mostly that of ER (Figs. 4d,f). Daily average NEE was positive during most of the time in this experiment, i.e. more carbon was released than bound in the field ecosystem. The NEE was negative only on 19 days in 2018, 23 days in 2019 and 5 days in 2020.

[FIGURE 4 HERE]

Measurements with the small soil respiration chamber showed more variability than those done with the larger opaque chamber (Fig. 5). This method also resulted in generally higher emissions compared to the opaque chambers during the winter season. For example, in the winter period from Oct 2019 to May 2020, average CO₂ flux was 0.22 g CO₂ m^− 2 h^− 1 with the opaque chamber method in both treatments (n = 88 in CT and NT) while the soil respiration chambers showed an average of 0.61 and 0.26 g CO₂ m^− 2 h^− 1 in CT and NT (n = 72), respectively. CT plots had higher respiration rates compared to NT plots especially in early summers, and in autumns after ploughing. Only in the early autumn 2019 had the NT plots higher respiration rates than the CT plots. Between sowing and harvesting, the total modelled soil respiration was 7.5 ± 1.2 and 6.3 ± 1.0 Mg CO₂-C ha^− 1 in CT and 7.6 ± 1.0 and 6.5 ± 0.9 Mg CO₂-C ha^− 1 in NT in 2019 and 2020, respectively. Correspondingly, ER was 11.1 ± 1.0 and 10.5 ± 1.2 Mg CO₂-C ha^− 1 in CT, and 10.3 ± 0.5 and 10.7 ± 0.9 Mg CO₂-C ha^− 1 in NT during the same period. On average, the proportion of soil respiration of ecosystem respiration was 71% in 2019 and 61% in 2020.

[FIGURE 5 HERE]

There was a high CO₂ peak in soil respiration immediately after ploughing, 14.6 ± 4.2 g CO₂ m^− 2 h^− 1 on average, but half an hour later the emission rate was only half of that (Fig. 6). In three days, the CO₂ flux settled down to approximately the same level as before ploughing. Soil carbon loss during the first three days after ploughing was 0.34 ± 0.7 Mg CO₂-C ha^− 1 which was 0.22 ± 0.06 Mg CO₂-C ha^− 1 more in comparison to the emission level before ploughing. After ploughing the CO₂ fluxes remained higher from the CT plots compared to the NT plots until 29th of January next year.

[FIGURE 6 HERE]

N₂O efflux

The daily rates of N₂O flux varied between zero and 1 g m^− 2 h^− 1. The highest N₂O rates occurred in the spring during frost melt in 2019 and 2021, whereas in 2020 the highest rates occurred in July (Fig. 7). During the experiment, there were 16 occasions when N₂O fluxes had statistically significant differences between the treatments. The occurrence of these deviations was random; in half of the cases higher emissions were observed in NT and in the rest in CT. Most of the latter cases occurred within the last study year.

[FIGURE 7 HERE]

CH₄ efflux

CH₄ fluxes varied from a slight sink to source during the whole monitoring period (Fig. 8). The fluxes were statistically higher (higher emissions or lower sink) in NT compared to CT four times, in November 2019 and in April 2019, 2020 and 2021.

[FIGURE 8 HERE]

Annual GHG balance

Total annual modelled NEE was lower in NT (5.4 Mg CO₂-C ha^− 1 yr^− 1) compared to CT (7.0 Mg CO₂-C ha^− 1 yr^− 1) (p = 0.02) in 2019 (Table 2). No significant differences were found between the treatments in the other years. The treatments did not differ with respect to the modelled annual GP values but ER showed slightly higher efflux in CT compared to NT in 2019. NT had lower net ecosystem carbon balance than CT in 2019 whereas no differences were observed in the other years. NEE played a major role in the carbon balance while carbon removed in the harvested yield had a minor role.

In terms of annual flux rates, NT had significantly (p = 0.02) lower N₂O emissions compared to CT in 2020 (6.6 ± 2.3 vs. 9.8 ± 2.4 kg N₂O-N ha^− 1 yr^− 1) (Table 2). On average, both treatments were a CH₄ sink during the experiment and there were no significant differences in the total annual balances between the treatments or between years. However, data for N₂O and CH₄ are missing from May and early June 2018 causing a small underestimation in the annual balance. The 100-year global warming potential was lower in NT than in CT in 2019 whereas no differences were observed in the other years.

[TABLE 2 HERE]

With the average NEE of 6.4 ± 2.4 Mg CO₂-C ha ^-1 yr^-1 during the experiment, our estimate of the carbon balance is in line with other studies from northern European cultivated peatlands. Maljanen et al. (2010) reported average NEE of 4.8 ± 3.2 Mg CO₂-C ha^-1 yr^-1 from cereal cultivation on peatlands in Nordic countries. Likewise, the NEE has been estimated to be 2.1 (Lohila et. al 2004), 4.1 (Maljanen et al. 2001) and 8.2 Mg CO₂-C ha^-1 yr^-1 (Maljanen et al. 2004) in Finnish barley sites. The results by Elsgaard et al. (2012) showed NEE of 8.6 ± 2.0 Mg CO₂-C ha^-1 yr^-1 for cultivated peatlands in Denmark (13 ± 4.5 Mg CO₂-C ha^-1 yr^-1 for barley). In the global scale, Tan et al. (2020) reported average of 5.3 ± 2.2 Mg CO₂-C ha^-1 yr^-1 for cultivated peat soils.

The emissions were relatively high, even though the field has been cultivated for a long time and the surface layer is highly decomposed (Table 1). It has been shown that the CO₂ emissions in cultivated peat soils does not necessarily decrease with lowering carbon content (Norberg et al. 2018; Säurich et al. 2019). The emissions might continue at a similar level as long as the majority of the two-meter peat layer is consumed. Our results for the net ecosystem carbon balance (mean 8.0 ± 2.4 Mg C ha^-1 yr^-1) fall within the confidence interval of the default IPCC emission factor for the boreal region, 6.5–9.4 Mg CO₂-C ha^-1 yr^-1 (Hiraishi et al. 2014). Although Finland is a relatively cold country, the aggregated IPCC emission factor for temperate and boreal regions thus seems to be suitable for reporting CO₂ emissions in northern conditions.

The mean N₂O emissions during our experiment were 7.6 ± 3.5 kg N₂O-N ha^-1 yr^-1 which is at the lower end of the reported range found in the literature. In the review study by Maljanen et al. (2010) the average N₂O emissions from cereal cultivated peatlands in Nordic countries was 10 ± 3.2 kg N₂O-N ha^-1 yr^-1. Values from Finnish barley sites were 8.3 (Maljanen et al. 2003c), 8.3 (Maljanen et al. 2004), 15 and 13 (Regina et al. 2004) kg N₂O-N ha^-1 yr^-1. The observed variation is, however, extremely high: e.g. Kandel et al. (2018) found 0.95 ± 0.76 kg N₂O-N ha^-1 yr^-1 (oats-spring barley rotation) in Denmark while Taft et al. (2017) reported 17 ± 1.7 kg N₂O-N ha^-1 yr^-1 for cultivated peat soils in UK. Our mean value is just below the 95% confidence interval of the IPCC default emission factor for N₂O emissions for boreal and temperate regions, 8.2–18 kg N₂O-N ha^-1 yr^-1 (Hiraishi et al. 2014).

The N₂O flux showed a high peak after the frost melted in spring 2019 and 2021. This is typical for melting soils, especially after a winter with frequent freeze-thaw episodes (Kaiser et al. 1998; Regina et al. 2004). High N₂O fluxes in the spring occur when the diffusion barrier of ice is removed, physical structures break and the substrate level increases, enhancing the microbial activity (Wagner-Riddle et al. 2017). In spring 2020, no distinct peaks were observed, likely due to a warm winter with no freeze-thaw cycles. CT had a higher and earlier peak of N₂O compared to NT most likely due to faster melting of the ploughed soil. Frost in the denser NT soil melted later which can be clearly seen in the flux in spring 2021. A peak in July 2020 and a small peak in July 2018 were most likely due to the first heavy rain after fertilization enhancing denitrification.

The soil was well drained and mainly acted as a sink of CH₄. On average, the annual CH₄ sink was − 0.35 ± 0.42 kg CH₄-C ha^-1 yr^-1 during 2018–2020. Most previously studied Finnish barley sites have also been small CH₄ sinks; the reported values have been − 1.8 (Maljanen et al. 2003b), -0.75 (Maljanen et al. 2004), -0.23 and 1.8 (Regina et al. 2007) CH₄-C ha^-1 yr^-1. Our observation is also in line with the findings of Kandel et al. (2018) who observed − 1.05 ± 2.2 kg CH₄-C ha^-1 yr^-1 CH₄ sink for oat-barley rotation on Danish peatland. Especially poorly drained cultivated peat soils can also be sources of CH₄. Taft et al. (2017) reported CH₄ emissions of 1.2 ± 0.6 kg CH₄-C ha^-1 yr^-1 and Maljanen et al. (2010) mean of 1.57 (range from − 1.8 to 11.7) kg CH₄-C ha^-1 yr^-1 for Nordic countries. No specific peaks clearly related to environmental variables occurred except an occasion of CH₄ emissions after a heavy rain following a dry period in 2020.

As soil respiration was 60–70% of the total respiration, it is the most significant component of the carbon balance in this field. It is thus important to find ways to reduce carbon losses via this pathway, especially in carbon-rich soils where the ordinary carbon input cannot counteract losses of peat decomposition (Freeman et al. 2022). With this respect, NT is a promising practice since it has the potential to reduce soil respiration due to less soil disturbance (Chatskikh et al. 2008; Akbolat et al. 2009). As in the study of Elder and Lal (2008a), there was a tendency to lower CO₂ emissions in NT compared to CT at our site. Our observations on increased CO₂ emissions immediately after ploughing are in line with Reicosky et al. (1997) and Vinten et al. (2002). Vinten et al. (2002) reported a high peak after ploughing but the emissions settled to the same level with NT within two days resulting in no difference in long-term CO₂ emissions from clay loam soil. Reicosky et al. (1997) reported the highest CO₂ emissions of 0.32 Mg CO₂-C ha^-1 within 24 hours from the ploughing on carbon rich black soil which is very close to our three-day result 0.34 ± 0.7 Mg CO₂-C ha^-1.

Although the CO₂ emissions were momentarily high right after ploughing, they had a minor effect on the cumulative annual emissions. Furthermore, there were no consistent differences in the annual CO₂ emissions between the treatments throughout the years. In 2019, when the CT treatment experienced ploughing both in the spring and in the autumn, its NEE and net ecosystem carbon balance were higher compared to NT. That year the NEE was 24% lower in NT compared to CT, and the difference resulted from the lower respiration rate in NT. The two soil respiration peaks due to ploughing increased the emissions in CT but the modelled annual ER without the peaks still plays a more significant role in explaining the difference between the treatments. No such a difference was observed in 2020, suggesting low significance of one-time ploughing in the total carbon loss of peat soils, where the annual loss rates are an order of magnitude higher than the peaks caused by ploughing.

Not only soil aeration status but also differences in the soil surface temperature caused by differences in crop residue placement may have a role in the temporal variation of the GHG fluxes in CT and NT. Higher CO₂ fluxes as well as higher soil temperature were found from CT compared to NT in early summers, when the residues left on the soil surface in NT “insulated” the soil as compared to CT (Blanco-Canqui and Ruis 2018). Both the large opaque chamber and soil respiration chamber measurements indicated a few times higher fluxes in NT compared to CT during late summers.

In the NT treatment the N₂O emissions were 33% lower compared to CT in 2020 mainly due to higher emission rates in CT during the summer period. Our results are similar with those of (Elder and Lal 2008a) who found a significant reduction (63%) of N₂O emissions in NT management on organic soil, likely due to increased topsoil density in NT. Denser soil in NT promotes more complete denitrification towards higher proportion of N₂ in the end products (Liu et al. 2007), but at the site of the present study we did not observe differences either in soil moisture (results not shown) nor in density or porosity. However, also increased N₂O emissions in NT have been commonly reported (Oorts et al. 2007; Regina and Alakukku, 2010; Sheehy et al. 2013). Ball (2013) concluded that the surface layer is the main source of N₂O emissions and a poor soil structure with restricted air permeability and aeration may result in high denitrification rate. These results are mainly based on studies on mineral soils, and it is not clear if the same applies in peat soils in which soil structure does not as strongly determine soil functions. The contrasting observations may partly result also from the differences in study duration. A meta-analysis of Van Kessel et al. (2013) showed that there was a significant decrease of N₂O emissions in long-term (> 10 year.) and increase in short-term NT management under dry climate conditions. Our experiment was relatively short, and longer NT management would confirm how much NT eventually compacts the soil, and what kind of implications it has on the trace gas fluxes.

Part of the uncertainty in the GHG results arises from the simplicity of the models. For example, soil respiration was modelled only based on soil temperature, although it can be affected also e.g. by changes in microbial community composition or activity (Yang et al. 2022) and soil moisture (Smith et al. 2018). Especially the soil respiration chamber measurements showed higher fluxes from CT compared to NT after ploughing in the growing seasons of 2019 and 2020, and the higher fluxes continued until the frost. It seems, however, that the model for soil respiration was not capable for showing such a difference between the treatments. One reason for the difference in soil respiration measurements may be due to stronger effect of air mixing in CT with more porous soil surface compared to NT. Air mixing has been found to be a major source of error (Pumpanen et al. 2004). Estimating vegetation cover using measured LAI is also problematic, as it reflects weakly the amount of active chlorophyll during cereal ripening (Gregersen et al. 2013; Delegido et al. 2015). The interpolation used in the models from the peak value of LAI to the end of August is a clear simplification, but it is in good agreement with the models and the measured GP values. Furthermore, LAI was at the same level in 2019 as in 2020 but lower in 2018 although there was no clear difference in yields or GP. This may be related to varying performance of the measuring device. The differences are not highly relevant in the modelling, as each year is dealt with separately, but they may cause variation to the results and explain the high error. Measurement results of PAR values may also contain uncertainties due to possible changes in cloudiness, fogging or dirt on the plexiglass and other shading, although seemingly erroneous observations were removed in the data filtering. The plexiglass surfaces were kept as clean as possible, and fogging was kept low with a short measurement time. Averaged air temperature in gap filling of soil temperature may cause some error but filled gaps were not long and error was mostly diurnal when no high error can occur on annual balances. With biweekly N₂O and CH₄ measurements, there is a high risk to miss short-term peaks for example due to freeze-thaw cycles (Lammirato et al. 2021). Also, if the measurements hit peaks, the emissions may be overestimated due to interpolation of the gaps in the data particularly during times with infrequent measurements. However, even with these uncertainties it is plausible to assume the climatic benefits of NT in boreal peat soils are moderate.

As there was no difference in the grain yields between the cultivation methods, NT can be considered a feasible practice in peat soils from the agronomic viewpoint. As hypothesized, reductions of soil CO₂ emissions from soil respiration were significant in NT during the wintertime due to less soil disturbance. However, as the major part of carbon loss occurred during the warm period of the year, the effect of NT on the annual carbon loss was of minor significance. Based on previous studies on Finnish NT field experiments, we hypothesized higher N₂O emissions in the NT treatment, but this assumption was not supported by the results of the present study. NT showed a tendency to slightly lower 100-year global warming potential and clearer effects might be expected in continuous long-term NT management.

Acknowledgements Assistance of Merja Myllys in the experiment setup and Luke technical staff in the field and laboratory work is gratefully acknowledged. The concept for this paper was developed at the workshop titled "Peatlands for climate change mitigation in agriculture" that took place in Aarhus, Denmark, on 4-5 October 2022, and which was sponsored by the Organisation for Economic Co-operation and Development (OECD) Co-operative Research Programme: Sustainable Agricultural and Food Systems. The opinions expressed and arguments employed in this publication are the sole responsibility of the authors and do not necessarily reflect those of the OECD or of the governments of its Member countries.

Author contributions KL and HK designed the study, HH and HK maintained the experimental site, performed sampling and analysed the data, HH performed statistical analyses, made the figures and tables and the first version of the manuscript. All authors commented the manuscript versions and read and approved the final version of the manuscript.

Funding The work was part of the project SOMPA (Novel soil management practices – key for sustainable bioeconomy and climate change mitigation), funded by the Strategic Research Council at the Academy of Finland, grant no. 312912. The work of HH was also funded by Maa- ja vesitekniikan tuki ry.

Data availability The datasets generated during the study are available from the corresponding author on request.

Competing interests The authors declare no competing interests.

Conflicting interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

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Table 1 Chemical and physical properties of the surface soil (0-20 cm)

Variable	Unit	Mean±standard deviation
Conductivity	10xmS cm^-1	1.4±0.1
Acidity	pH	5.5±0.1
Calcium	mg l^-1	3600±320
Phosphorus	mg l^-1	4.4±0.6
Potassium	mg l^-1	180±39
Magnesium	mg l^-1	260±35
Sulfur	mg l^-1	17±0.9
Decomposition status	von Post 1-10	8
Carbon content	mass-%	25±2.2
N content	mass-%	1.4±0.13
C/N		18±0.5
Ash content	mass-%	48±4.5
Average GWT^a	cm	68±12
Dry bulk density in CT	g cm^-3	0.43±0.05
Dry bulk density in NT	g cm^-3	0.43±0.06
Porosity in CT	%	0.78±0.03
Porosity in NT	%	0.78±0.03

^aLevel of ground water table

Table 2 Annual means and standard deviations of gross photosynthesis, ecosystem respiration, net ecosystem exchange, carbon removed by harvested yield, carbon balance and N₂O-N and CH₄-C effluxes and the global warming potential of conventional tillage (CT) and no-tillage (NT) treatments. Significant differences between treatments are marked with asterisks.

		CT	NT	P<0.05
2018
Gross photosynthesis	Mg CO₂-C ha^-1	-6.5±1.9	-7.4±1.5
Ecosystem respiration	Mg CO₂-C ha^-1	13.1±1.3	13.6±3.3
Net ecosystem exchange	Mg CO₂-C ha^-1	6.4±2.7	6.5±4.2
Carbon in crop yield	Mg C ha^-1	1.3±0.2	1.4±0.1
Net ecosystem carbon balance	Mg C ha^-1	7.7±2.8	7.9±4.3
N₂O	kg N₂O-N ha^-1	10.2±5.9	7.4±1.6
CH₄	kg CH₄-C ha^-1	-0.6±0.2	-0.5±0.3
GWP-100	Mg CO₂eq ha^-1	27.7±11.1	26.9±15.5

2019
Gross photosynthesis	Mg CO₂-C ha^-1	-8.1±0.7	-7.8±0.6
Ecosystem respiration	Mg CO₂-C ha^-1	14.9±1.5	13.2±1.0	*
Net ecosystem exchange	Mg CO₂-C ha^-1	7.0±1.4	5.4±0.9	*
Carbon in crop yield	Mg C ha^-1	2.0±0.3	2.0±0.2
Net ecosystem carbon balance	Mg C ha^-1	9.0±1.3	7.3±0.8	*
N₂O	kg N₂O-N ha^-1	5.3±1.1	6.4±1.8
CH₄	kg CH₄-C ha^-1	-0.2±0.3	-0.3±0.4
GWP-100	Mg CO₂eq ha^-1	28.0±5.7	22.4±3.2	*

2020
Gross photosynthesis	Mg CO₂-C ha^-1	-6.8±1.0	-7.1±0.7
Ecosystem respiration	Mg CO₂-C ha^-1	13.5±1.6	13.7±1.4
Net ecosystem exchange	Mg CO₂-C ha^-1	6.7±1.6	6.6±1.2
Carbon in crop yield	Mg C ha^-1	1.4±0.1	1.5±0.1
Net ecosystem carbon balance	Mg C ha^-1	8.1±1.6	8.1±1.1
N₂O	kg N₂O-N ha^-1	9.8±2.4	6.6±2.3	*
CH₄	kg CH₄-C ha^-1	-0.3±0.5	-0.1±0.5
GWP-100	Mg CO₂eq ha^-1	28.7±5.7	27.0±4.4

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Minor effects of no-till treatment on GHG emissions of boreal cultivated peat soil

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