Optimizing chamber methods for measuring nitrous oxide emissions from plot‐based agricultural experiments

Nitrous oxide emissions (N₂O) from agricultural land are spatially and temporally variable. Most emission measurements are made with small (? 1 m² area) static chambers. We used N₂O chamber data collected from multiple field experiments across different geo‐climatic zones in the UK and from a range of nitrogen treatments to quantify uncertainties associated with flux measurements. Data were analysed to assess the spatial variability of fluxes, the degree of linearity of headspace N₂O accumulation and the robustness of using ambient air N₂O concentrations as a surrogate for sampling immediately after closure (T₀). Data showed differences of up to more than 50‐fold between the maximum and minimum N₂O flux from five chambers within one plot on a single sampling occasion, and that reliability of flux measurements increased with greater numbers of chambers. In more than 90% of the 1970 cases where linearity of headspace N₂O accumulation was measured (with four or more sampling points), linear accumulation was observed; however, where non‐linear accumulation was seen this could result in a 26% under‐estimate of the flux. Statistical analysis demonstrated that the use of ambient air as a surrogate for T₀ headspace samples did not result in any consistent bias in calculated fluxes. Spatial variability has the potential to result in erroneous flux estimates if not taken into account, and generally introduces a far larger uncertainty into the calculated flux (commonly orders of magnitude more) than any uncertainties introduced through reduced headspace sampling or assumption of linearity of headspace accumulation. Hence, when deploying finite resources, maximizing chamber numbers should be given priority over maximizing the number of headspace samplings per enclosure period.     

Measuring forest floor CO2 fluxes in a Douglas-fir forest

CO₂ exchange was measured on the forest floor of a coastal temperate Douglas-fir forest located near Campbell River, British Columbia, Canada. Continuous measurements were obtained at six locations using an automated chamber system between April and December, 2000. Fluxes were measured every half hour by circulating chamber headspace air through a sampling manifold assembly and a closed-path infrared gas analyzer. Maximum CO₂ fluxes measured varied by a factor of almost 3 between the chamber locations, while the highest daily average fluxes observed at two chamber locations occasionally reached values near 15 μmol C m⁻² s⁻¹. Generally, fluxes ranged between 2 and 10 μmol C m⁻² s⁻¹ during the measurement period. CO₂ flux from the forest floor was strongly related to soil temperature with the highest correlation found with 5 cm depth temperature. A simple temperature dependent exponential model fit to the nighttime fluxes revealed Q₁₀ values in the normal range of 2–3 during the warmer parts of the year, but values of 4–5 during cooler periods. Moss photosynthesis was negligible in four of the six chambers, while at the other locations, it reduced daytime half-hourly net CO₂ flux by about 25%. Soil moisture had very little effect on forest floor CO₂ flux. Hysteresis in the annual relationship between chamber fluxes and soil temperatures was observed. Net exchange from the six chambers was estimated to be 1920±530 g C m⁻² per year, the higher estimates exceeding measurement of ecosystem respiration using year-round eddy correlation above the canopy at this site. This discrepancy is attributed to the inadequate number of chambers to obtain a reliable estimate of the spatial average soil CO₂ flux at the site and uncertainty in the eddy covariance respiration measurements.     

Divergent NEE balances from manual‐chamber CO2 fluxes linked to different measurement and gap‐filling strategies: A source for uncertainty of estimated terrestrial C sources and sinks?

Manual closed‐chamber measurements are commonly used to quantify annual net CO₂ ecosystem exchange (NEE) in a wide range of terrestrial ecosystems. However, differences in both the acquisition and gap filling of manual closed‐chamber data are large in the existing literature, complicating inter‐study comparisons and meta analyses. The aim of this study was to compare common approaches for quantifying CO₂ exchange at three methodological levels. (1) The first level included two different CO₂ flux measurement methods: one via measurements during mid‐day applying net coverages (mid‐day approach) and one via measurements from sunrise to noon (sunrise approach) to capture a span of light conditions for measurements of NEE with transparent chambers. (2) The second level included three different methods of pooling measured ecosystem respiration (R_ECO) fluxes for empirical modeling of R_ECO: campaign‐wise (19 single‐measurement‐day R_ECO models), season‐wise (one R_ECO model for the entire study period), and cluster‐wise (two R_ECO models representing a low and a high vegetation status). (3) The third level included two different methods of deriving fluxes of gross primary production (GPP): by subtracting either proximately measured R_ECO fluxes (direct GPP modeling) or empirically modeled R_ECO fluxes from measured NEE fluxes (indirect GPP modeling). Measurements were made during 2013–2014 in a lucerne‐clover‐grass field in NE Germany. Across the different combinations of measurement and gap‐filling options, the NEE balances of the agricultural field diverged strongly (–200 to 425 g CO₂‐C m⁻²). NEE balances were most similar to previous studies when derived from sunrise measurements and indirect GPP modeling. Overall, the large variation in NEE balances resulting from different data‐acquisition or gap‐filling strategies indicates that these methodological decisions should be made very carefully and that they likely add to the overall uncertainty of greenhouse gas emission factors. Preferably, a standard approach should be developed to reduce the uncertainty of upscaled estimates.     

A comparison of linear and exponential regression for estimating diffusive CH4 fluxes by closed-chambers in peatlands

The closed-chamber method is the most common approach to determine CH₄ fluxes in peatlands. The concentration change in the chamber is monitored over time, and the flux is usually calculated by the slope of a linear regression function. Theoretically, the gas exchange cannot be constant over time but has to decrease, when the concentration gradient between chamber headspace and soil air decreases. In this study, we test whether we can detect this non-linearity in the concentration change during the chamber closure with six air samples. We expect generally a low concentration gradient on dry sites (hummocks) and thus the occurrence of exponential concentration changes in the chamber due to a quick equilibrium of gas concentrations between peat and chamber headspace. On wet (flarks) and sedge-covered sites (lawns), we expect a high gradient and near-linear concentration changes in the chamber. To evaluate these model assumptions, we calculate both linear and exponential regressions for a test data set (n = 597) from a Finnish mire. We use the Akaike Information Criterion with small sample second order bias correction to select the best-fitted model. 13.6%, 19.2% and 9.8% of measurements on hummocks, lawns and flarks, respectively, were best fitted with an exponential regression model. A flux estimation derived from the slope of the exponential function at the beginning of the chamber closure can be significantly higher than using the slope of the linear regression function. Non-linear concentration-over-time curves occurred mostly during periods of changing water table. This could be due to either natural processes or chamber artefacts, e.g. initial pressure fluctuations during chamber deployment. To be able to exclude either natural processes or artefacts as cause of non-linearity, further information, e.g. CH₄ concentration profile measurements in the peat, would be needed. If this is not available, the range of uncertainty can be substantial. We suggest to use the range between the slopes of the exponential regression at the beginning and at the end of the closure time as an estimate of the overall uncertainty.     

An improved method for measuring soil N2O fluxes using a quantum cascade laser with a dynamic chamber

A dynamic chamber method was developed to measure fluxes of N₂O from soils with greater accuracy than previously possible, through the use of a quantum cascade laser (QCL). The dynamic method was compared with the conventional static chamber method, where samples are analysed subsequently on a gas chromatograph. Results suggest that the dynamic method is capable of measuring soil N₂O fluxes with an uncertainty of typically less than 1–2 µg N₂O‐N m^?2 hour^?1 (0.24–0.48 g N₂O‐N ha^?1 day^?1), much less than the conventional static chamber method, because of the greater precision and temporal resolution of the QCL. The continuous record of N₂O and CO₂ concentration at 1 Hz during chamber closure provides an insight into the effects that enclosure time and the use of different regression methods may introduce when employed with static chamber systems similar in design. Results suggest that long enclosure times can contribute significantly to uncertainty in chamber flux measurements. Non‐linear models are less influenced by effects of long enclosure time, but even these do not always adequately describe the observed concentrations when enclosure time exceeds 10 minutes, especially with large fluxes.     

Optimizing the use of open chambers to measure ammonia volatilization in field plots amended with urea

Measuring ammonia(NH₃)volatilization from urea-fertilized soils is crucial for evaluation of practices that reduce gaseous nitrogen(N)losses in agriculture.The small area of chambers used for NH₃volatilization measurements compared with the size of field plots may cause significant errors if inadequate sampling strategies are adopted.Our aims were:i)to investigate the effect of using multiple open chambers on the variability in the measurement of NH₃volatilization in urea-amended field plots and ii)to define the critical period of NH₃-N losses during which the concentration of sampling effort is capable of reducing uncertainty.The use of only one chamber covering 0.015%of the plot(51.84 m²)generates a value of NH₃-N loss within an expected margin of error of 30%around the true mean.To reduce the error margin by half(15%),3–7 chambers were required with a mean of 5 chambers per plot.Concentrating the sampling efforts in the first two weeks after urea application,which is usually the most critical period of N losses and associated errors,represents an efficient strategy to lessen uncertainty in the measurements of NH₃volatilization.This strategy enhances the power of detection of NH₃-N loss abatement in field experiments using chambers.     

Diurnal patterns of methane emissions from paddy rice fields in the Philippines

Methane (CH₄) emissions from rice paddies often show significant diurnal variations, most likely driven by diurnal changes of radiation and temperature in air, floodwater, and soil. Field measurements, however, are often scheduled at a fixed time of a given measuring day, thereby neglecting sub‐daily variations of CH₄ emissions. Here we evaluated diurnal patterns of CH₄ emissions from traditional paddy rice production as observed during field measurements in the Philippines. Field emissions were measured during three consecutive cropping seasons using an automated chamber and gas sampling system with fluxes being obtained every 4 h. Methane fluxes were monitored with a total of nine chambers during the dry seasons in 2012 and 2013 and 27 chambers during the wet season in 2012. Significant and consistent diurnal patterns of CH₄ emissions were mainly observed from the start of field flooding until the middle of cropping periods, i.e., periods with low leaf area of the rice crop. Our data show that disregarding the diurnal variability of fluxes results in an average overestimation of seasonal CH₄ emissions of 22% (16–31%) if measurements were conducted only around noon. Scheduling manual sampling either at early morning (7:00–9:00) or evening (17:00–19:00) results in estimations of seasonal emissions within 94–101% of the “true” value as calculated from multiple daily flux measurements. Alternatively, uncertainties of seasonal emissions can be reduced to an average of ≤3% by applying sinus function or Gauss function‐based correction factors. Application of correction factors allows the performance of flux measurements at any time of day. We also investigated N₂O emissions from rice paddies with respect to diurnal variations, but did not find, as in the case of CH₄, any significant and persistent diurnal pattern.     

Thermoelectric module cooling for photosynthesis chambers in plant nutrition studies 1

The water cooled system for controlling air temperature in photosynthesis assimilation chamber is cumbersome and requires a water tight system consisting of a double‐jacketed chamber. Manipulation of temperature control from one air temperature to another requires the adjustment of water bath temperatures. A simplified system for the air temperature control of the assimilation chamber and heat removal under high photon flux density would be desirable. An effective thermoelectric module cooling and heating system for a photosynthesis chamber was developed and evaluated for wheat (Triticum aestivum L.), sorghum [Sorghum bicolor (L.) Moench], and soybean [Glycine max (L.) Merr.] Air temperature variations within a chamber were maintained within 0.4°C, 0.9°C, and 0.3°C for the wheat, sorghum, and soybean chambers, respectively. The thermoelectric module system is simple and provides sufficient cooling and heating capacities to maintain chamber air temperature from 20°C to 30°C with 1100 μmol m^‐2 s^‐1 photon flux density for photosynthesis and dark respiration studies.

Air temperature within a photosynthesis chamber during photosynthesis in plant nutrition studies is one of the important environmental parameters that must be controlled. Due to excessive heat under the relatively high photon flux density used in photosynthesis measurements, air temperature has been traditionally cooled and controlled by passing chilled water through double walled water‐jacket chambers^{3,5,7,8,12,13}. Although the water cooled double‐jacket system has been successful in controlling temperature, maintaining water tight systems has been a problem. To alleviate some of the problems of a double‐jacketed system, air was cooled by passing over a water‐cooled radiator placed below the leaf^4,11 . Under conditions of relatively high photon flux densities (1100 ymol m^‐2 s^‐1), water‐cooled systems do not provide sufficient cooling capacity to maintain 25°C or less air temperature. Mauney, et al.⁶ reported photosynthetic data obtained from cuvettes that were electrically cooled by the Peltier device, but no details of the system were provided. In later studies^9,10,14, Peltier‐cooled systems appeared as a simple alternative to water‐cooled systems. This paper reports the details on an effective thermoelectric module cooling and heating system based on the Peltier principle for photosynthesis chambers.     

Short-range spatial variation of nitrous oxide fluxes in relation to compaction and straw residues

The spatial heterogeneity of N₂O flux at short distances (0.1–2 m) was characterized in relation to various soil physical and chemical properties and the location of incorporated crop residues in arable soils. Plots were prepared with uniform compaction (either zero or compacted by a laden two‐wheel‐drive tractor) in two field experiments, one under winter barley (Hordeum vulgare), the other under oil‐seed rape (Brassica napus). Flux measurements were made of N₂O using small chambers (7.3 cm diameter) placed at intervals of approximately 10 cm along a transect (c. 2 m long) across the direction of application of the treatments of compaction and residue incorporation. The flux of N₂O and many other measurements showed large variation over short distances, particularly when fluxes were small. The spatial variation of the flux was not closely related to the soil properties. Correlations showed that cone resistance, air permeability and closeness to incorporated residues were as important as soil NO₃, NH₄ and soluble C in determining flux of N₂O from non‐compacted soils. Most properties of compacted soils did not correlate with N₂O flux. Correlation and multiple regression analysis failed to establish consistent relations between soil environmental variables and N₂O flux, but principal component regression indicated that, overall, N₂O flux increased with decreasing distance from straw residues and air permeability, and with increasing cone resistance and wet bulk density.     

Applicability of the soil gradient method for estimating soil–atmosphere CO2, CH4, and N2O fluxes for steppe soils in Inner Mongolia

For evaluating the applicability of the soil gradient method as a substitute for CO₂‐, CH₄‐, and N₂O‐flux measurements in steppe, we carried out chamber measurements and determined soil gas concentration at an ungrazed (UG99) and a grazed (WG) site in Inner Mongolia, China. The agreement of the concentration‐based flux estimates with measured chamber‐based fluxes varied largely depending on the respective GHG in the sequence CO₂ > CH₄ >> N₂O. A calibration of the gas‐transport parameter used to calculate fluxes based on soil gas concentrations improved the results considerably for CO₂ and CH₄. After calibration, the average deviation from the chamber‐based annual cumulative flux for both sites was 11.5%, 10.5%, and 59% for CO₂, CH₄, and N₂O. The gradient method did not constitute an adequate stand‐alone substitute for greenhouse‐gas flux estimation since a calibration using chamber‐based measurements was necessary and vigorous production processes were confined to the uppermost, almost water‐saturated soil layer.     

An explanation of linear increases in gas concentration under closed chambers used to measure gas exchange between soil and the atmosphere

Earlier models describing the accumulation of gases under closed chambers are based on the assumption of a constant concentration source that does not change during the time of chamber deployment. A new model is proposed which is based on the assumption of a constant production source, and takes into account possible changes in gas concentrations at the source during chamber deployment. Using N₂O as an example, simulations have been carried out for different source strength and depth, diffusivities and air porosities. The main finding was a chamber‐induced increase in gas concentrations in the upper part of the soil profile, including the depth where the N₂O source is located. The increase started immediately after chamber closure. Nevertheless, fluxes calculated from increasing concentrations within the chamber's headspace were always less than those expected under undisturbed conditions, i.e. in the absence of a chamber. This was due to a proportion of the gas produced being stored within the soil profile while the chamber was in place. The discrepancy caused by this effect increased with increasing air‐filled porosity and decreasing height of the chamber, and a procedure for correcting chamber flux measurements accordingly is proposed. The increase in soil gas concentrations after chamber closure was confirmed in a laboratory experiment.     

Variations in the Diurnal Flux of Greenhouse Gases from Soil and Optimizing the Sampling Protocol for Closed Static Chambers

There is no standardized method for the sampling of greenhouse gas fluxes from soil. Two methods are primarily used: closed dynamic chamber (CDC) and closed static chamber (CSC) systems. The most complex and costly are the CDC systems, which can sample gases in situ. However, the low-cost CSC systems are being increasingly used in which the gas samples are collected manually and analyzed off-site at a later date. Given their growing popularity, it is important to optimize the sampling procedure of the CSC systems to ensure that the measurements are both repeatable and representative. Samples from a commercial potato crop were collected in the morning and afternoon at 0, 15, 30, 60, 90, and 120 min after the chambers were closed, and the concentrations of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) were determined using gas chromatography. The concentrations of CO₂ and N₂O inside chambers increased linearly over time, whereas the concentration of CH₄ remained constant. The fluxes of CO₂ and N₂O from soil were greater in the afternoon than the morning, whereas the flux of CH₄ was greater in the morning. For longer-term single-point soil flux monitoring using CSCs with a volume of 6.3 L, it is recommended that samples are collected in the morning at 0, 30, and 60 min after chambers are closed. This approach will ensure that the concentration of the gases are representative and will allow for a high level of repeatability and certainty in the results.     

Feldmethode zur Bestimmung der substrat‐induzierten Bodenatmung

A field method for the measurement of substrate‐induced soil respiration A novel method for in situ measurements of microbial soil activity using the CO₂ efflux combined with kinetic analysis is proposed. The results are compared with two conventional, laboratory methods, (1) substrate‐induced respiration using a ’︁Sapromat’ and (2) dehydrogenase activity. Soil respiration was measured in situ after addition of aqueous solutions containing 0 to 6 g glucose kg^—1 soil. The respiration data were analysed using kinetic models to describe the nutritional status of the soil bacteria employing few representative parameters. The two‐phase soil respiration response gave best fit results with the Hanes' or non‐parametric kinetic model with Michaelis‐Menten constants (K_m) of 0.05—0.1 g glucose kg^—1 soil. The maximum respiration rates (V_max) were obtained above 1 g glucose. Substrate‐induced respiration rates of the novel in situ method were significantly correlated to results of the ’︁Sapromat’ measurements (r² = 0.81***). The in situ method combined with kinetic analysis was suitable for the characterisation of microbial activity in soil; it showed respiration rates lower by 59% than measured in the laboratory with disturbed samples.     

Effect of methodological consideration on soil carbon parameter estimates obtained via the acid hydrolysis-incubation method

Many techniques such as the acid hydrolysis – incubation (AHI) method have been developed with the aim of elucidating the inherent complexity of soil organic carbon (SOC). While the utility of the AHI method has been demonstrated, there is no standardized protocol developed for conducting the long-term incubation component of the method. In the current study we evaluated the effects of chamber venting and mechanical headspace mixing on soil CO₂ flux rates and the resultant size and mean residence time of three operationally defined pools of SOC obtained via the AHI method. Continuous chamber venting resulted in an estimate of the readily mineralized carbon pool that was 2.3 times larger and turned over 2.9 times slower than the same pool estimated using periodically vented chambers. These differences were primarily attributed to the suppression of CO₂ flux in periodically vented chambers as a result of high internal CO₂ concentrations, and a concomitantly reduced diffusivity gradient. Prior to venting the periodically-vented chambers, CO₂ flux rates averaged 2.3 μg C (g soil)⁻¹ d⁻¹, while CO₂ flux rates following venting averaged 222.6 μg C (g soil)⁻¹ d⁻¹. We did not detect internal stratification of CO₂ suggesting that mechanical headspace mixing is unnecessary in incubation chambers ranging from 1 to 2 L. A standardized protocol is called for that isolates SOC fractions that are useful in hypothesis testing, while simultaneously seeking to minimize laboratory artifacts.     

Influence of lime and phosphorus on soybean (Glycine max (L.) Merrill) yield,leaf and seed composition on a paleudult soil

Abstract

This research was undertaken on a paleudult soil in southern Brazil, 30° south latitude, to quantify lime and P effect upon soybean (Glycine max (L.) Merrill). A lime x P factorial experience with lime treatments of 0, 0.5, 1, and 2 times SMP interpretation to pH 6.5, and 0, 44, 88, 132, and 176 kg P/ha with 3 replications were installed. The experiment was conducted for 2 years (1973–74, 1974–75), with leaf‐N, P, and K; yield; seed‐N, P, and K; Bray P₂ (0.03N NH₄F + 0.1N HC1) avail‐able‐P and soil pH measurements completed each year. Data was evaluated with linear, quadratic, logarithmic, polynomial, segmented line, and multiple regression using the coefficient of determination as goodness of fit.

The best model fit between P treatment and Bray P₂ available‐P was a quadratic equation; the model between relative yield and Bray P_2‐P with 54% of the relative yield attributed to Bray P₂ available‐P, a sigmented line. This model indicated point of maximum yield (91% relative yield) was obtained at 7.4 ppm‐P, with no increase in relative yield with increasing levels of soil available‐P. To calculate the P fertilizer necessary to increase available soil‐P to the level of maximum yield of equation Y_p = [1639(7.4 ‐ x_s)]^1/2, where Y_p = kg P/ha fertilizer needed; and x_s = initial Bray P₂ soil available‐P in ppm's. The lime effect upon soil pH was best described as a linear relationship. Yield increase with lime at this site was not significant at the 5% level.

The leaf‐N, P, and K increased significantly with soil available‐P levels. A second degree polynomial with logarithmic function best defined these relationships. The calculated DRIS indices and sum proved useful to evaluate the plant‐N, P, and K balance of each treatment.

Only seed‐P level was directly related to soil available‐P. Both seed‐N and seed‐K were highly correlated with indirect effects of soil available‐P levels.

Results from this study suggest the segmented line model would best interpret soybean yield response to Bray P₂ available‐P for this soil. To obtain maximum yield using this model rather than the second degree polynomial would require less fertilizer P. Foliar analyses interpretation confirmed adequate plant‐P level would be supplied for maximum yield at this level of fertilization.     

Small lateral air pressure gradients generated by a large chamber system have a strong effect on CO2 transport in soil

Gas transport in soils is usually assumed to be purely diffusive, although several studies have shown that non-diffusive processes can significantly enhance soil gas transport. These processes include barometric air pressure changes, wind-induced pressure pumping and static air pressure fields generated by wind interacting with obstacles. The associated pressure gradients in the soil can cause advective gas fluxes that are much larger than diffusive fluxes. However, the contributions of the respective transport processes are difficult to separate. We developed a large chamber system to simulate pressure fields and investigate their influence on soil gas transport. The chamber consists of four subspaces in which pressure is regulated by fans that blow air in or out of the chamber. With this setup, we conducted experiments with oscillating and static pressure fields. CO₂ concentrations were measured along two soil profiles beneath the chamber. We found a significant relationship between static lateral pressure gradients and the change in the CO₂ profiles (R² = 0.53; p-value <2e-16). Even small pressure gradients between −1 and 1 Pa relative to ambient pressure resulted in an increase or decrease in CO₂ concentrations of 8% on average in the upper soil, indicating advective flow of air in the pore space. Positive pressure gradients resulted in decreasing, negative pressure gradients in increasing CO₂ concentrations. The concentration changes were probably caused by an advective flow field in the soil beneath the chamber generated by the pressure gradients. No effect of oscillating pressure fields was observed in this study. The results indicate that static lateral pressure gradients have a substantial impact on soil gas transport and therefore are an important driver of gas exchange between soil and atmosphere. Lateral pressure gradients in a comparable range can be induced under windy conditions when wind interacts with terrain features. They can also be caused by chambers used for flux measurements at high wind speed or by fans used for head-space mixing within the chambers, which yields biased flux estimates.     

Selection of the most suitable sampling time for static chambers for the estimation of daily mean N2O flux from soils

Soil N₂O fluxes are frequently assessed by the use of static chambers with a single daily sampling. In this study, two experiments were conducted in two contrasting climatic locations, one in Edinburgh, UK, and the other at Seropédica, Rio de Janeiro State, Brazil. Soil N₂O fluxes were monitored every 6 h for 30 days during the summer in Edinburgh by the use of an automatic chamber system, and every 3 h for 5 days at Seropédica, using a manually-sampled static chamber. Air and soil temperatures were also measured at the same time as the N₂O fluxes. The principal driver of N₂O flux within any diurnal period was found to be soil temperature. Regression analysis showed that, for both places, the evenings (21:00–22:00 h) and mornings (09:00–10:00 h), were the times that the flux best represented the daily mean. The ability to work in daylight make the morning period the preferred one.     

Multiyear greenhouse gas flux measurements on a temperate fen soil used for cropland or grassland

To date there is still a lack of reliable data on greenhouse gas emissions from drained fens needed to determine the climatic relevance of land use and land use change on peatlands and to supply the National Inventory Report for the German Greenhouse Gas Inventory. In this study we present the results of monthly‐based multiyear measurements of CO₂, N₂O and CH₄ flux rates in two drained agriculturally used fen ecosystems in NW Germany (cropland and grassland) over a period of 4.5 y using transparent and opaque closed chambers. CO₂ exchange was modelled at high resolution with temperature and photosynthetic active radiation. The measured and modelled values fit very well (R² ≥ 0.93). Annual GHG and Global Warming Potential (GWP) balances were determined. Net CO₂ emissions at the cropland and grassland sites were similarly high, taking into account changes in management; net ecosystem C balance amounted to about 4.0 to 5.0 Mg C ha^?1 y^?1. Emissions of N₂O and CH₄ were low at both sites. The mean GWP balance for a time frame of 100 y (GWP100) amounted to about 17.0 to 19.0 Mg CO₂‐eq. ha^?1 y^?1. The unexpectedly low greenhouse gas emissions from the cropland site are attributed to the high water table and a change in crop management. The change from corn for silage to corn‐cob mix lead transiently to rather small greenhouse gas emissions. The study confirms the need for multiyear measurements taking climatic and management variation into account.     

Hierarchical Bayesian models for soil CO2 flux using soil texture: a case study in central Hokkaido,Japan

Hierarchical Bayesian (HB) methods are useful tools for modeling multifaceted, nonlinear phenomena such as those encountered in ecology, and have been increasingly applied in environmental sciences, e.g., to estimate soil gas flux from different soil textures or sites. We have developed a model of soil carbon dioxide (CO₂) flux based on soil temperature (T, 5 cm depth) and water-filled pore space (WFPS, 5 cm depth) using HB theory. The HB model was calibrated using a dataset of CO₂ flux measured from bare soils belonging to four texture classes in 14 upland field sites in a watershed in central Hokkaido, Japan, in the nonsnow-cover season from 2003 to 2011. The numerical software HYDRUS-1D was used to simulate daily WFPS, and the estimated values were significantly correlated with the measured WFPS (R² = 0.68, P < 0.001). Compared to a nonhierarchical Bayesian model (Bayesian pooled model), the CO₂ predictions with the HB model more accurately represented texture-specific observations. The simulation–observation fit of the CO₂ flux model was R² = 0.64 (P < 0.001). More than 90% of the observed daily data were within the 95% confidence interval. The HB model exhibited high uncertainty for high CO₂ flux values. The HB model calibration revealed differing sensitivity of CO₂ flux to T and WFPS in different soil texture classes. CO₂ flux increased with an increase in T, and it increased to a lesser degree with a finer texture, possibly because the clay and silt facilitated soil aggregation, thus reducing temperature fluctuations. WFPS values between 0.48 and 0.64 resulted in optimal conditions for CO₂ flux. The minimum WFPS value increased with an increase in clay content (P < 0.05). Although only a small number of soil types were studied in only one season in this study, the HB model may provide a method for predicting how the effects of soil temperature and moisture on CO₂ flux change with texture, and soil texture could be regarded as an upscaling factor in future research on regional extrapolation.     

An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed γ‐ray spectrometer data

The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time‐consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy‐to‐measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma‐ray (γ‐ray) spectrometry and apparent electrical conductivity (EC_a) data. The error budget uses empirical best linear unbiased prediction (E‐BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the γ‐ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the γ‐ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries.