Carbon degradation indicates the efficiency of anaerobic digestion processes. Common carbon degradation determination methods define gross carbon degradation (C deggross) of substrate and inoculum inseparably. The aim of this study was to test an isotope-based method defining solely substrate-based net carbon degradation (C degnet) on maize silage. As the natural abundance of stable isotopes in agricultural substrates vary, the method’s applicability was tested on (i) different maize silages sampled from agricultural farms, (ii) maize silage in fresh (MSfresh) and impaired storage (MSimpaired) conditions.Experiments included six maize silages digested in a total of 19 lab-scale batch reactors, analyzed for digestion parameters, stable isotopes, gross and net carbon degradation. MSimpaired showed significantly different stable carbon isotope composition at the start of the experiments, compared to MSfresh. Both methods indicated quality losses in MSimpaired. Results showed significantly higher C degnet values, ranging from 58.4% to 86.5%, compared to deggross values, ranging from 23.1% to 48.7%. This indicated the applicability of an isotope-based method C degnet to assess net carbon degradation of maize silages more detailed by excluding the masking effect of the inoculum. The isotope-based net carbon degradation method was found applicable on maize silages from (i) different farms and (ii) in different storage qualities.
Maize silage is one of the most important substrates in German biogas production (FNR 2017). An efficient transformation of substrate-bound carbon (C) to biogas during the anaerobic digestion process is one key aim for biogas plant operators. Therefore, many scientifically conducted experiments in lab-scale biogas reactors focused on maize silage degradations in order to analyze degradation efficiency. Commonly, degradation determination methods are based on volatile solids (VS), which serve as basis for specific biogas yield calculations. More detailed information on carbon degradation is gained by carbon mass quantification at start and end of the experiment. The carbon masses are used to determine gross carbon degradation of the substrate in anaerobic digestions (C deggross) (Bisutti et al. 2004). Anaerobic digestion experiments are often performed in batch processes which, in the simplest case, are set up with one inoculum and one substrate (e.g. maize silage). Gross carbon degradation determination methods, however, offer no possibility to identify solely substrate-bound carbon degradation without taking carbon of the applied inoculum into account. Often indirect approaches are presented by analyzing carbon degradation in separately digested inoculum (control). Interactions between inocula and substrates are, however, neglected. To achieve a better understanding of the net degradation process, it is required to gain additional information about substrate degradation independently of the contributing inoculum.
Environmental and geosciences have intensively used natural abundance of stable isotopes to investigate diverse physical, chemical and biological processes in oxic (Bowling et al. 2008; Brüggemann et al. 2011; Werth and Kuzyakov 2010) and anoxic (Conrad 2005) environments. Most biochemical processes favour the incorporation of the lighter isotope (12C) in the product. This leads to differences between the isotopic composition of the substrate and the product. In biological systems, such isotope fractionation is also defined as discrimination, because specific enzymatic reactions discriminate against the heavier (13C) and favour the lighter isotope. Natural variations of 13C/12C ratios have been used to trace C sources and fluxes in biological systems. The C isotope ratios are expressed relative to international standard as δ13C or δ15N, for nitrogen based research, values. Many studies are based on the natural variation of δ13C in C3 and C4 vegetation. The δ13C values of C3 and C4 plants range from − 10 to − 28 ‰ and from − 9 to − 17 ‰, respectively (Werth and Kuzyakov 2010). Maize is highly enriched in the heavier 13C isotope as a consequence of the C4 metabolic pathway in photosynthesis compared to C3 plants (Smith and Epstein 1971). This difference in natural 13C abundance has been exploited to study carbon turnovers (Amelung et al. 2008). In anaerobic processes, stable isotopes have been successfully used to identify microbiological reaction pathways (e.g. Conrad 2005; Francioso et al. 2010; Gehring et al. 2015; Gelwicks et al. 1994; Penger et al. 2014). Most studies either used in-vitro methods of specific microorganisms, municipal wastewater treatment plants, or concentrated exclusively on exhausted gasses. For instance, anaerobic CH4 production is known for a strong 13C fractionation factor (typically 10–70 ‰) which simultaneously affects δ13C values in the product (Conrad et al. 2012). Investigations into agricultural biogas reactor contents and substrates are, however, rare.
Our study focused on the implementation of a simple isotope-based method to identify maize silage-bound net carbon degradation (C degnet) in agricultural biogas plant inoculum. Maize silages cultivated under organic farming standards occur frequently as biogas substrates (Einfalt and Kazda 2016). However, so far maize silages cultivated according to organic farming standards, have not been qualitatively investigated regarding anaerobic digestion performance. This manuscript provides additional information on this aspect.
The main aim was to test the applicability of net carbon degradation assessment by stable carbon isotope analysis (C degnet). As biological processes continuously affect the natural variation of δ13C values in silages, the applicability of C degnet determination was tested on (i) maize silages from different agricultural farms in (ii) fresh (MSfresh) and impaired storage (MSimpaired) conditions. Additionally, we analyzed a critical setup with (iii) mixed C3 and C4 substrate pools. The hypothesis tested differences on applied gross and net carbon degradation determination methods, which were expected between experiments with MSfresh and MSimpaired. Additional analyses of digestion parameters of different maize silages offered the possibility to identify possible inhibition processes within the experiments. For validation of the C degnet values, the results were compared to specific biogas yields (sby) and specific methane yields (smy) from separately conducted experiments.
Material and methods
All samples were collected from farms equipped with biogas plants in the southern part of Germany. Inocula Inoc1 and Inoc2 originated from an organic farm with a biogas plant operating at 38 °C. Feedstock consisted of cattle manure, cattle dung and clover silage (52.8% fresh weight (FW) of total feedstock). For more detailed information, see biogas plant No. 11 in Einfalt and Kazda (2016). The inocula were sampled individually in September (Inoc1) and October (Inoc2) 2012 and stored under anaerobic conditions at 38 °C for 4 days. Samples were extracted at the outflow of the main reactor. Additionally conducted experiments with inoculum extracted in May 2014 from the same biogas plant showed a residual biogas potential of 15.5 L kg−1 d−1. Inoculum Inoc3 was extracted from a mesophilic (40 °C) biogas plant in April 2014, utilizing cattle manure, cattle dung, maize silage (50.0% FW of total feedstock) and grass silage (16.7% FW of total feedstock). The inoculum Inoc3 was stored in the reactors of experiment E3 for 11 days at 42 °C for microbial adaptation and reduction of background biogas production by the inoculum. It showed an average of 13.7 L kg−1 d−1 residual biogas production prior to the start of the experiment.
Maize (Zea mays L.) silage sampling was conducted according to VDLUFA (1997). Maize silage MS1 and MS2 were sampled on two conventional farms situated in Seligweiler (German postal zip code (PZC) 89,081) and Unterweiler (PZC 89079), respectively. MS3 and MS4 were sampled on organic farms situated in Mettenberg (PZC 88440) and Niederstetten (PZC 97996), respectively. All samples were collected in June and July 2012. MS5 and MS6 were sampled separately in April 2014 from similar farms in Unterweiler and Mettenberg. According to the farmer, organic maize silage MS6 was ensiled with sunflower plants (ca. 5% FW). Until start of experiments, all maize silages were stored in sealed plastic bags at 6 °C.
The experiments were conducted in three anaerobic batch digestion experiments. The first experiment (E1) was performed with nine one-liter glass flask reactors located in a closed PVC box and heated continuously to 38 ± 0.5 °C. Every flask had a gastight sealing including a biogas outlet and a dip tube, reaching below liquid surface, for sample extraction (Fig. 1). Exhausted biogas was led through flexible tubes into a water-filled glass to prevent reverse aeration before being diverted to discharged air. All reactors were continuously stirred on a platform shaker (110 rpm). They contained 700 mL inoculum (Inoc1) and MS1 to MS4. Experiment E1 focused on MSfresh. Every maize silage was digested in two reactors as duplicates. One control reactor contained only inoculum Inoc1. The second experiment (E2) had a similar setup with a new sampled inoculum (Inoc2, 700 mL) and the same maize silages MS1 to MS4 digested in duplicates. Prior to the start of experiment E2, subsamples (ca. 3.0 kg) of maize silages MS1 to MS4 from experiment E1 were kept for 47 days at 21 ± 1.0 °C while stored in gastight plastic bags to simulate poor storage conditions. Therefore, the maize silages of experiment E2 are referred to as maize silages with impaired storage qualities (MSimpaired). Here, reactor R6 (MS3) could not be evaluated, due to technical problems. One control reactor contained exclusively inoculum Inoc2. Each reactor in experiments E1 and E2 was filled with up to 150 mL of water to achieve consistent total volume of 900 mL. In this study, reactor contents are referred to as mixtures (mix) of maize and inocula. The organic loadings (OL; based on VS) of the reactors at start of experiments E1 and E2 were 13.3 ± 1.1 kg m−3 and 13.9 ± 0.0 kg m−3, respectively. The duration of both experiments was 29 days. In E1, 7 mL of reactor samples were extracted via dip tube at day 1, 4, 6, 8, 11, 13, 15, 18, 22 and 29 for analysis of digestion parameters.
A third experiment (E3) was carried out with four 12 l lab-scale batch reactors. A water bath was used to maintain the temperature of the digesters at 42 ± 0.5 °C. Reactors were mixed (40 rpm) every 15 min with installed paddle mixers. Maize silages MS5 and MS6 were digested in duplicates with inoculum Inoc3 (9.0 l). Each reactor was water-filled to a volume of 9.6 L. For analysis of digestion parameters, 7 mL samples were extracted via dip tube at day 1, 2, 7, 10, 14, 16, 21 and 29. Produced biogas was analyzed for volumes and methane concentrations and evaluated for specific biogas (sby) and methane yield (smy). A fifth reactor (1 l glass flask) with reverse aeration protection was added to the water bath and operated as a control containing exclusively inoculum Inoc3. The OL was set to 15.0 kg m−3 for an experimental duration of 64 days.
Sample analysis and evaluation
All inocula, maize silages and reactor mixtures at start and end of the experiments were analyzed for total solids (TS), VS and pH (excluding MS1 to MS4) according to standard methods ASTM E1756–08(2015), ASTM E1755–01(2015) and ASTM E70–07(2015), respectively. Total C and nitrogen (N) concentrations were analyzed in dried (60 °C) and ground (particle size <0.5 mm) samples with an elemental analyzer (LECO TrueSpec CN, LECO, St. Joseph, USA). Inocula were tested for VFA total alkaline capacity (TAC) buffer stability (VFA/TAC) (Voß et al. 2009) (Table 1).
In experiment E1, three major parameters were analyzed, including digestion parameters, stable isotope composition and carbon degradation. Experiment E2 focused on stable isotope compositions and carbon degradations. Data from reactors containing MSfresh (experiment E1) and MSimpaired (experiment E2) were compared to elucidate differences between maize silages of fresh and impaired storage qualities. Experiment E3 was analyzed for differences in digestion parameters, sby, smy, stable isotope compositions and carbon degradations.
Digestion parameters included pH, VFA/TAC, acetic, propionic and butyric acid concentrations, which were analyzed consecutively in experiments E1 and E3. Organic acid analysis was performed on subsamples of the extracted reactor contents. Subsamples were immediately diluted to 50% (v/v) with formic acid (≥ 99%) and stored at −21 °C to prevent further degradation processes. Organic acids were analyzed after centrifugation by gas chromatography (GC) (CP 9001, FID, SGE HT5, 300 °C, Chrompack Inc., USA). Samples of experiment E3 were analyzed with a Clarus 580 GC (FID, Elite Wax, 280 °C, PerkinElmer, USA).
In experiment E3, sby and smy were determined by recording produced gas volumes (MGC-1, Ritter GmbH & Co. KG, Germany) and methane concentrations (BCP-CH4, BlueSens gas sensor GmbH, Germany). Data were logged hourly with the software BACVis (Version 184.108.40.206, BlueSens gas sensor GmbH, Germany) and corrected to standard conditions (273.15 K and 101.325 kPa). The sby and smy are presented in normed L kg−1 based on VS of the according substrate. The yields refer to the final produced gas volumes within the reactors.
Stable carbon isotopes were evaluated in all maize silages, inocula and reactor mixtures at start and end of the experiments. In addition, nitrogen isotope signatures (δ15N) were assessed for all maize silages. Analyses were conducted with 0.7 mg of dried and ground samples at the Institute of Food Chemistry, University of Hohenheim. Acetanilide was used as the laboratory standard (isotopic and elemental drift control). Samples were measured with an elemental analyzer (Euro EA C/N analyser, EuroVector, Italy), which was coupled to an isotope ratio mass spectrometer (Thermo Finnigan Delta plus Advantage, Thermo Fisher Scientific Inc., USA). This instrument uses magnetic sector as mass analyzer. The applied laboratory reference gases were CO2 and N2 (USGS40 calibrated) (Qi et al. 2003).
Stable isotope signatures are expressed by convention as
where X is 13C or 15N and R is the corresponding isotope ratio of 13C/12C or 15N/14N. The isotope signatures are referred to Rstandard, being the international PDB (Pee Dee Belemnite) standard of 0.0112372 (Craig 1953) for δ13C and atmospheric N2 of 0.3663 for δ15N (Junk and Svec 1958).
The gross carbon degradation of the reactor mixtures (C deggross) was determined by carbon mass balances at start and end of the experiment according to
Generally, the carbon mass of mixtures (C massmix), maize silage (C massMS) and inoculum (C massInoc) was analysed by mass balances according to
with A = exclusively mix, MS or Inoc and B = exclusively start or end of the experiment. This formula included the fresh weight (FW in g), the percentage of total solids (TS in % FW) and carbon concentrations (C in % TS) of the samples.
For elucidating the maize-bound carbon degradation on the basis of the common gross carbon degradation (C degMS) we used the following equation:
As the carbon mass of the maize silage is not directly detectable at the end of the experiment, it was approximated by an indirect approach. Therefore, the carbon mass of the control inoculum (C massInoc end) was subtracted from the carbon mass of the reactor mixture at the end of the experiment (C massmix end).
The calculation of net carbon degradation was based on a new approach, focusing on stable isotope fractionation. The isotopic fractionation provides information about the contribution of one source to a mixing pool. The stable carbon isotope fraction (ƒMS) of the maize silages in the according pool was identified by applying a stable isotope mixing model, modified after Werth and Kuzyakov (2010). The fraction was determined by the δ13C values of the respective mixture (δ13Cmix), inoculum (δ13CInoc) and maize silage (δ13CMS).
With A = exclusively start or end of the experiment. The values for inocula were taken from the control reactors. Using the information of the analyzed maize silage fractions (ƒMS) on the mass balance of the mixture (C massmix) allows the determination of the maize silage carbon mass at start (C mass ƒMS start) and end (C mass ƒMS end) of the experiments.
With A = exclusively start or end of the experiment. This information can further be used to calculate the net carbon degradation of maize silage (C degnet) in a reactor.
Wilcoxon signed-rank tests were applied to find significant differences (p < 0.05) between experiments E1 and E2. This study considered all carbon degradation determination approaches C deggross, C degMS, C degnet and δ13C values at start and end of the experiments. Mean values (Ø) of the degradation determination methods and δ13C are given with standard deviation. The software SPSS 20 (SPSS Inc., USA) was used for statistical analysis. Diagrams were generated with Microsoft Excel 2010.
Digestion parameters differed slightly between reactors from experiment E1 and E3. In experiment E1, all reactors showed pH values ranging from 7.1 to 7.9 during the course of the experiment (Fig. 2). The VFA/TAC values ranged from 0.03 to 0.35 and developed relatively similar within all reactors. The maximum VFA/TAC value was found with 0.35 in reactor R2 (MS1) on day four. As expected, the pH levels developed according to the VFA/TAC values together with slightly different organic acid concentrations. On day one of experiment E1, acetic acid concentrations ranged from 3.5 g L−1 to 1.9 g L−1. The highest acetic acid concentrations were found in reactor R2 (MS1) and R3 (MS2) with 5.3 g L−1 and 4.7 g L−1, respectively. Highest propionic and butyric acid concentrations showed values of 2.0 g L−1 and 0.8 g L−1, respectively.
In experiment E3, reactors with maize silage MS6 showed higher pH values on day one compared to reactors with maize silage MS5. The VFA/TAC showed values of 0.18 (R3) and 0.15 (R4) on day seven in reactors with maize silage MS6, while both reactors with maize silage MS5 showed values of 0.04. Acetic and propionic acid concentrations were found in reactors with maize silage MS6 on day seven, but were already depleted in reactors with maize silage MS5.
Specific biogas and methane yields
Maize silage MS5 showed higher cumulated sby of 708 L kg−1 and 702 L kg−1 than MS6 with 648 L kg−1 and 621 L kg−1 (Fig. 3). The average methane concentration in the reactors ranged from 61 ± 7% to 69 ± 10%. The cumulated smy was also higher in reactors containing maize silage MS5 with 461 L kg−1 and 392 L kg−1 compared to reactors containing maize silage MS6 with 360 L kg−1 and 352 L kg−1. Reactors with maize silage MS6 produced 7.6% to 12.2% lower sby and 8.3% to 23.8% lower smy, respectively, than reactors with maize silage MS5. A shift in the steepness of sby and smy graph was visible. For reactors with maize silage MS5 the steepness changed from day six to seven and for reactors with maize silage MS6 on day eight to nine.
Maize silages MS1 to MS4 showed δ13C values from −12.16 ‰ to −13.07 ‰ (Table 2). The inocula Inoc1 and Inoc2 had δ13C values of −27.11 ‰ and − 26.84 ‰, respectively. In experiment E3, maize silage MS5 and MS6 showed δ13C values of −12.37 ‰ and − 18.63 ‰, respectively, and inoculum Inoc3 a δ13C value of −17.58 ‰.
The reactor mixtures of experiments E1 and E2 showed higher δ13C values compared to inocula Inoc1 and Inoc2. In experiment E1 (MSfresh) mixtures showed mean δ13C values of −21.98 ± 0.11 ‰ at the start (δ13Cmix start) and − 25.68 ± 0.20 ‰ at the end (δ13Cmix end) of the experiment (Fig. 4). In experiment E2 with MSimpaired, the mixtures showed mean δ13Cmix start values of −23.60 ± 0.78 ‰ and mean δ13Cmix end values of −25.84 ± 0.37 ‰. In both experiments δ13Cmix end values were more negative compared to the δ13Cmix start values. Also the δ13CInoc end values of Inoc1, Inoc2 and Inoc3 were more negative compared to the δ13CInoc start values. δ13Cmix start values were significantly more negative in experiment E2 compared to experiment E1 (Z = −2.37, p = 0.02).
As expected, the carbon mass in each reactor was higher at the start (C massmix start) compared to the end of the experiments (C massmix end) (Table 3). Similar results were found for the carbon mass of the maize silages (C massMS). Inoculum Inoc1 and Inoc2 also showed higher C massInoc start values of 17.2 g and 16.2 g compared to C massInoc end values of 13.1 g and 14.8 g, respectively. The carbon mass determination of maize silages via isotopic fractionation in experiment E1 resulted in higher starting values (C mass ƒMS start), ranging from 8.4 g to 9.2 g, compared to C massMS start with 6.0 g to 6.1 g. In experiment E2, C mass ƒMS start showed relatively lower values compared to the determined C massMS start values (exception found in R7).Analyzing the different carbon degradation determination approaches, C deggross values ranged from 31.6% to 48.7% in experiment E1, resulting in a mean gross carbon degradation of 39.3 ± 5.5%. Significantly higher C deggross values were found in experiment E1 compared to experiment E2 (Z = −2.20, p = 0.03) with a C deggross mean of 27.7 ± 4.8%. The carbon degradations of experiment E3 showed C deggross values ranging from 61.2% to 62.9%.
Values for C degMS ranged from 49.5% to 85.6% (mean 65.6 ± 14.5%) in experiment E1 and from 46.7% to 81.1% (mean 71.5 ± 11.9%) in experiment E2. No significant differences were found for C degMS between experiments E1 and E2. Experiment E3 showed C degMS values ranging from 79.5% to 95.3%.
The new stable isotope approach for net carbon degradation determination showed C degnet values ranging from 75.4% to 86.5% (mean 81.1 ± 3.5%) in experiment E1. Experiment E2 showed significantly lower C degnet values (Z = -2.37, p = 0.02) ranging from 58.4% to 79.2% (mean 67.7 ± 8.4%). The standard deviation indicated a higher variance in experiment E2 compared to experiment E1. The carbon degradation determination method using stable isotopes C degnet was found to be not applicable in experiment E3.
Determined digestion parameters did not show any inhibition within the conducted experiments. Different developments of digestion parameters between reactors of experiment E1 and E3 might be a result of different activities of inocula Inoc1 and Inoc3. The digestion parameters within all reactors of experiment E1 indicated relatively similar anaerobic digestion processes. Metabolic inhibitions due to high VFA/TAC or low pH values (Voß et al. 2009) were not detected in experiments E1 and E3. Acid production is the key intermediate during anaerobic digestion. In this study, acetic acid showed the highest concentrations of all analyzed acids (c.f. Mösche and Jördening 1999). Slightly different acetic acid concentrations at day one of the experiments indicated variabilities within the applied maize silages. Acetic acid is a main product of heterofermentative lactic acid bacteria during ensiling of biogas crops. As ensiling is an uncontrolled process (Hermann et al. 2011), different acetic acid concentrations were likely within maize silages after 24 h degradation. McDonald et al. (1991) stated that pH values less than 4.0 indicate a good silage quality, which was present in maize silages MS5 (pH = 3.78) of experiment E3. The higher acetic acid concentrations found in reactors with maize silage MS5, compared to maize silage MS6, might be a result of beneficial ensiling processes during lactic acid fermentation (McDonald et al. 1991). Possibly, this influenced the efficiency of the conversion processes in maize silage MS6, as indicated by higher pH values, and slowed the anaerobic digestion metabolism. As a result, this led to a prolonged presence of acetic acid in reactors with maize silage MS6. Our results indicated that digestion parameters in experiment E3 were mainly influenced by maize silage quality.
Specific biogas and methane yield
sby, smy and methane concentrations were similar to literature-values found for maize silage (c.f. Amon et al. 2007). Standard protocols for assignment of specific yields (VDI 2006) require that inoculum has a low residual biogas production potential at the start of the experiment, which was the case in all experiments. Considering the produced biogas volume at day 64, already 89.0% (R4) to 92.6% (R2) of the final biogas volume was produced after 29 days. This indicated that substrate degradation was already performed within the first 29 days. Of the analyzed acids, acetic acid has the highest conversion rates to methane (Wang et al. 2009). Herrmann et al. (2011) and Vervaeren et al. (2010) showed a close relationship of acetic acid concentrations to the smy. The effect of acetic acid on the biogas and methane production was also visible in experiment E3. With depletion of acetic acid the steepness of the sby and smy graphs changed visibly. Higher sby and smy were found for reactors with maize silage MS5 compared to reactors with MS6, despite higher variabilities between reactors R1 and R2 of maize silage MS5. sby and smy differences are likely to be a result of subsample variabilities within MS5. The higher sby and smy of maize silage MS5 compared to maize silage MS6 might be a result of the ensilage of sunflower plants within maize silage MS6. Sunflowers generally show lower biogas potentials compared to maize (Amon et al. 2007). Additionally, this difference can be related to better ensilage processes and good storage conditions of maize silage MS5 (Oude Elferink and Driehuis 2000).
Stable isotope signatures
The investigated maize silages showed δ13C values corresponding to literature values for maize (Gleixner et al. 1999; Haubert et al. 2009) and maize silages (Schwertl et al. 2005). An exception was found in maize silage MS6, which showed more negative δ13C values. As C3-plants, sunflowers have a δ13C of about −28 ‰ (Zhu and Cheng 2011), which explains the more negative values found in maize silage MS6 due to mixed ensilage. The δ13C values of MS6 indicated that more than 5% (FW) sunflowers were ensiled with the maize silage. The study showed a variability of δ13C values in different agricultural maize silages (Table 2). Such variations can be influenced by various parameters, including the cultivation system. For instance, Haubert et al. (2009) found more negative δ13C values in maize leaves (−13.3 ± 0.06 ‰) cultivated under organic farming standards, compared to conventionally cultivated maize (−12.9 ± 0.02 ‰). Differences in δ15N values of maize silages MS1 to MS6 might be a result of variable amounts of organic and mineral fertilizers applied for maize cultivation (EU 2007; Shearer et al. 1974).
Inocula Inoc1 and Inoc2 showed relatively similar δ13C values at the start of each experiment. The δ13C differences between these inocula (Inoc1 and Inoc2) compared to inoculum Inoc3 originated from different substrates utilized in the according biogas plants. Inocula Inoc1 and Inoc2 originated from an organic farm. Such biogas plants are generally prone to utilize high amounts of C3 plants and low amounts of maize silages (C4 plants) (Einfalt and Kazda 2016). This resulted in δ13C values more depleted in the 13C isotope than inoculum Inoc3, which originated from a conventional farm’s biogas plant that utilized preferably maize silage.
The maize silages with impaired storage conditions MS1 to MS4 (MSimpaired) in experiment E2 led to significantly lower δ13Cmix start values compared to the same maize silages in experiment E1 (MSfresh). This indicated that the δ13Cmix start values were significantly influenced by impaired storage conditions of the maize silages. Furthermore, this opens possibilities to draw conclusions about the maize silages’ storage conditions by negatively shifted δ13C values in reactor mixture. The relatively high variation in δ13Cmix start values of the maize silages in experiment E2 indicated that impaired storage conditions influence δ13C compositions of maize silages differently. This led to different 13C-depleted maize silages in reactor mixtures at the start of experiment E2. Also relatively high variations between reactor duplicates occurred in experiment E2, indicating high inner substrate variabilities in terms of δ13C values.
Despite different δ13Cmix start values between experiments E1 and E2, relatively similar δ13Cmix end values were found in both experiments. The δ13Cmix end values of experiments E1 and E2 were clearly shifted towards the δ13CInoc values. Most biochemical processes discriminate against the heavier carbon isotope creating products depleted in 13C and leaving reactants enriched in 13C (Conrad 2005; Fry 2006; Werth and Kuzyakov 2010). So far, such results were mainly observed in experiments of either pure C3 or pure C4 samples. In experiment E1 and E2 C3 and C4 sources were mixed in the reactors and resulted in a negative shift of all δ13Cmix and δ13CInoc values between start and end of the experiments. This indicated a 13C depletion of the reactant and a 13C discrimination during the degradation process. The 13C depletion in the reactor mixtures was therefore linked to the degradation of the maize silage. Similar results were found by Laukenmann et al. (2009) who digested C3 and C4 mixtures in batch biogas systems and analyzed δ13C values of produced methane and CO2. The authors found a shift of the gases towards more negative δ13C values for the first five days, leaving the mixture enriched in 13C isotopes. This was also found by Francioso et al. (2010) who reported a positive shift of δ13C values in the mixtures during the first six days. However, in the proceeding experimental time of Laukenmann et al. (2009) the gases shifted towards less positive δ13C values, demonstrating a depletion of the 13C isotopes in the mixture. This occurred despite methane formation, which is a strongly 13C discriminating biodegradation process (methane δ13C values from − 47 ‰ to − 89 ‰) (Krzycki et al. 1987). As Laukenmann et al. (2009) showed, exhausted gases were isotopically depleted at the beginning of the experiment. During this time the mixtures became enriched in 13C. However, as the carbon pool is limited in a batch digestion experiment, the 13C enriched mixture became eventually metabolized to methane and CO2 which, in turn, shows increasingly negative δ13C values. These results indicated a depletion of 13C in the reactor mixtures of anaerobic batch digestion experiments when the carbon pool becomes increasingly limited.The negative shift of all δ13Cmix and δ13CInoc values at the end of the experiment was also the result of variable degradability of different biochemical fractions. Sugars, starch, proteins and organic acids are considered easily degradable in anaerobic digestions, while having less negative δ13C values (Bowling et al. 2008; Hobbie and Werner 2004). Lignin and lipids are difficult substrates concerning degradability (Chen et al. 2008) and show generally more depleted δ13C values (Bowling et al. 2008; Hobbie and Werner 2004). Therefore, the negative shift of δ13C in the reactor mixtures during the experimental retention time is a result of the continuous depletion of maize silage and successively increasing amounts of biochemical fractions of low degradability.
Also all depleted δ13Cmix end values indicated more positive values compared to the according δ13CInoc values. This indicated an incomplete degradation of the maize silages after 29 days. On a long term run, as present in continuously fed reactors, it is likely that the δ13Cmix values will become more positive and close to δ13C values of the feedstock.
Carbon degradation assessment
Assessing the carbon degradation by different methodologies showed high variabilities in the determined values. The mean C degnet values compared to mean C deggross values (see Ø in Table 3) were increased by 42% and 40% in experiments E1 and E2, respectively. The lower C deggross values in experiments E1 and E2, compared to C degnet was due to the fact, that the carbon mass balance of the reactor mixtures included the carbon mass of the inoculum (c.f. equation 2). However, the carbon degradation occurs mainly in the added maize silage while the inoculum ideally only provides the necessary microbial communities and enzymes. All inocula showed relatively low residual biogas potentials prior to experiments, which can be linked to low inner carbon degradation processes. Therefore, the carbon degradation evaluation of the substrate in batch experiments is masked by the quantitatively high amount of carbon in the inoculum, resulting in an underestimation of the actual substrate-bound carbon degradation. Impaired storage conditions of the maize silages resulted in lower C deggross of experiment E2 compared to experiment E1. This can be explained as maize silage degradability is negatively influenced by temperature and long storage conditions (Ali et al. 2015). During the impaired storage of the maize silage available carbohydrates are successively degraded. Losses of easily degradable substances (e.g. non-structural carbohydrates) increased the relative amount of poorly degradable substances (e.g. fibers). Their anaerobic degradation is a relatively slow process and needs a long retention time (Golkowska and Greger 2013). Therefore, the relative amount of poorly degradable substances was likely to be higher in MSimpaired, compared to MSfresh, and resulted in lower carbon degradation over the retention time of 29 days.
The highest C deggross and C degMS values were found in experiment E3 due to retention time of 64 days. Higher degradation efficiency is majorly influenced by increased retention time (Ruile et al. 2015). Additionally, temperature is a key factor in biogas production (Naik et al. 2014; Pap et al. 2015; Sakar et al. 2009) influencing the metabolism of the microorganisms and thus the substrate degradation efficiency (Weiland 2010). As a longer retention time and higher digestion temperatures were present in experiment E3 (42 °C), compared to experiments E1 and E2 (38 °C), conditions in the former are likely to have improved carbon degradation.
The determination of C degMS is based on an estimation of the available maize silage carbon mass in the reactor mixtures at start and end of the experiment (c.f. equation 4). As C massMS end cannot be evaluated directly, it has to be approximated by an indirect approach. Therefore, the carbon mass of inoculum in the control reactor is analyzed at the end of the experiment (C massInoc end). Further, C massInoc end is subtracted from the carbon mass of the mixture at the end of the experiment (C massmix end). As two different reactors needed to be sampled, priming effects due to additional substrate within the reactor mixtures are not taken into account. The mean C degMS values indicated slightly higher carbon degradations in experiment E2 compared to experiment E1, albeit not significant. However, the impaired storage conditions of the maize silages (experiment E2) cannot have increased the available total carbon mass in experiment E2. Therefore, the indirect approach for elucidating C degMS did not show reliable values for the conducted experiments.
The analyzed net carbon degradation C degnet did show applicable on different maize silages, as applied in experiment E1. Despite described variabilities within the substrate characteristics of maize silages prior to the experiment mean net carbon degradation of 81.1 ± 3.5% indicated the applicability of this method to maize silages. It furthermore showed that the method can be applied on maize silages of fresh qualities despite inter-substrate variabilities due to differences in cultivation or ensilage. The net carbon degradation C degnet in experiment E2 showed significantly lower values compared to experiment E1. Thus, the artificially reduced qualities in MSimpaired were indeed detectable by the investigated method, despite their differences in δ13C compositions. Additionally, relatively large deviations within C degnet of the duplicates, indicated by high variability of δ13C, may point out inhomogeneous degradation processes within the reactors. Therefore, the method offers a possibility to determine the quality of maize silages for anaerobic digestion.
The determination of C degnet in experiment E3 was not possible. The stable isotope mixing model has to be applied to substrates of pure C3 sources in combination with substrates of pure C4 sources. As inoculum Inoc3 already contained a mixture of C3 and C4 sources, no precise 13C fractionation was determinable. Therefore, the experimental setup design must consist of inocula of biogas reactors utilizing solely C3 plants and pure C4 plant substrates (e.g. maize silages) or vice versa.
By comparing the applied carbon degradation methods, C degnet determination showed higher values compared to C deggross. As C degnet determination included information about isotope fractionation, it defined the contribution of maize silage to the total mixed carbon pool. Therefore, this information provided a detailed picture about the maize silage fraction and net carbon degradation of the investigated maize silage.
A direct validation of the C degnet values in the reactors by carbon mass balancing is generally not possible. C degnet values can, however, be compared to exhausted biogas. Continuous efflux of carbon-based gasses CO2 and CH4 is a result of continuous carbon degradation. Results from maize silage MS5 in experiment E3 showed that 92.5% to 92.6% of the final sby were already produced at day 29. It has to be considered, that biogas production in E3 was not finally completed after 64 days and poorly degradable substances from inoculum and substrate remained in the reactor. Comparing these with C degnet values of 81.1 ± 3.5% from E1, a validation of the stable isotope approach can be assumed viable.
Gross and net carbon degradation was determined by different methodical approaches within different experimental setups. The experiments revealed that net carbon degradation can be determined by including information about isotopic fractionation. The applicability of this method was tested positive on variable maize silages originating from different agricultural farms.
Artificially lowering the maize silage quality by unfavorable storage conditions (47 days, 21 ± 1 °C) significantly influenced stable isotope compositions. Expected differences in gross and net carbon degradation showed determinable, despite different carbon isotope signatures. This suggests the use of this method for determination of silage quality.
Gross carbon degradation determinations underestimate the actual substrate-bound carbon degradation. Compared to gross carbon degradation determination, net carbon degradation determination has the advantage to exclude the masking effect of the inoculum. This offers the possibility to implement a standardized stable isotope method for maize-silage bound net carbon degradation determination in anaerobic fermentation.
- C deggross :
gross carbon degradation determination method of reactor mixture
- C degMS :
net carbon degradation determination approach based on control reactor
- C degnet :
net carbon degradation determination method using stable isotopes
- C massMS :
Maize silage carbon mass analysed by carbon mass balance
- C massInoc :
Inoculum carbon mass analysed by carbon mass balance
- C mass ƒMS :
Carbon mass analysed by stable isotope fraction
- MSfresh :
fresh maize silage
- MSimpaired :
maize silage with impaired storage
Ali M, Cone JW, Khan NA, Hendriks WH, Struik PC (2015) Effect of temperature and duration of ensiling on in vitro degradation of maize silages in rumen fluid. J Anim Physiol Anim Nutr 99:251–257. https://doi.org/10.1111/jpn.12244
Amelung W, Brodowski S, Sandhage-Hofmann A, Bol R (2008). Combining biomarker with stable isotope analyses for assessing the transformation and turnover of soil organic matter. Adv Agron 100:155–250. 10.1016/S0065-2113(08)00606-8
Amon T, Amon B, Kryvoruchko V, Machmüller A, Hopfner-Sixt K, Bodiroza V, Hrbek R, Friedel J, Pötsch E, Wagentristl H, Schreiner M, Zollitsch W (2007) Methane production through anaerobic digestion of various energy crops grown in sustainable crop rotations. Bioresour Technol 98:3204–3212. https://doi.org/10.1016/j.biortech.2006.07.007
ASTM E70–07 (2015) Standard test method for pH of aqueous solutions with the glass electrode. ASTM International, West Conshohocken. https://doi.org/10.1520/E0070-07R15
ASTM E1755–01 (2015), Standard test method for ash in biomass, ASTM International, West Conshohocken, 10.1520/E1755-01R15
ASTM E1756–08 (2015), Standard Test Method for Determination of Total Solids in Biomass, ASTM International, West Conshohocken, https://doi.org/10.1520/E1756-08R15
Bisutti I, Hilke I, Raessler M (2004) determination of total organic carbon - an overview of current methods. Trends Anal Chem 23:716–726. https://doi.org/10.1016/j.trac.2004.09.003
Bowling DR, Pataki DE, Randerson JT (2008) Carbon isotopes in terrestrial ecosystem pools and CO2 fluxes. New Phytol 178:24–40. https://doi.org/10.1111/j.1469-8137.2007.02342.x
Brüggemann N, Gessler A, Kayler Z, Keel SG, Badeck F, Barthel M, Boeckx M, Buchmann N, Brugnoli E, Esperschütz J, Gavrichkova O, Ghashghaie J, Gomez-Casanovas N, Keitel C, Knohl A, Kuptz D, Palacio S, Salmon Y, Uchida Y, Bahn M (2011) Carbon allocation and carbon isotope fluxes in the plant-soil-atmosphere continuum: a review. Biogeosciences 8:3457–3489. https://doi.org/10.5194/bg-8-3457-2011
Chen Y, Cheng JJ, Creamer KS (2008) Inhibition of anaerobic digestion process: a review. Bioresour Technol 99:4044–4064. https://doi.org/10.1016/j.biortech.2007.01.057
Conrad R (2005) Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal. Org Geochem 36:739–752. https://doi.org/10.1016/j.orggeochem.2004.09.006
Conrad R, Klose M, Yuan Q, Lu Y, Chidthaisong A (2012) Stable carbon isotope fractionation, carbon flux partitioning and priming effects in anoxic soils during methanogenic degradation of straw and soil organic matter. Soil biol. Biochem 49:193–199. https://doi.org/10.1016/j.soilbio.2012.02.030
Craig H (1953) The geochemistry of the stable carbon isotopes. Geochim Cosmochim Acta 3:53–92. https://doi.org/10.1016/0016-7037(53)90001-5
Einfalt D, Kazda M (2016) Characterisation of biogas plants on organic farms and potentials for improvement. Org Agric 6:243–254. https://doi.org/10.1007/s13165-015-0133-3
EU (2007) Council regulation (EC) no 834/2007. European Union, Luxemburg
FNR (2017) Bioenergy in Germany facts and figures 2017. Agency for Renewable Resources, Gülzow-Prüzen
Francioso O, Rodriguez-Estrada MT, Montecchio D, Salomoni C, Caputo A, Palenzona D (2010) Chemical characterization of municipal wastewater sludges produced by two-phase anaerobic digestion for biogas production. J Hazard Mater 175:740–746. https://doi.org/10.1016/j.jhazmat.2009.10.071
Fry B (2006) Stable isotope ecology. Springer, New York
Gehring T, Klang J, Niedermayr A, Berzio S, Immenhauser A, Klocke M, Wichern M, Lübken M (2015) Determination of Methanogenic pathways through carbon isotope (δ13C) analysis for the two-stage anaerobic digestion of high-solids substrates. Environ Sci Technol 49:4705–4714. https://doi.org/10.1021/es505665z
Gelwicks JT, Risatti JB, Hayes JM (1994) Carbon isotope effects associated with Aceticlastic Methanogenesis. Appl Environ Microbiol 60:467–472
Gleixner G, Bol R, Balesdent J (1999) Molecular insight into soil carbon turnover. Rapid Commun Mass Spectrom 13:1278–1283. https://doi.org/10.1002/(SICI)1097-0231(19990715)13:13<1278::AID-RCM649>3.0.CO;2-N
Golkowska K, Greger M (2013) Anaerobic digestion of maize and cellulose under thermophilic and mesophilic conditions - a comparative study. Biomass Bioenergy 56:545–554. https://doi.org/10.1016/j.biombioe.2013.05.029
Haubert D, Birkhofer K, Fließbach A, Gehre M, Scheu S, Ruess L (2009) Trophic structure and major trophic links in conventional versus organic farming systems as indicated by carbon stable isotope ratios of fatty acids. Oikos 118:1579–1589. https://doi.org/10.1111/j.1600-0706.2009.17587.x
Herrmann C, Heiermann M, Idler C (2011) Effects of ensiling, silage additives and storage period on methane formation of biogas crops. Bioresour Technol 102:5153–5161. https://doi.org/10.1016/j.biortech.2011.01.012
Hobbie EA, Werner RA (2004) Intramolecular, compound-specific, and bulk carbon isotope patterns in C3 and C4 plants: a review and synthesis. New Phytol 161:371–385. https://doi.org/10.1111/j.1469-8137.2004.00970.x
Junk G, Svec HJ (1958) The absolute abundance of the nitrogen isotopes in the atmosphere and compressed gas from various sources. Geochim Cosmochim Acta 14:234–243. https://doi.org/10.1016/0016-7037(58)90082-6
Krzycki JA, Kenealy WR, DeNiro MJ, Zeikus JG (1987) Stable carbon isotope fractionation by Methanosarcina barkeri during Methanogenesis from acetate, methanol, or carbon dioxide-hydrogen. Appl Environ Microbiol 53:2597–2599
Laukenmann S, Heuwinkel H, Keppler F (2009) Stabile Kohlenstoffisotope: Eine neue Lasermethode zur Prozessanalyse und Identifikation mikrobiologischer Reaktionen im Biogasprozess. In: Int Wissenschaftstagung Biogas Sci, Bayerische Landesanstalt für Landwirtschaft (LfL), Freising-Weihenstephan, Germany. 369–376
McDonald P, Henderson AR, Heron S (1991) The biochemistry of silage, 2nd edn. Chalcombe Publications, Marlow
Mösche M, Jördening HJ (1999) Comparison of different models of substrate and product inhibition in anaerobic digestion. Water Res 33:2545–2554. https://doi.org/10.1016/S0043-1354(98)00490-4
Naik L, Gebreegziabher Z, Tumwesige V, Balana BB, Mwirigi J, Austin G (2014) Factors determining the stability and productivity of small scale anaerobic digesters. Biomass Bioenergy 70:51–57. https://doi.org/10.1016/j.biombioe.2014.01.055
Oude Elferink SJWH, Driehuis F (2000) Silage fermentation processes and their manipulation. In: FAO Electron Conf trop silage, food and agriculture organization (FAO), Rome, Italy 17–30
Pap B, Györkei Á, Boboescu IZ, Nagy IK, Bíró T, Kondorosi É, Marótibc G (2015) Temperature-dependent transformation of biogas-producing microbial communities points to the increased importance of hydrogenotrophic methanogenesis under thermophilic operation. Bioresour Technol 177:375–380. https://doi.org/10.1016/j.biortech.2014.11.021
Penger J, Conrad R, Blaser M (2014) Stable carbon isotope fractionation of six strongly fractionating microorganisms is not affected by growth temperature under laboratory conditions. Geochim Cosmochim Acta 140:95–105. https://doi.org/10.1016/j.gca.2014.05.015
Qi H, Coplen TB, Geilmann H, Brand WA, Böhlke JK (2003) Two new organic reference materials for d13C and d15N measurements and a new value for the d13C of NBS 22 oil. Rapid Commun Mass Spectrom 17:2483–2487. https://doi.org/10.1002/rcm.1219
Ruile S, Schmitz S, Mönch-Tegeder M, Oechsner H (2015) Degradation efficiency of agricultural biogas plants – a full-scale study. Bioresour Technol 178:341–349. https://doi.org/10.1016/j.biortech.2014.10.053
Sakar S, Yetilmezsoy K, Kocak E (2009) Anaerobic digestion technology in poultry and livestock waste treatment--a literature review. Waste Manag Res 27:3–18. 10.1177/0734242X07079060
Schwertl M, Auerswald K, Schäufele R, Schnyder H (2005) Carbon and nitrogen stable isotope composition of cattle hair: ecological fingerprints of production systems? Agric Ecosyst Environ 109:153–165. https://doi.org/10.1016/j.agee.2005.01.015
Shearer GB, Kohl DH, Commoner B (1974) The precision of determinations of the natural abundance of nitrogen-15 in soils, fertilizers, and shelf chemicals. Soil Sci 118:308–314
Smith BN, Epstein S (1971) Two categories of 13C/12C ratios for higher plants. Plant Physiol 47:380–384. https://doi.org/10.1104/pp.47.3.380
VDI (2006) VDI 4630 fermentation of organic materials – characterisation of the substrate, Sampling, Collection of Material Data, Fermentation Tests, Beuth Verlag, Berlin, Germany
VDLUFA (1997) Method book III - the chemical analysis of feedstuffs. 3rd ed.VDLUFA Verlag, Darmstadt, Germany
Vervaeren H, Hostyn K, Ghekiere G, Willems B (2010) Biological ensilage additives as pretreatment for maize to increase the biogas production. Renew Energy 35:2089–2093. https://doi.org/10.1016/j.renene.2010.02.010
Voß E, Weichgreber D, Rosenwinkel KH (2009) FOS/TAC – Deduction, Methods, Application and Significance. In: Int Wissenschaftskonferenz Biogas Sci, Bayerische Landesanstalt für Landwirtschaft (LfL), Freising-Weihenstephan, Germany
Wang Y, Zhang Y, Wang J, Meng L (2009) Effects of volatile fatty acid concentrations on methane yield and methanogenic bacteria. Biomass Bioenergy 33:848–853. https://doi.org/10.1016/j.biombioe.2009.01.007
Weiland P (2010) Biogas production: current state and perspectives. Appl Microbiol Biotechnol 85:849–860. https://doi.org/10.1007/s00253-009-2246-7
Werth M, Kuzyakov Y (2010) 13C fractionation at the root–microorganisms–soil interface: a review and outlook for partitioning studies. Soil Biol Biochem 42:1372–1384. https://doi.org/10.1016/j.soilbio.2010.04.009
Zhu B, Cheng W (2011) 13C isotope fractionation during rhizosphere respiration of C3 and C4 plants. Plant Soil 342:277–287. https://doi.org/10.1007/s11104-010-0691-9
Open Access funding provided by Projekt DEAL. We are grateful for the technical and analytical support by Ellen Salzer and Hans Malchus at Ulm University.
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Einfalt, D., Werth, M., Schropp, D. et al. Stable carbon isotope analyses offer insights into net carbon degradation of maize silages in anaerobic batch fermentations. Biologia 75, 1043–1054 (2020). https://doi.org/10.2478/s11756-019-00403-y
- Net carbon degradation
- Maize silage
- Stable isotopes