The effect of harvest date and the chemical characteristics of biomass from Molinia meadows on methane yield

Biomass and Bioenergy 130 (2019) 105391

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Research paper

The effect of harvest date and the chemical characteristics of biomass from Molinia meadows on methane yield � ska c, Mateusz Meserszmit a, *, Mariusz Chrabąszcz b, Monika Chylin c �ska-Chargot , Adriana Trojanowska-Olichwer d, Zygmunt Kącki a Monika Szyman a

Botanical Garden, University of Wrocław, Wrocław, Poland Department of Ecology, Biogeochemistry and Environmental Protection, University of Wrocław, Wrocław, Poland Institute of Agrophysics, Polish Academy of Sciences, Lublin, Poland d Institute of Geological Sciences, University of Wrocław, Wrocław, Poland b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Anaerobic digestion Renewable energy Grassland Biogas production Nature conservation

Molinia meadows are seminatural habitats of Natura 2000, and their continued existence is heavily dependent on human activities. The aim of the present study was to determine the effect of the harvest time of meadow biomass originating from Molinia meadows on the methane yield. After 35 days of conducting batch digestion tests, the average methane yield was found to be slightly different over the course of four biomass harvest dates: at the end of May, 221 � 8 NL CH4 kg 1 VS; at the beginning of July, 211 � 21 NL CH4 kg 1 VS; at the end of July, 200 � 8 NL CH4 kg 1 VS and on September 1, 197 � 2 NL CH4 kg 1 VS. During the initial stages of batch fermentation, a higher methane yield was obtained from the biomass harvested at the end of May. This biomass was charac­ terised by higher contents of N, P and K, as well as a lower C:N ratio and reduced Ca and cellulose content. Relative to the content of elements and cellulose as well as the C:N ratio observed, significant correlations were found for methane yields from biomass obtained between the fourth and eighth days of batch digestion. It was determined that the average methane yield on a per hectare basis for the harvested biomass differed depending on the harvest date under investigation. The use of biomass from Molinia meadows for biogas production has the potential to become an important factor in the environmental protection of this type of habitat.

1. Introduction Seminatural grasslands are communities of high species density that contain many endangered and protected species in their flora compo­ sition [1]. Due to their seminatural character, these habitats require technical conservation measures in order to maintain their viability. The traditional use of meadows has always been pastural or related to col­ lecting hay for cattle-rearing purposes; as such, their continued exis­ tence is highly dependent on human activities [2]. Since these types of measures hamper the succession of vegetation and positively influence the habitat’s biodiversity [3], such communities have been included on the ‘Habitat Types of Community Interest’ list in Annex I of the European Habitat Directive, and protection of these delicate spaces is of the highest priority. Following this regulation, all EU member states are, therefore, obliged to carry out the mandates of the Directive and to

establish methods for successfully restoring the most endangered species and habitats in Europe. Molinia meadows (Natura 2000 –habitat code 6410) are extensively used habitats and some of the most valuable seminatural communities in Poland. Unfortunately, the existing conditions of these meadows are considered to be generally ‘unfavourable’ or ‘deteriorating’ across the entire EU [4], the main reason for which is related to agricultural ac­ tivities, most notably the lack of proper maintenance procedures, such as mowing or grazing. This inevitably leads to an explosion in the density of shrubs and tree undergrowth [5]. The decreasing demand for meadow biomass as a result of the declining number of farm animals and agri­ cultural intensification has led to a marked reduction in the meadows’ size area [6]. Traditionally, Molinia meadows are mowed during the late growing season [7]. ‘Haying’ during this period results in lower fodder

Abbreviations: TS, total solids; VS, volatile solids; ODM, organic dry matter; DM, dry matter; NDF, neutral detergent fibre; ADF, acid detergent fibre; ADL, acid detergent lignin; CH4, methane; YCH4, methane yield. * Corresponding author. E-mail address: [email protected] (M. Meserszmit). https://doi.org/10.1016/j.biombioe.2019.105391 Received 10 June 2019; Received in revised form 16 September 2019; Accepted 30 September 2019 Available online 5 November 2019 0961-9534/© 2019 Elsevier Ltd. All rights reserved.

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digestibility and biomass quality due to an increase in the cellulose, hemicellulose and lignin content [8,9]. Nowadays, the biomass obtained from Molinia meadows is not used for agricultural purposes, although management of this habitat is still supported by many EU agro-environmental programmes. Finding solutions for more efficient utilisation of low feeding quality biomass is an important element in the conservation strategy for these types of habitats. One way to manage this type of waste is to use it as a substrate in agricultural biogas plants [10, 11]. Biogas production is a growing sector in the European energy market which, in the near future, has the potential to become a profit­ able economic alternative to bioenergy production [12]. Additionally, this sector will also be helpful in achieving the requirements of the European Commission’s Package ‘3 � 200 , which seeks to increase the percentage of energy derived from renewable sources in the continent’s gross final energy consumption to 20% by 2020 [13]. Generating energy from agricultural biogas plant reduces greenhouse gas emissions [14], positively impacts the natural environment [15] and promotes sustain­ able grassland management in a concerted effort to achieve established conservation targets [16]. Studies conducted in south-eastern England revealed that grassland management for conservation purposes pro­ duces 1.5 times more biogas per tonne of dry matter than cereals or crop waste [17]. Since meadow biomass is also used as a substrate or co-substrate in many agricultural biogas plants [6,18], the impact of harvest time on the volume of biomass obtained has been the focus of many past in­ vestigations [11,19,20]. Panagiotis et al. [21] showed that the grass-cutting period is one of the most important parameters to consider when determining the suitability of plant biomass for biogas production. Late harvest times often lead to reduced CH4 levels in the resulting biogas due to a percentage increase in the lignocellulosic matter in the biomass [6]. Therefore, the phenological state of the plants at the time of harvest should be taken into account when evaluating the biogas po­ tential of a meadow biomass. During the ‘growth’ phase, plants accu­ mulate varying amounts of macro- and microelements in their tissues which can have a significant impact on the fermentation processes involved in biogas production [22,23]. In particular, having a less-than-optimal C:N ratio in a substrate causes the incomplete con­ version of carbon to methane and leads to lower CH4 content in the biogas final product [24–27]. To this end, the aim of the present study is to determine the effect of harvest time on the methane yield obtained during energy production processes for biomass harvested from Molinia meadows.

2. Materials and methods 2.1. Location and characteristics of the study area The study was conducted in SW Poland (N 51� 230 56.80”; E �w in the Lower Silesia, during 17 100 45.8000 ) near the village of Skoroszo the growth season between May 24 and 1 September 2014. The study area is part of the Natura 2000 site (PLH020093). Sampling plots were located in a seminatural meadow classified as Molinia meadows (Natura 2000, habitat code 6410), which are found on meadow-brown soils (Gleyic Cambisols). The average atmospheric temperatures in the study area at the time of harvesting were 12.9 � C in May, 15.8 � C in June, 20.9 � C in July, 17.1 � C in August and 14.6 � C in September. �

2.2. Field study A 20-m-long transect was randomly marked in the Molinia meadow complex (Fig. 1). The transect consisted of five rectangular-shaped blocks of plots with the dimensions 2 � 4 m. Each block was divided into eight squares with an area of 1 m2 each. For each of the four harvest dates (May 24, July 1, July 23 and September 1), one square was randomly selected in each of the five sampling plots, and the following field evaluation operations were performed: (a) Species composition was assessed using the percentage scale. (b) Phenological evaluation of the plant’s growth stage was con­ ducted according to the scale as follows: 0, no flowers or buds; 1, flowers in bud; 2, full flowering; 3, full fruiting; 4, seed shed. Phenological data were determined for the dominant species in the sward (i.e. when plant cover was more than 10% of the spe­ cies composition according to percentage cover scale). (c) Biomass was harvested 5 cm above the ground and collected for chemical and biogas production analysis. (d) The biomass for dry matter yield measurements was taken from twenty squares that had an area of 1 m2 each. Biomass was collected using scissors 5 cm above the ground and kept in polyethylene bags. The twenty biomass samples that had been collected in total (five samples for each harvest date) were dried at 60 � C for 48 h in order to determine the dry weight. The calculated meadow biomass yield is expressed as t DM ha 1. 2.3. Biogas production The harvested meadow biomass was homogenised, cut into 4-cm sections and then mixed before being shredded into 3-mm pieces.

Fig. 1. The view on the scheme of transect, which consisted of five rectangular-shaped blocks of plots with the dimensions 2 � 4 m (P1 ¼ 24 May, P2 ¼ 1 July, P3 ¼ 23 July, P4 ¼ 1 September). 2

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Twenty experiment samples were prepared (five for each of the substrate harvest dates). Bottles with inoculum were incubated as controls. Biogas production was carried out over the course of several days via batch anaerobic digestion tests which were conducted in accordance with Standard DIN 38 414 [28]. Each test was performed in triplicate in glass fermentation bottles (500 cm3) containing 7.5 g of the ground meadow biomass and 192.5 g of the inoculum obtained from the post-digestion _ tank in the agricultural biogas plant in Zerniki Wielkie, SW Poland. Inoculum was characterised by following parameters: average pH values were 7.49, the total solids (TS) content was 5.03% and the volatile solids (VS) content was 76.82%TS. All bottles were purged with argon and then tightly sealed with a gas-tight cap. Incubation was carried out in a water bath with a constant temperature of 38 � C for 35 days. The con­ tents of the bottles were mixed manually after each reading, and the volume of biogas emitted was measured using a gas-tight syringe. For the first 10 days, the biogas volume was measured every 24 h, then af­ terwards, every 2–3 days. Gas samples were taken for testing and ana­ lysed using gas chromatography (Fisons GC8000, Restek QPlot column) in order to determine the biogas’ methane content. Biogas production was standardised under normal conditions (273 K, 1013 hPa), adjusted for the volume of gas produced by the inoculum of the reference sample only and then expressed in litres per kg for volatile solids (VS) (l N kg 1 ODM). Methane yield per hectare was calculated as the product of the dry matter yield, the percentage of VS in the biomass and the specific methane yield obtained [29]. The dry matter content and dry organic matter content in the inoculum, substrate and digestate were deter­ mined using the weight-drying method, which involved drying the samples at 105 � C for 17 h [30] and then combusting the samples at 550 � C for 7 h [31].

2.6. Statistical analyses Statistical analyses were carried out using Statistica 10.0 software (StatSoft, Inc., Tulusa, OK, USA). To compare methane yields, biogas yields, biomass yields, area-specific methane yields, fibre fractions and the elements obtained on the various harvest dates, one-way analysis of variance (ANOVA) was performed followed by a post hoc Tukey’s Honest Significant Difference (HSD) test. To investigate the relationship of the methane yield relative to the crude fibre and elemental content, linear regression analyses were performed with the significance level set at 0.05. 3. Results 3.1. Botanical characteristics and plant growth status A total number of sixty species were recorded (i.e. the mean number of species per square was 23). Table 1 shows the number of dominant species recorded in various phenological aspects of the grassland under investigation. The plant’s growth status is represented for each data collection period. For the first harvest date, referred to as P1, 6% of plants were in the first three earliest growth stages without flowers and buds, 59% were flowers in buds and 35% were in full bloom. The pro­ portion of grasses in the sward was 97%, whereas only 3% of the biomass harvested was herbs. For the second harvest date, P2, the biomass collected had plants exhibiting four different growth stages, namely, without flowers and buds (9%), in full bloom (15%), in full fruiting (17%) and in the seed shed stage (59%). The proportion of grasses in the sward was 69%, whereas 31% of the biomass collected consisted of herbs. For P3, the plants exhibited three growth stages, namely, in full bloom (10%), in full fruiting (21%) and in the seed shed stage (69%). The proportion of grasses and herbs in the sward were 75% and 25%, respectively. For the last harvest date, P4, only two growth stages were observed: no flowers and buds (22%) and seed shed (78%). The proportion of grasses and herbs in the sward were 91% and 9%, respectively.

2.4. Elemental analysis Samples were dried at 50 � C for 48 h and homogenised in laboratory mill (FexIKA M 20). The carbon content (C) was determined in raw material using Leco-144 SC. Other elements determined were chemi­ cally extracted from the material. Calcium (Ca), potassium (K) and phosphorus (P) were extracted via wet digestion with HNO3 (90%) and H2O2. The contents of Ca and K were determined using flame emission spectroscopy (FAAS, BWB Technologies UK Ltd.). P content was deter­ mined using flow injection analysis (Compact MLE). For nitrogen (N) results, Kjeldahl distillation was used with prior digestion of the samples in H2SO4 (95%) and catalyst. Analyses were performed on Par­ nas–Wagner apparatus Gerhardt [32]. All elements were determined in duplicate against blanks and a standard (Atomic Absorption Standard Solution from Sigma Chemical Co.). Results were calculated for con­ version to dry weight. Method accuracy was verified using certified reference materials (grass mixture-IPE 952).

3.2. Biomass yield The lowest biomass yield (2.3 t DM ha 1) was recorded for the first harvest date (P1). The P1 sample also differed significantly from the samples harvested later (ANOVA, F ¼ 19.9, p < 0.001; Tukey’s HSD p < 0.05). With regard to the three other harvests (P2, P3 and P4), the biomass yields were 4.41 t DM ha 1, 3.99 t DM ha 1 and 4.02 t DM ha 1, Table 1 List of dominant plant species on the Molinia meadow in four biomass harvest.

2.5. Determination of hemicellulose, cellulose and lignin content

List of dominant species

Determining the hemicellulose, cellulose and lignin content of the biomass was conducted using the method previously described by Van � ska et al. [34]. This enabled us to Soest [33] and modified by Chylin obtain plant cell wall fractions characterised as neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL). Hemicellulose content was obtained as the difference between NDF and ADF, the amount of cellulose in the sample was established by sub­ tracting ADL from ADF and the lignin content was taken as ADL; all of this was done in relation to the weight of the sample under investigation. NDF was taken to be the sum of the hemicellulose, cellulose and lignin percentage content in the sample. Thermogravimetric analyses with a crude fibre extractor FIWE 3 (VELP Scientifica, Italy) were performed in triplicates (1 g each).

P1 (May 24)

P2 (July 1)

P3 (July 23)

P4 (September 1)

Poa pratensis Avenula pubescens

Festuca rubra Arrhenatherum elatius Lathyrus pratensis

Festuca rubra Arrhenatherum elatius Galium mollugo

Festuca rubra Arrhenatherum elatius Festuca pratensis

Galium mollugo

Galium mollugo

Alopecurus pratensis Holcus lanatus

Arrhenatherum elatius Poa trivialis

3

Festuca rubra Alopecurus pratensis Festuca pratensis

Lotus uliginosus Festuca pratensis

Alopecurus pratensis Juncus effusus Molinia caerulea

Scirpus sylvaticus

Poa trivialis

Galium mollugo

Alopecurus pratensis Cirsium arvense Geum rivale Holcus lanatus Poa pratensis

Anthoxathum odoratum Holcus lanatus Lathyrus pratensis Selinum carvifolia Thalictrum lucidum

Poa pratensis Festuca rubra

Molinia caerulea

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440 NL kg 1 VS and 476 NL kg 1 VS for P2. Biogas production notice­ ably decreased, as evidenced by the average biogas yields obtained: 415 NL kg 1 VS and 406 NL kg 1 VS for P3 and P4 harvest dates, respectively (Table 2). Statistical analysis revealed that there were differences be­ tween the P2 and P4 samples (ANOVA, F ¼ 3.8, p ¼ 0.03; Tukey’s HSD p < 0.05).

respectively (Table 2). 3.3. Chemical characteristics of biomass It was found that the average NDF content in the substrate signifi­ cantly increased for the later harvest dates (Tukey’s HSD p < 0.05). The samples from the P1 and P2 harvest were not statistically different from each other. On the other hand, the biomass obtained for the next harvest periods showed successively significant increases in the NDF content, with the samples obtained for P4 being the highest of the lot (Table 2). The hemicellulose content of the biomass followed a similar trend for P1, P3 and P4 harvest periods, the only exception being that the P2 sample exhibited the lowest hemicellulose content and was significantly different from the other samples (Tukey’s HSD p < 0.05). Cellulose content was found to increase for later harvest dates, and the differences between samples were statistically significant (Tukey’s HSD p < 0.05, Table 2). On the other hand, while the lignin content showed no sta­ tistically significant differences, the average lignin content seemed to be higher in biomass collected on the later harvest dates. Analysis of the elements content revealed notable differences between P1 and the other samples (Tukey’s HSD p < 0.05, Table 2). Biomass obtained from the earliest harvest date contained substantially higher amounts of the el­ ements N, P and K, as well as a lower Ca content in comparison with the values seen in the other samples. The C:N ratio recorded for the P1 harvest date was significantly lower than the value recorded for the other samples (Table 2).

3.5. Methane yield per hectare The lowest values obtained for the methane yield per hectare were calculated for the P1 sample (482 m3 CH4 ha 1) whereas the highest values were found for the P2 (867 m3 CH4 ha 1), P3 (759 m3 CH4 ha 1) and P4 samples (730 m3 CH4 ha 1, Table 2). Statistical analysis showed that there were significant differences between the samples (ANOVA, F ¼ 4.1, p ¼ 0.025; Tukey’s HSD p < 0.05). 3.6. Total solids and volatile solids The average total solids (TS) content for P1 sample was 25.28%, P2 (31.21%), P3 (45.18%) and P4 (34.05%). Statistical analysis showed that there were significant differences between the samples (Tukey’s HSD p < 0.05). The average volatile solids (VS) content for P1, P2, P3 and P4 samples were 93.77%TS, 92.45%TS, 94.89%TS and 92.29%TS, respectively (Table 2). Statistical analysis showed that there were sig­ nificant differences between the samples (Tukey’s HSD p < 0.05). 4. Discussion

3.4. Methane yield and biogas yield

4.1. Total methane yield

After 35 days of batch digestion, the average methane yield was found to differ slightly, depending on the harvest dates (Fig. 2): (P1), 221 NL CH4 kg 1 VS; (P2), 211 NL CH4 kg 1 VS; (P3), 200 NL CH4 kg 1 VS; (P4), 197 NL CH4 kg 1 VS (Table 2). However, statistical analysis did not reveal any significant differences between the samples (ANOVA, F ¼ 0.84, p ¼ 0.49). Until the 17th day of batch digestion, i.e. until the half-time of anaerobic digestion had been achieved, most of the total methane volume obtained was 82% and 92% for harvest dates P1 and P2, respectively, whereas that value was 87% for both P3 and P4. Be­ tween the fourth and eighth day of the digestion, the methane yield was highest in the P1 samples and showed significant differences from the other samples (Fig. 3, Tukey’s HSD p < 0.05). After the eighth day of digestion, a sudden reduction in biogas production was observed in all replicas of the P1 sample (Fig. 2). On the fourth and the eighth days of batch digestion, significant correlations were found between the methane yields obtained and the K, N, P, Ca and cellulose content of the samples, as well as the C:N ratio (Table 3). After 35 days of batch digestion, the average biogas yield for P1 was

In our study, the average methane yield ranged from 221 (in May) to 197 NL CH4 kg 1 VS (in September) and was shown to decrease for later meadow biomass harvest dates. A similarly declining trend was seen in the yields calculated in other studies conducted on plants that were harvested in different months; these ranged from 298 NL CH4 kg 1 VS in June to 155 NL CH4 kg 1 VS in February [19], from 315 NL CH4 kg 1 VS in September to 137 NL CH4 kg 1 VS in November [20] and from 315 NL CH4 kg 1 VS in May to 80 NL CH4 kg 1 VS in February [11]. However, it is well known that weather conditions can influence the development of vegetation, e.g. a dry summer can significantly impact the biomass’ quality and characteristics, as well as the digestion process itself [11]. Therefore, in our opinion, caution must be taken when directly comparing the methane yields for biomass harvested even over the course of a relatively narrow time period. A decline in the methane yield can also be brought on by the progressive maturity of the plant [6,20,22, 35,36]. Despite the absence of statistically significant differences in our study’s results, we noticed that there was a decrease in the average

Table 2 Average values for different parameters by harvest time (n ¼ 20; NDF, Hemicelluloses, Cellulose, Lignins: n ¼ 12). Statistically significant differences between particular parameter found with ANOVA and Tukey’s HSD p < 0.05 test are indicated by different letters (a, b, c, n.s. – not significant). Mean � standard error. Parameters 1

K (g kg TS) N (g kg 1 TS) P (g kg 1 TS) Ca (g kg 1 TS) NDF (g kg 1 TS) Hemicelluloses (g kg 1 TS) Cellulose (g kg 1 TS) Lignins (g kg 1 TS) C:N (g kg 1 TS) Biomass yield (t DM ha 1) Total biogas yield (NL kg 1 VS) Total methane yield (NL CH4 kg Area CH4 yield (m3 CH4 ha 1) TS (%ww) VS (%TS)

1

VS)

P1 (May 24)

P2 (July 1)

P3 (July 23)

P4 (Sept. 1)

11.4 � 0.18 a 21.56 � 0.86 a 3.07 � 0.13 a 0.96 � 0.19 a 59.39 � 0.05 a 28.07 � 0.10 a 28.58 � 0.42 a 2.74 � 0.27 n.s 25.58 � 1.04 a 2.30 � 0.19 a 440 � 16 ab 221 � 8 n.s 482 � 71 a 25.28 � 0.71 a 93.77 � 0.17 ab

8.74 � 0.30 b 13.91 � 0.58 b 2.03 � 0.06 b 4.63 � 0.52 b 60.11 � 0.27 a 27.41 � 0.21 b 29.87 � 0.16 b 2.83 � 0.15 n.s 39.38 � 1.49 b 4.41 � 0.36 b 476 � 26 a 211 � 21 n.s 867 � 136 b 31.21 � 1.35 ac 92.42 � 0.23 a

8.40 � 0.50 b 12.14 � 0.51 b 2.22 � 0.09 b 4.85 � 0.59 b 61.97 � 0.02 b 28.02 � 0.01 a 30.65 � 0.17 bc 3.30 � 0.15 n.s 45.05 � 2.06 b 3.99 � 0.05 b 415 � 12 ab 200 � 8 n.s 759 � 39 ab 45.18 � 1.84 b 94.89 � 0.87 b

8.30 � 0.79b 13.91 � 0.65 b 1.92 � 0.24 b 4.10 � 0.29 b 63.50 � 0.41 c 28.55 � 0.10 a 31.45 � 0.25 c 3.49 � 0.43 n.s 38.85 � 1.64 b 4.02 � 0.16 b 406 � 5 b 197 � 2 n.s 730 � 32 ab 34.05 � 2.65 c 92.29 � 0.45 a

4

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Biomass and Bioenergy 130 (2019) 105391

Fig. 2. Effect of harvest date (P1 ¼ 24 May, P2 1 July, P3 ¼ 23 July, P4 ¼ 1 September) on cumulative methane yield in 35 – day batch digestion tests.

Fig. 3. Cumulative methane yield of biomass by different harvest time (P1 ¼ 24 May, P2 ¼ 1 July, P3 ¼ 23 July, P4 ¼ 1 September) in 4th, 5th, 6th, 7th, 8th - day batch digestion tests. Statistically different average values found with Tukey’s HSD (p < 0.05) tests between the same days batch digestion tests indicated by different letters (a, b, c). Vertical bars show the standard error of the mean value.

methane yield and an increase in NDF in the biomass acquired from later harvests. It has been reported that plant biomass rich in lignocellulosic fibres is difficult to degrade during the process of methane fermentation, whereas biomethane production slows down with higher lignin content and fibre fractions [37–39]. Based on the characteristics of the vegeta­ tion observed on the four dates during the ‘growth’ season, the biomass collected from the earlier harvests was at the initial stage of growth (94% of the plants were flowers in buds or in full bloom), which could have resulted in the higher average methane yield seen throughout our experiments. For later harvest dates, however, the vegetation was at a more advanced phenological stage (most of the plants were in full fruiting and in the seed shed stage) and therefore richer in NDF content.

As such, this resulted in a reduction in the average methane yield. 4.2. The methane yield rate for biomass obtained from different harvest dates Measurements made during the early stages of batch digestion (i.e. on the second and third days) showed that there were no significant differences in the cumulative methane yield obtained from the biomass on the various harvest dates. Between the fourth and eighth days of batch digestion, on the other hand, the methane yield was found to be significantly higher for the biomass feedstock taken on the first harvest date (P1). Afterwards, the methane yield rate was similar for all of the 5

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Table 3 Parameter estimates for the linear regression analysis of methane yield from 4th to 8th and 35th day batch digestion tests. Linear equation (y ¼ a þ bx) where ‘a’ is the intercept and ‘b’ is the linear coefficient (slope); Standard error of estimate; R2 ¼ Coefficient of determination. Significance level set at 0.05. Effect

n

Day

Regression equation

Standard error of estimate

R2

Level of significance

K

20

N

20

P

20

Ca

20

C:N

20

Cellulose

12

Hemicellulose

12

Lignin

12

4th 5th 6th 7th 8th 35th 4th 5th 6th 7th 8th 35th 4th 5th 6th 7th 8th 35th 4th 5th 6th 7th 8th 35th 4th 5th 6th 7th 8th 35th 4th 5th 6th 7th 8th 35th 4th 5th 6th 7th 8th 35th 4th 5th 6th 7th 8th 35th

YCH4 ¼ 12.461 YCH4 ¼ 17.821 YCH4 ¼ 19.333 YCH4 ¼ 33.754 YCH4 ¼ 58.557 YCH4 ¼ 177.84 YCH4 ¼ 20.060 YCH4 ¼ 29.219 YCH4 ¼ 25.963 YCH4 ¼ 45.961 YCH4 ¼ 67.923 YCH4 ¼ 182.78 YCH4 ¼ 29.851 YCH4 ¼ 38.893 YCH4 ¼ 50.535 YCH4 ¼ 58.839 YCH4 ¼ 80.322 YCH4 ¼ 176.32 YCH4 ¼ 106.07 YCH4 ¼ 125.31 YCH4 ¼ 148.07 YCH4 ¼ 160.74 YCH4 ¼ 169.55 YCH4 ¼ 218.67 YCH4 ¼ 152.93 YCH4 ¼ 184.36 YCH4 ¼ 225.12 YCH4 ¼ 228.58 YCH4 ¼ 229.82 YCH4 ¼ 218.67 YCH4 ¼ 381.67 YCH4 ¼ 578.08 YCH4 ¼ 718.89 YCH4 ¼ 724.89 YCH4 ¼ 679.61 YCH4 ¼ 386.94 YCH4 ¼ 85.680 YCH4 ¼ 462.64 YCH4 ¼ 629.49 YCH4 ¼ 210.62 YCH4 ¼ 519.78 YCH4 ¼ 367.12 YCH4 ¼ 119.66 YCH4 ¼ 179.41 YCH4 ¼ 212.47 YCH4 ¼ 210.62 YCH4 ¼ 204.74 YCH4 ¼ 229.12

12.992 15.733 21.931 18.512 17.973 26.892 9.4705 12.381 16.455 14.184 14.189 26.728 13.895 16.823 24.170 19.881 19.027 26.437 13.286 18.713 23.866 21.425 20.281 26.871 9.6563 13.015 16.134 15.248 15.172 26.991 15.611 14.671 19.379 15.020 15.274 23.423 19.678 23.395 30.011 27.640 25.890 24.462 18.656 20.274 26.512 21.996 24.500 24.112

0.517 0.518 0.430 0.514 0.462 0.044 0.743 0.702 0.680 0.715 0.664 0.056 0.447 0.449 0.308 0.440 0.397 0.077 0.495 0.318 0.325 0.349 0.616 0.046 0.733 0.670 0.692 0.670 0.314 0.037 0.384 0.637 0.617 0.722 0.673 0.096 0.021 0.078 0.081 0.060 0.060 0.014 0.120 0.307 0.283 0.231 0.158 0.042

<0.001 <0.001 0.002 <0.001 <0.001 0.372 <0.001 <0.001 <0.001 <0.001 <0.001 0.315 <0.001 <0.001 0.011 <0.001 0.003 0.238 <0.001 0.010 0.010 0.006 0.010 0.364 <0.001 <0.001 <0.001 <0.001 <0.001 0.414 0.030 0.002 0.003 <0.001 <0.001 0.327 0.651 0.380 0.370 0.443 0.443 0.715 0.270 0.061 0.075 0.114 0.201 0.523

þ 0.0074x þ 0.0089x þ 0.0104x þ 0.0104x þ 0.0091x þ 0.0032x þ 0.0040x þ 0.0047x þ 0.0059x þ 0.0055x þ 0.0049x þ 0.0016x þ 0.0221x þ 0.0268x þ 0.0285x þ 0.0311x þ 0.0272x þ 0.0134x 0.0070x 0.0068x 0.0088x 0.0083x 0.0073x 0.0031x 1.9390x 2.2480x 2.9280x 2.6350x 2.3300x 0.0030x 9.9820x 15.750x 19.910x 19.630x 17.740x 6.1790x 5.9440x 13.900x 18.230x 24.980x 13.380x 5.9390x 12.560x 24.610x 30.340x 24.980x 19.350x 9.1900x

remaining harvest dates. The higher content of N, P, K and other nutrients in plants from the first harvest date can explain the higher methane yields recovered during the early period of batch digestion. Melts et al. [40] presented similar conclusions in their study, in which they demonstrated that the higher N and P content in leguminous plants positively influenced methane yields in the early stages of the biochemical methane produc­ tion. Larger quantities of these elements promote an increase in the microbial biomass products and, thus, more effective methane produc­ tion [41]. The plant growth status can explain differences in the content of individual elements in plants. Based on the botanical characteristics, it can be noted that the plants collected on May 24 were mainly in the earlier growth stage (i.e. flowers in buds and in full bloom), whereas from July 1, the plants were mainly at a later stage of growth (i.e. in full fruiting and in the seed shed). Higher total nitrogen content in plants during the early growth stages is associated with a high protein content, which will inevitably decrease as plant maturation progresses [42]. However, it is always important that, during the process of digestion, the nitrogen content does not become too high, as this results in the release of ammonium which, in turn, inhibits microbial methane synthesis and

disrupts the internal acid and base balance system [43]. The optimal C:N ratio for methane fermentation is considered to be between 20:1 and 30:1 [44]. In our study, the optimal C:N ratio was found only in the biomass harvested on the first date (P1), thus making the digestion process more effective during the earliest stages only. However, after the eighth day of batch digestion, there was a sudden reduction in biogas production in the P1 sample. On the ninth and tenth days of experi­ mentation, the biogas yield from the P1 samples was higher in the inoculum controls than in all the substrate samples. This situation lasted for an additional 2 days, however, until the 11th day when the substrate samples again produced more biogas than the control samples. There are insufficient details from the chemical analysis of the feedstock to allow us to analyse this problem in depth. It can only be presumed that due to the higher nitrogen content in the substrate, more toxic ammonia could be released and the existing pH conditions could be changed, both of which possibly contribute to the inhibition of the biogas production process [45]. Analysis also showed that the amount of methane produced between the fourth and eighth days of batch digestion was negatively affected by high Ca and cellulose content in the raw material. The negative impact 6

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Biomass and Bioenergy 130 (2019) 105391

of cellulose on the methane yield during the initial phase was unex­ pected. Unfortunately, the results of our research were not detailed enough to prove a direct link between the Ca content of a biomass and its methane yield. Higher calcium levels (between 4.10 and 4.85 g kg 1) were found in the biomass harvested after July 1 (P2), which may have been the cause of the reduced methane yield during batch digestion. The literature data shows that while Ca positively influenced biogas pro­ duction at concentrations around 3 g L 1, higher levels (i.e. between 5 and 7 g L 1) were shown to inhibit anaerobic digestion [46]. According to Lar et al. [47], calcium supplementation up to 4 g L 1 proved to be very effective for the overall biogas and methane yields. Higher con­ centrations of Ca could possibly trigger precipitation of carbonates and phosphates from the biomass, which may have resulted in low specific methanogenic activity and extensive losses in the buffer capacity and the essential nutrients needed for anaerobic degradation [48].

Some homogeneous raw materials may have much higher methane yields per hectare. Maize is one of the most common type of co-substrate in agricultural biogas plants, particularly in Germany [55], and gives a methane yield typically ranging from 2100 up to 9000 m3 CH4 ha 1 [27, 56]. In comparison with energy crops, meadows are perennial plant communities that require less energy and financial expenditure, which reduces the overall production costs [57]. The production of energy crops requires arable land, which competes with food production for agricultural purposes [58]. According to the literature, the cultivation of maize for biogas production poses a threat to the existence of meadows due to the tendency to convert such fields into plantations. Studies carried out in the Hesse region in Germany showed that there was a decrease in the number of permanent grasslands with a simultaneous increase in the areas subjected to maize cultivation, especially in places where agricultural biogas plants were located [59]. Due to the fact that the conservation status of Molinia meadows has been assessed as unfavourable, it is very likely that the use of biomass from these habitats for biogas production could contribute to a better conservation status and enhance the biodiversity of this rare habitat.

4.3. Methane yield per hectare The methane yield per hectare, which is comprised of both the biomass yield per hectare and the methane yield, is an important parameter for determining the appropriate time for harvesting. In an earlier study conducted by Melts et al. [49] on three types of Estonian meadows, the methane yield ranged from 514 to 1375 m3 CH4 ha 1. On the other hand, for other functional group monocultures, such as grasses, legumes and herbs, the methane yield ranged from 623 to 876 m3 CH4 ha 1 [50]. Since Molinia meadows are only cut once a year and their composi­ tion includes many grasses and herbs species, there are few literature reports on the methane yields per hectare for extensively used semi­ natural meadows. Of those that exist, most are related to specific grass species, from which a yield of between 1200 and 3600 m3 CH4 ha 1 [35, 51] can be obtained. Yields ranging from 1157 to 2252 m3 CH4 ha 1 [36] can be achieved from grasses cut on different harvest dates, whereas yields from grasslands where intensive cutting is carried out several times per year may reach as high as 3459 m3 CH4 ha 1 [6]. When our data was compared with the results from these studies, it was found that cutting once a year gave lower biomass yields. According to Popp et al. [52], this type of activity decreases the methane yield per hectare. Nevertheless, it should be stressed that the environmental value of extensively used meadows is greater when compared with the value of intensively used grasslands [1]. Prochnow et al. [6] indicated that the biomass harvest time is also important in evaluating the methane yield per hectare. Meadow biomass harvested early during the ‘growth’ sea­ son had much lower yields when compared with those obtained from later harvest dates [23]. These results were also confirmed during the course of our research. The average biomass yield obtained from different types of extensively used meadows ranged from 1.6 to 6.7 t DM ha 1 [16,23], which was similar to the results obtained in our study. Our study included dates that allowed us to estimate the most efficient time for harvesting and, thus, ensuring the best methane yields per hectare. The results suggest that, from the point of view of methane production, the most favourable harvest date was early summer (i.e. P2 which was on July 1) when yields of 867 m3 CH4 ha 1 were achievable. According to the Polish Regulation for the Ministry of Agriculture and Rural Development [53], it is advisable to mow the Molinia meadow in September if rare butterfly species inhabit the habitat. However, if the meadow is endangered by expansive plants, it is recommended to mow annually in June. Habitats that are well maintained by local farmers and are not threatened by any invasive plant species can be mowed between June and September. In the current study, we were able to confirm that, in some cases, the increase in meadow biomass yields obtained on later harvest dates compensated for the corresponding decrease in the methane yields acquired during biogas production [36]. Similar con­ clusions were also presented by Prochnow [54], who reported that the highest biomass yield from meadow foxtail and purple moor meadows was noted when harvesting was undertaken later in the growth stage.

5. Conclusion Even though the biomass obtained from Molinia meadows can be used as a substrate in agricultural biogas plants due to the moderate methane yields obtained during digestion, the methane yield tended to decrease when the meadows were harvested later in the year. These studies did not confirm the presence of a direct link between the elemental content and fibres on the total methane yield from a meadow; however, we theorise that these factors could still exert some effect on the methane yields during the initial stages of experimentation. It was also demonstrated that biomass with high nitrogen, phosphorus and potassium content positively impacted the methane yield during the initial stages of batch digestion. According to the government protection regulations in place, Molinia meadows can be mowed at different times throughout the growth season. However, mowing at a later date (i.e. after July 1) increased the biomass yield and resulted in higher calcu­ lated methane yields per hectare. The use of Molinia meadows for biogas production has the potential to become an important element in con­ servation efforts targeted at this type of habitat. Acknowledgements These studies were funded by the University of Wroclaw, Poland Internal Grant in the Institute of Geological Sciences (No.: 1017/S/ING). Many thanks to Marta Czarniecka-Wiera for assistance in conducting field research and Beata Berbe�c for help with performing the laboratory tests. Thanks also to the Institute of Meteorology and Water Manage­ ment in Warsaw for providing temperature data and the anonymous reviewers for their comments and suggestions. References [1] S. Plantureux, A. Peeters, D. McCracken, Biodiversity in intensive grasslands – effect of management, improvement and challenges, Agron. Res. 3 (2005), 53–164. [2] M. Hejcman, P. Hejcmanov� a, V. Pavlů, J. Bene�s, Origin and history of grasslands in Central Europe—a review, Grass Forage Sci. 68 (2013) 345–363. [3] J.P. Grime, Plant Strategies, Vegetation Processes, and Ecosystem Properties, John Wiley, Chichester, UK, 2006, p. 456. [4] Reporting under article 17 of the habitats directive (period 2007 – 2012). Outcomes from the article 17 reports 2013. European topic centre on biological diversity, EIONET, Paris, https://nature-art17.eionet.europa.eu/article17/reports2 012/habitat/summary (5.04.2018). [5] Z. Kącki, Variability and long-terms changes in the species composition of Molinia meadows in Poland: a case study using a large data set from the Polish Vegetation Database, Bot. Silesiacae. Monogr. 7 (2012) 1–143. [6] A. Prochnow, M. Heiermann, M. Pl€ ochl, B. Linke, C. Idler, T. Amon, P.J. Hobbs, Bioenergy from permanent grassland – a review: 1. Biogas, Bioresour. Technol. 100 (21) (2009) 4931–4944.

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