Bioresource Technology 152 (2014) 107–115

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Suitability of giant reed (Arundo donax L.) for anaerobic digestion: Effect of harvest time and frequency on the biomethane yield potential Giorgio Ragaglini a,⇑,1, Federico Dragoni a,1, Marco Simone b, Enrico Bonari a,c a

Institute of Life Sciences, Scuola Superiore Sant’Anna, Piazza Martiri della Libertà 33, 56127 Pisa, Italy Dipartimento di Ingegneria Chimica, Chimica Industriale e Scienza dei Materiali, Università di Pisa, Via Diotisalvi 2, 56122 Pisa, Italy c CRIBE – Centro di Ricerche Interuniversitario Biomasse da Energia, Via Vecchia Livornese 748, 56122 Pisa, Italy b

h i g h l i g h t s  The Biochemical Methane Potential (BMP) of giant reed was assessed.  Biochemical Methane Potential was significantly influenced by harvest time and frequency.  The highest BMP and the best digestion kinetics was achieved at juvenile crop stages.  Double harvesting increased by 20–35% the methane yield per hectare.

a r t i c l e

i n f o

Article history: Received 27 August 2013 Received in revised form 29 October 2013 Accepted 2 November 2013 Available online 11 November 2013 Keywords: Biogas Double harvest Kinetics Biochemical Methane Potential Perennial energy crops

a b s t r a c t This study aimed to investigate the potential of giant reed for biomethane production by examining the influence of harvest time and frequency on the Biochemical Methane Potential (BMP), the kinetics of biomethane accumulation in batch reactors and the expected methane yield per hectare. The crop was cut at five different times, regrowths from early cuts were harvested in autumn and BMP of each cut was assessed. The highest BMP (392 NL kg VS1) and the best kinetics of methane production were associated to juvenile traits of the crop. By coupling the early cuts with the corresponding regrowths (double harvest), the dry biomass (from 35 to 40 Mg ha1) equaled that obtained by a single cut at end of the season (38 Mg ha1), while the methane yield per hectare (11,585–12,981 Nm3 ha1) exceeded up to 35% the methane produced with a single harvest at crop maturity (9452 Nm3 ha1). Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Anaerobic digestion is one of the most mature technologies for biomass energy production, allowing energy conversion from a broad variety of substrates, such as wastes, sludges, manures, and a wide range of crops and their residues. However, at present biogas production relies greatly on co-digestion of animal slurries and annual crops, and particularly on maize (Bauer et al., 2010; Herrmann and Rath, 2012). In Italy, maize cultivation is nowadays expanding from traditional regions (mainly Northern Italy) to less suitable areas because of the increase in biogas production, thus concerns may arise about water consumption and fertilizer requirements. Moreover, maize use as energy crop is often criticized because of the changes it may cause on the land use and on the price of food and feed commodities.

⇑ Corresponding author. Tel.: +39 050 883521. 1

E-mail address: [email protected] (G. Ragaglini). These authors equally contributed to this study.

0960-8524/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2013.11.004

Perennial grasses, and particularly giant reed, have already been recognized as high-yielding crops that can minimize environmental impacts, because of the reduced inputs requirements (Angelini et al., 2009; Nassi o di Nasso et al., 2011). Giant reed is a perennial rhizomatous species that has been traditionally cultivated in Southern Europe, North Africa, Asia and the Middle East and has been recently introduced in the USA, where it is usually considered an invasive species; it has already been studied for bioethanol production, direct combustion, and other thermal transformations (Pilu et al., 2012). Giant reed is considered a drought-tolerant species (Angelini et al., 2009; Pilu et al., 2012) and it can be grown in marginal or sub-marginal lands (Dragoni et al., 2011; Nasso o di Nasso et al., 2013), thus reducing competition with food crops for soil use. Harvest time significantly influences biomass yield of giant reed and its characteristics (Nassi o di Nasso et al., 2011), and it is usually seen as a major factor influencing digestibility and methane yields of energy crops (Massé et al., 2010; Bélanger et al., 2012; Kreuger et al., 2011; Gao et al., 2012; Kandel et al., 2012). The

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proportion of the stems respect to the leaves is expected to change, as well as chemical traits like nitrogen concentration, C/N ratio, NSC (Non-Structural Carbohydrates), and cell wall components, thus influencing its biodegradability and methane output (Heaton et al., 2009; Smith and Slater, 2011; Slewinski, 2012; Nasso o di Nasso et al., 2013). Previous studies assessed the capability of giant reed to regrow after an early cut (Dragoni et al., 2011), but regrowth of perennial grasses is expected to vary according to the length of the growing season and the environmental conditions (Kandel et al., 2012). Thus, biogas production could rely on multiple harvests per year, but several considerations are relevant to the question of whether single harvest or multiple harvest of a perennial crop for biogas is preferable, and many of these questions are not directly related to biodegradability (e.g., crop duration, machinery requirements, nutrient uptake, environmental and economic sustainability). Despite the fact that biogas production from giant reed has already been hypothesized by some authors (Schievano et al., 2012; Di Girolamo et al., 2013), it is still a novel crop for this purpose. Therefore, further questions about biomethane potential and digestibility of giant reed under different management conditions should be addressed. Biochemical Methane Potential (BMP) has been widely used to assess methane yields of organic matter when degraded at anaerobic conditions, including plant biomass (Chynoweth et al., 1993; Angelidaki et al., 2009). Grasses and other lignocellulosic substrates have been extensively studied as interesting feedstocks for anaerobic digestion by several authors and remarkable methane potentials are often reported (Nizami et al., 2009; Seppälä et al., 2009; Massé et al., 2010; Kandel et al., 2012). Nevertheless, BMP is not the only parameter to be considered. Kinetics of anaerobic digestion is also crucial, since rapid methane production is fundamental to shorten the residence time and to achieve better methane yield in real-scale, continuous plants (Mähnert and Linke, 2009; Grieder et al., 2012). Schievano et al. (2012) compared giant reed with other crops mainly in terms of Biochemical Biogas Potential (BBP) and biogas production costs per hectare, while Di Girolamo et al. (2013) recently reported methane potentials for giant reed harvested in early autumn, as affected by several pre-treatments. However, the BMP of giant reed as influenced by different harvest times has not previously been studied and double harvest systems have not yet been evaluated respect to the overall biomethane yield per hectare. Thus, the aim of this study was to determine the BMP and the methanisation kinetics of giant reed at different harvest times as influenced by the crop characteristics and the growth stage. Consequently, methane yields per hectare were evaluated as affected by harvest time and frequency, in order to assess the suitability of giant reed as an alternative feedstock for biogas production systems.

2. Methods 2.1. Field experiment and samples preparation A local ecotype of giant reed was cultivated since April 2007 in San Piero a Grado, Pisa, Italy (43° 400 49.2100 North, 10° 200 47.1500 East; 1 m above mean sea level and 0% slope). The establishment of the crop was performed using rhizomes, the canes were grown at a population density of 20,000 plants ha1 in rows having 1 meter of width. During the growing season of the year 2011, the crop was harvested at 5 different times from June to September (A1–A5) (Table 1), each one replicated 3 times (15 plots). Resprouting after first cut was expected, thus leading to perform a second cut in early fall (18th October 2011) from plots where crop regrowth

Table 1 date of harvest, total, and volatile solids content (TS and VS) on the fresh matter (FM), and ash concentration on the dry matter (DM) for first harvests (A1–A5) and second harvests (RA1, RA2) of giant reed and for maize silage (M). Cut

Date

TS (% of FM)

Ash (% of DM)

VS (% of FM)

A1 A2 A3 A4 A5 RA1 RA2 M

21-Jun 15-Jul 02-Aug 22-Aug 20-Sep 18-Oct 18-Oct Sep

46.9 49.1 51.9 47.0 51.0 42.3 39.6 34.8

7.9 6.6 4.4 6.4 4.8 7.7 6.7 4.9

43.2 45.9 49.6 44.0 48.5 39.1 37.0 33.1

was not negligible, that resulted those harvested in June and July (RA1 and RA2). At each harvest time, biomass fresh weight was determined by sampling a 2 m2 area within each plot (12  3 m). Nodes, green, and senescent leaves per plant were counted on a subsample (10 plants), while lost leaves were determined by difference with the total node number; plant heights were also measured. Subsequently, leaves and stems were separated, weighed and their dry matter content (DM) was determined by oven drying at 65 °C until constant weight, in order to assess the aboveground dry biomass yield (AGB, Mg ha1) and its partitioning. Biomass yield from the summer harvest and regrowth from the same plots were pooled to get the total biomass yield where double harvest was performed. Samples for chemical analyses were prepared for each field replication by milling in a Retsch SM1 rotor mill equipped with a 1 mm grid. Subsamples for Biochemical Methane Potential (BMP) determination were prepared by bulking biomass from the field replications. All the samples were stored at 20 °C. 2.2. Chemical analyses and nutrient evaluation Total solids (TS), volatile solids (VS), and total Kjeldahl nitrogen (TKN) were determined on fresh samples according to standard methods (APHA, 2005); C/N ratio was assessed by elemental analysis (Leco CHN 600). Lignin quantification was performed using the acetyl bromide method (Fukushima and Hatfield, 2004), absorbance was measured with a Beckman Coulter DU 800 UV/Vis spectrophotometer at 280 nm, then lignin content was calculated using the Lambert–Beer equation. Non-Structural Carbohydrates (NSC) were calculated as the sum of Water Soluble Carbohydrates (WSC) plus starch, determined according to Giovannelli et al. (2011). For each sample and parameter, three technical replicates were analyzed. 2.3. Biochemical Methane Potential (BMP) assay Biochemical Methane Potential (BMP) was determined in batch reactors (volume 2 L). The assays were conducted in triplicates on fresh biomass from the seven different cuts of giant reed (five first cuts, A1–A5, and two regrowths, RA1, RA2) and on maize silage (M), hybrid DKC6666, (wax ripeness, FAO 600, Dekalb) as a control assay. Each reactor received 300 g of inoculum that was suspended in a basal test medium up to a final filled volume of 1 L. Three blank experiments were also performed with 300 g of inoculum, Milli-Q water, and minerals only (Angelidaki et al., 2009). The medium was prepared using Milli-Q water, according to the ISO 11734 standard. The inoculum originated from the methanogenic stage of a mesophilic anaerobic digester fed with energy crops (maize and sorghum silages), agricultural residues, cattle and poultry manure; the acidogenic stage was an horizontal plug-flow, while the methanogenic stage was a CSTR. Inoculum

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was prepared according to the ISO 11734 standard, except for inorganic carbon removal procedure that has not been carried out, as proposed by Angelidaki et al. (2009). The anaerobic sludge was sieved through a 1 mm mesh (Chynoweth et al., 1993; Rincón et al., 2010); after removing large particles, it had a total solids (TS) content of 84.5 g kg1, a volatile solids (VS) content of 56.3 g kg1, and a pH of 7,8. Before the beginning of the assay, the inoculum was left for 5 days at 37 °C to reduce the amount of readily available organic matter and to be degassed. The substrates were added to the reactors according to a ratio between the inoculum and the substrate (I:S) equal to 2:1 on the basis of their volatile solids content (VS) (Table 1), as advised by Chynoweth et al. (1993). Once the reactors were loaded with the different substrates, the batches were sealed and flushed with N2, in order to obtain anaerobic conditions. Subsequently, the vessels were incubated at 37 ± 1 °C as long as the further production of biogas became negligible (40 days). Biogas pressure in each reactor was continuously measured by pressure piezo-resistive transducers and continuously recorded by a dedicated Programmable Logic Controller (PLC) connected to a PC. The cumulative volume of biogas produced in each reactor at each time (Br,t) was calculated according to the Ideal Gas Law and to the Molar Volume of Ideal Gases at Standard Temperature and Pressure conditions (1 bar, 273.15 K). Methane concentration (MC) was measured by gas chromatography (micro-GC Agilent 3000) (Angelidaki et al., 2009). Biogas was sampled and analyzed at five different times, considering intervals (i) of 2, 5, 10, 20, and 40 days after the start-up of the assay. Both the pressure reduction due to biogas removal at each sampling interval and the biomethane content of the sampled gas were considered in estimating the cumulative biogas production of each batch, as reported in Appendix A (Eqs. (A.1), (A.2)). Finally, in order to obtain the Biochemical Methane Potential (BMP) of each substrate, expressed in NL of CH4 per kg of volatile solids (VS), the residual (or intrinsic) methane potential of the inoculum as obtained by blank experiments was removed according to Eq. (A.3). Analogously, the Biochemical Biogas Potential (BBP) was obtained by subtracting the intrinsic biogas potential of the inoculum to the biogas produced in each reactor.

2.4. Kinetics of the methane production The kinetics of anaerobic digestion of giant reed and maize silage were examined by regressing on time the daily cumulated methane measured in each reactor. Six non-linear models were evaluated (Table 2), namely Michaelis–Menten, Asymptotic Regression, Weibull, Log–Logistic, Gompertz, and the five parameters Modified Gompertz (Beuvink and Kogut, 1993; Grieder et al., 2012). Goodness of fit (R2, RMSE) for the different models was assessed and their efficiency was tested by the Akaike Information Criterion (AIC). Thus, the most efficient model was identified and

used to calculate different kinetic parameters: the time, expressed in days, when 50% and 95% of methane production was reached (respectively, T50 and T95), the maximum daily production rate (Rmax) and the mean daily production rate from the beginning of the assay to T50 (R50). Curve fitting and model parameterization were performed using the R software, version 2.15.0, and then mle and rootSolve packages (Pinheiro et al., 2013; Soetaert, 2013). 2.5. Methane yields per hectare Biomass yield from first cuts and regrowths from the same plots were pooled to get the total biomass yield, in order to compare AGB production of double harvest systems (A1 + RA1, A2 + RA2) with that obtained from single harvest (A3–A5). Methane yields per hectare (Nm3 CH4 ha1) were determined by multiplying the mean BMP of each cut by the AGB production of each plot of the corresponding harvest time. Also in this case methane yields of first cuts and regrowths were combined in order to compare methane productivity of double harvest systems with single harvests. 2.6. Statistical analyses Accumulated biomass and its partitioning, biometric values, chemical traits, and anaerobic digestion parameters were compared for the different giant reed cuts by one-way ANOVA. Biomass crop productivity and methane yield per hectare were also compared by one-way ANOVA by coupling first and second harvest. When significant differences were evidenced, post hoc comparisons were made using the LSD test at the 0.05 p-level. Single linear regressions were performed in order to point out the main factors that influenced biogas and biomethane production and kinetics, testing several predictors among the anaerobic digestion parameters and the crop traits. 3. Results and discussion 3.1. Crop growth and biomass characteristics In 2011, giant reed sprouted on the 20th of March and the growing season was characterized by an average daily mean temperature of about 18 °C, while the maximum air temperature peaked above 35 °C in July and at the end of August (Fig. 1). Rainfall amounted to nearly 34% of the annual precipitations, mainly distributed in two most intensive events (up to 45 mm/day) at the beginning and at the end of the season. Total reference evapotranspiration (ET0) exceeded more than three times the total rainfall (740 vs 212 mm) and the highest daily values were reached during the third week of July, while after August ET0 rapidly decreased (Fig. 1). The first considered harvest time, namely A1, occurred 93 days after sprouting and the aboveground biomass (AGB) production

Table 2 Goodness of fit evaluation of investigated non-linear models. For each model are reported: the parameters description, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC) obtained from non-linear fitting of methane production curves (R2, RMSE, and AIC are means over all samples). Models

Parameters

Michaelis–Menten

y¼

Asymptotic regression Weibull Log–Logistic

y ¼ d  ½1  expð xeÞ y ¼ d  exp½ expðb  ðlog x  eÞ d y¼ 1 þ exp½b  ðlog x  log eÞ y ¼ d  exp½mc  expðc  xÞ

Gompertz Modified Gompertz

d eþx

y ¼ d  exp

h

mR cR

 expðcR  xÞ  mcSS

i  expðcS  xÞ

R2

RMSE

AIC

d = maximum y, e = time at which y is half d

0.976

10.73

301.3

d = upper limit, e = steepness of the increase d = upper limit, b = slope, e = inflexion point d = upper limit, b = slope, e = inflexion point

0.988 0.993 0.995

7.95 6.64 5.52

280.3 258.4 252.5

d = plateau, m = the initial relative growth rate, c = relative growth rate at inflection d = plateau, m = the initial relative growth rate, c = relative growth rate at inflection, R = rapid, S = slow

0.983

9.48

301.2

0.999

2.71

198.5

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Fig. 1. Meteorological data of the investigated period on daily scale: rainfalls are represented by vertical bars, maximum and minimum air temperature are described by dashed and dotted lines respectively, while the solid line represents the daily reference evapotranspiration (ET0) averaged on a weekly basis.

amounted to nearly 23 Mg DM ha1 (Fig. 2). AGB standard deviations increased sensibly in A2 and A3, probably depending on the variability of climatic conditions, thus not allowing to observe a significant yield increase, in spite of higher stem heights and leaves number respect to A1. In general, crop growth was limited by water deficit, as indicated by the green leaves number remaining roughly unvaried from A1 to A4, while senescent leaves number increased. The slow growth conditions are also evidenced by the fact that AGB in A4 was significantly higher than in A1 (P < 0.001) and appreciably but not significantly higher than in A2 and A3. Then, a substantial increase in stem height and green leaves number was achieved after A4, thus resulting in a significantly higher biomass production in A5 (P < 0.001), 38 Mg DM ha1. As expected, senescent leaves number increased from A3 to A5, and leaves loss was also observed from A4, thus determining a change in biomass partitioning from early to late crop stages (Fig. 2). In particular, the proportion of green leaves biomass on total AGB was higher in A1 (33%) and decreased constantly in the following cuts except A3, in which the increase in green leaves respect to A2 was probably due to leaf expansion, as consequence of the increased water availability occurred in the second half of July (Fig. 1). Regrowths from A1 and A2, namely RA1 and RA2, were about 17 and 13 Mg DM ha1 respectively. RA1 and RA2 were harvested 119 and 95 days after resprouting from the first cuts, corresponding to 1305 and 983 Growing Degree Day (GDD) respectively (Fig. 2). Despite the cumulative heat units were comparable with those of A3 and A4, RA1 and RA2 showed much more juvenile traits, thus being quite similar to A1 in height, green leaves and senescent leaves number and green leaves biomass, (Fig. 2). Biomass chemical traits are consistent with crop maturity and biomass partitioning. Total Kjeldahl Nitrogen concentration (TKN) was much higher in early cuts and in second cuts (about 0.75%) than in the mature crop (0.41% in A5) and a decreasing trend from A1 to A5 was showed. A3 had a TKN content slightly higher than A2 and comparable with RA1, probably as a consequence of the high proportion of green leaves (Fig. 2). Conversely, C/N ratio peaked (>120) in A4–A5 and exhibited its lowest values in A1 and in second cuts. Whereas RA2 had a higher TKN content and a lower C/ N ratio than RA1, it was evidenced that nitrogen concentration decreased at the increase of plant age both at first and second cuts.

The increase in C/N ratio over time is in line with previous findings on nutrient cycling in rhizomatous grasses and it can be explained by carbon accumulation (Kandel et al., 2012), by nitrogen relocation from the aerial parts to the rhizome and by leaf loss (Heaton et al., 2009; Nasso o di Nasso et al., 2013) (Fig. 2). High acetyl bromide lignin contents were observed (between 21% and 24% of DM), in line with those obtained using the same method by Lygin et al. (2011), but no significant variations were showed between the considered harvest times (Fig. 2). Analogously, in a previous study giant reed showed no significant variations in acid-detergent lignin (ADL) along the growing season (Nassi o di Nasso et al., 2011), although the values were substantially lower as typical for this method (Fukushima and Hatfield, 2004). At contrary, a high level of variability was observed in the Non-Structural Carbohydrates (NSC) content, with higher concentrations from midseason to mature stages (A3–A5) and in second harvests (RA1–RA2) (Fig. 2). Sucrose resulted to be the main WSC contained in giant reed biomass varying from 4.9 to 28.6 mg g1. Starch content ranged from 1.9 to 33.5 mg g1. Although further investigations are advisable to clarify NSC dynamics in giant reed, these observations are somehow consistent with previous studies on other species. Rapid stem elongation occurring at early stages of the growing season usually leads to relatively low NSC concentrations (Slewinski, 2012), then biomass NSC content typically increases over time. In switchgrass, once accumulation is interrupted by harvesting, a new accumulation cycle is performed and a steep increase in starch concentration can be achieved (Bélanger et al., 2012). This fact, as well as the slower growth rate and the increased leafiness, might explain the higher NSC content in RA1 and RA2. Moreover, the NSC content in the stem is often increased after the formation of new leaves and decreased when the photosynthetic rate is reduced by drought conditions, in order to mitigate energy losses (Slewinski, 2012), thus possibly explaining deviations in NSC respect to the expected trend (A2 < A1; A4 < A3). 3.2. Digestion kinetics and Biochemical Methane Potential Among the non linear models used for fitting the daily methane production data, the Modified Gompertz resulted the most efficient

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111

Fig. 2. Crop biometrics and yield, biomass partitioning and chemical traits according to the different cuts. On the bottom are reported the days after sprouting (DAS) and the Growing Degree Days (GDD) accumulated from the sprouting date to harvest. For RA1 and RA2, DAS and GDD were calculated from the date of the first cut. GDD were estimated according to NOAA method.

(Table 2). In fact, the average RMSE resulted the lowest and the cross-correlation test evidenced an homogeneous distribution of the prediction error, with an average R2 very close to 1. Moreover, the AIC showed the lowest level of redundancy, despite the fact that the Modified Gompertz model has the highest number of parameters, thus making clear its efficacy in describing the dynamic of methane production of each replication, as already reported by other authors (Beuvink and Kogut, 1993; Grieder et al., 2012). Hence, this model only was adopted to estimate the kinetics parameters T50, T95, Rmax, and R50. Details of the model parameterization are reported in Appendix B. Significant differences were evidenced among the investigated substrates (P < 0.001) (Fig. 3). During the first days of digestion, the methane production was quite faster in maize silage (M) than in giant reed, reaching the 50% of the accumulated production (T50) before the 4th day from the beginning of the assay. This fact was

evidenced by a higher methane production rate, with a maximum (Rmax) up to 51 NL kg VS1 day1, that was comparable to results obtained by Grieder et al. (2012) (61 NL kg VS1 day1). About giant reed, it was observed that T50 occurred at the end of the 4th day in A1, while it was recorded around the 5th day in the two regrowths. Harvest times from A2 to A4 reached the 50% of the methane production during the 6th day, while in A5 the early stage of digestion was much slower and T50 was reached at the 8th day. A similar pattern was found for T95, since RA1, RA2, A1, and A3 reached the 95% of the cumulative production within 29 days, while A2, A4, and A5 were significantly delayed. In particular, T95 occurred significantly earlier in RA1 respect to the other substrates (23rd day), while it was reached about 7 days later in A2 and A4, and 10 days later in A5. The 95% of total methane production in M was reached in 28 days, thus showing a noteworthy decrease in methane production after the early fast stage.

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Fig. 3. Kinetics of fermentation of giant reed harvested at different times; A1–A5 refer to first cuts, while RA1–RA2 refer to regrowths from A1 and A2. M is maize silage. (a) cumulative methane production along 40 days, T50 (d), and T95 (j). (b) Rmax (N) and daily methane production rate estimated as the first derivative of cumulate production curves. The significance level of ANOVA is showed using ⁄⁄⁄ symbol for p-values < 0.001 and standard error bars are reported.

Rmax clearly allowed to distinguish giant reed harvest times in accordance with the stage of development of the crop (Fig. 3). In the juvenile stages (A1, RA1, RA2) Rmax ranged between 37 and 44.4 NL kg VS1 day1 and a large variability among the replications was showed, while in the mid season stages (A2, A3, A4) Rmax was between 28.5 and 37.8 NL kg VS1 day1. At crop maturity (A5) Rmax was markedly the lowest, being less than 20 NL CH4 kg VS1 day1. Therefore, RA2, RA1, and A1 exhibited the highest methane daily production in the early stage of the batch anaerobic digestion process, as evidenced also by R50 (36.9, 31.8, and 34.5 NL kg VS1 day1 respectively) (Fig. 4). However, R50 in M was even higher (45.9 NL kg VS1 day1), consistently with Rmax. R50 was about 30 NL kg VS1 day1 in A3 and it was slightly lower in A2 and A4, while in A5 it drastically decreased (16.0 NL kg VS1 day1), being less than half that of A1. After 40 days of fermentation, the highest Biochemical Methane Potential (BMP) was measured in RA2, A3, and RA1 (391.7, 374.3, and 370.3 NL kg VS1, respectively), also showing a significantly higher potential than M (P < 0.001), that yielded 345 NL kg VS1 (Fig. 4). The BMP observed in A1, A2, and A4 was 332.9, 325.2, and 306.7 NL kg VS1 respectively, while the lowest value was measured in A5 (258.3 NL kg VS1), about 34% less than in RA2. By literature, BMP values obtained from maize silages at comparable experimental conditions were found to range from 310 to 371 NL kg VS1 (Bauer et al., 2010; Herrmann and Rath, 2012), thus being in line with the results of the present study. However, lower and higher values have also been reported (Amon et al., 2007; Gao et al., 2012), consequently to the large variability of

experimental conditions and maize silage characteristics, according to the variety, the maturity class, the harvest time and the climatic conditions. On average, the BMP of giant reed was 319.5 NL kg VS1 at the first cut, while at the second cut it was 381 NL kg VS1. Thus, it showed quite high BMPs that are comparable with other grasses (Seppälä et al., 2009), while much lower BMPs are reported for hemp (Kreuger et al., 2011). According to literature, switchgrass showed lower methane potentials (Massé et al., 2010; Frigon et al., 2012), but also in this case the BMPs and the digestibility (Bélanger et al., 2012) of the regrowths were higher than the first cuts. The Biochemical Biogas Potential (BBP) of maize silage was 576.8 NL kg VS1, significantly lower than the BBP measured in A3 and RA2 (P < 0.001), that were 641.6 and 632.7 NL kg VS1 respectively (Fig. 4). BBP of RA1 was similar to that observed in maize, while the lowest BBP was measured in A5 (less than 450 NL kg VS1). Obtained results evidenced that the low methane production of A5 depended both on a low biogas production and on a markedly reduced methane content (56%). At contrary, A1 showed a quite low BBP (499 NL kg VS1) but a significantly (P < 0.001) higher MC than the other substrates (68%) (Fig. 4). In particular, MC showed a clear decreasing trend from early harvests to late harvests and second harvests seemed to behave similarly to A1–A2. In general, no significant correlation between MC and BMP was found, while BMP seemed to be determined mainly by BBP (P < 0.001) and R50 (P < 0.01), as showed in Table 3. Interestingly, some crop characteristics showed a strong correlation with the digestion kinetics parameters (Table 4). Plant height was positively correlated with T95 (P < 0.001) and T50 (P < 0.05), while Rmax and R50 were correlated to the green leaves percentage (P < 0.01 and P < 0.001, respectively). The methane production rate decreased as the crop was more mature. Crop maturity is known to negatively affect specific methane yields and digestibility of grasses (Nizami et al., 2009; Seppälä et al., 2009). Double cutting (A1 + RA1, A2 + RA2) prevented the crop from senescing and the harvest of juvenile plants allowed to obtain biomass with a higher proportion of leaves, particularly of photosynthetically active and nitrogen-rich leaves, as commonly observed in giant reed (Nasso o di Nasso et al., 2013). In addition, the proportion of stems respect to the more digestible leaves (Nizami et al., 2009) increased with giant reed development. Given that TKN content and C/N ratio vary according to the harvest time and to the green leaves proportion, these parameters also contribute to explain how crop characteristics and biomass partitioning are related to kinetics, BMP, and BBP at different harvest times (Table 4). Nitrogen content possibly affected to some extent also the variations of the methane concentration between harvest times, since a significant correlation between TKN and MC was found (P < 0.05). Giant reed, and particularly giant reed leaves, are usually found to be richer in nitrogen than other substrates from perennial energy crops (Smith and Slater, 2011). Anaerobic digestion of proteins is known to result in higher methane concentrations in the biogas produced respect to simple carbohydrates (Amon et al., 2007; Rincón et al., 2010). In fact, biogas from A1, RA1, and RA2 was characterized by a higher MC, while NSC may have played a role lowering the MC in late harvests respect to early harvests. However, despite the significant variations between harvest times, the NSC content in giant reed showed no significant correlation with the anaerobic digestion parameters, as the overall performances were probably more influenced by other factors. Cellulose is recognized as a high-potential substrate, considering that its experimental BMP typically approaches the theoretical maximum of 415 NL kg VS1 (Kreuger et al., 2011; Triolo et al., 2011). Its reduced bioavailability is commonly acknowledged as the most important limiting factor in anaerobic conversion of

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Fig. 4. BBP, BMP, average MC, and R50 of the investigated substrates. Significance level of ANOVA and standard error bars are reported.

Table 3 Pearson’ r correlation matrix of anaerobic digestion parameters. BMP BBP MC T50 T95 Rmax R50

0.92 0.35 0.74 0.82 0.87 0.89

BBP

MC

T50

T95

Rmax

***

ns ns

0.03 0.5 0.58 0.65 0.68

* * **

ns ns ns ns ns

0.75 0.59 0.62 0.67

ns ns ns ns

0.75 0.9 0.95

ns **

*

0.85 0.84

**

*

0.97

***

p levels are represented: ns (p > 0.05). * (p < 0.05). ** (p < 0.01). *** (p < 0.001).

Table 4 Pearson’s r correlation between anaerobic digestion parameters, crop characteristics, and biomass chemical traits. Height BMP BBP MC T50 T95 Rmax R50

0.84 0.58 0.69 0.87 0.92 0.98 0.94

Leaves (% of DM) *

ns ns * *** *** ***

0.82 0.64 0.57 0.65 0.75 0.74 0.82

*

ns ns ns ns ns *

Green leaves (% of DM) 0.84 0.58 0.72 0.78 0.87 0.88 0.91

*

ns ns * * ** ***

TKN (% of DM) 0.78 0.51 0.76 0.85 0.75 0.93 0.93

*

ns * * * *** ***

Carbon (% of DM) 0.53 0.16 0.9 0.75 0.71 0.79 0.75

ns ns **

ns ns *

ns

C/N 0.83 0.56 0.77 0.86 0.78 0.92 0.94

*

ns * * * *** ***

Lignin (% of DM)

NSC (mg g1)

0.49 0.48 0.06 0.16 0.53 0.18 0.23

0.44 0.43 0.16 0.09 0.47 0.42 0.33

ns ns ns ns ns ns ns

ns ns ns ns ns ns ns

p levels are represented: ns (p > 0.05). (p < 0.05). (p < 0.01). *** (p < 0.001). *

**

lignocellulosic materials, mainly due to lignin accumulation over the vegetative season (Nizami et al., 2009; Triolo et al., 2011; Monlau et al., 2013). In fact, negative correlations between ADL content and BMP have often been reported (Triolo et al., 2011; Kandel et al., 2012). However, no correlation was found in the present study,

since acetyl bromide lignin content in giant reed showed no significant change in the considered period according to harvest time and frequency. Therefore, the reduced BMP and digestion rate in mature plants (A5) cannot be explained in terms of an increased lignin content but the reduced bioavailability of structural carbo-

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Fig. 5. Biomass yield (a) and methane yield per hectare (b) of first cuts (A1–A5) and second cuts (RA1–RA2) of giant reed. Significance level of ANOVA and LSD test results are reported.

hydrates can even so be inferred. In fact, an even higher lignin content (about 25%) was reported by Di Girolamo et al. (2013) in giant reed harvested at a maturity stage comparable to A5. Despite this, the methane potential found by the authors was higher than in other perennials cropped at full maturity and more similar to grasses harvested at earlier stages (Frigon et al., 2012; Di Girolamo et al., 2013). Hence, it is perceived that the chemical traits of giant reed should be further investigated to better understand the relationship between maturity and methane potential. Indeed, modifications of cellulose crystallinity, physicochemical properties of hemicelluloses, lignin polymerization, and composition over the crop cycle have been proposed by several authors (Nizami et al., 2009; Monlau et al., 2013) as key factors influencing the availability of both structural and non-structural carbohydrates for anaerobic digestion. 3.3. Methane yields per hectare Despite the highest BMP was reached in A3 and it decreased beyond early August, methane yields per hectare was higher in late harvests as consequence of the highest biomass production (Fig. 5). Considering first cuts only, A5 was in fact the most productive harvest time, exceeding 9580 Nm3 CH4 ha1, despite the relatively low BMP. Methane yields per hectare reached by giant reed resulted higher than those reported in other studies for hemp (Kreuger et al., 2011), reed canary grass (Kandel et al., 2012), and switchgrass (Massé et al., 2010), and equaled the levels reported by Amon et al. (2007) for maize. Schievano et al. (2012) reported a higher methane production per hectare (7,170–11,280 Nm3 CH4 ha1, under single harvest management) respect to maize that yielded about 20 Mg DM ha1 (6750 Nm3 CH4 ha1). Giant reed substantially exceeded those production levels under the double harvest system, achieving 11,585 and 12,981 Nm3 CH4 ha1 in A2 + RA2 and A1 + RA1 respectively, about 20% and 35% higher than A5 (Fig. 5). Considering that the dry biomass produced by A1 + RA1 and A2 + RA2 substantially matched that obtained by A5 (Fig. 5), the increase of methane yield achieved by double harvests essentially depended on the higher BMP showed by the juvenile stages. These results pointed out a high potential of giant reed for biomethane production, and they may lead to consider this crop as a suitable alternative to maize especially in Mediterranean environments, where its high productivity under rainfed conditions was already proven (Angelini et al., 2009; Nasso o di Nasso et al., 2013). A substantial reduction of soil use for biogas production could be allowed, especially in double harvest systems. Furthermore, the juvenile stages of giant reed showed a digestion

kinetics most favorable to the retention times of real scale continuously fed digesters (Mähnert and Linke, 2009; Grieder et al., 2012). Also other perennial grasses, such as switchgrass, tall fescue, cocksfoot, and reed canary grass, showed an increased methane potential when managed under multiple harvest systems (Massé et al., 2010; Seppälä et al., 2009; Kandel et al., 2012). Nevertheless, the age of the plantation, the water availability during the season, the length of the growing season as well as the nutritional status of the soil are crucial to support the regrowth when perennial grasses are harvested twice during the vegetative development (Kandel et al., 2012). Increased nitrogen requirements can be expected, due to higher TKN contents at juvenile stages, thus potentially leading to an intensification of the cropping systems that should be also taken into account. On the other hand, a single late harvest could be interesting, since reduced costs per hectare can be expected and AGB yield is maximized. Biomass pre-treatments could cope with the reduced bioavailability of carbohydrates that was shown in A4–A5, thus increasing giant reed biomethane potential (Di Girolamo et al., 2013). If compared with maize and other agricultural feedstocks (Amon et al., 2007; Rincón et al., 2010), giant reed showed a high C/N ratio at all the considered harvest times that could be a disadvantage in commercial plants and it can be overcome by co-digestion. 4. Conclusion This study showed that giant reed has a high potential for biomethane production, especially when managed under double harvesting systems. Double harvest increased the methane yield per hectare by 20–35% respect to the most productive single harvest time, as consequence of the highest BMP achievable by juvenile stages, and the digestion kinetics was also favored. This cropping system could allow a considerable land use saving for biomethane production respect to maize, but further researches are needed in order to evaluate the long term effect of double harvesting on the plantation and to assess the methane potential of the ensiled biomass. Acknowledgements The research was carried out under the BIOSEA Project, funded by MIPAAF (Italy). The authors wish to thank Sergio and Carlo Cattani for their help in building, setting-up and managing the batch anaerobic digestion system. Thanks are due to Cristiano Tozzini, Fabio Taccini, Nicoletta Nassi o Di Nasso, and the CIRAA (Pisa, Italy) for their

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contribution in managing the field trials. Thanks also to Elisa Pellegrino for her precious suggestions, to Federico Triana, Neri Roncucci, Maria Valentina Lasorella, Valentina Giulietti, and Nico Viligiardi for their support in field sampling, to Matteo Gnocato and Cooperativa Valle Bruna (Grosseto, Italy) for providing the inoculum, to Alessio Giovannelli and CNR IVALSA (Firenze, Italy) for collaboration. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.biortech.2013.11. 004. References Amon, T., Amon, B., Kryvoruchko, V., Zollitsch, W., Mayer, K., Gruber, L., 2007. Biogas production from maize and dairy cattle manure – influence of biomass composition on the methane yield. Agric. Ecosyst. Environ. 118, 173–182. Angelidaki, I., Alves, M.M., Bolzonella, D., Borzacconi, L., Campos, J.L., Guwy, A.J., Lier, J.V., 2009. Defining the biomethane potential (BMP) of solid organic wastes and energy crops: a proposed protocol for batch assays. Water Sci. Technol. 59, 927– 934. Angelini, L.G., Ceccarini, L., Nassi o Di Nasso, N., Bonari, E., 2009. Comparison of Arundo donax L. and Miscanthus x giganteus in a long-term field experiment in Central Italy: analysis of productive characteristics and energy balance. Biomass Bioenergy 33, 635–643. APHA, Awwa, WEF, 2005. Standard Methods for the Examination of Water and Wastewater, 21st ed. USA, Washington DC. Bauer, A., Leonhartsberger, C., Bösch, P., Amon, B., Friedl, A., Amon, T., 2010. Analysis of methane yields from energy crops and agricultural by-products and estimation of energy potential from sustainable crop rotation systems in EU27. Clean Technol. Environ. Policy 12, 153–161. Bélanger, G., Savoie, P., Parent, G., Claessens, A., Bertrand, A., Tremblay, G.F., Babineau, D., 2012. Switchgrass silage for methane production as affected by date of harvest. Can. J. Plant. Sci. 92, 1187–1197. Beuvink, J.M., Kogut, J., 1993. Modeling gas production kinetics of grass silages incubated with buffered ruminal fluid. J. Anim. Sci. 71, 1041–1046. Chynoweth, D.P., Turick, C.E., Owens, J.M., Jerger, D.E., Peck, M.W., 1993. Biochemical methane potential of biomass and waste feedstocks. Biomass Bioenergy 5, 95–111. Di Girolamo, G., Grigatti, M., Barbanti, L., Angelidaki, I., 2013. Effects of hydrothermal pre-treatments on Giant reed (Arundo donax) methane yield. Bioresour. Technol. 147, 152–159. Dragoni, F., Ragaglini, G., Nassi O Di Nasso, N., Tozzini, C., Bonari, E., 2011. Suitability of giant reed and miscanthus for biogas: preliminary results on harvest time and ensiling. Asp. Appl. Biol. 112, 291–296, Biomass and Energy Crops Conference IV. Frigon, J.C., Mehta, P., Guiot, S.R., 2012. Impact of mechanical, chemical and enzymatic pre-treatments on the methane yield from the anaerobic digestion of switchgrass. Biomass Bioenergy 36, 1–11. Fukushima, R.S., Hatfield, R.D., 2004. Comparison of the acetyl bromide spectrophotometric method with other analytical lignin methods for determining lignin concentration in forage samples. J. Agric. Food Chem. 52, 3713–3720. Gao, R., Xufeng, Y., Wanbin, Z., Xiaofen, W., Shaojiang, C., Xu, C., Cui, Z., 2012. Methane yield through anaerobic digestion for various maize varieties in China. Bioresour. Technol. 118, 611–614. Giovannelli, A., Emiliani, G., Traversi, M.L., Deslauriers, A., Rossi, S., 2011. Sampling cambial region and mature xylem for non structural carbohydrates and starch analyses. Dendrochronologia 29, 177–182.

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