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Cassava stem wastes as potential feedstock for fuel ethanol production: A basic parameter study
Renewable Energy 83 (2015) 970e978
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Cassava stem wastes as potential feedstock for fuel ethanol production: A basic parameter study €rn A. Lestander a, Shaojun Xiong a, * Maogui Wei a, b, Wanbin Zhu c, Guanghui Xie b, Torbjo a
Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences, SE 901 83 Umeå, Sweden National Energy R&D Centre for Non-food Biomass, China Agricultural University, Beijing 100094, China c Centre of Biomass Engineering, China Agricultural University, Beijing 100094, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 13 September 2014 Received in revised form 18 May 2015 Accepted 26 May 2015 Available online 5 June 2015
The cassava stem is found to be one of few crop residues containing starch (up to 42% of dry mass) that may be converted to fuel ethanol. The current study was to evaluate the influence of parameters genotype, growth location and harvest time on cassava stem starch contents and yields as well as consequences in ethanol production (non-cellulosic process), based on 180 samples from a full factorial design experiment (3 varieties 3 locations 5 harvest times) in Guangxi, China. The potential utilization of stem starch and soluble sugar that varied 14e42% and 3e12.1% of dry mass, respectively, can correspond to an increase of 26% in ethanol production compared to that produced by roots only. The cassava stem starch content was significantly affected by all three studied parameters and location had the largest effect followed by variety and harvest time, while the stem starch yield was significantly affected by location only. The starch and soluble sugar content were significantly correlated with soil properties, e.g., soil pH and organic carbon, S and P contents. A general and positive correlation was also found between the stem and root starch, suggesting a promising potential of using stem starch without reducing root starch production. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Crop residue Biomass Fuel Soil property Non-structural sugar
1. Introduction Crop residues in the world alone amount to about 6.7 Pg, of which 60% could be available for bio-based production [1,2], corresponding to about 570 kg per world citizen. The use of these leftovers for the feedstock of fuel and bio-based products may make it possible to increase bioenergy and biorefinery production without largely expanding land use and threatening food security [3e5]. It is, therefore, important to study the possibilities of utilizing non-food biomass as feedstock for biofuel and bio-based products [6], in order to better meet global challenges: food security for a growing population and for the development of sustainable societies. Cassava (Manihot esculenta Crantz.) stem is currently wasted after harvesting of the starchy roots that are today an important food resource for as many as one billion people in tropical and subtropical areas in Africa, Asia, and Latin America but also as feed and biofuel feedstock [7]. Previous studies [8,9] indicate that
* Corresponding author. E-mail address: [email protected] (S. Xiong). http://dx.doi.org/10.1016/j.renene.2015.05.054 0960-1481/© 2015 Elsevier Ltd. All rights reserved.
cassava stems have a high content of starch (up to 30% of dry mass). Globally, the quantity of cassava stems can be estimated to be about 32e38 Tg dry mass based on a stem/root mass ratio of 42e50% [9,10] and the average cassava root production in 2009e2013 [7]. This suggests a considerable alternative resource to substitute cassava roots that today are used for biofuel production and other non-food products, thus, saving the roots as the food resource for a growing population up to 100 million people by 2030 [9]. However, most cassava stems are abandoned or burned in the wild. Only 10e20% of the stems are used for propagation, mushroom substrates or recycled to maintain soil fertility [11]. The lack of knowledge about cassava stem starch and its variation in content makes the cassava stem an unimportant resource so far. It was not until recently, that cassava stem was studied as a feedstock for bioethanol production based on its potential to produce a considerable amount of fermentable sugars [12e14]. Either sole cellulosic process hydrolyzing cellulose and hemicellulose [12,13], or using enzyme mixtures (cellulase, amyloglucosidase and amylase [14]; were proposed as approaches of bioethanol conversion from cassava stems. However, the high concentration of stem starch [9], which can be easily extracted and/or directly hydrolysed
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into glucose using simple and low-cost process in comparison to that for lignocellulos [15], can be a very important factor in designing industrial processes for efficient and sustainable production of ethanol. The fundamental information on quantitative variations of cassava stem starch is lacking, as its considerable stem starch quantity is a rather “new” property. There is a great need to have such information to be able to provide possible recommendations for industrial processes e.g., pretreatments. This knowledge is also necessary to be able to avoid any possible misinterpretations of research results. Being an important chemical component of cassava stem, starch may vary with both biotic and abiotic parameters such as genotype, environment, harvest time and management, as it was found with other biomass characteristics [8,16e21]. Few studies, if any, have been made to understand the variability of stem starch and the parameters that may affect cassava stem starch quantity. Important questions remaining to be answered include: (1) Is there a large span of variability in stem starch? (2) What is the major parameter that may influence cassava stem starch? (3) Would use of stem starch influence root starch production? (4) How much can stem starch contribute to bioethanol production? Answering these questions will certainly improve the possibilities to design and optimize the process chain from the feedstock to ethanol. The current study was mainly to examine variations in stem starch, and evaluate the impacts of the parameters location, variety and harvest time on stem starch content and yield as well as consequences in bioethanol production. The influence of root starch on stem starch was also assessed, as an uncontrolled factor in the experiment, because of their physiological associations. As soluble sugar is closely related to the starch, it was also included as a response variable. The chosen parameters are of major interest, as they are known to influence embedded starch in biomass but
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also due to their ease of control in feedstock production management. 2. Materials and methods This current study is a continuity of the previous study [9] that was primarily based on three-year field surveys and analyses, where the variations of cassava stem starch between 28 geographic locations and 8 varieties were observed. 2.1. Plant materials and their origins One hundred and eighty (180) samples were originated in 2011 from a full factorial experiment composed of parameters location (3 sites), variety (3 ones) and harvest time (5 occasions) (in total, 45 treatments four replicates ¼ 180 plots). The three locations were Heng (22 480 2500 N, 109 050 4700 E), Longan (23 030 4000 N, 107 520 2700 E) and Wuming (23 080 5600 N, 108 090 1700 E), the largest cassava production counties in, Guangxi, China. Varieties South China 205 (SC205), South China 5 (SC5) and Xinxuan 048 (XX048) were chosen as they are the most favourable ones by farmers mostly because of good root yields. The five harvest times was set on 230, 245, 260, 275 and 290 days after planting (DAP), covering the most possible harvest periods in practice in the region. The data about the local weather can be found in Fig. 1. More information about the field experiment setting and management was described by Wei et al. [22]. 2.2. Sampling of stems and soil Eight plant stems were harvested and merged into one sample for each treatment. Nine root tubers from those eight plants were randomly chosen and mixed as one root sample. The fresh root
Fig. 1. Monthly air temperature ( C) and precipitation (mm) in Nanning city, covering three study sites. The history data were obtained at: http://www.travelchinaguide.com/ climate/nanning.htm, accessed on 2 December 2013. The data for the growth season 2011e2012 are average values for Nanning City, covering the three study sites, from Nanning Meteorology Bureau, Guangxi.
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yield from those eight plants and the above ground stem yield of two randomly chosen plants (representing 1.6 m2) were measured for each plot on 230, 260 and 290 DAP. All samples were immediately put into plastic bags after harvesting and transported to a laboratory at Guangxi University where they were cleaned and chopped, and dried at 75 C until constant weight. The samples were further transported and analysed in the laboratory at China Agricultural University (CAU). Before the analyses, the biomass moisture content was re-determined at 105 C. Further details of the sampling of plant and soil samples were presented in Wei et al. [22].
- CS: content of starch; CSS: content of soluble sugar; BY: biomass yield (Mg e million grams). - 1.11 (in E2-2) is the ratio of converting starch into sugar, which is based on the hydrolysis reaction of starch: (C6H10O5)n þ nH2O ¼ nC6H10O6; i.e., every unit of starch can produce 1.11 units of sugar (in weight). - for E2-3: 0.51 is the coefficient of sugar converting into ethanol; 0.85 is the process efficiency; 1000/0.79 is the density of ethanol (The Institution of Japan Energy, 2006).
2.5. Data analysis 2.3. Starch, soluble sugar and soil analysis All of the stem and root samples were milled into 0.5 mm particle size before analysing. Starch and soluble sugar content were then determined using the method recommended by The International Rice Research Institute [23], with which ethanol and perchloric acid were used to extract soluble sugar and starch respectively while anthrone was used as a testing reagent. The soil samples were analyzed at CAU. The results as well as methods of the soil analysis were summarized in our previous study [22] and the mean values for each location could be found in Table 1. 2.4. Estimation of starch yield and ethanol production potential The starch and sugar in cassava stem may potentially contribute to industrial productions of starch and ethanol that use the root only today. Here the first generation ethanol is concerned, i.e., soluble sugar and starch are directly fermented to produce ethanol. To review these potentials, the yields of non-structural sugar (NSS), whole plant starch (WPS) and the first generation ethanol (FGE) were estimated/calculated using the data from this current experiment. The calculation was based on equations as follows:
WPS Mg ha1 ¼ CSstem ð%Þ BYstem Mg ha1 þ CSroot ð%Þ BYroot Mg ha1 (E2-1) NSS Mg ha1 ¼ ½CSS ð%Þ þ CS ð%Þ 1:11 BY Mg ha1 (E2-2) FGE L ha1 ¼ NSS Mg ha1 0:51 0:85 ð1000=0:79Þ (E2-3) where Table 1 Physical and chemical propertiesa of topsoil (0e20 cm) samples taken from study sites by the end of season.
pH Organic carbon (OC) (g kg1) Nitrogen (N) (g kg1) Sulphur (S-al) (mg kg1) Chlorine (Cl) (mg kg1) Phosphorus (P) (g kg1) Potassium (K) (g kg1) Calcium (Ca) (g kg1) Magnesium (Mg) (g kg1) Silicon (Si-al) (mg kg1)
Heng
Longan
Wuming
5.6 8.6 0.7 11.5 41.2 0.8 1.4 0.7 1.2 128.5
3.9 18.9 2.3 21.9 21.2 0.6 1.7 0.3 2.0 96.9
5.5 26.9 2.8 29.9 30.2 1.4 4.9 1.5 2.4 234.8
a S-al & Si-al ¼ available S and Si, respectively. The data were all from our previous study (Wei et al., 2014).
All data presented are based on oven dry mass (DM). Analysis of variances was performed using statistical software IBM SPSS 20.0 for Windows (GLM Univariate Analysis; IBM SPSS, 2012). Responses (i.e., starch and soluble sugars) were treated as dependent variables, while parameters location, variety and harvest time were independent variables. Both tests of between-subjects effects and pairwise comparison were conducted. The root starch and sugar were treated as uncontrolled factors that would have influences on the stem starch and soluble sugar, due to their interactions [24]. The IBM SPSS was also used to examine correlations between stem and root starch and between stem and root sugar, in which linear regression (stepwise) analyses were conducted and root data (Table 1) were treated as independent variables. Prior to the correlation analysis, the normality of data was tested and logarithm transformation was made according to kurtosis and skewness analyses. The same software and multiple linear regression was used to analyze the correlations between stem starch and soil composition, to elaborate the impact of location or environmental conditions, but no data transformation was made according to the test of data normality. 3. Results and discussion 3.1. Contents of starch and soluble sugar in cassava stems 3.1.1. Variations Fig. 2a shows the averages of starch and soluble sugar content for each of 45 treatments. The stem starch content ranged from 14 to 42% of DM, being 25% on average and >20% in 36 out of all 45 treatments, which was comparable to those reported in a previous study [9]. It is very rare to find such a high starch content in crop residues, except for those reported for transgenic rice dedicated for energy (up to 24.4% in the entire above-ground straw, except panicles and seeds) [25] and whole plants including fruit grains of ‘fibre corn’ (25e35%) [26]. The highest starch content in this experiment was found in stem of SC205 in Heng, and the lowest in XX048 in Wuming. In general (Fig. 2a), stems starch content tended to peak at 260e275 DAP within the studied harvest time (230e290 DAP). The content of soluble sugar in cassava stems was also considerably high, 3e12.1% in most cases, and comparable to that of varieties of rice dedicated for bioenergy (4.4e18.2%) [27]. Sugar content in the stems from Longan site and variety SC205 were significant higher (p < 0.01) than other sites and varieties (Fig. 2a). Peak time of sugar content in cassava stem was found at the 245 DAP, i.e., 15e30 days before the peaking of starch content. 3.1.2. Effects of parameters location, variety and harvest time All of three studied parameters had significant effects on the starch and soluble sugar contents of the stem (p < 0.001 for all, Table 2). Location (L) had the largest effect on stem starch and soluble
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Fig. 2. Variations of cassava stem starch content (a), biomass yield (b) and starch yield (c) with location, variety and harvest time Mg e million grams. The harvest time is indicated by days after planting (DAP). The content and yield of soluble sugar are also shown in (a) and (c) respectively. Each bar refers to mean ± standard error for each treatment.
sugar according to the relative contribution to the mean square of variance (RC > 70.66%). Variety (V) was second to location but more influential than harvest time (T) on starch content, showing an RC of 21.30% in comparison with a <1% for harvest time. The harvest time had a bit larger role on soluble sugar; its RC was about 5%. All interactions between any two of the three factors, i.e., twoway interaction, had significant effects on stem starch content (Table 2). L V had stronger effects on starch content than other interactions while soluble sugar content was more affected by L T. The three-way interaction (L V T) had a significant effect on both response variables, with an especially strong effect on soluble
sugar. Based on the mean square of variance values and RC, however, the interactive effects on stem response variables constituted only a small proportion (about 7% for starch and 3% for sugar) of the total sum of mean square of variance (Table 2). The fact that more than 70% variations in contents of stem starch and soluble sugar were due to the effects of location (Table 2) was found to be correlated with specific soil conditions where the studied cassava was growing. Using linear multiple regression (stepwise), three highly significant correlations were established between stem starch and sugar contents, and soil chemistry variables (Table 3). The models explained 64e92% of the variances, and
Table 2 Univariate analysis of variance on stem starch and sugar contents and yields from the factorial experiment in 2011. df ¼ degrees of freedom; MS ¼ mean square of variance; p ¼ significance level of ANOVA. Source
Location (L) Variety (V) Harvest time (T) LV LT VT LCT Error Total a
df
2 2 4 4 8 8 16 135 180
Starch content
Soluble sugar content
df
MS
p
RCa
MS
p
RC
2164.12 652.46 23.93 170.31 28.77 12.34 7.00 3.97
<0.001 <0.001 <0.001 <0.001 <0.001 0.003 0.042
70.66 21.30 0.78 5.56 0.94 0.40 0.23 0.13
548.30 10.97 32.87 0.72 13.70 0.71 2.40 0.51
<0.001 <0.001 <0.001 0.234 <0.001 0.213 <0.001
89.86 1.80 5.39 0.12 2.25 0.12 0.39 0.08
2 2 2 4 4 4 8 81 108
Starch yield
Soluble sugar yield
MS
p
RC
MS
p
RC
24.34 0.12 0.01 0.26 0.10 0.30 0.11 0.08
<0.001 0.237 0.902 0.017 0.320 0.008 0.233
96.19 0.46 0.03 1.01 0.38 1.18 0.43 0.32
0.269 0.004 0.079 0.006 0.021 0.005 0.002 0.003
<0.001 0.232 <0.001 0.089 <0.001 0.136 0.612
68.89 1.12 20.29 1.57 5.32 1.36 0.60 0.75
RC: relative contribution of the effects (%), calculated as the ratio of the mean squares of each factor source to the total sum of mean-squares.
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Table 3 Correlations between soil and content of cassava stem soluble sugar and starch (Linear Regression, Stepwise). The soil data were summarized in Table 1. Stem
Soil variable
R2 Adj
p
n
Sugar (%) Starch (%) Starch þ sugar (%)
21.198e2.964 pH e 0.047 S (mg kg1) 34.39e0.776 OC (g kg1) þ 51.495 P (%) 69.325e0.869 OC (g kg1) e 5.961 pH þ 125.537 P (%)e0.243 S (mg kg1)
0.919 0.638 0.850
<0.001 <0.001 <0.001
36 36 36
soil pH, organic carbon, S and P were included as a key predictor in the models (Table 3). These phenomena are consistent with reported correlations between fuel and soil properties [9]. However, the exact mechanism behind these findings has to be studied in future research, which may be achieved by dedicated laboratory and field experiments. 3.2. Yields of stem starch and soluble sugar Based on stem starch and soluble sugar content (Fig. 2a) and biomass production (Fig. 2b), yields of stem starch and soluble sugar were calculated (Fig. 2c). The stem starch yields from the 27 treatments ranged 0.2e2.06 Mg ha1 (Fig. 2c), with an average of 1.2 Mg ha1. Location had the most significant effect on the stem starch yield (p < 0.001; Table 2) that was highest in Heng (average 1.87 Mg ha1), followed by Wuming (1.45 Mg ha1) and Longan (0.28 Mg ha1); neither variety nor time showed a significant influence. Although the interactions L V and V T affected stem starch significantly (p < 0.017 and 0.008, respectively), their RC was together about 2.2% only. The yields of stem soluble sugar was between 0.05 and 0.51 Mg ha1 (Fig. 2c) and averaging 0.19 Mg ha1. A similar trend to stem starch, location had most influential effect (p < 0.001, Table 2) and the average value for Heng (0.27 Mg ha1) was higher than for Wuming (0.21) and Longan (0.10). Harvest time showed also a significant effect and had as much as 20% of RC (Table 4). Across locations and varieties, stem soluble sugar yield was on average 0.14, 0.22 and 0.22 Mg ha1 on 230, 260 and 290 DAP, respectively. The patterns of variations in the starch and sugar yields were more similar to the biomass (Fig. 2b) than to the corresponding contents (Fig. 2a), in which location is the dominant factor. Heng is situated at lower latitude and receives more solar radiation than other two sites and Wuming is characterized by a higher soil fertility (Table 1), while in Longan soil pH is rather low (4.0, Table 1) that is not only below the range of 5.5e6.5 for a proper growth but also at a threshold of hydrogen ion injury for cassava plant [28]. A lower biomass yield in Longan was therefore expected. 3.3. Root starch and soluble sugar 3.3.1. Variations of root starch and soluble sugar Fig. 3 shows the results of root starch and soluble sugar content
(Fig. 3a), root biomass (3b) and yield of root starch and sugar (3c). The contents of root starch and soluble sugar were in a range of 64e87% and 1.7e9.8%, respectively, while the corresponding yields were 2.00e12.18 and 0.05e1.08 Mg ha1. All main (individual) effects of location, variety and time on root starch and soluble sugar contents were significant (p < 0.001 for all but p < 0.038 for starch yield, Table 4). Location was more influential than variety and time. These results were similar to the results for stem starch and soluble sugar, and as expected in agreement to previous findings for root starch [18,29,30]. Both root starch content and yield were higher in Heng (on average 81.6%, 8.9 Mg ha1) than in Longan (76.0%, 4.1 Mg ha1) and Wuming (71.8%, 7.5 Mg ha1) (Fig. 3a). The yields of root starch and sugar was largely driven by the root biomass (Fig. 3b). Variety SC205 had a root starch content of 78.2% which was significantly higher than SC5 (75.0%) and XX048 (76.1%), however a higher yield of root starch was found for XX048 (8.3 Mg ha1) than SC205 and SC5 (7.2 respect 5.2 Mg ha1). In contrast to starch content, harvest time is more important than variety for soluble sugar, which is also in consistent to the results of stem sugar (Table 4). Additionally, the impacts of interactions between the factors seem to be rather low. 3.3.2. Correlations between root and stem starch and soluble sugar Correlations in carbohydrate concentrations between the stem and root are of interest in plant physiology [24]. The current study, however, focused on an evaluation of root starch and soluble sugar and their correlations with their stem starch and soluble sugar; the plant physiological mechanism was not investigated. An exponential correlation was found between stem and root starch contents derived from all 45 treatments (Fig. 4): y ¼ 20230.19 10x(0.0007x0.0912), where y ¼ stem starch content and x ¼ root starch content (R2 adj ¼ 0.573, n ¼ 45, p < 0.001, F-test). The correlation was analyzed at different grouping levels as well. Similar significant and positive correlations for starch content were also found for all three varieties (across location and time), and five harvest occasion (across cultivar and location), but for Heng location only (Table 5). Interestingly, the correlations in content of stem soluble sugar was negative on 260 and 275 DAP, which probably signalizes the move of sugar from stem to root. When starch and sugar yield were concerned, significant correlations were found in most cases and all were positive (Table 5),
Table 4 Univariate analysis of variance (ANOVA) on root starch and soluble sugar from the factorial experiment in 2011. df ¼ degrees of freedom; MS ¼ mean square of variance; p ¼ significance level of ANOVA. Source
Location (L) Variety (V) Harvest time (T) LV LT VT LVT Error Total a
df
2 2 4 4 8 8 16 134 180
Starch content
Soluble sugar content
df
MS
p
RCa
MS
p
RC
1438.70 163.59 69.46 103.59 93.32 13.83 21,59 9.96
<0.001 <0.001 <0.001 <0.001 <0.001 0.207 0.009
75.17 8.55 3.63 5.41 4.88 0.72 1.13
53.89 28.69 53.01 10.80 15.31 2.59 2.56 0.69
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
32.17 17.13 31.64 6.44 9.14 1.55 1.53
2 2 2 4 4 4 8 81 108
Starch yield
Soluble sugar yield
MS
p
RC
MS
p
RC
223.73 97.76 5.81 69.48 0.48 1.79 1.19 1.71
<0.001 <0.001 0.038 <0.001 0.888 0.388 0.694
55.66 24.32 1.45 17.29 0.12 0.44 0.30 0.42
0.910 0.625 0.518 0.187 0.296 0.036 0.032 0.010
<0.001 <0.001 <0.001 <0.001 <0.001 0.010 0.004
34.80 23.92 19.80 7.17 11.31 1.39 1.22
RC: relative contribution of the effects (%), calculated as the ratio of the mean squares of each factor source to the total sum of mean-squares.
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Fig. 3. Variations of cassava root starch content (a), biomass yield (b) and starch yield (c) with location, variety and harvest time. The harvest time is indicated by days after planting (DAP). The content and yield of soluble sugar are also shown in (a) and (c) respectively. Each bar refers to mean ± standard error for each treatment.
irrespective of grouping. We had expected a negative correlation between stem and root starch, due to plant physiological transform and move of hydrocarbon from stems to roots and possible internal competition for non-structural carbohydrate between them [24,31]. However, the results of the current study were mostly in contrast to this expectation. The starch appeared to be accumulating almost simultaneously and co-ordinately in both stem and root (Fig. 4). The process and mechanism, however, need to be investigated in future studies.
3.4. Potential contribution of stems to starch and ethanol production Table 6 summarizes calculated potential yields of whole plant starch (WPS), non-structural sugar (NSS) and first generation ethanol (FGE) based on the equations described in section 2.4. While WSP was 2.20e13.94 Mg ha1; stem proportion was on average 17.9%, or an increase of 24.5% on top of that from roots only. Due to the contribution of stem NSS that would be eventually converted from starch and soluble sugar, an average proportion of 20.61% or an increase of 26.0% in FGE (i.e., about 1066 L ha1) could be reached on top of that produced from the root only. The potential contribution of stem to starch and ethanol production are dependent on the parameter location, variety and harvest time, in association with the variations of stem and root starch and soluble sugar (Sections 3.1e3.3). On average, cassava stems from Heng and Wuming had much larger proportions than Longan (24.8% and 21.4% vs. 7.5% for total starch; 26.8% and 23.1% vs. 9.7% for FGE; Table 6), and in these two locations the ratio of SC5 was much higher than other two varieties (total starch, 35.6% vs. 17.8% for SC205 and 15.9% for XX048; FGE, 38.6% vs. 17.8% for SC205 and 17.1% for XX048). 3.5. General discussion
Fig. 4. Correlations between stem and root starch content. SC205, SC5 and XX048 were marked as blue cub, green triangle and purple cross, respectively. The correlation was established as Y ¼ 20230.19 10x(0.0007x0.0912), where y ¼ stem starch and x ¼ root starch. R2 adj ¼ 0.573, n ¼ 45, p < 0.001, F-test. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
The results demonstrate that the cassava stem starch is a considerable resource with a potential to increase starch production that is today based on root only, and that the stem starch quantity varies to a large extent with location, a small extent with variety and to a minor extent with harvest time. This will influence the yield of subsequent ethanol production or any other products
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Table 5 Correlations (Pearson coefficient) in starch and soluble sugar content between stem and root. The asterisks refer to statistic significant levels (*p < 0.05,**p < 0.01, two tailed).
Correlations based on contents Location
Variety
Harvest time (DAP)
Correlations based on yields Location
Variety
Harvest time (DAP)
n
Starch (stem ~ root)
Soluble sugar (stem ~ root)
Heng Longan Wuming SC205 SC5 XX048 230 245 260 275 290
60 60 60 60 60 60 36 36 36 36 36
0.544** 0.096 0.228 0.606** 0.665** 0.671** 0.665** 0.831** 0.816** 0.462** 0.462**
0.158 0.297* 0.416** 0.212 0.140 0.208 0.055 0.134 0.402* 0.509** 0.066
Heng Longan Wuming SC205 SC5 XX048 230 260 290
36 36 36 36 36 36 36 36 36
0.026 0.541** 0.059 0.805** 0.162 0.690** 0.450** 0.580** 0.391*
0.143 0.385* 0.450** 0.621** 0.371* 0.790** 0.256 0.497** 0.522**
based on cassava starch. The study showed also that there are significant correlations between stem starch content and soil chemistry (as environment constitutes of location) and between the stem and root starch. These findings not only support our previous proposals that cassava stems as supplementary or alternative feedstock may be integrated into existing starch (food) and ethanol (fuel) production [9], but also provide fundamental facts that may be used for further research and technology development for utilizing cassava stems for variable bio-products. The
knowledge about role of the studied parameters in influencing the stem starch will be definitely helpful in practice when a feedstock production and industrial infrastructure are concerned. Conventionally cassava root is sole product that people cultivate for, but our results indicate that the stem production needs to be included in existing cultivation management. Processing cassava stems for starch and ethanol production is new [9]. Feedstock quality, i.e., content of starch (and soluble sugars), is urgent to be understood and has to be paid more
Table 6 Potential production of non-structural sugar (NSS), whole plant starch (WPS) and first generation ethanol (FGE). Data are shown as Mean ± standard error (SE). Time ¼ harvest time, DAP refers to days after planting. Location
Variety
Time (DAP)
NSS
WPS
Stem (Mg ha1) Heng
SC205
SC5
XX048
Longan
SC205
SC5
XX048
Wuming
SC205
SC5
XX048
Mean Maximum Minimum
230 260 290 230 260 290 230 260 290 230 260 290 230 260 290 230 260 290 230 260 290 230 260 290 230 260 290
2.53 2.55 2.49 2.51 2.05 2.10 1.99 2.59 2.25 0.30 0.39 0.41 0.58 0.47 0.39 0.37 0.36 0.39 1.22 2.03 1.77 2.13 1.70 2.37 1.40 1.86 1.86 1.52 2.55 0.30
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.29 0.29 0.25 0.28 0.27 0.25 0.30 0.21 0.22 0.20 0.18 0.19 0.19 0.16 0.13 0.13 0.16 0.15 0.19 0.31 0.26 0.36 0.43 0.51 0.21 0.29 0.23
FGE
Root (Mg ha1)
Yield (Mg ha1)
Stem proportion (%)
Yield (L ha1)
12.14 ± 0.76 12.07 ± 0.56 12.84 ± 0.56 4.79 ± 0.41 6.01 ± 0.52 5.39 ± 0.47 12.14 ± 0.60 13.52 ± 0.81 14.60 ± 0.43 2.27 ± 0.51 2.90 ± 0.52 3.23 ± 0.58 5.62 ± 0.42 6.15 ± 0.53 6.06 ± 0.60 5.16 ± 0.38 4.79 ± 0.60 5.94 ± 0.51 8.59 ± 0.70 10.48 ± 0.65 9.85 ± 0.71 6.42 ± 0.72 5.48 ± 0.72 6.34 ± 0.58 8.78 ± 0.69 10.28 ± 0.56 11.71 ± 0.65 7.91 14.60 2.27
12.69 ± 0.77 12.59 ± 0.57 12.87 ± 0.51 6.24 ± 0.44 6.79 ± 0.45 6.15 ± 0.50 12.20 ± 0.62 13.67 ± 0.73 13.94 ± 0.42 2.20 ± 0.51 2.75 ± 0.50 3.06 ± 0.56 5.35 ± 0.40 5.61 ± 0.51 5.55 ± 0.58 4.79 ± 0.37 4.38 ± 0.57 5.40 ± 0.48 8.61 ± 0.66 10.60 ± 0.54 9.99 ± 0.70 7.42 ± 0.71 5.94 ± 0.75 7.34 ± 0.65 8.79 ± 0.65 9.91 ± 0.48 11.47 ± 0.58 8.01 13.94 2.20
19.60 ± 0.68 18.94 ± 0.61 18.56 ± 0.81 49.11 ± 1.54 32.06 ± 1.52 36.51 ± 0.90 15.70 ± 0.93 18.41 ± 1.08 14.52 ± 0.62 9.91 ± 0.68 11.21 ± 0.99 10.29 ± 0.65 8.62 ± 0.73 5.91 ± 0.62 5.38 ± 0.45 5.88 ± 0.48 5.62 ± 0.31 5.03 ± 0.38 14.36 ± 1.08 18.78 ± 1.19 16.50 ± 0.76 33.63 ± 1.77 28.44 ± 1.36 34.05 ± 1.89 15.48 ± 0.73 17.27 ± 1.04 14.11 ± 0.75 17.92 49.11 5.03
8047 8021 8411 4002 4424 4110 7751 8835 9246 1408 1809 1994 3405 3634 3540 3029 2828 3474 5381 6867 6376 4690 3940 4776 5587 6663 7445 5174 9246 1408
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
19 14 13 11 11 12 15 18 10 13 13 14 10 13 14 9 14 12 16 14 18 18 19 17 17 12 15
Stem proportion (%) 21.13 ± 0.75 21.16 ± 0.69 19.53 ± 0.81 52.94 ± 1.44 35.41 ± 1.58 39.30 ± 0.86 16.47 ± 0.89 19.86 ± 1.11 15.43 ± 0.63 13.13 ± 0.74 14.69 ± 1.18 13.21 ± 0.78 10.43 ± 0.80 7.83 ± 0.69 6.53 ± 0.52 7.08 ± 0.49 7.61 ± 0.39 6.51 ± 0.40 14.97 ± 1.10 20.11 ± 1.23 18.23 ± 0.79 35.29 ± 1.82 30.96 ± 1.32 37.88 ± 1.95 16.24 ± 0.74 18.44 ± 1.05 16.05 ± 0.79 26.61 52.94 6.51
M. Wei et al. / Renewable Energy 83 (2015) 970e978
attention as in this study than the yield, because the knowledge about the stem starch content is crucial for designing infrastructure and processes for an efficient use of stems for starch and ethanol production. The fact that stem starch content was strongly and significantly affected by all three studied parameters (Table 2) whereas the stem starch yield was significantly affected by location only emphasizes a more complexity in managing starch content than doing yield. The effects of variety and harvest time have to be counted, though they are less important, when the content is concerned but not for the yield. The location was key and dominant parameter in determining both content and yield of stem starch (Table 2), as indicated by the fact that the highest content and yield was from Heng, while Wuming had the lowest content and Longan the lowest yield (Fig. 2), which was largely attributed to soil properties (Table 3 and Section 3.2). However, location is a complex of many environmental conditions; there is no doubt that climate conditions such as temperature, precipitation and sunlight have influences on the starch, which should be included in future studies at an expanding geographic scale. Utilization of cassava stems has to be coordinated with the root production, apparently because the stem is the byproduct. It is important to understand the relations between stem starch and root starch, not only for the root is the primary sink of photosynthate [11], but also for management practice: e.g., when it is optimized time for harvesting both stem and root. In the current study several similarities were found between stem and root starch: (1) the location is much more influential than variety and harvest time; (2) starch content is less variable than yield across the treatments (Figs. 2 and 3); and (3) may be of the most importance, the stem and root starch is positively correlated (Fig. 4, Table 5). With these findings, the question “Would use of stem starch influence root starch production” is well answered. Also, the normal root harvest regime seems to suit well stem harvesting. Since harvest time had no large influence on the starch content of stem and root, a rather flexible and less restrict period for harvest and feedstock management can be suggested. The difference that starch content of stem is more diverse than that of root (the highest one was 3 times of the lowest in stems, but the corresponding value for root was 1.4 times), however, indicates some small modifications and/or optimizations in the industrial processes existing for the root may need to be considered to suit the stem processing. The industry has to be able to handle and process the feedstock with lower but more variable starch contents than those in roots. Remarkably, stem starch is found by this study to have an average potential to increase the production of the first generation ethanol by up to about 26% on top of that based on root feedstock only. This estimation was based on about 25% of stem being starch and 5% soluble sugars (in averages) and did not count for those fermentable sugars that could be converted from cellulose and hemicellulose [12,13]. Using a mixture of enzymes cellulase, amyloglucosidase and amylase, Nuwamanya et al. [14] obtained as high as 60% of conversion rate from stem to reducing sugars, however information of exact stem composition was lacking, especially starch content. A large potential to use all additional sugars in cassava stem for food and fuel is therefore expected in future but this has to be further studied based on a well-understanding about the feedstock composition or properties, keeping in mind that the annual cassava stem production is about 30 Tg and that 500e1000 million people in the world are dependent on cassava starch production. 4. Conclusion The results from the factorial design experiment show: (1) a large span of variability in cassava stem starch between the
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treatments, which was even larger than those for roots; (2) that cassava stem starch content was significantly affected by the three studied parameters where location had the largest effect followed by variety and harvest time, while the stem starch yield was significantly affected by location only; (3) in general positive correlations between the stem and root starch suggest that a collaborated management of stem and root harvest is possible and that the stem and root achieve high starch contents simultaneously; (4) on average, that stem starch had a theoretical potential to increase 24.5% of starch or 26% ethanol production based on the root only today. Acknowledgements Guangcan Tao, Ronghui Qin and Jishi Wang at China agricultural of University (CAU), and Xinglu Luo and Xiran Cheng at Guangxi University (GXU) are gratefully acknowledged for their helps with the field and laboratory work. Our thanks also go to Professor Xu Cheng at CAU for valuable comments during the course of the study. This research was funded by the EU-China Energy and Environment Program (EEP-PMU/CN/126077/RE006), the Swedish Energy Agency (32805-1), the Chinese Ministry of Agriculture 948Project (“948”-2011-S7), the Royal Swedish Academy of Engineering Science, the China Academy of Engineering, and the Swedish Governmental Strategic Project Bio4Energy. References [1] G.C. Tao, T.A. Lestander, P. Geladi, S.J. Xiong, Biomass properties in association with plant species and assortments I: a synthesis based on literature data of energy properties, Renew. Sust. Energy Rev. 16 (2012a) 3481e3506. [2] G.C. Tao, P. Geladi, T.A. Lestander, S.J. Xiong, Biomass properties in association with plant species and assortments II: a synthesis based on literature data for ash elements, Renew. Sust. Energy Rev. 16 (2012b) 3507e3522. [3] J. Fargione, J. Hill, D. Tilman, S. Polasky, P. Hawthorne, Land clearing and the biofuel carbon debt, Science 319 (2008) 1235e1238. [4] T. Searchinger, R. Heimlich, R.A. Houghton, et al., Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change, Science 319 (2008) 1238e1240. [5] J.J. Cheng, G.R. Timilsina, Status and barriers of advanced biofuel technologies: a review, Renew. Energy 36 (2011) 3541e3549. [6] R.D. Perlack, L.L. Wright, A. Turhollow, R. Graham, B. Stokes, D. Erbach, Biomass as a feedstock for a bioenergy and bioproducts industry: the technical feasibility of a billion-ton annual supply, in: Tech. Rep., ORNL/TM, Oak Ridge, TN, 2006. [7] FAOSTAT. http://faostat.fao.org/. (accessed 05.07.13). [8] J.L. Molina, M.A. El-Sharkawy, Increasing crop productivity in cassava by fertilizing production of planting material, Field Crop Res. 44 (1995) 151e157. € [9] W.B. Zhu, T.A. Lestander, H. Orberg, M.G. Wei, B. Hedman, J.W. Ren, G.H. Xie, S.J. Xiong, Cassava stems: A new resource to increase food and fuel production, GCB Bioenergy (2013), http://dx.doi.org/10.1111/gcbb.12112. http:// onlinelibrary.wiley.com/doi/10.1111/gcbb.12112/ (accessed 05.09.13). [10] R.H. Howeler, The Cassava Handbook e a Reference Manual Based on the Asian Regional Cassava Training Course, International Centre for Tropical Agriculture, Cali, Colombia, 2012. [11] R.H. Howeler, N. Lutaladio, G. Thomas, Save and Grow: Cassava, 2013. http:// www.fao.org/ag/save-and-grow/cassava/ (accessed 09.08.13). [12] C. Martin, B. Alriksson, A. Sjode, N.O. Nilvebrant, L.J. Jonsson, Dilute sulfuric acid pretreatment of agricultural and agro-Industrial residues for ethanol production, Appl. Biochem. Biotech. 137e140 (2007) 339e352. [13] M. Han, Y. Kim, Y. Kim, B. Chung, G.W. Choi, Bioethanol production from optimized pretreatment of cassava stem, Korean J. Chem. Eng. 28 (2011) 119e125. [14] E. Nuwamanya, L. Chiwona-Karltun, R.S. Kawuki, Y. Baguma, Bio-ethanol production from non-food parts of cassava (Manihot esculenta Crantz), Ambio 41 (2012) 262e270. [15] R.T. Brown, C.R. Brown, A review of cellulosic biofuel commercial-scale projects in the United States, Biofuels, Bioprod. Bioref 7 (2013) 235e245. [16] I.R.M. Benesi, M.T. Labuschagne, A.G.O. Dixon, N.M. Mahungu, Genotype environment interaction effects on native cassava starch quality and potential for starch use in the commercial sector, Afr. Crop Sci. J. 12 (2004) 205e216. [17] L.O. Pordesimoa, B.R. Hamesb, S. Sokhansanjc, W.C. Edensd, Variation in corn stover composition and energy content with crop maturity, Biomass Bioenergy 28 (2005) 366e374. [18] I.R.M. Benesi, M.T. Labuschagne, L. Herselman, N.M. Mahungu, J.K. Saka, The effect of genotype, location and season on cassava starch extraction,
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Euphytica 160 (2008) 59e74. [19] S.J. Xiong, Y.F. Zhang, Y. Zhuo, T.A. Lestander, P. Geladi, Variations in fuel characteristics of corn (Zea mays) stovers: general spatial patterns and relationships to soil properties, Renew. Energy 35 (2010) 1185e1191. €m, Ash [20] M. Díaz-Ramírez, C. Boman, F. Sebastian, J. Royo, S.J. Xiong, D. Bostro characterization and transformation behavior of the fixed-bed combustion of novel crops: poplar, brassica, and cassava fuels, Energy Fuel 26 (2012) 3218e3229. [21] S.V. Vassilev, D. Baxter, L.K. Andersen, C.G. Vassileva, T.J. Morgan, An overview of the organic and inorganic phase composition of biomass, Fuel 94 (2012) 1e33. [22] M.G. Wei, W.B. Zhu, G.H. Xie, T.A. Lestander, J.S. Wang, S.J. Xiong, Ash composition in cassava stems originating from different locations, varieties, and harvest times, Energy Fuels (2014), http://dx.doi.org/10.1021/ef5009693. [23] S. Yoshida, D.A. Forno, J.H. Cock, K.A. Gomez, Chapter 11 Determination of Sugar and Starch in Plant Tissue. Laboratory Manual for Physiological Studies os, Laguna, of Rice, third ed., The International Rice Research Institute, Los Ban Philippines, 1976, pp. 46e49. [24] T.T. Kozlowski, Carbohydrate sources and sinks in woody-plants, Bot. Rev. 58 (1992) 107e222. [25] T. Hattori, S. Murakami, M. Mukai, et al., Rapid analysis of transgenic rice
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straw using near-infrared spectroscopy, Plant Biotechnol. 29 (2012) 359e366. [26] B. Godin, S. Lamaudiere, R. Agneessens, et al., Chemical characteristics and biofuel potential of several vegetal biomasses grown under a wide range of environmental conditions, Ind. Crop Prod. 48 (2013) 1e12. [27] J. Park, E. Kanda, A. Fukushima, et al., Contents of various sources of glucose and fructose in rice straw, a potential feedstock for ethanol production in Japan, Biomass Bioenergy 35 (2011) 3733e3735. [28] A.K.M.S. Islam, D.G. Edwards, C.J. Asher, pH optima for crop growth results of a flowing solution culture experiment with six species, Plant Soil 54 (1980) 339e357. [29] E. Baafi, O. Safo-Kantanka, Agronomy and processing attributes of some cassava (Manihot esculenta Crantz) genotypes as affected by location and age at harvest in Ghana, Int. J. Agric. Res. 3 (2008) 211e218. [30] B. Gu, Q.Q. Yao, K.M. Li, S.B. Chen, Change in physicochemical traits of cassava roots and starches associated with genotypes and environmental factors, Starch-Starke 65 (2013) 253e263. [31] K. Sriroth, V. Santisopasri, C. Petchalanuwat, K. Kurotjanawong, K. Piyachomkwan, C.G. Oates, Cassava starch granule structure-function properties: influence of time and conditions at harvest on four cultivars of cassava starch, Carbohyd. Polym. 38 (1999) 161e170.
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Cassava stem wastes as potential feedstock for fuel ethanol production: A basic parameter study €rn A. Lestander a, Shaojun Xiong a, * Maogui Wei a, b, Wanbin Zhu c, Guanghui Xie b, Torbjo a
Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences, SE 901 83 Umeå, Sweden National Energy R&D Centre for Non-food Biomass, China Agricultural University, Beijing 100094, China c Centre of Biomass Engineering, China Agricultural University, Beijing 100094, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 13 September 2014 Received in revised form 18 May 2015 Accepted 26 May 2015 Available online 5 June 2015
The cassava stem is found to be one of few crop residues containing starch (up to 42% of dry mass) that may be converted to fuel ethanol. The current study was to evaluate the influence of parameters genotype, growth location and harvest time on cassava stem starch contents and yields as well as consequences in ethanol production (non-cellulosic process), based on 180 samples from a full factorial design experiment (3 varieties 3 locations 5 harvest times) in Guangxi, China. The potential utilization of stem starch and soluble sugar that varied 14e42% and 3e12.1% of dry mass, respectively, can correspond to an increase of 26% in ethanol production compared to that produced by roots only. The cassava stem starch content was significantly affected by all three studied parameters and location had the largest effect followed by variety and harvest time, while the stem starch yield was significantly affected by location only. The starch and soluble sugar content were significantly correlated with soil properties, e.g., soil pH and organic carbon, S and P contents. A general and positive correlation was also found between the stem and root starch, suggesting a promising potential of using stem starch without reducing root starch production. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Crop residue Biomass Fuel Soil property Non-structural sugar
1. Introduction Crop residues in the world alone amount to about 6.7 Pg, of which 60% could be available for bio-based production [1,2], corresponding to about 570 kg per world citizen. The use of these leftovers for the feedstock of fuel and bio-based products may make it possible to increase bioenergy and biorefinery production without largely expanding land use and threatening food security [3e5]. It is, therefore, important to study the possibilities of utilizing non-food biomass as feedstock for biofuel and bio-based products [6], in order to better meet global challenges: food security for a growing population and for the development of sustainable societies. Cassava (Manihot esculenta Crantz.) stem is currently wasted after harvesting of the starchy roots that are today an important food resource for as many as one billion people in tropical and subtropical areas in Africa, Asia, and Latin America but also as feed and biofuel feedstock [7]. Previous studies [8,9] indicate that
* Corresponding author. E-mail address: [email protected] (S. Xiong). http://dx.doi.org/10.1016/j.renene.2015.05.054 0960-1481/© 2015 Elsevier Ltd. All rights reserved.
cassava stems have a high content of starch (up to 30% of dry mass). Globally, the quantity of cassava stems can be estimated to be about 32e38 Tg dry mass based on a stem/root mass ratio of 42e50% [9,10] and the average cassava root production in 2009e2013 [7]. This suggests a considerable alternative resource to substitute cassava roots that today are used for biofuel production and other non-food products, thus, saving the roots as the food resource for a growing population up to 100 million people by 2030 [9]. However, most cassava stems are abandoned or burned in the wild. Only 10e20% of the stems are used for propagation, mushroom substrates or recycled to maintain soil fertility [11]. The lack of knowledge about cassava stem starch and its variation in content makes the cassava stem an unimportant resource so far. It was not until recently, that cassava stem was studied as a feedstock for bioethanol production based on its potential to produce a considerable amount of fermentable sugars [12e14]. Either sole cellulosic process hydrolyzing cellulose and hemicellulose [12,13], or using enzyme mixtures (cellulase, amyloglucosidase and amylase [14]; were proposed as approaches of bioethanol conversion from cassava stems. However, the high concentration of stem starch [9], which can be easily extracted and/or directly hydrolysed
M. Wei et al. / Renewable Energy 83 (2015) 970e978
into glucose using simple and low-cost process in comparison to that for lignocellulos [15], can be a very important factor in designing industrial processes for efficient and sustainable production of ethanol. The fundamental information on quantitative variations of cassava stem starch is lacking, as its considerable stem starch quantity is a rather “new” property. There is a great need to have such information to be able to provide possible recommendations for industrial processes e.g., pretreatments. This knowledge is also necessary to be able to avoid any possible misinterpretations of research results. Being an important chemical component of cassava stem, starch may vary with both biotic and abiotic parameters such as genotype, environment, harvest time and management, as it was found with other biomass characteristics [8,16e21]. Few studies, if any, have been made to understand the variability of stem starch and the parameters that may affect cassava stem starch quantity. Important questions remaining to be answered include: (1) Is there a large span of variability in stem starch? (2) What is the major parameter that may influence cassava stem starch? (3) Would use of stem starch influence root starch production? (4) How much can stem starch contribute to bioethanol production? Answering these questions will certainly improve the possibilities to design and optimize the process chain from the feedstock to ethanol. The current study was mainly to examine variations in stem starch, and evaluate the impacts of the parameters location, variety and harvest time on stem starch content and yield as well as consequences in bioethanol production. The influence of root starch on stem starch was also assessed, as an uncontrolled factor in the experiment, because of their physiological associations. As soluble sugar is closely related to the starch, it was also included as a response variable. The chosen parameters are of major interest, as they are known to influence embedded starch in biomass but
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also due to their ease of control in feedstock production management. 2. Materials and methods This current study is a continuity of the previous study [9] that was primarily based on three-year field surveys and analyses, where the variations of cassava stem starch between 28 geographic locations and 8 varieties were observed. 2.1. Plant materials and their origins One hundred and eighty (180) samples were originated in 2011 from a full factorial experiment composed of parameters location (3 sites), variety (3 ones) and harvest time (5 occasions) (in total, 45 treatments four replicates ¼ 180 plots). The three locations were Heng (22 480 2500 N, 109 050 4700 E), Longan (23 030 4000 N, 107 520 2700 E) and Wuming (23 080 5600 N, 108 090 1700 E), the largest cassava production counties in, Guangxi, China. Varieties South China 205 (SC205), South China 5 (SC5) and Xinxuan 048 (XX048) were chosen as they are the most favourable ones by farmers mostly because of good root yields. The five harvest times was set on 230, 245, 260, 275 and 290 days after planting (DAP), covering the most possible harvest periods in practice in the region. The data about the local weather can be found in Fig. 1. More information about the field experiment setting and management was described by Wei et al. [22]. 2.2. Sampling of stems and soil Eight plant stems were harvested and merged into one sample for each treatment. Nine root tubers from those eight plants were randomly chosen and mixed as one root sample. The fresh root
Fig. 1. Monthly air temperature ( C) and precipitation (mm) in Nanning city, covering three study sites. The history data were obtained at: http://www.travelchinaguide.com/ climate/nanning.htm, accessed on 2 December 2013. The data for the growth season 2011e2012 are average values for Nanning City, covering the three study sites, from Nanning Meteorology Bureau, Guangxi.
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yield from those eight plants and the above ground stem yield of two randomly chosen plants (representing 1.6 m2) were measured for each plot on 230, 260 and 290 DAP. All samples were immediately put into plastic bags after harvesting and transported to a laboratory at Guangxi University where they were cleaned and chopped, and dried at 75 C until constant weight. The samples were further transported and analysed in the laboratory at China Agricultural University (CAU). Before the analyses, the biomass moisture content was re-determined at 105 C. Further details of the sampling of plant and soil samples were presented in Wei et al. [22].
- CS: content of starch; CSS: content of soluble sugar; BY: biomass yield (Mg e million grams). - 1.11 (in E2-2) is the ratio of converting starch into sugar, which is based on the hydrolysis reaction of starch: (C6H10O5)n þ nH2O ¼ nC6H10O6; i.e., every unit of starch can produce 1.11 units of sugar (in weight). - for E2-3: 0.51 is the coefficient of sugar converting into ethanol; 0.85 is the process efficiency; 1000/0.79 is the density of ethanol (The Institution of Japan Energy, 2006).
2.5. Data analysis 2.3. Starch, soluble sugar and soil analysis All of the stem and root samples were milled into 0.5 mm particle size before analysing. Starch and soluble sugar content were then determined using the method recommended by The International Rice Research Institute [23], with which ethanol and perchloric acid were used to extract soluble sugar and starch respectively while anthrone was used as a testing reagent. The soil samples were analyzed at CAU. The results as well as methods of the soil analysis were summarized in our previous study [22] and the mean values for each location could be found in Table 1. 2.4. Estimation of starch yield and ethanol production potential The starch and sugar in cassava stem may potentially contribute to industrial productions of starch and ethanol that use the root only today. Here the first generation ethanol is concerned, i.e., soluble sugar and starch are directly fermented to produce ethanol. To review these potentials, the yields of non-structural sugar (NSS), whole plant starch (WPS) and the first generation ethanol (FGE) were estimated/calculated using the data from this current experiment. The calculation was based on equations as follows:
WPS Mg ha1 ¼ CSstem ð%Þ BYstem Mg ha1 þ CSroot ð%Þ BYroot Mg ha1 (E2-1) NSS Mg ha1 ¼ ½CSS ð%Þ þ CS ð%Þ 1:11 BY Mg ha1 (E2-2) FGE L ha1 ¼ NSS Mg ha1 0:51 0:85 ð1000=0:79Þ (E2-3) where Table 1 Physical and chemical propertiesa of topsoil (0e20 cm) samples taken from study sites by the end of season.
pH Organic carbon (OC) (g kg1) Nitrogen (N) (g kg1) Sulphur (S-al) (mg kg1) Chlorine (Cl) (mg kg1) Phosphorus (P) (g kg1) Potassium (K) (g kg1) Calcium (Ca) (g kg1) Magnesium (Mg) (g kg1) Silicon (Si-al) (mg kg1)
Heng
Longan
Wuming
5.6 8.6 0.7 11.5 41.2 0.8 1.4 0.7 1.2 128.5
3.9 18.9 2.3 21.9 21.2 0.6 1.7 0.3 2.0 96.9
5.5 26.9 2.8 29.9 30.2 1.4 4.9 1.5 2.4 234.8
a S-al & Si-al ¼ available S and Si, respectively. The data were all from our previous study (Wei et al., 2014).
All data presented are based on oven dry mass (DM). Analysis of variances was performed using statistical software IBM SPSS 20.0 for Windows (GLM Univariate Analysis; IBM SPSS, 2012). Responses (i.e., starch and soluble sugars) were treated as dependent variables, while parameters location, variety and harvest time were independent variables. Both tests of between-subjects effects and pairwise comparison were conducted. The root starch and sugar were treated as uncontrolled factors that would have influences on the stem starch and soluble sugar, due to their interactions [24]. The IBM SPSS was also used to examine correlations between stem and root starch and between stem and root sugar, in which linear regression (stepwise) analyses were conducted and root data (Table 1) were treated as independent variables. Prior to the correlation analysis, the normality of data was tested and logarithm transformation was made according to kurtosis and skewness analyses. The same software and multiple linear regression was used to analyze the correlations between stem starch and soil composition, to elaborate the impact of location or environmental conditions, but no data transformation was made according to the test of data normality. 3. Results and discussion 3.1. Contents of starch and soluble sugar in cassava stems 3.1.1. Variations Fig. 2a shows the averages of starch and soluble sugar content for each of 45 treatments. The stem starch content ranged from 14 to 42% of DM, being 25% on average and >20% in 36 out of all 45 treatments, which was comparable to those reported in a previous study [9]. It is very rare to find such a high starch content in crop residues, except for those reported for transgenic rice dedicated for energy (up to 24.4% in the entire above-ground straw, except panicles and seeds) [25] and whole plants including fruit grains of ‘fibre corn’ (25e35%) [26]. The highest starch content in this experiment was found in stem of SC205 in Heng, and the lowest in XX048 in Wuming. In general (Fig. 2a), stems starch content tended to peak at 260e275 DAP within the studied harvest time (230e290 DAP). The content of soluble sugar in cassava stems was also considerably high, 3e12.1% in most cases, and comparable to that of varieties of rice dedicated for bioenergy (4.4e18.2%) [27]. Sugar content in the stems from Longan site and variety SC205 were significant higher (p < 0.01) than other sites and varieties (Fig. 2a). Peak time of sugar content in cassava stem was found at the 245 DAP, i.e., 15e30 days before the peaking of starch content. 3.1.2. Effects of parameters location, variety and harvest time All of three studied parameters had significant effects on the starch and soluble sugar contents of the stem (p < 0.001 for all, Table 2). Location (L) had the largest effect on stem starch and soluble
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Fig. 2. Variations of cassava stem starch content (a), biomass yield (b) and starch yield (c) with location, variety and harvest time Mg e million grams. The harvest time is indicated by days after planting (DAP). The content and yield of soluble sugar are also shown in (a) and (c) respectively. Each bar refers to mean ± standard error for each treatment.
sugar according to the relative contribution to the mean square of variance (RC > 70.66%). Variety (V) was second to location but more influential than harvest time (T) on starch content, showing an RC of 21.30% in comparison with a <1% for harvest time. The harvest time had a bit larger role on soluble sugar; its RC was about 5%. All interactions between any two of the three factors, i.e., twoway interaction, had significant effects on stem starch content (Table 2). L V had stronger effects on starch content than other interactions while soluble sugar content was more affected by L T. The three-way interaction (L V T) had a significant effect on both response variables, with an especially strong effect on soluble
sugar. Based on the mean square of variance values and RC, however, the interactive effects on stem response variables constituted only a small proportion (about 7% for starch and 3% for sugar) of the total sum of mean square of variance (Table 2). The fact that more than 70% variations in contents of stem starch and soluble sugar were due to the effects of location (Table 2) was found to be correlated with specific soil conditions where the studied cassava was growing. Using linear multiple regression (stepwise), three highly significant correlations were established between stem starch and sugar contents, and soil chemistry variables (Table 3). The models explained 64e92% of the variances, and
Table 2 Univariate analysis of variance on stem starch and sugar contents and yields from the factorial experiment in 2011. df ¼ degrees of freedom; MS ¼ mean square of variance; p ¼ significance level of ANOVA. Source
Location (L) Variety (V) Harvest time (T) LV LT VT LCT Error Total a
df
2 2 4 4 8 8 16 135 180
Starch content
Soluble sugar content
df
MS
p
RCa
MS
p
RC
2164.12 652.46 23.93 170.31 28.77 12.34 7.00 3.97
<0.001 <0.001 <0.001 <0.001 <0.001 0.003 0.042
70.66 21.30 0.78 5.56 0.94 0.40 0.23 0.13
548.30 10.97 32.87 0.72 13.70 0.71 2.40 0.51
<0.001 <0.001 <0.001 0.234 <0.001 0.213 <0.001
89.86 1.80 5.39 0.12 2.25 0.12 0.39 0.08
2 2 2 4 4 4 8 81 108
Starch yield
Soluble sugar yield
MS
p
RC
MS
p
RC
24.34 0.12 0.01 0.26 0.10 0.30 0.11 0.08
<0.001 0.237 0.902 0.017 0.320 0.008 0.233
96.19 0.46 0.03 1.01 0.38 1.18 0.43 0.32
0.269 0.004 0.079 0.006 0.021 0.005 0.002 0.003
<0.001 0.232 <0.001 0.089 <0.001 0.136 0.612
68.89 1.12 20.29 1.57 5.32 1.36 0.60 0.75
RC: relative contribution of the effects (%), calculated as the ratio of the mean squares of each factor source to the total sum of mean-squares.
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Table 3 Correlations between soil and content of cassava stem soluble sugar and starch (Linear Regression, Stepwise). The soil data were summarized in Table 1. Stem
Soil variable
R2 Adj
p
n
Sugar (%) Starch (%) Starch þ sugar (%)
21.198e2.964 pH e 0.047 S (mg kg1) 34.39e0.776 OC (g kg1) þ 51.495 P (%) 69.325e0.869 OC (g kg1) e 5.961 pH þ 125.537 P (%)e0.243 S (mg kg1)
0.919 0.638 0.850
<0.001 <0.001 <0.001
36 36 36
soil pH, organic carbon, S and P were included as a key predictor in the models (Table 3). These phenomena are consistent with reported correlations between fuel and soil properties [9]. However, the exact mechanism behind these findings has to be studied in future research, which may be achieved by dedicated laboratory and field experiments. 3.2. Yields of stem starch and soluble sugar Based on stem starch and soluble sugar content (Fig. 2a) and biomass production (Fig. 2b), yields of stem starch and soluble sugar were calculated (Fig. 2c). The stem starch yields from the 27 treatments ranged 0.2e2.06 Mg ha1 (Fig. 2c), with an average of 1.2 Mg ha1. Location had the most significant effect on the stem starch yield (p < 0.001; Table 2) that was highest in Heng (average 1.87 Mg ha1), followed by Wuming (1.45 Mg ha1) and Longan (0.28 Mg ha1); neither variety nor time showed a significant influence. Although the interactions L V and V T affected stem starch significantly (p < 0.017 and 0.008, respectively), their RC was together about 2.2% only. The yields of stem soluble sugar was between 0.05 and 0.51 Mg ha1 (Fig. 2c) and averaging 0.19 Mg ha1. A similar trend to stem starch, location had most influential effect (p < 0.001, Table 2) and the average value for Heng (0.27 Mg ha1) was higher than for Wuming (0.21) and Longan (0.10). Harvest time showed also a significant effect and had as much as 20% of RC (Table 4). Across locations and varieties, stem soluble sugar yield was on average 0.14, 0.22 and 0.22 Mg ha1 on 230, 260 and 290 DAP, respectively. The patterns of variations in the starch and sugar yields were more similar to the biomass (Fig. 2b) than to the corresponding contents (Fig. 2a), in which location is the dominant factor. Heng is situated at lower latitude and receives more solar radiation than other two sites and Wuming is characterized by a higher soil fertility (Table 1), while in Longan soil pH is rather low (4.0, Table 1) that is not only below the range of 5.5e6.5 for a proper growth but also at a threshold of hydrogen ion injury for cassava plant [28]. A lower biomass yield in Longan was therefore expected. 3.3. Root starch and soluble sugar 3.3.1. Variations of root starch and soluble sugar Fig. 3 shows the results of root starch and soluble sugar content
(Fig. 3a), root biomass (3b) and yield of root starch and sugar (3c). The contents of root starch and soluble sugar were in a range of 64e87% and 1.7e9.8%, respectively, while the corresponding yields were 2.00e12.18 and 0.05e1.08 Mg ha1. All main (individual) effects of location, variety and time on root starch and soluble sugar contents were significant (p < 0.001 for all but p < 0.038 for starch yield, Table 4). Location was more influential than variety and time. These results were similar to the results for stem starch and soluble sugar, and as expected in agreement to previous findings for root starch [18,29,30]. Both root starch content and yield were higher in Heng (on average 81.6%, 8.9 Mg ha1) than in Longan (76.0%, 4.1 Mg ha1) and Wuming (71.8%, 7.5 Mg ha1) (Fig. 3a). The yields of root starch and sugar was largely driven by the root biomass (Fig. 3b). Variety SC205 had a root starch content of 78.2% which was significantly higher than SC5 (75.0%) and XX048 (76.1%), however a higher yield of root starch was found for XX048 (8.3 Mg ha1) than SC205 and SC5 (7.2 respect 5.2 Mg ha1). In contrast to starch content, harvest time is more important than variety for soluble sugar, which is also in consistent to the results of stem sugar (Table 4). Additionally, the impacts of interactions between the factors seem to be rather low. 3.3.2. Correlations between root and stem starch and soluble sugar Correlations in carbohydrate concentrations between the stem and root are of interest in plant physiology [24]. The current study, however, focused on an evaluation of root starch and soluble sugar and their correlations with their stem starch and soluble sugar; the plant physiological mechanism was not investigated. An exponential correlation was found between stem and root starch contents derived from all 45 treatments (Fig. 4): y ¼ 20230.19 10x(0.0007x0.0912), where y ¼ stem starch content and x ¼ root starch content (R2 adj ¼ 0.573, n ¼ 45, p < 0.001, F-test). The correlation was analyzed at different grouping levels as well. Similar significant and positive correlations for starch content were also found for all three varieties (across location and time), and five harvest occasion (across cultivar and location), but for Heng location only (Table 5). Interestingly, the correlations in content of stem soluble sugar was negative on 260 and 275 DAP, which probably signalizes the move of sugar from stem to root. When starch and sugar yield were concerned, significant correlations were found in most cases and all were positive (Table 5),
Table 4 Univariate analysis of variance (ANOVA) on root starch and soluble sugar from the factorial experiment in 2011. df ¼ degrees of freedom; MS ¼ mean square of variance; p ¼ significance level of ANOVA. Source
Location (L) Variety (V) Harvest time (T) LV LT VT LVT Error Total a
df
2 2 4 4 8 8 16 134 180
Starch content
Soluble sugar content
df
MS
p
RCa
MS
p
RC
1438.70 163.59 69.46 103.59 93.32 13.83 21,59 9.96
<0.001 <0.001 <0.001 <0.001 <0.001 0.207 0.009
75.17 8.55 3.63 5.41 4.88 0.72 1.13
53.89 28.69 53.01 10.80 15.31 2.59 2.56 0.69
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
32.17 17.13 31.64 6.44 9.14 1.55 1.53
2 2 2 4 4 4 8 81 108
Starch yield
Soluble sugar yield
MS
p
RC
MS
p
RC
223.73 97.76 5.81 69.48 0.48 1.79 1.19 1.71
<0.001 <0.001 0.038 <0.001 0.888 0.388 0.694
55.66 24.32 1.45 17.29 0.12 0.44 0.30 0.42
0.910 0.625 0.518 0.187 0.296 0.036 0.032 0.010
<0.001 <0.001 <0.001 <0.001 <0.001 0.010 0.004
34.80 23.92 19.80 7.17 11.31 1.39 1.22
RC: relative contribution of the effects (%), calculated as the ratio of the mean squares of each factor source to the total sum of mean-squares.
M. Wei et al. / Renewable Energy 83 (2015) 970e978
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Fig. 3. Variations of cassava root starch content (a), biomass yield (b) and starch yield (c) with location, variety and harvest time. The harvest time is indicated by days after planting (DAP). The content and yield of soluble sugar are also shown in (a) and (c) respectively. Each bar refers to mean ± standard error for each treatment.
irrespective of grouping. We had expected a negative correlation between stem and root starch, due to plant physiological transform and move of hydrocarbon from stems to roots and possible internal competition for non-structural carbohydrate between them [24,31]. However, the results of the current study were mostly in contrast to this expectation. The starch appeared to be accumulating almost simultaneously and co-ordinately in both stem and root (Fig. 4). The process and mechanism, however, need to be investigated in future studies.
3.4. Potential contribution of stems to starch and ethanol production Table 6 summarizes calculated potential yields of whole plant starch (WPS), non-structural sugar (NSS) and first generation ethanol (FGE) based on the equations described in section 2.4. While WSP was 2.20e13.94 Mg ha1; stem proportion was on average 17.9%, or an increase of 24.5% on top of that from roots only. Due to the contribution of stem NSS that would be eventually converted from starch and soluble sugar, an average proportion of 20.61% or an increase of 26.0% in FGE (i.e., about 1066 L ha1) could be reached on top of that produced from the root only. The potential contribution of stem to starch and ethanol production are dependent on the parameter location, variety and harvest time, in association with the variations of stem and root starch and soluble sugar (Sections 3.1e3.3). On average, cassava stems from Heng and Wuming had much larger proportions than Longan (24.8% and 21.4% vs. 7.5% for total starch; 26.8% and 23.1% vs. 9.7% for FGE; Table 6), and in these two locations the ratio of SC5 was much higher than other two varieties (total starch, 35.6% vs. 17.8% for SC205 and 15.9% for XX048; FGE, 38.6% vs. 17.8% for SC205 and 17.1% for XX048). 3.5. General discussion
Fig. 4. Correlations between stem and root starch content. SC205, SC5 and XX048 were marked as blue cub, green triangle and purple cross, respectively. The correlation was established as Y ¼ 20230.19 10x(0.0007x0.0912), where y ¼ stem starch and x ¼ root starch. R2 adj ¼ 0.573, n ¼ 45, p < 0.001, F-test. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
The results demonstrate that the cassava stem starch is a considerable resource with a potential to increase starch production that is today based on root only, and that the stem starch quantity varies to a large extent with location, a small extent with variety and to a minor extent with harvest time. This will influence the yield of subsequent ethanol production or any other products
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Table 5 Correlations (Pearson coefficient) in starch and soluble sugar content between stem and root. The asterisks refer to statistic significant levels (*p < 0.05,**p < 0.01, two tailed).
Correlations based on contents Location
Variety
Harvest time (DAP)
Correlations based on yields Location
Variety
Harvest time (DAP)
n
Starch (stem ~ root)
Soluble sugar (stem ~ root)
Heng Longan Wuming SC205 SC5 XX048 230 245 260 275 290
60 60 60 60 60 60 36 36 36 36 36
0.544** 0.096 0.228 0.606** 0.665** 0.671** 0.665** 0.831** 0.816** 0.462** 0.462**
0.158 0.297* 0.416** 0.212 0.140 0.208 0.055 0.134 0.402* 0.509** 0.066
Heng Longan Wuming SC205 SC5 XX048 230 260 290
36 36 36 36 36 36 36 36 36
0.026 0.541** 0.059 0.805** 0.162 0.690** 0.450** 0.580** 0.391*
0.143 0.385* 0.450** 0.621** 0.371* 0.790** 0.256 0.497** 0.522**
based on cassava starch. The study showed also that there are significant correlations between stem starch content and soil chemistry (as environment constitutes of location) and between the stem and root starch. These findings not only support our previous proposals that cassava stems as supplementary or alternative feedstock may be integrated into existing starch (food) and ethanol (fuel) production [9], but also provide fundamental facts that may be used for further research and technology development for utilizing cassava stems for variable bio-products. The
knowledge about role of the studied parameters in influencing the stem starch will be definitely helpful in practice when a feedstock production and industrial infrastructure are concerned. Conventionally cassava root is sole product that people cultivate for, but our results indicate that the stem production needs to be included in existing cultivation management. Processing cassava stems for starch and ethanol production is new [9]. Feedstock quality, i.e., content of starch (and soluble sugars), is urgent to be understood and has to be paid more
Table 6 Potential production of non-structural sugar (NSS), whole plant starch (WPS) and first generation ethanol (FGE). Data are shown as Mean ± standard error (SE). Time ¼ harvest time, DAP refers to days after planting. Location
Variety
Time (DAP)
NSS
WPS
Stem (Mg ha1) Heng
SC205
SC5
XX048
Longan
SC205
SC5
XX048
Wuming
SC205
SC5
XX048
Mean Maximum Minimum
230 260 290 230 260 290 230 260 290 230 260 290 230 260 290 230 260 290 230 260 290 230 260 290 230 260 290
2.53 2.55 2.49 2.51 2.05 2.10 1.99 2.59 2.25 0.30 0.39 0.41 0.58 0.47 0.39 0.37 0.36 0.39 1.22 2.03 1.77 2.13 1.70 2.37 1.40 1.86 1.86 1.52 2.55 0.30
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.29 0.29 0.25 0.28 0.27 0.25 0.30 0.21 0.22 0.20 0.18 0.19 0.19 0.16 0.13 0.13 0.16 0.15 0.19 0.31 0.26 0.36 0.43 0.51 0.21 0.29 0.23
FGE
Root (Mg ha1)
Yield (Mg ha1)
Stem proportion (%)
Yield (L ha1)
12.14 ± 0.76 12.07 ± 0.56 12.84 ± 0.56 4.79 ± 0.41 6.01 ± 0.52 5.39 ± 0.47 12.14 ± 0.60 13.52 ± 0.81 14.60 ± 0.43 2.27 ± 0.51 2.90 ± 0.52 3.23 ± 0.58 5.62 ± 0.42 6.15 ± 0.53 6.06 ± 0.60 5.16 ± 0.38 4.79 ± 0.60 5.94 ± 0.51 8.59 ± 0.70 10.48 ± 0.65 9.85 ± 0.71 6.42 ± 0.72 5.48 ± 0.72 6.34 ± 0.58 8.78 ± 0.69 10.28 ± 0.56 11.71 ± 0.65 7.91 14.60 2.27
12.69 ± 0.77 12.59 ± 0.57 12.87 ± 0.51 6.24 ± 0.44 6.79 ± 0.45 6.15 ± 0.50 12.20 ± 0.62 13.67 ± 0.73 13.94 ± 0.42 2.20 ± 0.51 2.75 ± 0.50 3.06 ± 0.56 5.35 ± 0.40 5.61 ± 0.51 5.55 ± 0.58 4.79 ± 0.37 4.38 ± 0.57 5.40 ± 0.48 8.61 ± 0.66 10.60 ± 0.54 9.99 ± 0.70 7.42 ± 0.71 5.94 ± 0.75 7.34 ± 0.65 8.79 ± 0.65 9.91 ± 0.48 11.47 ± 0.58 8.01 13.94 2.20
19.60 ± 0.68 18.94 ± 0.61 18.56 ± 0.81 49.11 ± 1.54 32.06 ± 1.52 36.51 ± 0.90 15.70 ± 0.93 18.41 ± 1.08 14.52 ± 0.62 9.91 ± 0.68 11.21 ± 0.99 10.29 ± 0.65 8.62 ± 0.73 5.91 ± 0.62 5.38 ± 0.45 5.88 ± 0.48 5.62 ± 0.31 5.03 ± 0.38 14.36 ± 1.08 18.78 ± 1.19 16.50 ± 0.76 33.63 ± 1.77 28.44 ± 1.36 34.05 ± 1.89 15.48 ± 0.73 17.27 ± 1.04 14.11 ± 0.75 17.92 49.11 5.03
8047 8021 8411 4002 4424 4110 7751 8835 9246 1408 1809 1994 3405 3634 3540 3029 2828 3474 5381 6867 6376 4690 3940 4776 5587 6663 7445 5174 9246 1408
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
19 14 13 11 11 12 15 18 10 13 13 14 10 13 14 9 14 12 16 14 18 18 19 17 17 12 15
Stem proportion (%) 21.13 ± 0.75 21.16 ± 0.69 19.53 ± 0.81 52.94 ± 1.44 35.41 ± 1.58 39.30 ± 0.86 16.47 ± 0.89 19.86 ± 1.11 15.43 ± 0.63 13.13 ± 0.74 14.69 ± 1.18 13.21 ± 0.78 10.43 ± 0.80 7.83 ± 0.69 6.53 ± 0.52 7.08 ± 0.49 7.61 ± 0.39 6.51 ± 0.40 14.97 ± 1.10 20.11 ± 1.23 18.23 ± 0.79 35.29 ± 1.82 30.96 ± 1.32 37.88 ± 1.95 16.24 ± 0.74 18.44 ± 1.05 16.05 ± 0.79 26.61 52.94 6.51
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attention as in this study than the yield, because the knowledge about the stem starch content is crucial for designing infrastructure and processes for an efficient use of stems for starch and ethanol production. The fact that stem starch content was strongly and significantly affected by all three studied parameters (Table 2) whereas the stem starch yield was significantly affected by location only emphasizes a more complexity in managing starch content than doing yield. The effects of variety and harvest time have to be counted, though they are less important, when the content is concerned but not for the yield. The location was key and dominant parameter in determining both content and yield of stem starch (Table 2), as indicated by the fact that the highest content and yield was from Heng, while Wuming had the lowest content and Longan the lowest yield (Fig. 2), which was largely attributed to soil properties (Table 3 and Section 3.2). However, location is a complex of many environmental conditions; there is no doubt that climate conditions such as temperature, precipitation and sunlight have influences on the starch, which should be included in future studies at an expanding geographic scale. Utilization of cassava stems has to be coordinated with the root production, apparently because the stem is the byproduct. It is important to understand the relations between stem starch and root starch, not only for the root is the primary sink of photosynthate [11], but also for management practice: e.g., when it is optimized time for harvesting both stem and root. In the current study several similarities were found between stem and root starch: (1) the location is much more influential than variety and harvest time; (2) starch content is less variable than yield across the treatments (Figs. 2 and 3); and (3) may be of the most importance, the stem and root starch is positively correlated (Fig. 4, Table 5). With these findings, the question “Would use of stem starch influence root starch production” is well answered. Also, the normal root harvest regime seems to suit well stem harvesting. Since harvest time had no large influence on the starch content of stem and root, a rather flexible and less restrict period for harvest and feedstock management can be suggested. The difference that starch content of stem is more diverse than that of root (the highest one was 3 times of the lowest in stems, but the corresponding value for root was 1.4 times), however, indicates some small modifications and/or optimizations in the industrial processes existing for the root may need to be considered to suit the stem processing. The industry has to be able to handle and process the feedstock with lower but more variable starch contents than those in roots. Remarkably, stem starch is found by this study to have an average potential to increase the production of the first generation ethanol by up to about 26% on top of that based on root feedstock only. This estimation was based on about 25% of stem being starch and 5% soluble sugars (in averages) and did not count for those fermentable sugars that could be converted from cellulose and hemicellulose [12,13]. Using a mixture of enzymes cellulase, amyloglucosidase and amylase, Nuwamanya et al. [14] obtained as high as 60% of conversion rate from stem to reducing sugars, however information of exact stem composition was lacking, especially starch content. A large potential to use all additional sugars in cassava stem for food and fuel is therefore expected in future but this has to be further studied based on a well-understanding about the feedstock composition or properties, keeping in mind that the annual cassava stem production is about 30 Tg and that 500e1000 million people in the world are dependent on cassava starch production. 4. Conclusion The results from the factorial design experiment show: (1) a large span of variability in cassava stem starch between the
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treatments, which was even larger than those for roots; (2) that cassava stem starch content was significantly affected by the three studied parameters where location had the largest effect followed by variety and harvest time, while the stem starch yield was significantly affected by location only; (3) in general positive correlations between the stem and root starch suggest that a collaborated management of stem and root harvest is possible and that the stem and root achieve high starch contents simultaneously; (4) on average, that stem starch had a theoretical potential to increase 24.5% of starch or 26% ethanol production based on the root only today. Acknowledgements Guangcan Tao, Ronghui Qin and Jishi Wang at China agricultural of University (CAU), and Xinglu Luo and Xiran Cheng at Guangxi University (GXU) are gratefully acknowledged for their helps with the field and laboratory work. Our thanks also go to Professor Xu Cheng at CAU for valuable comments during the course of the study. This research was funded by the EU-China Energy and Environment Program (EEP-PMU/CN/126077/RE006), the Swedish Energy Agency (32805-1), the Chinese Ministry of Agriculture 948Project (“948”-2011-S7), the Royal Swedish Academy of Engineering Science, the China Academy of Engineering, and the Swedish Governmental Strategic Project Bio4Energy. References [1] G.C. Tao, T.A. Lestander, P. Geladi, S.J. Xiong, Biomass properties in association with plant species and assortments I: a synthesis based on literature data of energy properties, Renew. Sust. Energy Rev. 16 (2012a) 3481e3506. [2] G.C. Tao, P. Geladi, T.A. Lestander, S.J. Xiong, Biomass properties in association with plant species and assortments II: a synthesis based on literature data for ash elements, Renew. Sust. Energy Rev. 16 (2012b) 3507e3522. [3] J. Fargione, J. Hill, D. Tilman, S. Polasky, P. Hawthorne, Land clearing and the biofuel carbon debt, Science 319 (2008) 1235e1238. [4] T. Searchinger, R. Heimlich, R.A. Houghton, et al., Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change, Science 319 (2008) 1238e1240. [5] J.J. Cheng, G.R. Timilsina, Status and barriers of advanced biofuel technologies: a review, Renew. Energy 36 (2011) 3541e3549. [6] R.D. Perlack, L.L. Wright, A. Turhollow, R. Graham, B. Stokes, D. Erbach, Biomass as a feedstock for a bioenergy and bioproducts industry: the technical feasibility of a billion-ton annual supply, in: Tech. Rep., ORNL/TM, Oak Ridge, TN, 2006. [7] FAOSTAT. http://faostat.fao.org/. (accessed 05.07.13). [8] J.L. Molina, M.A. El-Sharkawy, Increasing crop productivity in cassava by fertilizing production of planting material, Field Crop Res. 44 (1995) 151e157. € [9] W.B. Zhu, T.A. Lestander, H. Orberg, M.G. Wei, B. Hedman, J.W. Ren, G.H. Xie, S.J. Xiong, Cassava stems: A new resource to increase food and fuel production, GCB Bioenergy (2013), http://dx.doi.org/10.1111/gcbb.12112. http:// onlinelibrary.wiley.com/doi/10.1111/gcbb.12112/ (accessed 05.09.13). [10] R.H. Howeler, The Cassava Handbook e a Reference Manual Based on the Asian Regional Cassava Training Course, International Centre for Tropical Agriculture, Cali, Colombia, 2012. [11] R.H. Howeler, N. Lutaladio, G. Thomas, Save and Grow: Cassava, 2013. http:// www.fao.org/ag/save-and-grow/cassava/ (accessed 09.08.13). [12] C. Martin, B. Alriksson, A. Sjode, N.O. Nilvebrant, L.J. Jonsson, Dilute sulfuric acid pretreatment of agricultural and agro-Industrial residues for ethanol production, Appl. Biochem. Biotech. 137e140 (2007) 339e352. [13] M. Han, Y. Kim, Y. Kim, B. Chung, G.W. Choi, Bioethanol production from optimized pretreatment of cassava stem, Korean J. Chem. Eng. 28 (2011) 119e125. [14] E. Nuwamanya, L. Chiwona-Karltun, R.S. Kawuki, Y. Baguma, Bio-ethanol production from non-food parts of cassava (Manihot esculenta Crantz), Ambio 41 (2012) 262e270. [15] R.T. Brown, C.R. Brown, A review of cellulosic biofuel commercial-scale projects in the United States, Biofuels, Bioprod. Bioref 7 (2013) 235e245. [16] I.R.M. Benesi, M.T. Labuschagne, A.G.O. Dixon, N.M. Mahungu, Genotype environment interaction effects on native cassava starch quality and potential for starch use in the commercial sector, Afr. Crop Sci. J. 12 (2004) 205e216. [17] L.O. Pordesimoa, B.R. Hamesb, S. Sokhansanjc, W.C. Edensd, Variation in corn stover composition and energy content with crop maturity, Biomass Bioenergy 28 (2005) 366e374. [18] I.R.M. Benesi, M.T. Labuschagne, L. Herselman, N.M. Mahungu, J.K. Saka, The effect of genotype, location and season on cassava starch extraction,
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