Vp1.3.13 barro proceeding

VARIABILITY IN THE PROPERTIES OF TRITICALE AND RYE BIOMASS DUE TO THE DIFFERENT

VARIETIES AND GROWING CONDITIONS

Ruth Barro*, Pilar Ciria, Emiliano Maletta, Miguel Fernández, Javier Pérez, Jaime Losada, and Juan E. Carrasco

Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CEDER -CIEMAT)

Autovía de Navarra A15, salida 56, 42290 Lubia (Soria), Spain

*Corresponding author: [email protected], phone: +34 975281013, fax: +34 975281051

ABSTRACT: There is a large interest in Spain surrounding the maximum variability that can be obtained in the main

physico-chemical properties of biomass, when it is produced or used for energy purposes. Multiple sources could

cause that expected variability. Winter cereals might be considered as a potential biomass source for energy purposes,

particularly triticale and rye. On the one hand, a statistical study was carried out, and differences between varieties

cultivated in the same location and cereal development states were evaluated. On the other hand, different varieties of

cereals were cultivated in 11 different locations around the north and central part of the country under multiple

conditions. A Cochran´s test was run to check up on variances (variance of same location vs. variance of different

locations) and compare the variability originated by the genetics of cereals, and that caused by the different growing

conditions. Some parameters as carbon or heating values seem to be dependent on the variety cultivated, while the

variability associated to some others like ash, nitrogen, sulphur, and chlorine contents increases significantly by being

directly influenced by some other factors like the different growing and farming conditions.

Keywords: biomass, composition, characterization, grain, sampling, quality

1 INTRODUCTION

There is a large interest in Spain among biofuel

producers, professionals, and final consumers,

surrounding the maximum variability that can be

obtained in the main physico-chemical properties of

biomass, when it is produced or used for energy

purposes. A full discussion brought up not only about the

source of variability, but also the range of values that

could be found from a real point of view. In such a long

process since biomass is grown until it is converted in a

solid biofuel and finally transformed into energy, many

steps are involved, and thus multiple sources could cause

that expected variability. For example, main sources

involving the first step of the process are e.g. the genetics

of the plant (species, variety, clon...), the growing

conditions (type of soil, fertilizers...) or the collecting

step, transportation and storage, which could easily

pollute biomass by adding particles from soil.

With regard to the utilization of biomass as an energy

source, the investigation of characteristics of biomass

fuels is beneficial for biomass fuels to find suitable

energy conversion technologies and for various energy

conversion processes to utilize favorable biomass

feedstock [1]. Researches in several countries have

carried out extensive studies to determine the quality

properties of their own available biomass resources [1-6].

In addition, several comprehensive reviews have been

published regarding physical characterization and

chemical composition of different biomass fuels [7-11].

Spain is a country with a marked tradition and

expertise on cereal crops, which are cultivated in dry land

for alimentary purposes, but suffering as a consequence

problems derived from a surplus of production. 81 % of

the total existing growing surface is non-irrigated land,

and around 37 % is dedicated to cereal grain farming.

There is a conspicuous lack of knowledge with regard to

the varibiality of physico-chemical properties of cereal

fuels in Spain, which is very important for their use as

energy sources. Winter cereals might be considered as a

potential biomass source for energy purposes, particularly

triticale (Triticosecale) and rye (Secale cereale), due to

their rusticity, tolerating adverse edafoclimatic conditions

(dryness, freezing, different soils, etc.). In addition, these

two species, triticale and rye, were previously found to

have better yield and quality demands than other grown

cereals like wheat or oat for energy use [12,13].

This paper deals with the task of obtaining a

variability range that can be found in Spain for the main

properties of triticale and rye biomass, but it only

pretends to be a first approach to this problem.

Preliminary results are presented in the frame of a much

more ambitious comprehensive study that it is performing

in Spain, supported by the national project for energy

crops development: “PSE – On crops” Project, and where

other additional variability sources such as the collection

process (which is a potential source of pollution with

soil) are going to be added as this project gets developed.

The aim of this study is to estimate the variability

ranges in the properties of triticale and rye biomass and

how it is affected by the different varieties and growing

conditions.

2 MATERIALS AND METHODS

2.1 Biomass

In this work, two different cereal species were

considered: triticale (Triticosecale) and rye (Secale

cereale). Four triticale varieties (Bienvenue, Trimour,

Trujillo and Collegial) and rye varieties (Askari and

Petkus) were evaluated.

2.2 Locations and agronomic practices This study was performed during 2009-2010. Plots

were sown in November 2009 and sampling of plants was

carried out between May and July 2010.

On the one hand, the four above-mentioned triticale

varieties and the two rye varieties were cultivated in 6

small plots of the same location (see Fig. 1): Escobosa de

Almazan (EDA), a village in the province of Soria, in the

region of Castilla y León (central-northern Spain),

characterizaed by a continental mediterranean climate

with cold winters. EDA can be located in the Spanish

map of Fig. 2.

Different triticale varieties were sown in plots

between 2700 and 3000 m2, while Petkus and Askari ryes

were sown in 2500 and 900 m2, respectively.

Biomass grown in this location was carefully

controlled by our organization. It was cultivated strictly

applying the same farming techniques and sampled

manually to avoid biomass contamination. Therefore,

hypothetical differences between samples of the same

species should be attributed to the inherent variability of

each variety, and so to the genetics of plants.

Figure 1: Sown plots in Escobosa de Almazán

Before sowing, soil was prepared and a basal dose

(300 kg ha-1) of N-P2O5-K2O (8-24-8) fertilizer was

applied. In November 2009, sowing was done by

broadcasting the seed at the rate of 250, 120 and 60 kg

ha-1 for triticales, Petkus rye and Askari rye, respectively.

Four months after sowing (March 2010), calcium

ammonium nitrate (27 wt %) was applied at a 270 kg ha-1

dose. A month later, two herbicides were applied: 2,4-

dichlorophenoxyacetic acid and tribenuron-methyl 75 %

(granulated) at 0.25 L ha-1 and 16 g ha-1 dosages,

respectively.

On the other hand, three triticale varieties (Trujillo,

Collegial and Trimour) and a rye variety (Petkus) were

cultivated in 11 locations randomly distributed in 8

Spanish provinces all around the north and central part of

the country (colored in Fig. 2). Exact locations are

marked in Fig. 2, as well. Table I shows all the varieties

cultivated in the different locations all around the

country.

All the locations are characterized by a continental

mediterranean climate with cold winters or by a sub-

humid continental mediterranean climate, except the

coastal mediterranean climate of VDO and LTE.

Biomass was grown by 6 different companies, and so

multiple conditions (fertilizers, herbicides, seed rate,

soils, etc.) were applied, following their own local

traditional agricultural methods.

Surface of plots varied remarkably in a range from 8

to 10000 m2. Fertilizers (NPK and urea), herbicides, and

seed-rates were applied at different doses.

Key: CDR = Cabreros del Río, Fu = Fuentesaúco, PDN = Palencia de

Negrilla, Za = Valladolid, SL = San Llorente, Ce = Cerratón de Juarros,

Go = Golmayo, EDA = Escobosa de Almazán, Al = Aldealafuente, Ga =

Galar, VDO = Vilobi DÓnyar, LTE = La Tallada DÉmpordà

Figure 2: Sampling locations

Table I: Studied varieties per location

Species Variety Location Province

Cabreros del Río León

Galar Navarra

Vilobi DÓnyar Girona

La Tallada DÉmpordà Girona

Fuentesaúco Zamora

Valladolid Valladolid

San Llorente Valladolid

Cerratón de Juarros Burgos

Palencia de Negrilla Salamanca



Fuentesaúco Zamora





Fuentesaúco Zamora



Aldealafuente Soria

Golmayo Soria

Fuentesaúco Zamora



San Llorente Valladolid

Palencia de Negrilla Salamanca

Rye Petkus

Triticale

Trujillo

Collegial

Trimour

Such huge differences among the sampling locations

are desireable in order to get a wide range of samples that

could be considered representative of the different

scenarios that could occur around a country marked by

different climates, orography and agricultural practices.

2.3 Sampling Sampling was carried out between May and July

2010. Whole plants (straw + grains) were manually

collected to prevent biomass pollution, e.g. soil particles,

and add an additional source of variability derived from

the increase of the ash content.

Six samples from the positions shown in Fig. 3 were

collected per each plot located in Escobosa de Almazán.

Each sample was obtained by collecting all the available

biomass in 1.66 lineal meters, which is equivalent to the

biomass produced in 0.25 m2. Therefore, 24 samples of

triticale and 12 samples of rye where collected from this

location for their further characterization and to evaluate

their associated variabilities.

In addition, half of the samples collected in EDA

were sampled when the grains were in a different

development state according to Zadoks growth scale: 7

and 8, which are corresponded to milk and dough

development, respectively. In a milk development state,

the grain is squeezed, and a milky solution is apparent,

while in the dough development state, the grain will still

deform slightly, but no liquid is apparent. Samples with

grains in a 7 development state were collected at the end

of June 2010, and those with their grains in the dough

state were collected 20 days later.

Key: md = minimum distance

Figure 3: Sampling positions per plot in a) EDA, and b)

rest of locations

In the rest of locations, samples were collected after

the reaping season between May and June 2010. All the

cut biomass was left laid on the ground and a V sampling

was performed (extracting samples from positions

forming a V shape, see Fig. 3), paying special attention

not to introduce particles from soil (sand, stones, clays,

earth) into the bags and pollute biomass samples.

A minimum of 5 samples were extracted from each

plot, and combined to form a 3-5 kg final sample,

representative of the plot. 26 final samples were formed,

meaning more than 130 subsamples were collected. Each

final sample was considered representative of the overall

composition of the biomass grown in every plot.

After sampling, collected samples were dried

naturally or at 45 ºC, and sent to the laboratory to be

conveniently analyzed. All the analyzed aliquots were

taken out from each sample after following a cone and

quartering sampling procedure.

2.4 Characterization methods Samples were analyzed to determine volatile matter,

ash, carbon, hydrogen, nitrogen, sulpur, and chlorine

contents, heating values, as well as major elements

constituting the ashes and their fusibility temperatures.

Moisture was determined following the norm UNE-EN

14774-2. To determine ash content, a portion of a sample

was calcinated at 550 ºC following UNE-EN 14775. The

volatile matter (VM) was calculated as the loss of weight

in a sample placed in a closed crucible at a temperature of

900 ºC for 7 minutes. Norm UNE-EN 15148 was

followed to carry out the analysis.

Carbon, hydrogen, and nitrogen where directly

determined using a LECO elemental analyzer equipped

with an infrared cell to quantify the carbon and hydrogen

contents and with a thermal conductivity detector to

quantify nitrogen by following EN 15104.

Chlorine and sulphur determinations were carried out

by ion chromatography after sample combustion in a

calorimetric bomb followed by lixiviation of the ashes

with an aqueous solution, a procedure derived from EN

15289.

To determine the gross calorific value, 1 g-sample

was burnt in an IKA C-5000 calorimetric bomb following

the norm EN 14918. Gross calorific value at constant

volume in dry basis (GCVv,0) and net calorific value at

constant pressure in dry basis (NCVp,0) were calculated.

Biomass ashes (obtained at 550 ºC) were digested in

a microwave oven using HNO3, H2O2 and HF in a first

step and H3BO3 in a second step, and inorganic elements

were analyzed accordingly to EN 15290 by inductively

coupled plasma with atomic emission spectroscopy (ICP-

AES) using a Thermo Jarrell Ash simultaneous

spectrometer.

The ash fusibility test was based on the shape

changes detected during the heating of a cylindrical ash

pellet (ashes produced at 550 ºC) from room temperature

to 1400 ºC in an air atmosphere. Four characteristic

temperatures were measured by an optical heating

microscope (LEICA) following CEN/TS 15370-1: initial

deformation (IDT), sphere (ST), hemisphere (HT), and

fluid (FT) temperatures.

2.5 Statistical analysis To evaluate the effect of the different studied

conditions on the biomass composition, different

statistical tests were carried out by using the software

Statgraphics Plus [14].

First of all, a one-way analysis of variance (ANOVA)

was performed on the species factor for the whole data

set obtained in Escobosa de Almazán (36 samples, i.e. 36

observations for each biomass property). Results obtained

from this test allow identifying statistical differences of

each independent variable (each analyzed biomass

property) for the 2 different levels of the species factor

(triticale and rye). The F-test in the ANOVA table will

test whether there are any significant differences amongst

the obtained means of each evaluated parameter for

triticale and rye. ANOVA table decomposes the variance

of each biomass property into two components: a

between-group component and a within-group

component. If the P-value of the F-test is less than 0.05,

there is a statistically significant difference between the

mean of the property from one level of the species factor

(triticale) to another (rye) at the 95.0 % confidence level.

Secondly, a two-way multifactor ANOVA was

performed on each species (first triticale and then rye) by

separate. The considered factors for each species were the

variety and the grain development state. Four varieties

were studied (Trujillo, Collegial, Bienvenue and

Trimour) for triticale, and two (Petkus and Askari) for

rye. Regarding the grain development state, as it was

previously explained, it was studied at two growth stages:

milk (7) and dough (8) development. The 24 and 12

triticale and rye samples, respectively, cultivated in EDA

were included in this study.

Finally, a Cochran´s test was run to check up on

variances in EDA and those obtained for different

locations. This test was run for each species by separate.

It is a statistical test for homogeneity of variance. The

hypothesis is that the variances across the two included

levels (same location/farming conditions vs. different

locations/farming conditions) of each biomass property

are equal. A reported significance level (P-value) greater

than or equal to 0.05 means that hypotheris shall be

md = 2

m

m = 2

md = 2

m md = 2

m md = 2 m

md = 2 m

md = 2 m md = 2 m

a) b)

accepted; meaning that variances are not significantly

different (they are equal). Such approach allows

comparing the variability for each biomass property when

biomass is cultivated in the same location and conditions

with the variability when biomass is cultivated in

different locations by using multiple agricultural

techniques. Results were confirmed by applying also

other variance check tests such as Bartlett´s, Hartley´s

and Levene´s test. 50 samples were included in this

study; 30 cultivated in EDA and 20 in other locations.

3 RESULTS AND DISCUSSION

3.1 Variability in the same location

As it was previously commented, four triticale

varieties and two rye varieties were cultivated in the same

location (EDA), and collected when the grains were in a

different development state (7 and 8 in the cereal

development Zadoks scale).

Obtained results from biomass characterization, the

composition of ashes obtained from biomass by

calcination at 550 ºC, and ash fusibilities were found to

be in the typical range for these species [7,8,12,14], and

they are shown in Tables II-IV. The number of analyzed

samples (n) for each condition is also included in all

tables.

Table II: Composition and variability of the biomass cultivated in Escobosa de Almazán

Table III: Composition of the major components (expressed as oxides) of ashes from biomass cultivated in Escobosa de

Almazán

CaO MgO Na2O K2O P2O5 Al2O3 SiO2

(wt%, d.b.) (wt%, d.b.) (% b.s.) (% b.s.) (% b.s.) (% b.s.) (% b.s.)

Mean (n=2) 5.6 2.4 0.23 18 9.2 0.26 47

Range 1.8 0.7 0.01 5 1.6 0.00 11

Min. Val. 4.7 2.0 0.22 15 8.4 0.26 41

Max. Val. 6.5 2.7 0.23 20 10.0 0.26 52

Mean (n=2) 5.1 1.8 0.25 16 6.7 0.36 49

Range 0.6 0.1 0.15 4 0.0 0.32 2

Min. Val. 4.8 1.7 0.17 14 6.7 0.20 48

Max. Val. 5.4 1.8 0.32 18 6.7 0.52 50

Mean (n=2) 5.1 2.0 0.16 15 7.6 0.44 54

Range 1.1 0.6 0.08 1 0.2 0.42 0

Min. Val. 4.5 1.7 0.12 14 7.5 0.23 54

Max. Val. 5.6 2.3 0.20 15 7.7 0.65 54

Mean (n=2) 5.5 2.0 0.28 17 8.0 0.63 45

Range 1.7 0.3 0.27 4 1.8 0.95 6

Min. Val. 4.6 1.8 0.14 15 7.1 0.15 42

Max. Val. 6.3 2.1 0.41 19 8.9 1.10 48

Mean (n=2) 7.1 2.0 0.39 23 8.2 0.22 42

Range 0.4 0.1 0.03 5 0.6 0.04 6

Min. Val. 6.9 1.9 0.37 20 7.9 0.20 39

Max. Val. 7.3 2.0 0.40 25 8.5 0.24 45

Mean (n=2) 8.3 2.3 0.18 30 8.8 0.30 30

Range 1.0 0.0 0.19 5 0.3 0.05 8

Min. Val. 7.8 2.3 0.09 27 8.6 0.27 26

Max. Val. 8.8 2.3 0.27 32 8.9 0.32 34

Rye

Petkus

Askari

Triticale

Collegial

Trujillo

Bienvenue

Trimour

Species Variety

Ash VM C H N S Cl O GCVv,0 NCVp,0

(wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (MJ kg-1

) (MJ kg-1

)Mean (n=6) 4.6 78.8 44.1 6.1 0.87 0.08 0.07 44.1 17.78 16.45

Range 1.7 1.6 2.0 0.2 0.54 0.05 0.08 1.9 0.94 0.95

Min. Val. 3.9 78.2 43.1 6.0 0.73 0.06 0.04 43.1 17.36 16.02

Max. Val. 5.6 79.8 45.1 6.2 1.27 0.11 0.12 45.1 18.30 16.97

Std. Dev. 0.7 0.6 0.8 0.06 0.2 0.02 0.03 0.7 0.38 0.38

Mean (n=6) 5.1 78.1 44.1 6.1 0.77 0.07 0.08 43.7 17.68 16.35

Range 1.1 2.6 2.1 0.1 0.40 0.03 0.06 2.5 0.91 0.89

Min. Val. 4.6 77.1 43.2 6.1 0.56 0.06 0.04 42.4 17.32 15.99

Max. Val. 5.7 79.7 45.3 6.2 0.96 0.09 0.10 44.9 18.23 16.88

Std. Dev. 0.4 1.0 0.9 0.05 0.1 0.01 0.03 1.0 0.42 0.42

Mean (n=6) 5.2 77.1 45.4 6.1 0.76 0.08 0.08 42.5 18.22 16.90

Range 1.1 3.7 1.2 0.2 0.33 0.07 0.07 1.4 0.47 0.47

Min. Val. 4.6 75.2 44.8 5.9 0.60 0.06 0.04 41.9 17.99 16.66

Max. Val. 5.7 78.9 46.0 6.1 0.93 0.13 0.11 43.3 18.46 17.13

Std. Dev. 0.4 1.3 0.4 0.08 0.1 0.03 0.03 0.5 0.17 0.16

Mean (n=6) 5.3 77.2 45.0 6.0 0.76 0.07 0.06 42.8 18.05 16.75

Range 0.6 4.2 0.5 0.1 0.20 0.02 0.04 0.4 0.24 0.24

Min. Val. 4.9 75.4 44.8 5.9 0.67 0.06 0.04 42.6 17.96 16.66

Max. Val. 5.5 79.6 45.3 6.0 0.87 0.08 0.08 43.0 18.20 16.90

Std. Dev. 0.2 1.9 0.2 0.04 0.1 0.01 0.01 0.2 0.09 0.09

Mean (n=6) 4.4 77.7 45.7 6.1 0.72 0.08 0.08 43.0 18.28 16.96

Range 0.6 3.0 1.0 0.1 0.19 0.01 0.05 1.0 0.48 0.51

Min. Val. 4.1 76.5 45.1 6.0 0.61 0.07 0.06 42.5 18.04 16.71

Max. Val. 4.7 79.5 46.1 6.1 0.80 0.08 0.11 43.6 18.52 17.22

Std. Dev. 0.3 1.0 0.4 0.05 0.1 0.01 0.02 0.4 0.19 0.20

Mean (n=6) 4.2 77.2 46.2 6.1 1.04 0.10 0.06 42.3 18.51 17.19

Range 1.2 2.1 0.7 0.1 0.74 0.05 0.03 2.1 0.34 0.34

Min. Val. 3.5 76.2 45.8 6.0 0.82 0.08 0.05 40.9 18.28 16.95

Max. Val. 4.7 78.3 46.5 6.1 1.56 0.13 0.08 43.1 18.62 17.29

Std. Dev. 0.4 0.7 0.2 0.04 0.3 0.02 0.01 0.8 0.13 0.13

Species Variety

Triticale

Rye

Collegial

Trujillo

Bienvenue

Trimour

Petkus

Askari

Table IV: Fusibility of the ashes obtained from the

biomass cultivated in Escobosa de Almazán

IDT ST HT FT

(oC) (oC) (oC) (oC)

Mean (n=2) 855 970 1100 1180

Range 90 40 100 40

Min. Val. 810 950 1050 1160

Max. Val. 900 990 1150 1200

Mean (n=2) 890 1015 1125 1225

Range 160 70 110 10

Min. Val. 810 980 1070 1220

Max. Val. 970 1050 1180 1230

Mean (n=2) 865 1055 1155 1215

Range 50 90 50 70

Min. Val. 840 1010 1130 1180

Max. Val. 890 1100 1180 1250

Mean (n=2) 830 1005 1095 1165

Range 140 190 130 130

Min. Val. 760 910 1030 1100

Max. Val. 900 1100 1160 1230

Mean (n=6) 793 917 1055 1135

Range 120 140 40 60

Min. Val. 730 830 1040 1100

Max. Val. 850 970 1080 1160

Std. Dev. 40 52 16 24

Mean (n=6) 783 883 1080 1107

Range 80 160 120 150

Min. Val. 750 820 1040 1060

Max. Val. 830 980 1160 1210

Std. Dev. 33 54 45 56

Rye

Petkus

Askari

Species Variety

Triticale

Collegial

Trujillo

Bienvenue

Trimour

Means and standard deviations (Std. Dev.) were

calculated, and ranges, minimum values (Min. Val.) and

maximum values (Max. Val.) were established for each

property and studied variety, trying to be representative

of the differences due to a genetic factor, since the

growing conditions were the same (soil, fertilization,

weather, seed rate, etc.).

In a first attempt to estimate the differences between

the two studied species, a one-way analysis of variance

(ANOVA) was carried out, and results are shown in the

first raw of Table V. Secondly, a two-way multifactor

ANOVA was performed over the entire data set, and

differences between varieties and cereal development

states were evaluated. Results are also included in Table

V. P-values below 0.05 denote a statistically significant

difference between the mean of one level of the property

to another, at the 95.0% confidence level. For a full

comprehension of results, trends between levels for

significant properties were also included in the table.

They were obtained by running a multiple range test

which is a multiple comparison procedure to determine

which means are significantly different from each others

at the same confidence level. Refer to the key below

Table V for abbreviations.

C, H, N, S, Cl, O contents and heating values were

not found to be dependent on the grain development

state. Samples collected when their grains were in a

dough development state showed very slightly higher

volatile matter (77 wt% for milky grains and 78-79 wt%

for doughy grains). Ash content was also slightly higher

for triticales with doughy grains (5.3 wt% vs. 4.8 wt%),

being more noticeable for Collegial samples with doughy

grains (5.3 wt% for doughy grains vs. 4.0 wt% for milky

grains), as it can be seen in the screening plot of Fig. 4.

Figure 4: Screening plot for the triticale ashes cultivated

in EDA

Carbon content, as well as calorific values

(differences of 0.46 MJ kg-1 between means) were found

to be higher for rye samples, probably as a consequence

of the higher ash contents found for triticale samples.

Significant differences between ashes and net calorific

values for both species can be appreciated in Fig. 5.

Negative effect that high ash contents causes into

biomass calorific value and C content is well-known.

Significant correlations have been found when plotting

heating values as a function of the ash or carbon contents

[15]. Heating values decrease with the increase of ash

content in biomass materials, and increase with the

increase of C and H contents, which is consistent with

commonsense that higher C and H contents mean a

higher energy content of a biomass [15,16].

Figure 5: Mean and 95.0 % low square differences

(LSD) intervals for the ash content and net calorific value

of samples cultivated in EDA

Table V: ANOVA results for biomass cultivated in Escobosa de Almazán (P-values and trends between levels).

Key: T = triticale, R = Rye, B = Bienvenue, Ti = Trimour, Tu = Trujillo, C = Collegial, D = Dough, M = Milk


(wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (MJ kg-1) (MJ kg-1)Species n=36 0.0000 0.4321 0.0000 1.0000 0.1767 0.0725 0.9575 0.0460 0.0002 0.0002

T>R R>T R>T R>TTriticale n=24

Variety 0.0381 0.0156 0.0065 0.0033 0.4626 0.5047 0.7623 0.0015 0.0260 0.0183(B=Ti=Tu)>C C>(B=Ti) (B=Ti)>(C=Tu) (C=Tu)>Ti (C=Tu)>(B=Ti) (B=Ti) > Tu (B=Ti) > Tu

Co=Tu Co=TuGrowth stage 0.0119 0.0005 0.8805 0.5236 0.0776 0.2443 0.5786 0.3460 0.9527 0.9209

D>M D>MRye n=12

Variety 0.4686 0.2735 0.0211 0.2861 0.0119 0.0350 0.0660 0.0740 0.0361 0.0480A>P A>P A>P A>P A>P

Growth stage 0.7690 0.0258 0.2724 1.0000 0.1452 0.2887 0.3666 0.3922 0.5055 0.5240D>M

Factor

Ashes

wt%

, d.b

.

3.9

4.2

4.5

4.8

5.1

5.4

5.7

6.0

6.3

Bienvenue Collegial Trimour Trujillo

Milky grains

Doughy grains

Ashes

wt%

, d.b

.

3.9

4.2

4.5

4.8

5.1

5.4

5.7

6.0

6.3


Milky grains

Doughy grains

Ashes

wt%

, d.b

.

Rye Triticale4.0

4.2

4.4

4.6

4.8

5.0

5.2

5.4

NCVp,0

MJ

kg-1

Rye Triticale16.5

16.7

16.9

17.1

17.3

17.5

Ashes

wt%

, d.b

.

Rye Triticale4.0

4.2

4.4

4.6

4.8

5.0

5.2

5.4

NCVp,0

MJ

kg-1

Rye Triticale16.5

16.7

16.9

17.1

17.3

17.5

Found differences regarding ashes, C and heating

values must be attributed to the genetics of the plant, and

not to the contamination of the biomass with external

particles because all samples were manually collected

avoiding this kind of pollution. Additionally, no

differences among varieties were found when analyzing

the ashes obtained from biomass, which clearly supports

the afore-mentioned hypothesis. For instance, Al is

usually considered as a marker for contamination of

biomass by soil inclusions (predominantly clays and

oxides). When biomass is polluted with sand, clays and

soil components, other elements such as Si, Ti, Fe and Na

are also introduced [8]. It can not be attributed either to

the different grain development state, due to the only very

slight differences found for ash (only for the Collegial

variety) and volatile matter contents (differences of only

1-2 wt%), regarding this factor.

No significant differences were found for N, S and Cl

among triticale varieties. However, differences were

found for heating values, as well as for ash, volatile, C,

H, and O contents. ANOVA found that mean ash content

for Collegial varieties was lower than for the rest of the

varieties, but it is only because of the low ash contents

found for samples of this variety containing milky grains

(see Fig. 4). Plots were depicted in Fig. 6 for some of

these significant properties. It should be mentioned that

Bienvenue and Trimour are the triticale varieties with the

highest C content and mean heating values.

Figure 6: Mean and 95.0 % LSD intervals for C and net

calorific value for the triticale varieties cultivated in EDA

Attending rye varieties, C and heating values were

slightly higher for the Askari variety (a difference of 0.24

MJ kg-1 between the means). Although N and S contents

were also found to be significant properties, this is not a

reliable result due to the presence of a likely outlier

sample, which exhibits disproportionate high contents of

both elements, increasing thus the mean value and the

variability for this variety.

As it can be seen in Table IV, fusibility temperatures

were found to be higher for triticale samples (e.g. IDT of

760-970 ºC) in comparison with those obtained for rye

samples (e.g. IDT of 730-850 ºC). It could be due to the

lower K2O contents (mean of 17 wt%) of triticale ashes

compared to those found for rye samples (27 wt%).

Fusibility or composition of ashes obtained from

biomass does not depend on the variety of the studied

species or the development state of rye grains. However,

ash fusibility temperatures for triticale samples were

found to be dependent on the growth state of grains,

finding lower temperatures for milky grains (e.g. IDT of

760-840 for milky grains vs. 890-970 for doughy grains),

probably as a consequence of their higher K2O content

(15-20 for ashes from biomass containing milky grains

vs. 14-15 wt% for doughy grains, see Fig. 7).

Figure 7: Fusibility temperatures and K2O content as a

function of grains growth stage

The importance of the K content is due to its

influence on the ash melting behaviour and on aerosol

formation. According to literature, potassium is relatively

volatile, forming chlorides, hydroxides and sulfates,

which play an important role in the corrosion

mechanisms relevant for boilers [17]. Increased K

concentrations rise the amount of aerosols formed during

combustion, and thus fouling in boilers and fine

particulate emissions. Moreover, an increased K content

leads to a decreased ash melting point, which can cause

slag and hard deposit formation in the furnace and boiler

[9]. Straw, cereal, grass, and grain ashes, which contain

low concentrations of Ca and high concentrations of Si

and K start to sinter and melt at significantly lower

temperatures than wood fuels [7]. Therefore, triticale

presents a lower tendency to fouling and slagging when it

is collected with doughy grains than with grains in a

milky development state.

3.2 Variability in different locations

Three triticale varieties (Trujillo, Collegial and

Trimour) and one of rye (Petkus) were cultivated in 11

different locations around the north and central part of

of Spain under multiple conditions (companies,

fertilization, soil, etc.). More than 130 samples were

manually collected to avoid the contamination of the

samples with stones, sand, etc. to form the 26 final

samples that were characterized. Each final sample was

considered representative of the composition of the

biomass grown in its corresponding plot.

Standard deviations and ranges were obtained for

each variety and property, trying to be representative of

the differences due to an environment factor, given that

this biomass was grown under multiple conditions and

locations. Means, ranges, standard deviations as well as

minimum and maximum found values are shown in

Tables VI-VIII.

Carbon

wt%

, d.b

.


43.0

43.5

44.0

44.5

45.0

45.5

46.0

NCVp,0

MJ

kg-1

Bienvenue Collegial Trimour Trujillo16.1

16.3

16.5

16.7

16.9

17.1

Carbon

wt%

, d.b

.


43.0

43.5

44.0

44.5

45.0

45.5

46.0

NCVp,0

MJ

kg-1

Bienvenue Collegial Trimour Trujillo16.1

16.3

16.5

16.7

16.9

17.1

Initial deformation temperature

600

650

700

750

800

850

900

950

1000

Collegial Trujillo Bienvenue Trimour

º C

Sphere temperature

800

850

900

950

1000

1050

1100

1150

1200


º C

Hemisphere temperature

1000

1050

1100

1150

1200

1250

1300


º C

Fluid temperature

900

950

1000

1050

1100

1150

1200

1250

1300


º C

K2O

10

12

14

16

18

20

22


wt%

, d.b

.

Milky grains

Doughy grains

Initial deformation temperature

600

650

700

750

800

850

900

950

1000


º C

Sphere temperature

800

850

900

950

1000

1050

1100

1150

1200


º C

Hemisphere temperature

1000

1050

1100

1150

1200

1250

1300


º C

Fluid temperature

900

950

1000

1050

1100

1150

1200

1250

1300


º C

K2O

10

12

14

16

18

20

22


wt%

, d.b

.

K2O

10

12

14

16

18

20

22


wt%

, d.b

.

Milky grains

Doughy grains

Table VI: Composition and variability of the biomass cultivated in different locations around the north and central part of

Spain

Table VII: Composition of the major components (expressed as oxides) of ashes from the biomass cultivated in different

location

Table VIII: Fusibility of the ashes obtained from the

biomass cultivated in different locations

3.3 Comparison of variabilities

Finally, a Cochran´s test was run for each biomass

property to check up on variances (variance of same

location vs. variance of different locations). Variance of

same location can be equivalent to the variability

originated by the genetics of the grown variety, while

variance of different locations includes the variability

originated by the different grown varieties plus that

caused by the multiple growing and farming conditions

applied.

Cochran´s test results for triticale samples (including

Collegial, Trujillo and Trimour varieties) and rye

samples (including Petkus variety) are shown in Tables

IX and X, respectively. Standard deviations (SD) and

ranges (R) were also included in tables.


(wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (wt%, d.b.) (MJ kg-1

) (MJ kg-1

)Mean (n=5) 6.3 75.3 45.1 5.9 0.60 0.12 0.34 41.6 18.11 16.83

Range 1.9 2.2 1.2 0.2 0.89 0.10 0.69 2.4 0.18 0.21

Min. Val. 5.5 74.4 44.5 5.8 0.32 0.06 0.07 40.1 18.06 16.77

Max. Val. 7.4 76.6 45.7 6.0 1.21 0.16 0.76 42.4 18.24 16.98

Std. Dev. 0.8 0.9 0.4 0.1 0.37 0.04 0.27 0.9 0.07 0.09

Mean (n=9) 4.7 78.0 45.3 6.1 1.12 0.12 0.19 42.5 18.37 17.05

Range 3.0 3.1 1.3 0.3 0.55 0.09 0.53 2.9 0.46 0.50

Min. Val. 3.4 76.8 44.7 6.0 0.89 0.07 0.05 40.7 18.18 16.81

Max. Val. 6.4 79.9 46.0 6.3 1.44 0.16 0.58 43.6 18.64 17.31

Std. Dev. 0.8 1.3 0.4 0.1 0.17 0.02 0.17 0.9 0.18 0.18

Mean (n=5) 5.1 76.3 45.6 6.0 0.75 0.10 0.22 42.2 18.54 17.24

Range 2.3 3.0 1.4 0.2 0.99 0.09 0.55 2.5 0.38 0.40

Min. Val. 4.0 74.9 44.8 5.9 0.35 0.06 0.05 40.8 18.41 17.08

Max. Val. 6.3 77.9 46.2 6.1 1.34 0.15 0.60 43.3 18.79 17.48

Std. Dev. 0.8 1.3 0.6 0.1 0.41 0.03 0.23 0.9 0.15 0.15

Mean (n=7) 4.1 78.0 46.0 6.0 0.88 0.09 0.19 42.7 18.51 17.20

Range 1.9 3.9 0.7 0.3 1.06 0.07 0.43 1.6 0.27 0.21

Min. Val. 3.2 76.4 45.7 5.8 0.36 0.04 0.04 42.2 18.37 17.10

Max. Val. 5.1 80.3 46.4 6.1 1.42 0.11 0.47 43.8 18.64 17.31

Std. Dev. 0.7 1.5 0.3 0.1 0.36 0.03 0.15 0.6 0.09 0.07

Rye

Collegial

Trujillo

Trimour

Petkus

Species Variety

Triticale

IDT ST HT FT

(o

C) (o

C) (o

C) (o

C)

Mean (n=5) 766 940 1024 1126

Range 110 140 30 250

Min. Val. 720 850 1010 1050

Max. Val. 830 990 1040 1300

Std. Dev. 50 78 11 101

Mean (n=9) 868 955 1082 1146

Range 280 340 150 140

Min. Val. 780 830 1040 1070

Max. Val. 1060 1170 1190 1210

Std. Dev. 85 105 45 48

Mean (n=5) 836 923 1088 1128

Range 200 180 230 270

Min. Val. 740 840 1030 1040

Max. Val. 940 1020 1260 1310

Std. Dev. 86 74 97 108

Mean (n=7) 876 861 967 1040

Range 400 300 290 220

Min. Val. 750 840 930 1040

Max. Val. 1150 1140 1220 1260

Std. Dev. 137 110 97 73

Species Variety

Rye Petkus

Triticale

Collegial

Trujillo

Trimour

CaO MgO Na2O K2O P2O5 Al2O3 SiO2

(wt%, d.b.) (wt%, d.b.) (% b.s.) (% b.s.) (% b.s.) (% b.s.) (% b.s.)

Mean (n=5) 7.2 2.4 0.35 29 6.5 0.50 39

Range 3.6 1.8 0.61 10 10.0 0.63 20

Min. Val. 5.1 1.6 0.15 24 2.0 0.17 31

Max. Val. 8.7 3.4 0.76 34 12.0 0.80 51

Std. Dev. 1.4 0.7 0.26 4 4.3 0.25 8

Mean (n=9) 6.7 3.5 0.43 24 8.4 0.54 37

Range 4.0 3.8 0.51 14 10.3 0.81 28

Min. Val. 4.6 2.4 0.15 19 4.7 0.19 21

Max. Val. 8.6 6.2 0.66 33 15.0 1.00 49

Std. Dev. 1.2 1.4 0.18 4 3.4 0.30 8

Mean (n=5) 7.4 2.7 0.41 26 5.5 0.71 41

Range 4.0 2.3 0.52 12 9.0 1.01 25

Min. Val. 5.4 1.9 0.17 21 2.0 0.29 29

Max. Val. 9.4 4.2 0.69 33 11.0 1.30 54

Std. Dev. 2.0 0.9 0.20 5 3.8 0.46 10

Mean (n=7) 7.7 3.9 0.16 29 8.9 0.29 32

Range 2.8 1.7 0.09 9 8.4 0.46 21

Min. Val. 6.4 2.9 0.11 23 5.6 0.13 21

Max. Val. 9.2 4.6 0.20 32 14.0 0.59 42

Std. Dev. 1.0 0.5 0.04 3 2.7 0.14 8

Species

Petkus

Variety

Triticale

Collegial

Trujillo

Trimour

Rye

Table IX: Cochran´s test results for triticale samples (Collegial, Trujillo and Trimour varieties included)

Property Units n P-value SDDL SDSL RDL RSL

Ash wt%, d.b. 37 0.00646 1.1 0.5 4.0 1.8 not equal

VM wt%, d.b. 37 0.46920 1.7 1.4 5.5 4.4 equal

C wt%, d.b. 37 0.04081 0.48 0.80 1.70 2.20 not equal

H wt%, d.b. 37 0.27460 0.11 0.08 0.50 0.30 equal

N wt%, d.b. 37 0.00029 0.37 0.15 1.12 0.71 not equal

S wt%, d.b. 37 0.00103 0.03 0.01 0.10 0.05 not equal

Cl wt%, d.b. 37 0.00000 0.22 0.02 0.71 0.08 not equal

O wt%, d.b. 37 0.79582 0.95 0.90 3.51 2.65 equal

GCVv,0 MJ kg-1 37 0.06510 0.22 0.35 2.65 0.98 equal

NCVp,0 MJ kg-1 37 0.04481 0.22 0.36 0.71 0.98 not equal

Key: SD = satnadard deviation, R = range, DL = different locations, SL = same location

Table X: Cochran´s test results for rye samples (Petkus variety included)

Property Units n P-value SDDL SDSL RDL RSL

Ash wt%, d.b. 25 0.03914 0.7 0.3 1.9 0.6 not equal

VM wt%, d.b. 25 0.44961 1.5 1.0 3.9 3.0 equal

C wt%, d.b. 25 0.42253 0.28 0.39 0.70 1.00 equal

H wt%, d.b. 25 0.12559 0.11 0.06 0.30 0.10 equal

N wt%, d.b. 25 0.00149 0.36 0.07 1.06 0.19 not equal

S wt%, d.b. 25 0.00183 0.03 0.01 0.07 0.01 not equal

Cl wt%, d.b. 25 0.00013 0.15 0.02 0.43 0.05 not equal

O wt%, d.b. 25 0.25364 0.59 0.35 1.60 1.01 equal

GCVv,0 MJ kg-1 25 0.09821 0.09 0.19 0.27 0.48 equal

NCVp,0 MJ kg-1 25 0.02862 0.07 0.20 0.21 0.51 not equal

Key: SD = standard deviation, R = range, DL = different locations, SL = same location

This test assumes the hypothesis that both variances

are equal and reports a significance level for each

evaluated property. P-values below 0.05 means that

variances are significantly different, which indicates in

turn that the variability of that group of samples is

originated by the condition exhibiting the highest

variance. It is worth mentioning that the same test was

performed for each individual variety, obtaining the

same results. Therefore, if variances can be considered

statistically different (raws labelled as “not equal”) , the

variability of the results can be attributed to a genetic

factor (SDSL > SDDL) or, on the contrary, the different

growing and farming conditions adds more variability to

the results (SDDL > SDSL).

Taking a thorough look at Table IX, it can be

deduced that the variability of parameters such as C and

net calorific value can be associated with the cultivated

variety of triticale, while main differences in the ash, N,

S, and Cl contents seem to be linked to the growing

conditions. Dispersion plots for some triticale properties

are also presented in Figure 8. Regarding rye, it can be

said from Table X that the variability of the net calorific

value can be associated with the cultivated rye variety,

while differences in the growing conditions seem to add

a significant extra source of variability to the ash, N, S,

Cl contents. Bartlett´s, Hartley´s and Levene´s tests

confirmed the obtained results. Higher variability for

e.g. Cl, and S was expected for samples cultivated under

different conditions, since it was demonstrated that

precipitation removes substantial amounts of K, Cl and

S from triticale and rye, or the use of K fertilizers

containing Cl and S resulted in a significantly increased

concentration of these elements in the straw [12].

Future research could involve the study of how

different environmental conditions contribute to the

found variability by separate, e.g. factors like rainfall,

type of soil, physical conditions of the land, nutrients, or

some others more related to the pollution during

sampling and handling of the fuel, such as sawthing with

windrowers or bailing.

4 CONCLUSIONS

This study constitutes a first approach to estimate the

variability of cereals in Spain in terms of biomass quality.

The variability among the properties of unpolluted raw

biomass (straw + grains) of triticale and rye due to the

different varieties and growing conditions was evaluated.

Variability ranges that can be expected for each variety

and physico-chemical property of triticale and rye were

set in north central Spain.

It is really worth mentioning that parameters such as

carbon or heating values seem to be somehow dependent

on the variety cultivated, while different growing and

farming conditions seem to add a significant extra source

of variability to some others like ash, nitrogen, sulphur,

or chlorine contents.

Of particular interest are some other conclusions that

can be extracted from this study. For instance, the rye

variety Askari is characterized by the highest mean

heating values among all the considered varieties.

Attending triticale, Bienvenue and Trimour are

characterized by higher mean heating values than

Collegial or Trujillo. Triticale presents a lower tendency

to fouling and slagging than rye, particularly when

triticale is collected with doughy grains instead of milky

grains.

.

Figure 8: Dispersion plots for triticale samples as a function of the location: EDA/same growing conditions vs. the rest of

locations/different growing conditions

5 REFERENCES

[1] L. Cuiping, W. Chuangzhi, Yanyongjie, H. Haitao,

Chemical elemental characteristics of biomass fuels

in China, Biomass Bioenergy 27, (2004), pag. 119-

130.

[2] B. Sander, Properties of Danish biofuels and the

requirements for power production, Biomass

Bioenergy 12, (1997), pag. 177-183.

[3] R. Kataki, D. Konwer, Fuelwood characteristics of

some indigenous woody species of north-east India,

Biomass Bioenergy 20, (2001), pag. 17-23.

[4] A. van der Drift, J. van Doorn, J. W. Vermeulen, Ten

residual biomass fuels for circulating fluidized-bed

gasification, Biomass Bioenergy 20, (2001), pag. 45-

56.

[5] K. Senelwa, R. E. H. Sims, Fuel characteristics of

short rotation forest biomass, Biomass Bioenergy 17,

(1999), pag. 127-140.

[6] L. Rytter, Nutrient content in stems of hybrid aspen

as affected by tree age and tree size, an nutrient

removal with harvest, Biomass Bioenergy 23, (2002),

pag. 13-25.

[7] I. Obernberger, T. Brunner, G. Bärnthaler, Chemical

properties of solid biofuels – significance and impact,

Biomass Bioenergy 30, (2006), pag. 973-982.

[8] S. V. Vassilev, D. Baxter, L. K. Andersen, C. G.

Vassileva, Fuel 89, (2010), pag. 913-933.

[9] I. Obernberger, G. Theka, Physical characteristion

and chemical composition of densified biomass fuels

with regard to their combustion behaviour, Biomass

Bioenergy 27, (2004), pag. 653–669.

[10] R. Saidur, E. A. Abdelaziz, A. Demirbas, M. S.

Hossain, S. Mekhilef, A review on biomass as a fuel

for boilers, Renew Sust Energ Rev 15, (2011), pag.

2262-2289.

[11] A. Nordin, Chemical elemental characteristics of

biomass fuels, Biomass Bioenergy 6, (1994), pag.

339-347.

[12] J. R. Jorgensen, L. C. Deleuran, B. Wollenweber,

Prospects of whole grain crops of wheat, rye and

triticale under different fertilizer regimes for energy

production, Biomass Bioenergy 31 (2007) 308-317.

Same location (EDA)

Rest of locations

Ashes

2.0

3.0

4.0

5.0

6.0

7.0

8.0

0 5 10 15 20

wt%

, d.b

.

Volatile matter

70.0

72.0

74.0

76.0

78.0

80.0

82.0

84.0

0 5 10 15 20

wt%

, d.b

.

Sample Sample

Hydrogen

5.0

5.3

5.5

5.8

6.0

6.3

6.5

6.8

7.0

0 5 10 15 20

wt%

, d.b

.

Sample

Nitrogen

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

0 2 4 6 8 10 12 14 16 18 20

wt%

, d.b

.

Sample

Sulphur

0.00

0.04

0.08

0.12

0.16

0.20

0 5 10 15 20

wt%

, d.b

.

Sample

Chlorine

0.00

0.04

0.08

0.12

0.16

0.20

0 5 10 15 20

wt%

, d.b

.

Sample

Carbon

42.0

43.0

44.0

45.0

46.0

47.0

0 2 4 6 8 10 12 14 16 18 20

wt%

, d.b

.

NCVp,0

15.75

16.00

16.25

16.50

16.75

17.00

17.25

17.50

17.75

0 5 10 15 20

MJ

kg-1

Sample

Sample

Same location (EDA)

Rest of locations

Ashes

2.0

3.0

4.0

5.0

6.0

7.0

8.0

0 5 10 15 20

wt%

, d.b

.

Volatile matter

70.0

72.0

74.0

76.0

78.0

80.0

82.0

84.0

0 5 10 15 20

wt%

, d.b

.

Sample Sample

Hydrogen

5.0

5.3

5.5

5.8

6.0

6.3

6.5

6.8

7.0

0 5 10 15 20

wt%

, d.b

.

Sample

Nitrogen

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

0 2 4 6 8 10 12 14 16 18 20

wt%

, d.b

.

Sample

Sulphur

0.00

0.04

0.08

0.12

0.16

0.20

0 5 10 15 20

wt%

, d.b

.

Sample

Chlorine

0.00

0.04

0.08

0.12

0.16

0.20

0 5 10 15 20

wt%

, d.b

.

Sample

Carbon

42.0

43.0

44.0

45.0

46.0

47.0

0 2 4 6 8 10 12 14 16 18 20

wt%

, d.b

.

NCVp,0

15.75

16.00

16.25

16.50

16.75

17.00

17.25

17.50

17.75

0 5 10 15 20

MJ

kg-1

Sample

Sample

[13] R. Barro, M. J. Fernández, J. Losada, L. Rovira, A.

Salvadó, J. Serra, J. E. Carrasco, Differences on the

quality of the biomass obtained from different species

of winter cereals, Proceedings of the 17th European

Biomass Conference & Exhibition From Research to

Industry and Markets, 3-7 May, Lyon, (2010).

[14] Statgraphics-Plus V.5.1, Manugistics, Rockville,

MD, (2002).

[15] C. Sheng, J. L. T. Azevedo, Estimating the higher

heating value of biomass fuels from basic analysis

data, Biomass Bioenergy 28, (2005), pag. 499-507.

[16] B. M. Jenkins, L. L. Baxter, Jr. T. R. Miles, T. R.

Miles, Combustion properties of biomass, Fuel

Processing Technology 54, (1998), pag. 17-46.

[17] I. Obernberger, F. Biedermann, W. Widmann, R.

Riedl, Concentrations of inorganic elements in

biomass fuels and recovery in the different ash

fractions, Biomass Bioenergy 12, (1997), pag. 211-

224.

6 ACKNOWLEDGEMENTS

This research has been performed in the frame of the

2R Subproject (PSE-120000-2009-15) from the Project

for Development, Demonstration and Evaluation of the

Viability of the Commercial Production of Energy from

Dedicated Crops in Spain “PSE – On crops”, which has

been recognized as a national singular and strategic

project. This project is being supported by the Spanish

Ministry of Science and Innovation.

Vp1.3.13 barro proceeding

Technology

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