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Energy potential through agricultural biomass usinggeographical information system—A case study of Punjab

Jagtar Singha,�, B.S. Panesarb, S.K. Sharmac

aMechanical Engineering Department, SLIET Longowal, Punjab 148106, IndiabSenior Project Professional, SCS Engineers, 11260 Roger Bacon Drive, #300, VA 20190, USAcMechanical Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India

a r t i c l e i n f o

Article history:

Received 21 June 2006

Received in revised form

24 September 2007

Accepted 1 October 2007

Available online 19 November 2007

Keywords:

Agricultural biomass

Paddy straw

Barley straw

Cotton stalks

Rapeseed–mustard stalks

Collection cost

GIS

Residue to product ratio

nt matter & 2007 Elsevieioe.2007.10.003

thor. Tel.: +91 94635 15101j_s_ranu@rediffmail.com

a b s t r a c t

Agricultural biomass has immense potential for power production in an Indian state like

Punjab. A judicious use of biomass energy could potentially play an important role in

mitigating environmental impacts of non-renewable energy sources particularly global

warming and acid rain. But the availability of agricultural biomass is spatially scattered.

The spatial distribution of this resource and the associate costs of collection and

transportation are major bottlenecks for the success of biomass energy conversion

facilities. Biomass, being scattered and loose, has huge collection and transportation

costs, which can be reduced by properly planning and locating the biomass collection

centers for biomass-based power plants. Before planning the collection centers, it is

necessary to evaluate the biomass, energy and collection cost of biomass in the field. In this

paper, an attempt has been made to evaluate the spatial potential of biomass with

geographical information system (GIS) and a mathematical model for collection of biomass

in the field has been developed. The total amount of unused agricultural biomass is about

13.73 Mt year�1. The total power generation capacity from unused biomass is approxi-

mately 900 MW. The collection cost in the field up to the carrier unit is US$3.90 t�1.

& 2007 Elsevier Ltd. All rights reserved.

1. Introduction

Punjab has made tremendous progress not only in the

agricultural sector but also in the industrial, transport and

household sectors. This has increased energy demand

significantly. Electricity consumption has increased from

15.8 TWh in 1995–1996 to 32.12 TWh in 2005–2006 and the

average annual growth rate of electricity consumption is

14.98%. It is feared that conventional resources may not be

able to meet the rising electricity demand as the annual

growth rate of installed capacity during the last 5 years has

declined to 2.85% in comparison to more than 7% during the

three decades [1,2]. This state does not have its own resources

of conventional fuels such as coal, petroleum products and

r Ltd. All rights reserved.

; fax: +91 1672 280057.(J. Singh).

hydro-electricity energy. The state has to depend on neigh-

boring states for hydro-electricity energy and on the far-off

states for coal. Thus, the development can be jeopardized by

natural calamities not within the control of the Punjab

government and can threaten the sustainability of economic

development. The state has plenty of renewable energy

sources, such as biomass, wind and solar energy, which can

be exploited to provide sustainable energy base for socio-

economic development.

Nowadays, agricultural biomass is widely used as a source

of energy in developed as well as developing countries. In

United States, the biomass resource currently constitutes

about 1% of total electric generating capacity [3]. In China, the

share of agricultural residues, fuel wood and animal waste in

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Table 1 – Identification of agricultural biomass

Category Type of biomass Name of crop

A1 Straw Wheat

Paddy

Barley

Pulses

A2 Stalk Cotton

Maize

Arhar

Rapeseed and mustard

Seasum

A3 Bagasssea Sugar cane

Tops and leaves Sugar cane

A4 Cobsa Maize

Huska Paddy

Shellsa Groundnut

a Indicates that these are processing residues.

B I O M A S S A N D B I O E N E R G Y 3 2 ( 2 0 0 8 ) 3 0 1 – 3 0 7302

total biomass production are 57.7%, 39.2% and 3.2%, respec-

tively [4]. The biomass energy consumption in Pakistan,

Philippines and Sri Lanka has been observed to be 2.72, 1.36

and 1.51 EJ, respectively [5–7]. We know that renewable energy

technologies offer the prospect of increasing energy supplies

in a sustainable way. They also contribute to economic, social

and security benefits at the national and local levels [8]. Due

to technological developments and cost reductions, renew-

able especially solar, hydro, wind and biomass energy are

gaining momentum. Further, the renewable sources, particu-

larly biomass, are less environmentally destructive than the

current fossil-fuel sources [9]. Of all the renewable energy

sources, agricultural biomass is the largest, most diverse and

most readily exploitable resource. Bioenergy technologies

provide opportunities for conversion of biomass into liquid

and gaseous fuels as well as electricity [10].

The estimate of types of agricultural biomass, their

geographical distribution and energy content is important in

assessing the feasibility of plant installation. It is well known

that thermo-chemical properties, such as density, moisture,

ash content and volatile yield are highly dependent on the

types of residues. These affect the conversion process, so that

the design and operation of conversion units should be

properly modified or adapted. In this study, results are

presented of an extensive evaluation of the type and

availability of agricultural biomass in Punjab state with a

view to energy recovery. The availability of agricultural

biomass is in scattered form, so GIS is potentially well suited

for identifying biomass energy potential zones as it can

manage and analyze the multidisciplinary spatial and tem-

poral data needed in this application. This is useful as a

planning tool as it provides the user with the freedom to use

their individual expertise in analyzing the conditions and in

the decision-making process. GIS are being used in many

bioenergy studies. One of the earliest applications is a

decision support system presented for forest biomass ex-

ploitation for energy. In their proposed approach, GIS-based

techniques were integrated with mathematical program and

also assessed the possibility of biomass exploitation for both

thermal and electric energy production in a given area [11].

2. Materials and methods

The methodology adopted for conducting the present study is

given below.

2.1. Categorization of agricultural biomass

In the present study, production of agricultural biomass from

all the major crops is identified. Two main categories are

identified: processed-based (from both annual and perennial

agricultural crops) and field-based biomass. Processed-based

biomass is created at one easily accessible place, where the

product is processed. Field-based biomass remains in the

field, where crops are harvested and normally have a lower

bulk density. The main types of agricultural biomass from

crop production are identified and divided into four categories

based on the study [12] and shown in Table 1.

2.2. Potential of agricultural biomass

The data related to production of crops have been obtained by

consulting agricultural statistics, corresponding governing

authorities (Ministry of Agriculture), research institutes and

available literature. In literature, the biomass types corre-

sponding to the most common agricultural and forestry

products are described. The annual gross potential of

agricultural biomass is determined by using residue-to-

product ratio (RPR). In order to determine the amount of

biomass produced, it is necessary to know the RPR. The

potential of the entire biomass (A1, A2, A3 and A4) in each

block of the state has been cumulated on the basis of the

following model [12]:

ðCRÞi ¼ ðRPRÞi � ðPrCÞi, (2.1)

where (CR)i is the amount of agricultural biomass of ith crop

in ton, (RPR)i the RPR of the ith crop on dry mass basis and

(PrC)i the amount of crop production in ton.

2.3. Availability of unused agricultural biomass for energy

The total sum of agricultural biomass cannot be utilized as an

energy source, because much has already been used for

domestic purposes, heating, animal fodder, bedding, etc. [13].

Unused agricultural biomass means the quantity of biomass

that is being burnt by the farmers itself in the field. The

availability of unused agricultural biomass for energy is

determined by subtracting the current utilization of biomass

from the total production of each crop residue [14]. This is the

actual availability of biomass that can be supplied for energy

generation.

2.4. Spatial energy from unused agricultural biomass

Energy potential from unused agricultural biomass can be

determined by multiplying the net supply potential of unused

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Table 2 – Biomass and energy intensity group

Group Biomass intensity(t ha�1)

Energy intensity(GJ ha�1)

Low o2.0 o30

Semi-

medium

2.0–3.0 30–45

Medium 3.0–4.0 45–60

High 44.0 460

B I O M A S S A N D B I O E N E R G Y 3 2 ( 2 0 0 8 ) 3 0 1 – 3 0 7 303

agricultural biomass by the lower heating value (LHV). Each

biomass source has a different LHV. Bhattacharya [15]

developed the model for estimation of energy potential as

Qi ¼Xn

i¼1

ðNAPi � LHViÞ � nc, (2.2)

where Qi is the energy potential (GJ year�1), NAPi the amount

of unused agricultural biomass for category Ai (t year�1), LHVi

the LHV in (GJ t�1) of air-dry biomass and nc the conversion

efficiency.

Spatial distribution of unused biomass and energy potential

can be developed with GIS. The spatial availability of unused

biomass comprises area, yield and production of crops. These

components have been integrated in a spreadsheet. The

spatial model has been implemented on a computer using

ARC-INFO and spreadsheets. The availability of unused

biomass in each block of Punjab was determined for the year

2000–2001. Apparent biomass intensity was calculated by

dividing the corresponding total quantity of unused biomass

in each block with the net surface area of that block. The

energy potential intensity was calculated by dividing total

energy potential from unused biomass in each block with net

surface area of the block and classified into four groups, viz.

low, semi-medium, medium and high as shown in Table 2.

A GIS/spreadsheet can manage the spatial data. The output

of the spatial model in the spreadsheet was transferred to GIS

for spatial analysis. A map of Punjab state with 137 blocks was

scanned and a bitmap obtained. The raster image of the map

was vectorized using a raster to vector-conversion software.

The block boundary lines, international boundaries lines and

state boundary lines were identified on the vector map. This

map was then transferred to ARC-INFO software to generate

coverage. Coverage with block polygons was generated using

the BUILD and CLEAN commands of the ARC-INFO program.

The area of the block polygons was transformed into the

geographical units and then compared with the geographical

area of the blocks. This was done to prevent any error at the

vectorization stage.

3. Results and discussion

3.1. Potential of agricultural biomass

The geographical area of Punjab state is 53,600 km2, out of

which 82.6% is under cultivation, 4.45% under forests, 10.43%

not available for cultivation, 1.55% under fallow land and

0.97% under other uncultivated land [16]. The net area sown

during 2000–2001 was 41,600 km2. The availability of various

types of biomass from all the main crops has been evaluated.

The potential of biomass at block level was calculated

by using an appropriate RPR value on dry basis and shown

in Table 3.

3.2. Availability of unused biomass

The availability of unused wheat straw is 20%, barely straw

80%, paddy straw 83.55%, seasum straw 80% and pulses 20%.

After analyzing the availability of straw, it is clear that the

paddy straw is the main source of biomass. The cotton stalks

68.7%, maize stalks 75.8%, arhar stalks 30% and stalks of

rapeseed and mustard are 30%. So the main source from this

category is cotton stalks. Sugar cane bagasse and its

tops–leaves are 45% and 40%, respectively. The availability

of unused maize cobs, paddy husk and groundnut shells are

75.8%, 51% and 64%, respectively.

Agricultural biomass has been identified and divided into

four categories. It is observed that category A1 residues are

the largest available unused dry biomass 10.34 Mt year�1.

Category A2 residue is about 1.14 Mt year�1 woody biomass.

Other residues A3 and A4 are available in smaller amounts

and are about 0.95 and 1.30 Mt year�1, respectively, as men-

tioned in Table 3. It is also observed that the main source of

agricultural biomass is straw, which is 2/3rd of the currently

available biomass in the state.

3.3. Spatial availability of unused agricultural biomass

The total amount of unused agricultural biomass in Punjab is

about 13.73 Mt year�1. This corresponds to a surface concen-

tration of 274.30 t km�2. Fig. 1 reports the spatial variation in

the biomass at the block level. The analysis of the data

presented in Fig. 1 shows that the area under low range, semi-

medium, medium and high range is 20%, 35%, 32.5% and

12.5%, respectively. The results of spatial distribution of

biomass availability for the year 2000–2001 indicate that the

major area of low range of agricultural biomass is in the east

and west-southern parts of the state. The major area under

semi-medium range is the central and southern-east parts of

the state. The area under medium range is the northern and

west parts of the state. The major area with high range

availability of biomass was observed in the southern-east part

comprising Sangrur, Patiala, Ludhiana districts and Bamyal

block (Gurdaspur) in the northern side. The regions of

Fathegarh Sahib, Sangrur, Patiala, Ludhiana, Moga are areas

of interest for thermo-chemical conversion of agricultural

residues, with a surface fuel concentration in the range of

2.0–5.0 t ha�1.

3.4. Potential of energy from unused biomass

The energy potential of biomass was estimated for all the

districts of the state by using LHV. The average value of LHV is

to be considered for evaluating the energy potential from

unused biomass. The average value of various categories of

biomass A1–A4 has been reported in Table 4.

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Table 3 – Production of agricultural biomass, cultivated area and unused biomass for different crops

Category Residuetype

Crop Cultivatedarea (km2)a

Moisturecontent (%)b

Total biomass(dry basis) (kt)

Used(%)b

Unused biomass(dry basis) (kt)

A1 Straw Wheat 34,765 9.2 14,317.30 80 2863.45

Barley 70 – 45.28 20 36.22

Paddy 25,406 10.6 8774.14 16.45 7417.70

Seasum 206 – 19.35 20 15.48

Pulses 274 – 31.92 80 6.88

Total – 23,187.99 – 10,339.73

A2 Stalk Cotton 6043 12 707.47 31.3 486.03

Maize 1649 11.5 800.44 24.2 598.70

Arhar 91 – 39.00 70 11.70

Rapeseed

and mustard

498 – 142.49 70 42.75

Total – 1689.43 – 1139.18

A3 Bagassse Sugar Cane 1441 15 1154.14 40-50 577.07

Tops and

leaves

59.2 940.99 60 376.40

Total – 2095.13 – 953.47

A4 Cobs Maize 1649 8.6 207.78 24.2 154.70

Husk Paddy 25,406 9.6 2417.18 49 1152.76

Shells Groundnut 37 9.87 0.92 36 0.49

Total – 2625.88 – 1307.95

Cultivated area represents total cultivated area during the year.a Source: [16].b Source: [14].

< 2 tha-1

< 5 tha-1

2 - 3 tha-13 - 4 tha-14 - 5 tha-1

Fig. 1 – Spatial availability of unused biomass in Punjab.

B I O M A S S A N D B I O E N E R G Y 3 2 ( 2 0 0 8 ) 3 0 1 – 3 0 7304

The total theoretical energy potential of agricultural

biomass was estimated for all the districts of the state. This

is only the first basis of evaluation, which should be corrected

to take into account the efficiency of the conversion process.

The conversion efficiency was not considered as the present

research shows only the energy potential resource. The

maximum energy potential covers Sangrur and Ferozpur.

Minimum energy potential is observed in Nawanshar, Ropar,

Faridkot and Mansa districts as shown in Table 5.

3.5. Model for unit collection cost of agricultural biomassin the field

Collection cost is the cost to collect the biomass from the field

in scattered form near the transport unit for its loading. For

manually and reaper-harvested fields, the collection costs are

to be assumed zero, because biomass is already collected at

one location in the field. So collection costs are considered for

combine-harvested fields only. Collection costs depend on the

spatial density, unit costs of recovery and capacity of the

transportation units. The collection costs are the sum of total

recovery costs for harvesting biomass and transport costs for

moving the biomass from a loosely spread form to the

transport unit. The recovery costs depend on technology

used for biomass recovery. It is assumed that recovery costs

are proportional to the area from where the biomass is

recovered. A mathematical model has been developed for

unit collection cost in the field and is presented below.

Let the transport unit be placed at ‘O’ in Fig. 2 and biomass

be recovered from a circular area of radius ‘ro’ surrounding the

transport unit. If qc is the load capacity of transport unit, r is

the spatial density of biomass availability, Cr is the biomass

recovery costs (US$ km�2); and Ct is the unit cost of biomass

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Table 4 – Average heating value of agricultural biomass

Category Biomass Crop LHV (GJ t�1)a Average value of LHV (GJ t�1)

A1 Straws Wheat 17.15 16.02

Barley 17.56b

Paddy 15.03

Seasum 14.35

Pulses 16.02c

A2 Stalks Cotton 17.40 17.40

Maize 16.67

Arhar 18.58

Rapeseed and mustard 17.00

A3 Sugar cane Bagasse 20.00 20.00

Tops and leaves 20.00c

A4 Maize Cobs 17.39 17.65

Paddy Husk 15.54

Groundnut Shells 20.01

a Source: [17].b Source: [18].c Signifies the average value of LHV of the corresponding category.

Table 5 – Energy potential from unused biomass invarious districts of Punjab

Sl. no. Name of district Energy content (TJ)

1 Hoshiarpur 12.61

2 Jalandhar 13.06

3 Nawan Shahar 6.22

4 Ludhiana-I 19.89

5 Ferozepur 26.17

6 Amritsar 22.06

7 Gurdaspur 16.17

8 Kapurthala 8.44

9 Bathinda 10.17

10 Patiala 19.06

11 Sangrur 26.28

12 Ropar 6.72

13 Faridkot 6.89

14 Moga 8.78

15 Mukatsar 11.67

16 Mansa 7.17

17 Fathegarh S. 8.50

dr

r

O

ro

'O' Position of Transport Unit

Fig. 2 – Collection cost in the field.

B I O M A S S A N D B I O E N E R G Y 3 2 ( 2 0 0 8 ) 3 0 1 – 3 0 7 305

transport (manual or machine transport) from place where it

is lying to the transportation unit (US$ km�1 t�1).

qc ¼

Z ro

0r2pr dr ¼ prr2

o ) ro ¼

ffiffiffiffiffiffiqc

pr

r. (3.1)

Total collection costs of biomass in the field

Z ro

0ðCr2pr drþ Ctrr2pr dr ¼ prr2

oCr

rþ

23

Ctro

� �

¼ qc

Cr

rþ

23

Ctro

� �. ð3:2Þ

Unit collection cost (Cc) is defined as the ratio of total

collection cost to the carrying capacity of transport unit (qc):

Cc ¼ Cr1rþ

23

Ctro (3.3)

From Eq. (3.3) it is clear that unit cost of biomass collection

(Cc) is a function of r, Cr and Ct. The values of Cr and Ct

are US$1862.43 km�2 {US$1 ¼ Rs.39.42 (Indian rupees)} and

US$6.21 t�1 km�1, respectively [1].

The effects of spatial density of biomass and carrying

capacity of the transport unit on unit cost of biomass

collection in the field are shown in Fig. 3. The collection cost

has been calculated with Eq. (3.3) with a variation of carrying

capacity of the transport unit from 1 to 5 t, and biomass

density of the collection area varies from 100 t km�2 to

1 kt km�2. It is clear from Fig. 3 that unit collection costs

decrease with increase in spatial density of biomass. It is also

observed that there was a marginal increase in unit cost of

biomass collection when carrying capacity was increased

from 1 to 5 t. The main reason of unit collection cost increases

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0.00

5.00

10.00

15.00

20.00

0 100 200 300 400 500 600 700 800 900 1000

Col

lect

ion

cost

of

biom

ass

in th

e fi

eld

[US$

t-1 ] qc = 1t

qc = 2tqc = 3tqc = 4tqc = 5t

Cr = 18.62 US$ ha-1

= 1862.43 US$ km-2

Ct = 6.21 US$ t-1 km-1

Biomass density [t km-2]

Fig. 3 – Effect of collection cost in the field versus biomass density.

B I O M A S S A N D B I O E N E R G Y 3 2 ( 2 0 0 8 ) 3 0 1 – 3 0 7306

while carrying capacity increases from 1 to 5 t is be-

cause larger carrying capacity requires larger quantity of

biomass, which is to be collected from a larger radius. When

radius increases then definitely unit collection cost increases

in the field. Further, decreases in unit collection 50%,

65%, 75%, 80%, 83%, 85%, 87%, 89% and 90% were observed

when spatial density increased to 2, 3, 4, 5, 6, 7, 8, 9 and 10

fold.

4. Conclusion

The state of Punjab has plenty of agricultural biomass, which

can augment energy generation to an extent of 235.14 TJ per

annum in the state. Spatial availability of unused agricultural

biomass evaluated and the observed area under low range,

semi-medium, medium and high range is 20%, 35%, 32.5%

and 12.5%, respectively. It has been observed that the unit

collection cost in the field decreases with increase in spatial

density of biomass, while it marginally increases with

increase in carrying capacity of transport unit. The average

unit collection cost in the field for spatial biomass density of

500 t km�2 has been found to be US$3.90 t�1.

Acknowledgments

The authors are grateful to the Director, School of Energy

Studies for Agriculture, Punjab Agricultural University (PAU),

Ludhiana, Punjab, for cooperation, guidance and facilities

provided for completion of this work.

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