BIORESOURCE INVENTORY USING REMOTE SENSING FOR
REGIONAL ENERGY PLANNING
RAMACHANDRA, T.V.,
VIJAYA PRASAD, B.K.
AND
SAMAPIKA
PADHY
CENTRE FOR ECOLOGICAL SCIENCES
INDIAN INSTITUTE OF SCIENCE
BANGALORE 560 012, INDIA
Address for Communication:
Dr. Ramachandra, T.V.
Energy Research Group [CES]
Centre for Ecological Sciences
Indian Institute of Science
Bangalore 560 012, India
E Mail:
cestvr@ces.iisc.ernet.in
cestvr@hamsadvani.serc.iisc.ernet.in
energy@ces.iisc.ernet.in
Telefax: 91 - 080 - 3315428 / 3342085 / 3341683 [CES-TVR]
Telephone: 91 - 080 - 3340985 / 309 2506 / 3340985
BIORESOURCE INVENTORY USING REMOTE SENSING FOR
REGIONAL ENERGY PLANNING
Ramachandra, T.V., Vijaya Prasad, B.K. and Samapika Padhy
Centre for Ecological Sciences
Indian Institute of Science
Bangalore 560 012, India
Abstract:
Energy is the fundamental tool to attain minimum quality of life. It is also an
indicator of the economic status of a region. One of the main concerns today is
to meet the ever-growing energy demand in an environmentally sustainable
manner. Bioenergy continues to contribute significantly to the total energy
consumption. It plays a critical role in sectors such as domestic and rural
industries. In this context it is necessary that the regional planning exercises
formulate policies to develop sustainable bioenergy systems consistent with the
objectives of ecodevelopment and environmental conservation. However, lack
of adequate relevant information of different bioenergy resources in a regional
planning framework, hampers effort to develop alternatives to achieve multiple
goals set by environmental objectives and the energy demand on the resource.
Hence, for sustainable development, there is a need to determine the inventory
of demand and resource situation in a region. In this regard a study has been
carried out in Kolar district to explore an environmentally sustainable bioenergy
strategy by developing bioresource supply and consumption database. Our
study shows that the bioenergy constitute 82-85% of the district’s total energy
consumption. This paper provides insight to the bioresource inventory at district
level, using spatial and temporal tools and implications on policy structures.
1.0
Introduction:
Bioenergy continue to contribute significantly to the total energy consumption.
In sectors such as domestic and rural industries sector, they play a critical role.
In this context it is necessary that the regional planning exercises formulate
policies to develop sustainable bioenergy systems consistent with the objectives
of ecodevelopment and environmental conservation. However, lack of adequate
relevant information on a different bioenergy resources in a regional planning
framework hamper efforts to develop alternatives to achieve multiple goals set
by environmental objectives and the energy demand on the resource. This
paper provides insight to the bioresource inventory using spatial and temporal
tools and implications on policy structures are examined.
Detailed planning would be required from National, to State, to District, to
Taluk and village levels. The inappropriate selection and site matching of
species or management strategies can have adverse effects and lead to
degradation and abandonment of land. However, the correct selection of plant
species can allow the economic production of energy crops in areas previously
capable of only low plant productivities. Simultaneously multiple benefits may
accrue to the environment. Such selection strategies allow synergistic increases
in food crop yield and decreased fertiliser applications while providing the local
source of energy and employment.
Bioresources play a dominant role in the energy balance of various states
in India, and shortages of bioenergy exist in many regions. One of the
important databases needed for management of forests , regional energy
planning, etc. is the total quantity of biomass per unit area referred to as
biomass density. This also aides in estimating emissions of carbon dioxide
resulting from changes in the vegetation cover. Bioresource inventories have
shown to be valuable sources of data for estimating biomass density, but
inventories for any states in India are few in number and poor in their quality.
This lack of reliable data has been overcome by use of a promising approaches
that produces geographically referenced, estimates
by modelling in a
geographic information system. (GIS) and remotely sensed data. This approach
3
used to produce geographically referenced, spatial distributions of potential
and actual above ground biomass density in Kolar district.
Multi temporal satellite imagery (LISS III) with GIS would help in
developing agro ecological zones in the district - as the first stage of a multi
level sampling frame for estimating available bioresources. Use of multi stage
sampling design enables a regional inventory of bio resources inventory quickly
and effectively. Automated selection of sampling units from digitally classified
satellite imagery are proved very efficient, and the methodology for deriving
sampling expansion factors makes the result highly robust with respect land
cover classification accuracy. The regional agro ecological zones provides a
consistent sampling frame to identify the bioresource status (surplus / deficit).
The methodology could be successfully applied to other regions / states.
The resource base in Kolar under each sector such as forests, agriculture,
horticulture and animal residues are analysed spatially.
2.0
Objectives
1.
To estimate the bioresources available in the arid
(Kolar) zone of
Karnataka State, India. The resource base in a region under each sector
such as forests, agriculture, horticulture and animal residues would be
analysed spatially. the estimates of woody biomass derived from field
data includes a)
Estimates of biomass productivity, above ground biomass - forest
inventory,
b)
2.
Bioresidues available from agriculture and horticulture sectors,
Annual increment and regeneration potential of the bioresources within
the region and
3.
To assess the bioresource status in the Kolar district
3.0
Study Area: Kolar District
The Kolar District is located in the southern plains region of the Karnataka
State, India. It lies between 770 21' to 780 35' east longitude and 120 46' to
4
130 58' north latitude and extends over an area of 8,225 sq.kms. The
population was 22.17 lakhs in 1991 (as per census report). For administrative
purposes the District has been divided into 11 Taluks. There are 15 towns and
3,325 inhabited villages in the District.
Kolar belongs to the semi arid zone of Karnataka. In the semi arid zone
apart from the year to year fluctuations in the total seasonal rainfall, there are
also large variations in the time of commencement of rainfall adequate for
sowing as well as in the distribution of drought periods within the crop growing
season. Kolar district depends upon the distribution of rainfall during the
southwest and northeast monsoon seasons. Out of about 280 thousand
hectares of land under cultivation 35% is under well and tank irrigations. There
are about 951 big tanks and 2934 small tanks in the district. The average
population density of the district is 2.09 persons/hect (rural) and 2.69
persons/hect (rural+urban). The population density ranges from 1.44
(Bagepalli), 1.69 (Gudibanda), 1.70 (Srinivaspura) to the maximum of 2.55
(Kolar). While, the population density in taluks lies within this range Bangarapet (2.52), Malur (2.38), Gauribidanur (2.36), Sidlaghatta (2.16),
Chintamani (2.10), Mulbagal (2.04), Chikkaballapur (1.92). Fig 1a, depicts
talukwise population and livestock densities.
4.0
Methodology
An integrated approach involving
compilation of data from government
agencies and institutions, application of spatial and temporal analyses using
remote sensing data, Geographic Information System (GIS technique) and
conventional field survey (ground truthing) have been adopted in this study
(Fig 1b). The vegetation map for Kolar district (talukwise) were prepared based
on the interpretation of IRS-1C satellite imageries, using the visual
interpretation keys such as tone, colour, texture, pattern, association, size,
shape, topography and drainage. This involves
Data acquisition, loading, composite generation and georeferencing
Training sites, Ground truth collection (field data collection).
Signature generation for classification
Demarcation of boundaries and transfer of administrative features
5
Extraction of statistics
Taluk and district boundaries, and road network were digitised from SOI
toposheets.
Georeferencing has been done by extracting GCPS from topographical map and
using GPS. Multispectral classifications was carried out using soft classifiers
(based on Bayesian probability theory).
5.0
Biomass Resources Assessment
Non-availability of accurate, reliable and up-to-date data for various biomass
resources is the main reason hindering an accurate assessment of the
bioresources of a region. Such data need to be obtained and updated
periodically.
Surveying, sampling and analytical procedures is used for the
collection of this data.
5.1
Wood Biomass
-
Average biomass content per unit area in different forests, forest
reserves, plantations, woodland transitions in various climatic zones;
-
Average biomass being removed and added in above areas on annual
basis in various climatic zones;
-
Wood supply and demand for rural and urban areas for all States of the
Federation for various applications of wood;
-
Energy value of various types of wood available and its present mode of
utilization as fuel.
5.2
Forrage Grasses and Shrubs
-
Average biomass content per unit area in wooded shrub grasslands,
shrublands, grassland/shrubland transitions and grasslands, annual
harvestable and renewable percentages and methods, energy values, and
present utilization.
5.3
Residues and Wastes
Crop Residues and Wastes
-
Total annual crop production of food and cash crops and their hectarage
6
for various climatic zones.
-
Average crop residues and wastes per unit area for each of food and cash
crop in different climatic zones, their proportion to net grain yields,
harvestable percentages and methods, energy values, and present
utilization.
Animal Wastes
-
Total number of animals and their categories in different climatic
zones/States.
-
Average amount and type of feed, and waste production per day for each
type of animal, waste collectable percentages and methods, energy
values, and present utilization.
The resource base in a region under each sector such as forests, agriculture,
horticulture and animal residues is analysed spatially.
5.4
GIS in Bioresources Assessment and Monitoring
The acquisition of basic inventory data is fundamental in the regional energy
planning endeavour. Data include vegetation type, soil type, species type,
class/stand structure, canopy details, density and the boundaries of
management units. Data collection techniques range from selecting sample
plots (quadrats or transects) for ground surveys to using topographic maps,
and emerging Global Positioning Systems (GPS), alongwith Remotely Sensed
data and Geographic Information System (GIS) make direct and substantial
contributions. Geographic Information System can contribute to assessment of
bioresoorce availability, demand and offers the potential to predict future
needs.
Spatial data input, editing, maps creation, overlaying, reclassification and
suitability analyses characterize the inventorying, monitoring and decision
making process. Resource assessment include
1.
inventorying bioresources available for fuel, food and fodder from
various categories of land cover,
2.
related data such as topography, soils, roads and hydrology and
7
3.
assessment of bioresource productivity (from forests, agriculture,
horticulture, etc.).
In addition to remote sensing, spatial positioning technologies have begun
to influence surveying techniques and, thus resource inventories.
Global
Positioning System (GPS) technology is based on a set of orbiting satellites (a
total of 24), which provides three dimensional positional fixes with an accuracy
within tens of meters. With four or more satellites in view, a GPS receiver can
interpret the carefully timed satellite signals to determine geometrically the
latitude, longitude and altitude at the operators position. GIS applications of
GPS include georeferencing of satellite imagery and navigating to sample sites
for ground truth exercises particularly relevant for forest and plantation
inventories.
5.5
IRS-1C LISS -III Data for Bioresource Assessment
IRS-1C
with 23.5 m
spatial resolution provide data outputs adequate or
comparable to the scale of 1:50,000. Eleven taluks in Kolar district has been
selected using March 1998 data and was analysed using soft classifiers (based
on Bayesian probability theory). Bioresource is estimated using yield data for
each vegetation type in the inventory: Forests, Agriculture, Horticulture, Shrub
land, etc. Yields were multiplied by spatial coverage (area) for each land use
category. Talukwise residues from livestock were computed from population
and dung yield data for each type of animal.
5.6
Interpretation of Remotely Sensed Data for Land Use / Land
Cover
Sensor records response based on many characteristics of land surface,
including natural and artificial cover. Usually the elements of tone, texture,
pattern, shape, size, site and association are used to derive information about
land use / cover mapping.
5.7
Sampling Frame
8
An important initial objective is to develop the agro-ecological zonation to
provide a valid basis for extrapolating the results of the supply survey to the
regional level. The woody biomass and agricultural residues surveys requires a
regional zonation which reflected the range of natural vegetation as well as
agricultural land use. A suitable zonation is also required to provide a valid
sampling frame to spatially link the results of the supply and demand surveys.
The use of satellite imagery enables actual land cover classes to be
mapped at a regional level, which is a more preferable approach to developing
a valid and robust sampling frame. There are well-established methodologies
for developing land cover zonation at national scales by using multi-temporal
imagery to distinguish between patterns of vegetation activity with time.
The
imageries, with Geographic Information System and combined with ancillary
data on rainfall, topography, climate, and the extent of irrigated farmland to
produce a zonation of land cover types for the whole Region.
Sampling units was selected within each IRS scene for field measurement
of woody biomass and crop residues. Acquisition dates depends on cloud free
period for both woody vegetation and crop sampling. This required a trade-off
between the optimal season for classifying woody vegetation (June-July)_and
for classifying crops at their stage of maximum greenness (March-April for the
spring or rabi harvest and September for the autumn or kharif harvest).
5.8
Selection of Sampling units for Measurement of Woody Biomass
Sampling units for field measurements of biomass fuels would fit within
each scene. Both imagery, topographic maps, GPS were used the field work.
Digital classifications of vegetation cover from the LISS be used as a
second-level sampling frame for drawing field samples for the woody biomass
survey. For a selection of primary sampling units for field work, to ensure
robustness with respect to any variation in classification accuracy between
images. The following approaches were used.
1.
The agro-ecological zonation provide a sound sampling frame at the
regional level. The zonation provides a basis for introducing consistency
between scenes, in that the proportion of vegetation cover classes falling
9
within each zone would be typical of that zone.
2.
For the woody biomass survey, ground truthing was carried out to
confirm whether vegetation cover classes derived from the imagery
contained significant woody biomass resources. This included cultivated
farmland.
6.0
Literature Review - Present Role of Bioenergy:
Bio energy is one of the primary sources of fuel in our country. Recent study by
us on energy utilisation in Karnataka considering all types of energy sources
and sectorwise consumption reveals that traditional fuels such as firewood
(7.440 million tonnes of oil equivalent - 43.62%), agro residues (1.510 million
tonnes of oil equivalent - 8.85%), biogas, cowdung (0.250 million tonnes of oil
equivalent-1.47%) accounts for 53.20% of total energy consumption in
Karnataka [Ramachandra, T.V. and Subramanian, D.K., 1997]. In rural areas
the dependence on bio energy to meet the domestic requirements such as
cooking and water heating purposes is as high as 80-85%. Fuelwood and
agricultural residues are also widely used as fuel in rural industries such as
cashew processing and other agro processing industries, brick kilns, and in
commercial sectors such as hotels etc. Investigations of energy consumption in
few selected
villages of Kolar
Taluk reveal that per capita fuel wood
consumption for domestic purposes such as cooking, water heating etc., are in
the range 1.3 to 2.5 kg/person/day [Pramod Dabrase, et.al. 1997.].
In developing countries such as India there is seen to be a large
difference in the Energy Consumption Patterns in the Urban and Rural areas.
According to a survey carried out in 1963-64 and 1973-74 (by National Sample
Survey), the average per capita consumption of energy has not changed
significantly during this period and it also indicated that the per capita
consumption of energy in Rural areas is more than that in Urban areas, which is
mainly due to the relatively low efficiency of traditional(energy) devices and the
availability of free fuel. These surveys also indicated that the share of
commercial and Non-commercial energy in the rural areas works out to 20%
10
and 80% respectively, Corresponding figures for Urban areas are 49% and 51%
[NCAER, 1985]
6.1
Remote Sensing Applications in Bioresource Inventory
Kimothi,M.M. et.al. (1997) carried out a study of horticultural plantations in
Kumarsain Tahsil in Simla district of Himachal Pradesh using remote sensing
IRS-LISS II satellite data, Survey of India Topographic maps, Forest working
plan maps of Kumarsain range along with Ground truth data on location, site
characteristics, growth stages and cultural practices of horticultural plantations.
This study shows that the identification and discrimination of horticultural
plantation needs multi-season satellite data. FCC of IRS LISS II revealed that
during the April month maximum contrast between the horticultural area,
forests, agriculture and other land use categories was observed. The overall
interpretation accuracy assessed on 40 sample points was found to be 87% at
90% confidence limits.
Palaniyandi, M. and Nagarathinam, V. (1997) carried out land use / land
cover mapping of Thiruvallur area of Chengai-MGR district in Tamil Nadu for
1986-90 using Landsat 5 TM, and IRS-1A LISS II, Sept.1986 and Survey of
India Topographic maps. This study shows very little increase of 60 ha in
agricultural land and a declination in forested areas from 6,593 ha in 1986 to
6,415 ha in 1990.
Degraded/open forests areas have declined during the
period of the analysis from 3,928 ha to 3,043 ha accommodated by an increase
in forest blanks from 490 ha in 1986 to 1,297 ha in 1990.
Murthy,K.S.R. and Rao,V.V. (1997) carried out temporal studies of land
use/ land cover in Varaha River basin, Andhra Pradesh in 1986-92 using
Landsat TM (April ‘86), IRS LISS II (May’92) and Survey of India toposheets.
This study shows that the irrigated area has increased in sub-basins 3,4,5,13
and 16 by 9.63% from 1986 to 1992 whereas in sub-basins 13 the irrigated
area has reduced from 50.87 to 40.00 and in 19 from 39.5 to 19.75.At higher
levels, jowar and maize are the main crops. In the lower parts of the valley
jowar, maize, bajra and pulses are the main crops. Paddy is the major irrigated
crop followed by rain fed sugarcane cultivated in low lying plains near
11
Kotauratla during rainy season. The un-irrigated land in 1986 was 274.67
sq.kms and it decreased to 272.85 sq.kms in 1992.The total area of deciduous
forests was only 1.52 sq.kms in 1986 which increased to 4.70 sq.kms in
1992.The area of degraded forests has increased from 89.90 sq.kms in 1986 to
107.50 sq.kms in 1992.The upland with or without scrub has decreased from
249 sq.kms in 1986 to 245 sq.kms in 1992.
Samant,H.P. and Subramanyan,V. (1997) carried out studies of landuse
/landcover in Mumbai and its effects on the drainage basins and channels using
Landsat TM, Survey of India topographical maps. This study shows that drastic
reduction of about 55% in forest/agricultural land has taken place while 300%
increase in built-up area is seen.
Madhavan Unni et.al. (1991) carried out a study of IRS-1A application in
forestry using LISS I & II, IRS-1A. The study of Narmada valley and
environments from 1972–89 has been done by interpreting two submergible
areas viz., Sardar Sarovar in Gujarat and Indira Sagar in Madhya Pradesh. It
revealed that a little forest area exists in the project area of Sardar Sarovar
where as a considerable area of good forest will be lost in Indira Sagar area if
the project is taken up as proposed. In the study of forest area in Western
Himalaya from 1983 –89 shows that the net depletion of forest during the
period has been 2.96%.The study of forest biomass in Rajaji National Park of
Uttar Pradesh revealed that 1km x 1km of
forest sinks 0.188*109 gm
carbon/year.
Rao et.al. (1991) carried out wasteland mapping in India using IRS-1A,
LISS-II, Survey of India and revenue and census maps. For this study 146
critically affected districts were selected. The study revealed that the total area
under wastelands in India was estimated to be 53.3 million ha which is 16.21%
of the total geographical area. The interpretation accuracy has been found to be
85-90%.
Yadav et.al. (1988) carried out a study on forest type mapping using IRS
LISS-I data and compared the result with Landsat MSS data using Survey of
India topographical map, forest map of South India, Landsat MSS (April 1985),
IRS-1A LISS-I (April 1988) and IRS-1A LISS-I (April 1988) data. The study area
12
is the forest divisions of Haliyal and Belgaum of the Karnataka state. The result
shows that the discrimination of evergreen, moist deciduous and dry deciduous
forest types is very clear in LISS-I data compared to Landsat MSS data.
Gaussian stretch digital enhancement product depicts forest type differentiation
more clearly in IRS LISS-I data. Teak plantations are not emerging clearly in
IRS LISS-I data compared to Landsat MSS data.
Jadhav et.al. (1989) carried out a study on forest mapping and damage
detection using Landsat MSS and TM, and IRS LISS I and LISS II, SPOT data
along with SOI topographical maps, forest management maps, forest type maps
and forest stock maps. The sites selected for this study are Yellapur
(Karnataka), Jatga (M.P.), Nepanagar (M.P.), Shirpur and Sanghvi ranges
(Maharastra). Forest type map of Yellapur forest division were prepared with
accuracy of 90% at 95% confidence limit. Forest type map of Jatga range of
M.P. have been prepared with accuracy of 90% at 95% confidence limits. Forest
density map of Nepanagar(M.P.) was prepared with accuracy of 80% at 85%
confidence limits. Afforestation and deforestation map of Matin and Jatgablocks
of Jatga range(M.P.) were prepared with accuracy of 85% at 90% confidence
limit.
Navalgund et.al. (1996) carried out a study on crops using IRS-1C data.
They have used WiFS data over Andhra Pradesh, Punjab and West Bengal and
LISS –III data of Palli District (Rajastan). The study of Punjab gave an early
pre-harvest estimate of 3.4715 Mha for 1995-96 rabi season. The fully revised
estimates have been released by the Ministry of Agriculture for wheat, gram
and mustard for crop seasons 1990-91 and 1991-92 add up to 3.406 and 3.398
Mha, respectively. The estimated acreage of paddy and pulses are 5.09 lakh ha
and 2.69 lakh ha, respectively.
Rao et.al. (1996) carried out a study on land use mapping and planning
using WiFS data for the entire Andhra Pradesh, LISS-III data for Kaziranga
National Park (Assam) and its environs, composit data of LISS-III and PAN of
urban fringe of Hyderabad, NRSA carried out wasteland mapping using 1980-82
Landsat MSS data and it revealed that about 53.3 Mha (16.2%) area to be
wasteland. The study of Andhra Pradesh shows that due to the limitation of
13
data (single season) some of the classes like open/degraded forest and uplands
could not be separated, in some areas, with the result that some of the uplands
in parts of Nizamabad, Medak and Ranga Reddy districts were mixed with
degraded forest. Some of the fallow lands in south-western part of the state
have been misclassified as upland. The interpretation accuracy has found to be
80-85%.Using multi season data the problem can be solved.
Roy et.al. (1996) conducted a study of forestry application using IRS-1C data.
The site selected for this study are Western Ghats and Gujarat. WiFS coverage
of 24 January 1996 has been analysed. The result shows that the data are
excellent sources for rapid forest survey. Around 6.5 lakh km2 area in Western
Ghats could be analysed with a high degree of consistency. The study also
suggests that WiFS data could be used for annual in season forest fire
monitoring and to account damages and suggest control measures for large
scale forest fires.
7.0
RESULTS AND DISCUSSION
Forests provides both tangible and non tangible benefits towards
amelioration of soil, protection of environment besides economic benefits
through timber, minor forest produce, fuel and other products.. Most parts of
Kolar district experiences severe bioresource scarcity and immediate policy
interventions are needed to restore the ecological balance of the region. Hence
this study is aimed to determine the supply and demand situation of Biomass
fuels in this region and spatially link this database to determine the energy
surplus and deficit areas.
Secondary data collected from the forest department
shows that 7
taluks have forest cover less than 10%, 2 taluks are in the range 10 - 20%
(Gudibanda and Srinivaspura) while, remaining two taluks (Bagepalli and
Chikballapur) have forest cover greater than 20%. Actual resource cover based
on conventional method and satellite remote sensing carried out for Kolar
district (talukwise) are given in Fig 2a-k. Woody biomass annual availabilities in
Kolar taluks taking into account woody biomass productivity of 3.6 t/h/yr
(evergreen, semi evergreen), 3.9 t/ha/yr (deciduous) and 0.9 t/ha/yr (Scanty
14
and Scrub vegetation).
7.1
Bio Energy from Animal Residues:
Livestock:
Livestock are an important component of an agro ecosystem.
For
instance, livestock provide the critical energy input to the crop lands required
for ploughing, threshing and other farm operations. Animal dung provides
essential nutrients required for soil fertility and crop yields in the form of
organic manure. Data collected from respective Taluk's Veterinary Department
regarding livestock population. Livestock density (cattle and buffalo), talukwise
in Kolar district is given in Table 1.
Table 1: Talukwise density (number of villages in each range) in Kolar
District:
Taluk
<0.5
0.5 - 1 1 - 1.5 1.5 - 2 2 - 2.5 >2.5
138
114
70
33
12
23
Chikballapur 100
65
22
26
14
27
Chintamani 140
121
69
42
17
19
Gauribidnur 81
81
37
18
7
13
Gudibanda
41
28
15
9
5
8
Kolar
132
130
70
15
15
0
Malur
143
123
57
18
11
13
Mulbagal
160
96
54
17
6
13
Sidalghatta 140
49
35
13
5
48
Bagepalli*
Bangarpet
15
Srinivasapur 151
92
47
22
9
27
* livestock data for Bagepalli not available
This shows that in Kolar taluk 132 villages have an average density less than
0.5 (livestock/hectare), 130 villages have density in the range 0.5 - 1.0, 70
villages have a density 1.0 -1.5, while remaining 30 villages have density
greater than 1.5.
Quantity of the dung yield per cattle varies from place to place - It is seen
that dung available per animal (cattle) is about 3-7.5 kg/adult animal,
buffaloes 12-15 kg, stall fed buffalo about 15-18 kg, hybrid ones about 15-18
kg. By considering lower figures (such as 3 kg per animal for cattle, 12
kg/animal for buffaloes), total dung available is about 3,71,991 kg/day. With
the assumption of 0.036 m3 of biogas yield per kg of cattle/buffaloes dung
(Khandelwal and Mahdi, 1986), we estimate that total quantity of gas available
(if all is used for biogas) is about 13,391 m3/day. It is estimated that per
capita requirement of gas is about 0.34-0.43 m3/day for domestic purposes.
Which means, that gas generated by animal dung is sufficient to meet the
requirement of 19.62% of total population of Kolar taluk.
7.2
Sectorwise Energy Demand:
This involved
survey of the present energy consumption
in
domestic,
agriculture, industrial, commercial and services sector for various enduses
covering the magnitude, types of energy sources, trends and preferences in
consumption.
Domestic sector: Energy survey is being carried out in sample villages in
each taluk. villages are selected based on per capita agricultural land, per
capita livestock population, forest land as % of total land, per capita waste land.
These parameters reflect the availability of energy resources in the village
energy system.
questionnaire based
stratified random sampling of Domestic sector
(based on land holding, community etc.) in each selected village, to
16
determine cooking, lighting and other domestic energy demand. Samples
from the population would be selected in a manner so as to represent all
category and number of samples so as to represent atleast
5% of
population. From this set of samples we select few households (who are
cooperative) to carry out energy survey in each season in order to see
the seasonal variation in fuel consumption.
8.0
Status of Bioresource Availability and Demand for Kolar District:
The ratio of bioresource availability and demand gives an idea of level of
bioresource in a region. The availabilty to demand ratio ranges from 0.11
(Kolar)
to
0.73
(Srinivasapura),
fuelwood
demand
kg/person/day. The ratio being less than one indicates
is
taken
that
as
1.3
there is bio
resource scarcity. Talukwise bioresource status is given in Fig 3.
9.0
Alternatives: Techno Economic Analyses of Bio Energy Systems.
The fundamental forms of bio energy use are:
(i) The traditional domestic uses: for household cooking, lighting and
water heating (for bath). The efficiency of conversion of the biomass to
useful energy is between 5% and 15%.
(ii) The rural industrial use in agro processing, bricks and tiles, pig iron
where the biomass is considered as free energy source. There is generally
little incentive to use the biomass efficiently so conversion of feedstock to
useful energy commonly occurs at an efficiency of 15% or less.
(iii) Biological conversion including anaerobic digestion for biogas
production and fermentation for alcohol.
The overall efficiency of biomass utilisation depends on the moisture
content of the fuel and type of stoves used. Freshly cut wood contains about
25-60% moisture. The removal of a kilo gram of water from wood involves an
expenditure of about 620 - 670 kcals. It is noticed that a reduction of 25%
moisture in fuelwood would cause a saving of nearly 15% of the fuel wood. It
is observed that dried wood with moisture content of 8% releases heat too fast
and the whole log tends to burn bringing the flame out of the stove.
17
9.1
Fuel Efficient Stoves:
Most commonly used stoves in most of households for cooking are either mud
stoves or three stone stoves also referred as traditional stoves (TC'S). The
efficiency of these stoves are less than 10%. Applying the principles of
combustion and heat transfer, fuel efficient wood and other biomass burning
designed by ASTRA (Lokras, 1992) also called as Astra stoves or Improved
cook stoves (IC's). In Astra stoves complete combustion of fuel wood with as
little excess air as practicable to generate the highest temperature of flue
gases. In IC's combustion of fuelwood is carried out over a grate in an enclosed
fuel box with ports of suitable size for entry of air. The grate helps in entry of
air below the fuel bed to burn the char as well as for separation of ash from
fuel. Air required for burning the volatile matter released as consequence of
heating the fuel (also referred as secondary air), enters through a port at a
level slightly above the grate. Heat gets transferred to pans by the mechanism
of conduction convection and radiation. Fuel efficiency studies conducted in 82
households of the cluster of villages in Sirsimakki microcatchment of Sirsi Taluk
[Ramachandra, T.V., and Shastri, C.M., 1995] have shown that the fuel need
for cooking is about 1.92(avg)±1.02(Sd) kg per person per day for cooking in
traditional stoves while in IC's about 1.1(avg)±0.78(Sd) kg per person per day.
Which, means that there is a saving of about 42% in the quantity of fuel used
by switching over to IC's from TC's.
9.2
Energy Plantations:
Technically speaking, energy plantations mean growing select species of trees
and shrubs that are harvestable in a comparably shorter time and are specific
means for fuel. The fuelwood may be used either directly into wood burning
stoves, boilers or processed into Methanol, Ethanol and Producer Gas. These
plantations help to provide wood either for purposes of cooking in homes and
for industrial use so as to satisfy local energy needs in a decentralised manner
(Vimal and Tyagi, 1984). The energy plantations provide almost inexhaustible
renewable sources (has total time constant of 3-8 years only for each cycle) of
18
energy that is essentially local and independent of unreliable and finite sources
of fuel. The attractive features of wood are (a) its heat content is similar to that
of Indian coal, (b) wood has low
sulphur that is not likely to pollute the
atmosphere, (c) ash obtainable from burning is a valuable fertiliser, (d)
Utilisation of erosion prone lands for raising wood plantations help to reduce
wind and water erosion, by that minimising hazards from floods, siltation, loss
of nitrogen and minerals from soil. (e) helps in rural employment generation: It
is estimated that an acre of energy plantation provides the job for at least three
persons regularly. Selection of multipurpose species provides number of by
products like oils, organic compounds, fruits, edible leaves, forage for livestock
etc. Data collected from Forest Department (plantations in Bagepalli,
Srinivasapur, Chintamani Taluks under Social Forestry Programme) reveals that
annual woody biomass available is in the range 11.9 to 21 tonnes/ha/yr. An
energy plantation at the Hosalli village, Tumkur District to support wood gasifier
plant has annual yield of 6t/ha/yr. In each taluk about ten percent of lands are
unsuitable for agriculture. These wastelands available in each taluk could be
used for energy plantations with species acceptable to local people.
9.3
Biogas Technology:
Biogas is a product of anaerobic fermentation of organic matters, and consists
of about 60-70% Methane, 30-40% Carbon-di-oxide etc. The input materials for
biogas digesters are the wastes that are found locally such as animal dung,
agricultural residues, and leaf litter from forests. The residues are introduced
into a closed digester, where, without the presence of free oxygen, the
responsible micro organisms work successively to convert complex organic
matter in to CH4, CO2, H2, H2S, etc. The optimum conditions for biogas
production are: temperature 30-35C, Ph 6.8-7.5, Carbon/Nitrogen Ratio 2030, solid contents 7-9%, retention time 20-40 days. Among these parameters,
temperature is the most difficult or costly to control. The gas formation virtually
stops when the temperature drops below 10C. Retention time decides the rate
at which the waste is digested. The longer the time, the larger the volume of
gas produced from a given amount of waste and vice versa. Thus if the
19
available amount of input materials is limited, a bigger digester can be adopted
to more fully exploit the gas potential; and where the waste is abundant, the
waste can be fed at a higher loading rate into a small digester to maximise the
gas production per unit volume of the digester. The optimum retention time
depends on the temperature. In practice, a longer retention time is usually
adopted to cope with cool seasons. There are various designs of biogas
digesters such as:
(1)
Floating gas holder type designed by Kadhi and Village Industries
Commission (Directorate of Gobar Gas Scheme, 1979).
(2)
Optimised design developed by Application of Science and
Technology to Rural Areas (ASTRA) at our institute (Subramanian,
1984).
(3)
Fixed dome type designed by University of Agricultural Sciences Bhagyalaxmi design.
(4)
Raitabandu Biogas Plant - designed by a farmer from Sagar Taluk,
Shimoga District to suit the needs of the malnad region.
(i) Biogas Usage: Biogas can be used for many purposes, mainly for cooking
and lighting in rural area. Biogas can be burned with a gas mantle or can be
converted to electricity using a dual mode engine. The per capita requirement
of gas for cooking is in the range 0.34-0.43 m3/day (efficiency of a standard
burner is about 60%). The gas requirement to generate one unit of electricity
(kWh) is about 0.54 m3. The calorific value of m3 of gas is about 4713 kcals. As
indicated, biogas can meet the cooking requirement of at least 20% of the total
District population. Villagewise biogas availability and demand is computed for
the following four cases.:
Case I:
taking dung yield for cattle as 3 kg/animal/day, for buffalo 12
kg/animal/day. Per capita requirement of biogas as 0.34 m3/day.
Case II:
taking dung yield for cattle as 3 kg/animal/day, for buffalo 12
kg/animal/day. Per capita requirement of biogas as 0.43 m3/day.
Case III:
taking dung yield for cattle as 7.5 kg/animal/day, for buffalo 15
kg/animal/day. Per capita requirement of biogas as 0.34 m3/day.
Case IV:
taking dung yield for cattle as 7.5 kg/animal/day, for buffalo 15
20
kg/animal/day. Per capita requirement of biogas as 0.43 m3/day.
Biogas potential assessment for these four cases were carried out for
each taluk. Results are listed in Table 2.
Table2: Biogas potential in Kolar district
Scenari
Dung Yield
o
(kg/animal/day)
Cattle
Buffalo
Case I
3
12
Case II
7.5
Case
PCGR
Percentage of population - Domestic
(m3/da
Energy requirement met by Biogas
y)
option
<15
15-30
30-45
45-60
0.34
4
7
0
0
15
0.34
0
5
5
1
3
12
0.43
8
3
0
0
7.5
15
0.43
1
8
2
0
III
Case IV
Table 2 shows that Biogas can meet 15-30% populations domestic energy
requirement in seven taluks as per case I. In Case II, Biogas is sufficient to
meet 15-30%, 20-45% and 45-60% of
populations domestic energy
requirement in five, five and one taluks of Kolar district respectively.
Community biogas plants are the best solution to meet the domestic
energy requirements. In order to select villages suitable for this purpose,
similar analyses were carried out villagewise and results are listed in Table 3a3d.
This shows that in 99 villages of Kolar taluk, biogas can meet energy
demand of 15% of population. In 112 villages biogas potential is sufficient to
meet 15-30% of population's energy requirements. 30-45% of population
21
energy requirements is met in 86 villages by switching over to biogas. About
65 villages have potential which can meet more than 45% of population
requirements.
Table 3a: Case I - Talukwise Biogas potential (number of villages in each
range) in Kolar District:
Percentage of population - Domestic
Energy requirement met by Biogas
option
Taluk
<15
15 -30 30- 45 45 -60 >60
235
119
17
5
14
Chikballapur 123
59
30
15
27
Chintamani 152
149
70
23
14
Gauribidnur 80
96
42
8
11
Gudibanda
45
40
12
3
6
Kolar
132
130
70
15
15
Malur
184
131
26
11
13
Mulbagal
195
108
23
6
14
Sidalghatta 135
65
32
8
50
Srinivasapur 147
120
45
14
22
Bagepalli
Bangarpet
Table 3b: Case II - Talukwise Biogas potential (number of villages in each
range) in Kolar District:
Taluk
<15
15 -30 30- 45 45 -60 >60
Bagepalli
22
Bangarpet
294
71
9
9
7
Chikballapur 132
70
23
9
20
Chintamani 181
172
37
9
9
Gauribidnur 105
98
23
2
9
Gudibanda
60
34
6
1
5
Kolar
171
137
37
9
8
Malur
249
81
18
8
9
Mulbagal
234
83
14
6
9
Sidalghatta 156
61
22
14
37
Srinivasapur 176
118
31
9
14
Table 3c: Case III -Talukwise Biogas potential (number of villages in each
range) in Kolar
Taluk
<15
15 -30 30- 45 45 -60 >60
109
100
105
46
30
Chikballapur 85
38
33
34
64
Chintamani 106
53
91
78
80
Gauribidnur 47
46
64
41
39
Gudibanda
28
13
29
17
19
Kolar
81
81
84
61
55
Malur
112
94
83
37
39
Mulbagal
130
56
92
31
37
Bagepalli
Bangarpet
23
Sidalghatta 102
36
42
30
80
Srinivasapur 94
49
77
59
69
Table 3d: Case IV-Talukwise Biogas potential (number of villages in each
range) in Kolar:
Taluk
<15
15 -30 30- 45 45 -60 >60
131
149
78
11
21
Chikballapur 97
41
46
20
50
Chintamani 115
87
113
51
42
Gauribidnur 60
64
68
25
20
Gudibanda
31
27
27
11
10
Kolar
99
112
86
39
26
Malur
129
123
67
20
26
Mulbagal
143
99
63
19
22
Sidalghatta 114
50
43
17
66
Srinivasapur 105
82
83
41
37
Bagepalli
Bangarpet
10.0 Conclusions:
The bioresource availabilty to demand ratio ranges from 0.11 (Kolar) to 0.73
(Srinivasapura), fuelwood demand is taken as 1.3 kg/person/day. The ratio
being less than one indicates that there is bio resource scarcity. Biogas seems
to be a viable option to use bioresource prudently, can meet the cooking
requirement of at least 20-30% of the total District population.
11.0 Acknowledgements
24
We thank Mr.Pramod Dabrase and Mr.Lakshmi Naryana for their assistance in
field data collection. This research is supported by the Ministry of Science and
Technology, Department of Science and Technology, Government of India.
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