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Joshi et al (1992) study confirms the predominance of firewood use in rural areas with analysis indicating the 95% confidence interval for per capita consumption (national aggregates) for cooking in the range of 1.10-1.34 kg/capita/day. The consumption of dung cake was lower with 95% confidence interval of the per capita consumption showing 0.40-0.49 kg/capita/day. The agricultural residue consumption was marginally higher with 95% confidence interval range of 0.47-0.63 kg/capita/day. The rural energy database generated across the agro climatic zones showed variation in the firewood used for cooking to have a high degree of statistical significance and a significant use in the variation of dung cakes in cooking across the agro - climatic zones.

In India, different groups such as the Energy Survey of India Committee- ESI (1965), Fuel Policy Committee (1974), Working Group on Energy Policy - WGEP (1979), Advisory Board on Energy- ABE (1985), and Energy Demand Screening Group - EDSG (1986) have assessed the rural energy requirements. The study by ESI projects the variation in the consumption of commercial and biomass fuels at the urban and rural areas. The figures are aggregated at the national level and the regional variations were not examined. The per capita daily useful energy requirement was assumed at 510 kcal for rural areas and 535 kcal for urban areas. The WGEP also made similar estimations assuming the per capita energy requirements of the ESI. The projections were forecast at the national level and the regional variations owning to economic disparities at the household level were ignored. The EDSG was established to bring out common estimates of energy requirements as the values given by ABE were felt to be high at the rural level. The survey results of this group predicted the level of requirement for 2004/2005 as 520 kcal/capita/day. To arrive at this figure the committee used the energy requirements and the level of growth of the energy requirements over time as suggested by the ESI committee. The wide variations that were noticed in the estimates given by the above study groups were attributed mainly to the change in population figures.

Swaminathan Committee (1982) reports the percentage share of non-commercial energy sources to be 80%. It also reported that in the rural and urban sectors per capita energy consumption had 68.5 and 45.5% share of firewood.

Dwivedi (1994) reports the total consumption of fuel wood as 265 million tonnes in 1991, assuming an average consumption of fuel wood as 400 kg/capita/year.

The National Council of Applied Economic Research data shows that between 1978-79 and 1992-93, the share of biofuels in the total household energy remains unchanged at 95%.

Natarajan (1995) showed that the availability of crop residues and cattle wastes have not increased considerably in the last two decades and so firewood constituted 61.2% of total rural household energy in 1992-93 as against 54.57% in 1978-79.

Sinha and Joshi (1995) report that contribution of firewood has remained around 65% of the rural energy during the last three decades. The demand of fuelwood in rural sector for domestic needs in 1991 was 180 million tonnes, with the average consumption in villages being 1.22-kg/capita/day.

Ramachandra et al (2000) examine the present role of biomass in the energy supply of Uttara Kannada district, Karnataka and calculate the potential for future biomass provision and scope for conversion to both modern and traditional fuels. The study reveals that fuel wood was mainly used for cooking and horticulture residues from coconut and areca nut trees were used for water heating purposes. Most of the households in this region still use traditional stoves where efficiency is less than 10%. Energy from various crop residues were calculated: paddy husk-1701.23 lakh kWh, bagasse-1363 lakh kWh, groundnut-116.4 lakh kWh, and maize-16.65 lakh kWh. The total residues available for the district were calculated as 42020.37 tonnes. The total energy available from horticulture residues: areca-5405.83 lakh kWh, coconut-2470.44 lakh kWh and cashew-383.65 lakh kWh. The total gas available was calculated to be 46290.85 thousand m 3 , which could meet 30% of the population's energy demand. The fodder requirement was estimated to be 10.85 lakh tonnes of which 2.08 lakh tonnes could be met by agro-residues. As nearly 50% of the forests are inaccessible only 50% of the forests of the district are available to meet the remaining fodder demand.

Misra et al (1995) have studied the domestic fuel energy consumption in an Indian urban ecosystem and concluded that 49% of its energy used for cooking and heating was from biomass. Fuel wood was the only fuel used by all income groups.

Johansson and Lundqvist (1999) have estimated the potential of biomass energy supplies regarding prices, technical progress and energy policy.

Shin-Ya-Yokoyama (2000) has estimated the biomass energy potential of Thailand from biomass and forest residues. According to the estimation, 65 PJ can be obtained from agricultural and forestry waste and 770 PJ can be generated if half of the area allocated for the cultivation of plantation forest could be used for biomass energy plantation.

Kingeri Senelwa (1999) has studied the fuel characteristics of short rotation forest biomass comprising of the species E. globules, E. nitens, A. dealbata, A. glutinosa, P. tomentosa and S. matsundan alba. High heating values were known to vary from 19.6-20.5 MJ/kg for wood, 17.4-20.6 MJ/kg for bark and 19.5-24.1MJ/kg for leaves, with highest values for wood and bark being obtained from Pinus radiata .

Gunter Helas et al (2001) have studied the annual domestic consumption levels and patterns of various common biofuels in Kenya. The main fuel wood sources were farmland trees, indigenous forests, woodlands and timber off-cuts from plantations. Biofuel availability was the major factor influencing the reported annual spatial species use and consumption patterns. High demand for locally available species for commercial and other purposes was responsible for their reducing numbers.

Gunter Helas et.al (2001) have studied the rural and urban biofuel consumption rates and patterns in Kenya. In the rural sector, fuel wood was the main biofuel used with an average consumption rate of 0.80-2.7 kg/capita/day. The urban households largely consumed charcoal at weighted average rates of 0.18-0.69 kg/capita/day. The consumption rates were largely dependent on fuel availability but varied significantly among restaurants, academic institutions and urban and rural households.

Xiaohua and Zhenmin (2001) have studied the variation of rural household energy consumption with the economic development in China. The above study analyzed the rural household energy consumption of three typical regions, i.e., out-of-poverty, well-off and rich regions in terms of effective heat per capita per day, percentage of commercial energy consumption in total effective heat, electricity consumption per capita and room temperature of northern areas in winter.

Schrattenholzer and Fischer (2001) have estimated the global bioenergy potential through the year 2050. Consistent results were seen in the scenarios of agricultural production and land use developed at the International Institute For Applied System Analysis, Austria.

Kaygusuz (2002) has evaluated the sustainable development of biomass energy in Turkey. Biomass energy is the second important energy source for Turkey. In 1998, biomass contributed 10% to the total energy consumption of the country. Considering the total cereal products and fatty seed plants, approximately 50-60 million tonnes per year of biomass and 8-10 million tonnes of solid animal waste matter are produced. The study projects the potential utilization of 70% of the available biomass to be used as an energy source.

Mahapatra and Mitchell (1999) have examined the patterns of biomass fuel use in rural India and supply effects on household consumption. Their analyses showed that socio-economic factors influence the bioenergy use, but scarcity of forests does not lower the demand for biofuel nor is it a driving force for farm forestry.

Assimacopoulos et al (2000) have developed a GIS based decision support system, which implements the method and provides the tools to identify the geographic distribution of the economically exploited biomass potential that can be used for power production. The study revealed that the main parameters that affect the location and number of bioenergy conversion facilities are plant capacity and spatial distribution of the available biomass potential.

Mitchell (2001) has developed a hypertext based information system and decision support system that helps in handling the information in a manner that is helpful for decision making. These approaches were with reference to short rotation forestry production information system and decision support system for harvesting wood for energy from conventional rotation forestry.

Sarkar and Islam (1998) reveal that 70% of the rural energy sources of Bangladesh is met by biomass. The study revealed that cooking consumed most of the energy and straw and hulls contributed more in meeting the energy requirements.

Devdas (2001) has studied the rural energy scene in depth. The study identifies the important parameters that control the economy in the rural system, particularly in relation with energy inputs and outputs. Secondary data was utilized for segregating the study area into various categories. The study identifies several parameters that influence energy consumption in a rural economic system. In a subsequent study, the complex interactions within the subsystems that contribute a rural energy system were analyzed. Also the differences between the energy related primary and secondary data in two villages in Kanyakumari district of Tamil Nadu were brought out. A Linear Programming Model for optimum resource allocation in a rural system was also suggested. The objective function of the LPM was to maximize the revenue of the rural system where in optimum resource allocation was made subject to a number of energy and non-energy related relevant constraints. Based on the above two studies, a technique for forecasting future scenarios was developed. The forecast was based on a set of projected inputs for the target year along with a projected set of technical coefficients. These values were arrived at through a regression of historical data or based on socio-economic conditions of the study area. Scenarios for analyzing the impact of replacing field crop by plantation crop, introducing energy plantation, introducing fuel efficient stoves, increasing fertilizer price, increased fertilizer application, increased population growth, drought conditions and decreased fuel wood availability were developed and discussed for arriving at plausible recommendations for energy resource generation and optimum usage of available energy resources in a given rural system under various conditions.

Baath et al (2002) have evaluated a method for assessing the local biofuel potentials. A sparse grid of field sample plots from an existing national forest inventory was used as a reference data for satellite image based estimates of forest condition. This method was evaluated in the communes of Vindeln and Alvsbyn of Northern Sweden. With a maximum distance of 300 m to road, the available forest fuel potential was shown to decrease by more then 50%.

Xiaquing et al (2002) have studied the domestic energy consumption in rural China. About 384 households in 12 villages of 4 towns in Sheyang County Country were subject to stratified sampling method and the annual energy consumption per capita was worked out to be 8.62 GJ (294 kilogram of coal equivalent), with an average energy consumption of 16.56 MJ per family per day in the form of straw. The average energy consumption was found to be dependent on income, stalk yield, and the number of persons and pigs in a family.

Ramachandra et al (2000) have analyzed the village level domestic energy consumption patterns across the coastal, interior, hilly and plain zones considering seasonal and regional variations. The study revealed variation in the average energy consumption for cooking and water heating in various seasons across the zones. A survey of 1304 households from 90 villages in Kumta taluk revealed that most of them use traditional stoves for cooking (97.92%) and water heating purposes (98.3%). The average consumption (kg/person/day) varied between 2.01±1.49 (coastal) to 2.32±2.09 (hilly). Seasonwise cooking fuel wood requirement for coast and hilly zones, ranged from 1.98 and 2.22 (summer) to 2.11 to 2.51 (monsoon), while for water heating (bath and washing) ranged from 1.17±0.02 (coastal) to 1.63±0.05 (hilly). The seasonal variation ranged from 1.12 and 1.53 (summer) to 1.22 and 1.73 (monsoon) for coastal and hilly zones. Based on fuel consumption norms (regionwise, seasonwise and end use wise) the total fuelwood required (cooking, water heating, space heating, jaggery making and parboiling) worked out to be 1668698.23 tonnes/year.

Bhel and Goel (2001) have studied the genetic selection and improvement of hardwood tree species for fuel wood production on sodic wastelands (pH 8.6-10.5). Field trials were conducted using leguminous species like Acacia auriculiformis, A.nilotica, Albizia lebbeck, Acacia procera, Dalbergia sissoo, Leucaena leucocephala, Pongamia pinnata, Prosopis juliflora and Pithecellobium dulce. Other tree species like Azadirachta indica, Eucalyptus teriticornis and Terminalia arjuna were also chosen for this study. Prosopis juliflora was the most promising species in terms of biomass productivity (68.7 tonne/ha) and fuel index (148.8) after 8 years of growth. Acacia nilotica ranked second.

Rupam Kataki and Dolon Konwer (2001) have studied the fuel wood characteristics of four indigenous perennial species of northeast. Physio-chemical parameters like moisture and ash content, density, solubility in cold water, hot water and alkali, cellulose, holocellulose, lignin and extractive contents of different parts of these species were determined. Leaf component of all the species contained the highest calorific value presumably because of the higher extractive content, followed by heartwood. The study identified A . lucida, S. fruticosum and P.lanceaefolium to have better fuel wood properties to be raised as energy plantation.

Bungart and Huttl (2001) have studied the production of biomass for energy in post-mining landscape of Lusatian mining region. The yield potential of fast growing tree species under poor soil conditions of the mined area were studied. The study revealed that even under unfavorable soil conditions of N and P supply, above ground biomass supply after 4 years since planting ranged from 5.3 tonnes to 19.6 tonnes of dry matter per hectare. For the biomass accumulated within this four-year period, a calorific value of 42 MJ/ha/year was calculated.

Patrick and lowore (1998) have identified and discussed the management options of some indigenous firewood species of Malawi's central region. This study proposes the local people's involvement in the management domestic firewood on a coppice rotation of five years upwards.

Pal and Sharma (2001) have studied the reclamation of salt affected wastelands in arid and semi-arid regions of Aravalli Hills. Twelve tree species were planted and were analyzed for growth parameters, soil pH, organic carbon, electrical conductivity, available phosphorous and various socio-economic benefits after 5.5 and 13.5 years. Only five species survived and three grew more than 40 cm in GBH as well as 10 m high in 13.5 years. The soil pH, electrical conductivity, organic carbon and available Phosphorous recorded significant improvement in three depths (up to 45 cm) and ages over the initial status. The village community was also benefited through fodder supply, employment generation, income generation and other community facilities.

Pesonen et al (2001) have determined the potential of logging residues and first thinning as wood fuel in Southern Finland based on the four cutting scenarios. The calculations were carried out using the MELA program which simulates a finite number of feasible forest management schedules for each forest stand according to the given simulation instructions. The annual potential harvest of energy wood was 3.6 million m 3 /year.

Yamamoto et al (2001) have evaluated the global bioenergy potential in the future using a multi-regional global-land-use-and–energy-model (GLUE-11). Through a set of simulations the following results were obtained. North America, Eastern and Western Europe, Latin America and former U.S.S.R will have the potential of energy produced from crops growing on surplus arable land. Variation in food supply and demand will be influencing this potential. The ultimate bioenergy supply potential of biomass residues in the world will be 265 EJ/year in the year 2100. The practical potential of biomass residues in the world will be 114 EJ/year, which is equivalent to one-third of the energy consumption in the world in 1990. Considering the current land use pattern, the global mature forest area will decrease by 24% between 1990 and 2100 because of growth of both population and wood biomass demand per capita in the developing regions.

Seiler et al (2002) estimate the potential hydrocarbon in selected species of Western Ghats, Tamil Nadu, which show that Sarcostemma brevistigma has the highest concentration of hydrocarbon of 3.6%. While species such as Tylophora asthmatica, Euphorbia tirucalli, Cryptostegia grandiflora, Ficus ealstica and Euphorbia antisyphylitica contained more than 2%. The gross heat value of hydrocarbon fraction in Ficus elastica (leaf) is 9834 Cal/g, which is comparable to fuel oil calorific value.

The standing woody biomass of trees is the weight of the trees above ground in a given area, if harvested at a given time. The change in the standing biomass over a period of time is referred to as productivity and is expressed as tonnes/ha/year. In ecological terms (Whittaker, 1970) primary productivity is the rate at which energy is bound or organic matter created by photosynthesis per unit of earth's surface per unit of time.

Rai (1982) found the above ground biomass in tropical rain forests of Karnataka to be 465.61 tonnes/ha and root biomass to be 13.21 tonnes/ha based on field investigations at four locations namely Agumbe, Bannadpare, Kagneri and Bhadra and subsequent regression analyses and the average net primary productivity ranges between 7.77 to 11.76 tonnes/ha/year.

Narendra Prasad (1987) showed the average standing biomass at sample locations - Nagur, Santgal, Sonda and Bidralli in Uttara Kannada district reserve forests to be 243.25 tonnes/ha.

Ravindranath et al (1999) assessed the biomass potential across the agro ecological zones and show an estimate of 321Mt, based on biomass productivity in the range of 2-17 tonnes/ha/year, considering a conservative estimate of 43 Mha land.

Bhat et.al (2002) investigations in differentially managed forests of Uttara Kannada indicate that tree biomass productivity decreases and herb productivity increases with increasing light gap (canopy gaps).

Haripriya (2000) measured biomass resources in Indian forests for the year 1993, using species-wise volume inventories for all forest strata in various States and show the above ground biomass densities ranging from 14-210 Mg/ha, with a mean of 67.4 Mg/ha, which equals around 34 Mg C/ha.

Shanmughavel et al (2000) have estimated the biomass production in Bambusa bambos plantations of different age class, and compared with its interspecies natural stands and between genera of natural and plantation stands. The study shows the mean annual biomass production of 49.6 tonnes/ha (over a period of six years) and with an increase in age, there was a linear increase in the total biomass of all compartments. In the aboveground biomass, the percentage contribution of culms (81%), branches (14%), and leaves (1%) was 96%, whereas in the below ground, the rhizome contribution was 4%.

Shanmughavel et al (2001) have studied the biomass and their distribution using standard regression analysis and the clear-cut method for shrubs and herbs. Results show the total biomass of 360.9 tonnes/ha and its allocation among different layers - tree layer 352.5, shrub layer 4.7, liana 3.1 and herb layer 0.5 tonnes/ha.

Fuwape et al (2001) have developed biomass equations and estimation for Gmelina arborea and Nauclea diderrichii stands in Akure forest reserve. The study revealed that more than 75% of total biomass yield of both species are from the stem and over 90% of the total above ground biomass is available as biofuel.

Dadhwal et al (2001) have estimated the forest biomass growing stock as 8683.7 MT based on the field inventory information of growing stock volume and corresponding area under different crown density classes for India for the year 1992-1993. The average growing stock volume density in Indian forests is about 74.42 m 3 /ha, but varied amongst the States with a range of 7.1 m 3 /ha in Punjab to 224.5 m 3 /ha in Jammu and Kashmir. The mean biomass density in Indian forests was estimated as 135.6 tonnes/ha and amongst the States it varied from 27.4 tonnes/ha in Punjab to 251.8 tonnes/ha in Jammu and Kashmir, respectively.

Seimons (2001) has identified the potential of biomass gasification for electrification of rural areas in developing countries taking the feasibility prospects into account. The analysis was carried out on the basis of an annuity-costing model taking into account the

time value of money, technological and site parameters. The capacity range considered was 10-200 kW, which was investigated in 10, 40 and 160 kW cases. For 10 kW the fuel considered was charcoal, wood and charcoal for 40 kW and wood for 160 kW. For smaller capacity plants, conditions for economically feasible projects were found to be satisfied easily under prevailing fuel price and investment levels. Charcoal even though more expensive cannot compete wood as a fuel for gasification systems up to a capacity of 40 kW. Mass production of gasifiers will result in considerable cost reduction that would promote the technology.

Junginger et al (2001) have evaluated the availability of agricultural and forest residues for electricity generation in northeastern Thailand considering the variations in the residue produced, limited accessibility, utilization by other competitors and logistical risks. In the current Thailand situation, only a combustion plant seemed to operate economically.

Zanzi et al (2002) have studied the effect of the process conditions such as heating rate, temperature and particle size on the product distribution, gas composition and char reactivity on the rapid pyrolysis of agricultural residues at high temperature. The study reveals that higher temperature leads to lower yields of tar and higher yields of gaseous products. At higher temperature, the heating rate is higher, which will favor a decrease in char yield. The yield of hydrogen is increased at higher temperature when the cracking of hydrocarbons is favored. Use of smaller particles increases the heating rate. Agricultural residues having higher ash content favour the formation of a more reactive char.

Gamborg (2000) has studied the different ways of increasing the production of whole tree chips for energy production. The influences of silvicultural factors like tree species, thinning programme, plant density and the quality of nursery trees on the production of wood chips were examined.

Akinbami et al (2001) have studied the biogas energy use in Nigeria. The study projects the quantity of family-sized biogas digesters in the future to vary between 144,350 and 2165,250 units. The projected energy from biogas would range between 5.0 -171.0 x 10 12 J in the period 2000-2030 and the associated aggregate financial commitment (first cost) was worked out to be US$72.16-1083.00 million. Other factors such as technology, political will, economics and personal motivation were also found to influence its popularization.