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Fuelwood consumption patterns in Fakot watershed, Garhwal Himalaya, Uttarakhand
Energy 36 (2011) 4769e4776
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Fuelwood consumption patterns in Fakot watershed, Garhwal Himalaya, Uttarakhand Y.S.C. Khuman a, *, Ranjita Pandey b, K.S. Rao a a b
Department of Botany, University of Delhi, Delhi 11007, India Department of Statistics, University of Delhi, Delhi 11007, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 October 2010 Received in revised form 7 April 2011 Accepted 11 May 2011 Available online 16 June 2011
Biomass is one of the key energy sources in rural India and constitutes 75% of the total energy consumption. In the tough mountain terrain, the consumption pattern depends on the availability of the resources as well as socio-economic conditions of the people. The present study is conducted at Fakot micro-watershed in Garhwal district of Uttarakhand, India. The present paper explores the variations in fuelwood consumption pattern in the watershed level at different altitudes with respect to family size. In the present study, the fuelwood consumption in the watershed is in the range of 455e2388 gm/person/ day. Absence of serial autocorrelation in the seasonal fuel consumption data is observed. Season specific models for fuel consumption are proposed. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Bioenergy Per capita fuelwood consumption Predictive seasonal model
1. Introduction Energy is one of the vital inputs for social and economic development of a country and its demand patterns vary from region to region depending upon their social and geographical conditions. The developing countries of the world vastly depend on biomass as primary energy source that covers all energy forms derived from organic fuels of biological origins. It comprises all purposely grown energy crops, multipurpose plantations and byproducts (residues/wastes). On the other hand, the capital intensiveness of fossil fuel and utility alternatives makes the biomass better for national and local economies. For a secure energy supply and energy independence, bio-energy and other renewable energy sources play important role in overcoming increasing external dependence [1]. Bioenergy can bring about other environmental benefits including the recovery of degraded land, reduction of soil erosion and protection of watersheds [2,3]. Bioenergy has the potential to provide a self reliant decentralized village level energy system to meet needs in a sustainable and environmentally sound way [4]. Amongst developing countries such as India, bio-energy is a source of fuel for people surviving at the subsistence level. But there is an acute shortage of this biomass fuel due to combined effects of diminishing supplies and growing demand in India [5].
* Corresponding author. Tel.: þ91 (0)9953172074; fax: þ91 11 29532374. E-mail address: [email protected] (Y.S.C. Khuman). 0360-5442/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2011.05.011
Continued growth of population without enhanced productivity of land, along with a lack of ecological conservation, may jeopardize the self sufficiency of the village ecosystems and as such careful planning and management of the ecosystem is necessary if standard of living to be maintained/improved [5]. Biomass consumption is dependent on the level of availability, access to the resource, local food and cooking practices among other factors and thus, likely to vary widely across small geographical units. Therefore, a careful study of the local factors influencing the supply and consumption pattern is imperative for designing effective intervention plans [6]. Factors influencing the variations in biomass consumption pattern at micro level are discussed in this paper. In the tough mountain terrain, accessibility is the limitation to the availability. Variations in biomass consumption pattern and its relationship to family size and accessibility of resources at micro level are discussed in the present paper. An attempt is made at seasonal modelling which is more precise in prediction as compared to the existing annual models. 1.1. The resource flow in Himalaya and its impact Mountain regions typically combine ecological heterogeneity and socio-cultural variety [7]. Forests and pastures, arable land, cattle and human population are the four basic components of hill agricultural system and all these components are linked in a series of dynamic relationships involving the production, transfer and consumption of energy [8e12]. Availability of fodder, fuel and litter
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Fig. 1. Study Area.
is important for the survival of the rural settlements since almost ninety percent of energy demand is met from the biomass in the Himalayan region [13]. The forest thinning, in government controlled forest and community forests, is largely the result of
increased demand for fuelwood, fodder and manure for subsistence [14,15]. Therefore, forest resource-based economic development opportunities for local people [16] with a biomass plan that integrate resource conservation [13] will mitigate biomass stress in the region. Several authors [17e19] have found seasonal variation in the nature of biofuel resource extraction from the forest ecosystems in the Himalayan region. Firewood, being the major source of fuel, was extracted throughout the year whereas lopping for leaffy fodder mostly occurred during the months of FebruaryeMay and leaf litter was collected during winter season. Thus, the
Table 1 Standard Adult Equivalents.
Fig. 2. Land use of Fakot watershed in 2004.
Family Size
Standard Adult Equivalent
Men 18e59 yr Women 18e59 yr Men >59 yr Women >59 yr Boys 5e18 yr Girls 5e18 yr Kids 1e5 yr Child <1 yr
1.0 0.8 0.8 0.8 0.5 0.5 0.35 0.25
Y.S.C. Khuman et al. / Energy 36 (2011) 4769e4776
occurs during the monsoon months of June to September. The winter precipitation is mainly in the form of snow particularly at higher elevations.
Table 2 Land use statistics of Phakot Watershed (area in Ha). Village
Non-Agric Agric. Land
Naur 3.6 Katkor 3.5 Bhaintan 17.1 Malas 2.9 Tachhla 2.1 Ghadsera 8.0 Patta 1.4 Jangleth 2.2 Bhengarki 9.0 Agar 11.4 Chillogi 1.5 Lamyali 2.0 Total 64.8
Total Village Ratio Land (TVL) Non-Ag/ Crop/ Agr Fallow
Fallow Crop Land Land
Total
31.6 116.8 86.2 49.5 21.6 41.6 17.8 24.0 44.1 136.4 4.3 11.0 584.9
57.9 61.5 154.0 157.5 131.4 148.5 74.5 77.5 36.8 38.9 57.9 61.5 31.1 32.5 34.9 37.1 83.9 92.8 229.0 240.5 7.7 9.2 57.9 61.5 956.9 1018.9
26.3 37.2 45.2 25.0 15.2 26.3 13.3 11.0 39.8 92.6 3.4 26.3 361.4
1:16 1:43 1:7.5 1:25 1:17 1:7 1:22 1:16 1:9 1:20 1:5 1:28 1:15
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2.1. General resource use pattern
1:1 1:3 1:2 1:2 1:1.5 1:1.5 1:1 1:2 1:1 1:5 1:1 1:0.4 1:2.3
The main source of fuel and fodder in the watershed is forest and agricultural areas, mainly collected from the forests in the form of dry and fallen trees. Majority of the middle income group families own LPG but traditional chulha is popular for cooking purpose. The perception behind the use of wood as fuel is that it is available free. 2.2. Land use pattern Forest, fallow land and agriculture fields are the main land use categories in the study area. The land use pattern is influenced by the weather conditions of the year. During the study period i.e. 2006e2008, most of the agricultural fields are kept as temporary fallows. Most of the rain-fed fields in the higher elevation are left as fallow from the last three to four years. Using IKONOS data 29 land use/land cover classes belonging to six broad land use classes i.e. built up land, agricultural land, forests, wastelands, water bodies and others was identified by Kunwar et al. [29]. Fakot Watershed (Fig. 1) has a geographical area of 1466 ha. Fig. 2 gives the details of the land use in the study area during 2006e08. Sixty five percent of the watershed is under forest cover, while 29% is under agriculture and 3% are wastelands and others. Agar (240.476 ha), Bhentan (148.546 ha) and Katkor (157.486 ha) are the bigger villages and Chillogi (9.181 ha) is the smallest village. Village wise land use statistics shown in Table 2 is based on the information provided by village patwaris (Government official who maintains the records) of state revenue department. The Villagers are not allowed to access the reserved forest for fuelwood and fodder. As a result, the villagers have access to only 48% of the total forest area in the watershed. Fallow land also contributes to fuelwood supply. Village Agar has largest cropped area to fallow land (1:1.5) followed by Katkor (1:3) and Lamyali has the smallest (1:0.4). Fuel is extracting from the forest comprising of evergreen forest (293.68 ha) dominated by Oak (Quercus leucotriphora) and deciduous forest (654.3 ha) dominated by mixed Bakli (Anogeissus latifolia).
surrounding forest ecosystem provided a considerable amount of energy for sustenance of the human societies in terms of animal fodder and fuelwood [20] and is the most critical factor for the viability of the hill agro-ecosystems [21]. The fuelwood consumption pattern in the Himalayan regions such asmigratory villages in Uttarkashi district [19], along altitudinal gradients in Garhwal Himalaya [22], different tribal communities in Northeast India [23], Pindar basin [24], Himachal Pradesh [25,26], and Tehri district in Uttaranchal [27] were studied in detail by different authors. Spatio-temporal variation in resource extraction was observed among the migratory villages of Uttarkashi by Awasthi et al. [19]. Studies based on administrative boundary level data indicate that the firewood consumption is influenced by the climate and altitude of the place. On the other hand, studies based on the region level data indicate non-suitability of aggregated data in developing specific policies for the sustainable use of forest resources [26]. Rural energy planning is a subject of village development and should focus on study of the village as a management unit [28]. Thus, the main objective of the present paper is to find the variations in household energy consumption pattern in a small micro-watershed and develop specific seasonal consumption model applicable to the area and usable for similar areas in the Himalayan region. 2. Study area
2.3. Interventions initiated by state The Fakot watershed (in Tehri district) forms the parts of Huini watershed, a part of Alaknanda-Bhagirathi sub catchments of the upper Ganga Catchment. It lies at a distance of about 35 km from Rishikesh on Rishikesh-Tehri-Uttarkashi highway between the geographical range of 30190 5700 to 30 220 2000 N latitude and 78 160 5200 to 79 2105000 E longitude occupying an area of 1466 ha with an elevation range of 630e2015 m. The general climate is of sub-tropical monsoonal type. The winter is usually dry and mild while the summer is hot. The rugged topography and geographical position leads to a high variability in rainfall. It has an average rainfall of 1185 mm. Most of the rainfall
The watershed got much intervention under the Integrated Watershed Management Programme of United Nations Development Programme (UNDP) during 1970e1990s. 3. Methodology The initial phase of the research comprised of an exploratory survey in the watershed. Villages present in three altitudinal zones are identified. Socio economic data was collected from 246 households of nine villages (out of total 12 settlements) in the watershed.
Table 3 Average Seasonal Variations of fuelwood consumption in Fakot watershed. Fuelwood
g/day/person ton/annuma a
Summer
Winter
Monsoon
Annual
Max
Min
Avg.
Max
Min
Avg.
Max
Min
Avg.
Max
Min
Avg.
3030 3523.55
64 74.42
642 746.57
4545 5285.33
128 148.85
946 1100.09
3261 3792.18
101 117.45
680 790.76
3535 4110.81
107 124.42
756 879.14
It is calculated based on population figure of 2001 census (total population in the watershed ¼ 3186).
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Table 4 Fuelwood consumption in Fakot Watershed season wise and category-wise fuelwood (gm/person/day). Village Categories
Remote Lower Village Mid Easily Accessible Remote Higher Village
N
62 134 50
Summer
Winter
Monsoon
Annual
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
780 658 428
481 424 166
61.71 64.48 38.69
120 72 46
1122 969 666
481 638 219
42.9 65.9 32.9
120 108 61
851 668 501
564 438 169
66.21 65.5 33.82
140 74 47
918 765 532
560 492 168
61 64.3 31.6
139 83 47
3.1. Collection of primary data A questionnaire was prepared for the collection of household resource use pattern. Door to door survey was conducted for two years (2006e2008) in summer, rainy and winter seasons. The sum of seasonal data reflects the annual totals. Questionnaire used for survey data consisted of the following information: 1. Household information: Members details, Educational qualification, land holding, income and expenditure 2. Livestock: Number, fodder consumption 3. Fuelwood: Collection, quantity per day, season, Source and place of collection (actual measurement was conducted at the selected households) 4. Agriculture: Cropping pattern, input and production Data from 246 households from nine villages were collected during two years of field visit. Of the total 246 respondents, 25.2% represents Remote lower altitude villages, 54.5% represents Easily accessible Mid altitude villages and the rest 20.3% represents Remote higher altitude villages. 4. Data analysis The household population is converted into total adult equivalent (TAE), which is computed by using conversion factor provided by Ramachandra et al. [30]. During the study period, the Katkor village has the highest TAE (181.05) followed by Ghadsera (187.75), Bhengarki (174.1) and the Jangleth (24.6) has the lowest.
in the lower elevation and high elevation are accessible only through footpaths in varying lengths from this motorable highway. In order to see the impact of altitude and accessibility on the variations of fuelwood resource consumption in the watershed, the study area is segregated into three segments viz. Remote Lower altitude villages, Mid Easily Accessible Villages and Remote Higher altitude Villages. Remote Lower Altitude Villages are in the lower part of the state highway and majority of the households are in the foothill or between two hillocks. The Mid Easily Accessible Villages are near the state highway. The Remote Higher Altitude Villages are in upper part of the highway and most of the settlements are on the hill top with greater exposure to sunlight. 4.2. Statistical analysis MS Excel has been used for fitting the statistical distribution to data. Average values are represented using the arithmetic mean. Standard deviation (S. D.) represents degree of scatteredness around the respective arithmetic means, in order to assess the extent of probable dispersion on each side of arithmetic mean in the data set. The S. D. of the sampling distribution is given by standard error (S. E.). Coefficient of variation (C. V.) measures consistency of the values relative to arithmetic mean. Confidence Interval (C. I.) indicates the range inside which the true parameter value is likely to be found. Correlation measure studies the extent of degree and direction of linear relationship between the two involved factors. The autocorrelation function expresses how the correlation between any two values of the series changes as their extent of separation in time changes. This phenomenon is quantified using Durbin-watson statistic and is estimated using SPSS.
4.1. Per capita fuelwood consumption (PCFC) 5. Results and discussion To determine the fuelwood consumption pattern, we computed the Per Capita Fuelwood Consumption (PCFC) as: PCFC ¼ FC/p [30]. Where FC ¼ fuel consumed in kgs/day and p ¼ number of adult equivalents. The daily fuelwood consumption of each household is further converted to fuelwood consumption per adult using the adult equivalent of the number of household members (Table 1). The total annual income of the household (GRINCM) is converted into per capita income (PCINCM) for further analysis. The data are further analysed to see the role of income, household size and available village level fuelwood sources on per capita fuelwood consumption. It is observed in the study region that fuelwood is used for cooking and heating purpose only. The family size is categorised into small (1e3), medium (3.1e6), large (6.1e9) and very large (>9) classes. Of the total 246 respondents in the watershed, 23% represents the small, 61% medium, 12% large and 4% as very large. Based on the data collected during field observation, it is noticed that the watershed is dominated by medium size households. A state highway passes through the middle of the watershed which provides motorable vehicle accessibility to the habitations situated in the mid elevation in the watershed while the habitations
5.1. Fuelwood consumption 5.1.1. Total fuelwood consumption in the watershed The average seasonal variations of fuelwood consumption in Fakot watershed are shown in Table 3.
Fig. 3. Seasonal variations of Per Capita Fuelwood Consumption (Altitudinal).
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Table 5 Seasonal variations fuelwood consumption of different villages in Fakot Watershed (gm/person/day). Altitudinal Category
Village
Remote Lower altitude Higher altitude Mid-easily accessible
Chillogi Ghadsera Katkor Malas Agar Bhaintan Jangleth Bhengarki Patta
N
30 32 39 11 38 36 5 45 10
Summer
Winter
Monsoon
Annual
Avg.
SD
CV
CI
Avg.
SD
CV
CI
Avg.
SD
CV
CI
Avg.
SD
CV
CI
759 799 452 342 631 597 1936 642 409
579 376 158 169 226 311 1046 317 313
76 47 35 49 35 52 54 49 76
207 130 50 100 72 101 917 93 194
1063 1177 706 526 974 899 3063 869 610
828 523 201 232 399 486 1384 402 390
77 44 28 44 41 54 45 46 64
296 181 63 137 127 159 1213 118 242
796 903 502 497 636 598 2165 642 409
693 412 145 246 219 308 860 317 313
87 45 28 49 34 51 39 49 76
248 143 46 145 70 101 754 93 194
873 960 553 455 747 698 2388 718 476
679 426 152 206 264 365 1076 336 335
77 44 27 45 35 52 45 46 70
243 148 48 121 84 119 943 98 208
The per capita fuelwood consumption in the summer is in the range of 64e3030 gm/person/day with an average value of 642 g/ person/day in summer. It is in the range of 128e4545 g/person/day with an average value of 946 g/person/day in winter and 1013262 g/person/day with an average value of 680 g/person/day in monsoon. The annual average fuelwood consumption in the watershed is from a maximum value of 3535 g/person/day to a minimum 107 g/person/day with an average consumption of 756 g/person/day at the watershed level. 5.1.2. Altitudinal and seasonal variations in energy consumption The fuelwood consumption of different village categories is shown in Table 4. The average per capita fuelwood consumption in the watershed ranges from 428 to 780 g in summer, 666 to 1.122 g in winter, 501 to 851 g in monsoon. The annual average ranges between 532 and 918 gm per capita. The PCFC is higher in the remote lower altitude villages (780 481 in summer, 1122 481 in winters and 851 564 in monsoon) than other counterparts in mid easily accessible (658 424, 969 638 and 668 438, respectively, in summer, winter and monsoon seasons) and remote higher altitude villages (428 166, 666 219 and 501 169 in summer, winter and monsoon seasons, respectively). The Lower altitude has 1.73 times higher PCFC than the higher altitude villages and it is 1.44 times higher than that of mid-altitude villages. The lower altitude has more access to the available fuelwood. Altitudinal variation in PCFC is shown in Fig. 3. There is a decrease in PCFC with the increase in altitude. 5.1.3. Village wise variations in energy consumption with respect to altitude variation 5.1.3.1. Remote lower altitude village. Chillogi and Ghadsera are the villages in the lower altitude category. Chillogi is in the northern slope of the hill range with limited exposure to the sunlight and the Ghadsera is in the foothill between two hill ranges and on the bank of Huini stream, a tributary of Bhagirathi River. Chillogi is in the higher altitude than the Ghadsera. The seasonal variations in fuelwood consumption pattern between the two villages are shown in Table 5. Annually, Ghadsera have 1.09 times higher consumption
rate than that of Chillogi and it is 1.05, 1.10, and 1.13 in summer, winter and monsoon seasons, respectively. 5.1.3.2. Remote higher altitude village. Katkor, Malas and Naur are the remote higher altitude villages in the watershed. Katkor and Naur have some similar characteristics in terms of landscape, social structure and so of the three villages, we have taken Katkor and Malas as sample villages. Katkor is in the higher elevation with the highest point in the watershed and of flat land. The village is of Rawat and Harijan community. Malas is in the southern slope of the hill range with a greater availability of sunlight. Socially it is a Brahmin village. The seasonal variations in fuelwood consumption in the remote higher altitude villages are shown in Table 5. Katkor have higher PCFC (452 158, 706 201, 502 145 and 553 152 in summer, winter, monsoon seasons and annual average respectively) than Malas (342 169, 526 232, 497 246 and 455 206 in summer, winter, monsoon and annual average, respectively). 5.1.3.3. Easily accessible mid-altitude village. Easily Accessible Mid Altitute villages are those settlements near the state highway that passes through the middle of the watershed. Agar, Bhaintan, Jangleth, Bhengarki and Patta are the villages in this category. Agar which was once famous for Ginger production is one of the semiurban areas in the watershed. The State highway passes through this village. It is a Rawat community village. Most of the households are on the North facing slopes and a few in the east facing slope of the hill range. Bhaintan is in the south facing slope of the hill range and the highway passes through the village. It is also a Rawat community village. Jangleth is a small village with only a few households. It is in the east facing slope of the hill range. Bhengarki is a mixed community village of SC and general caste. Patta is a general community village. It is also named as Fakot. It is semiurban area and the surrounding villages are depending on this small market for their day to day commodities. Seasonal variations in fuelwood consumption in these villages are shown in Table 5. The higher PCFC is observed in Jangleth Village (1936 1046 in summer, 3063 1384 in winter, 2165 860 in Monsoon and
Table 6 Seasonal variations in Fuelwood consumption of different family size in Fakot Watershed (gm/person/day). TAECTR
1e3 (Small) 3.1e6 (Medium) 6.1e9 (Large) >9 (Very Large) Total
N
57 151 29 9 246
PCFCW
PCFCS
PCFCM
PCFCA
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
994 973 793 690 946
561 646 500 522 609
56 66 63 75 64
146 103 182 341 76
681 662 510 476 642
383 446 307 401 418
56 67 60 84 65
100 71 112 262 52
708 696 587 532 680
400 478 394 442 450
56 68 67 83 66
104 76 143 289 56
794 777 630 566 756
431 514 387 452 481
54 66 61 79 63
112 82 141 295 60
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Y.S.C. Khuman et al. / Energy 36 (2011) 4769e4776 Table 8 Statistical relationship. Variables
PCFCW PCFCS PCFCM PCFCA a b
Fig. 4. Seasonal variations in per capita fuelwood consumption by different household size.
2388 1076 as annual average) and lower value in Patta (409 313 in summer, 610 390 in winter, 409 313 in monsoon and 476 335 as annual average). 5.1.4. Family size The seasonal variations in fuelwood consumption of different family size classes are shown in Table 6. The size of household is found to be inversely proportional to per capita fuelwood consumption at micro level (Fig. 4). 5.1.5. Village level land use wise variations in energy consumption The PCFC level corresponding to altitudinal sectors is given in Table 7. There is a steady increase in village PCFC with the available non-agriculture village land under different altitudinal categories viz. Remote lower, easily accessible mid altitude and Remote higher villages. There is a steady increase in village PCFC with that of village agriculture fallow land at different altitudinal regions. A close perusal of Table 7, indicates the absence of causative relationship between PCFC and non-agriculture land as well as agriculture fallow land. 6. Model proposed It is observed that fuelwood consumption of a family diminishes as TAE increases though the income level of such families also increases marginally (Table 8).Thus, PCINCM is dropped as an explanatory variable in the further analysis. rTAE,GRINC ¼ 0.538, else problem of multicollinearity arises. It is observed that the two independents GRINC and TAE are moderately positively correlative and therefore it is inferred that due to existence of collinearity, TAE
r Value GRINC
PCINCM
TAE
0.183a 0.192a 0.158b 0.182a
0.127b 0.114 0.107 0.120
0.190a 0.199a 0.163b 0.188a
Durbin-Watson
Standard Error GRINC
TAE
1.522 1.604 1.545 1.534
0 0 0 0
0.02 0.014 0.015 0.016
Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed).
alone will be retained as the cause of fuel consumption in the subsequent analysis. The actual data is segregated on the basis of the season into 3 distinct groups and plotted on the graph in order to assess the presence of trend if any. Annual figure is a non-existent observation, since it is condensed as the mean value of the true seasonal data, which is the recorded value from the sample survey. Hence, the following predictive seasonal models are proposed for the specific watershed: 1. PCFCW ¼ 1.176e0.051 TAE 2. PCFCS ¼ 0.807e0.037 TAE 3. PCFCM ¼ 0.825e0.032 TAE The annual model is also constructed as: PCFCA ¼ 0.9360.040 TAE These seasonal models were fitted to the real data collected from the sample villages and the split half model validation has been conducted to authenticate proposed seasonal models. It is found that seasonal predictive errors are log normally distributed with E (ei)w ¼ 0. More specifically,
Eðeiw Þ ¼ 0:001; Eðeis Þ ¼ 0:001; Eðeim Þ ¼ 0:002; Eðeia Þ ¼ 0:000 Where eix ¼ component error for the ith case in the xth season. From the above, we notice that the prediction error is zero in the computed (data based) annual linear model. Analysts may therefore be tempted to adopt this model for the future fuel-demand forecasts. But doing so is incorrect as the annual values are arrived at by averaging over the recorded within-the-year observations, which in-effect cancels out the net fluctuations above and below the average level. Actual demand levels in the watershed are season dependent. We observe that:
tws ¼ 17:54; tsm ¼ 4:67; tmw ¼ 15:7; tcrit ð0:05Þ ¼ 1:96 Hence we conclude that the actual fuelwood demand varies significantly among the seasons.
Table 7 Seasonal statistics for per capita fuelwood consumption at village level available non-agriculture and agriculture fallow land. Altitude Code
Village name
Non Agric Land
AGRIC FALLOW
PCFCW Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
Remote Lower Village
Chillogi Ghadsera Patta Bhengarki Agar Bhaintan Malas Katkor
1.49 8 1.42 8.95 11.4 17.1 2.93 3.53
4.29 41.6 17.8 44.1 136.4 86.23 49.5 117
1063 1177 610 869 974 899 526 706
828 523 390 402 399 486 232 201
77 44 63 46 41 54 44 28
296 181 242 118 127 159 137 63
759 799 409 642 631 597 342 452
579 376 313 317 226 311 169 158
76 47 76 49 36 52 49 35
207 130 194 93 72 101 100 50
796 903 409 642 636 598 497 502
693 412 313 317 219 308 246 145
87 45 76 49 34 51 49 29
248 143 194 93 70 101 145 46
873 960 476 718 747 698 455 553
679 426 335 336 264 365 206 152
77 44 70 46 35 52 45 27
243 148 208 98 84 119 121 48
Mid Easily Accessible Village
Remote Higher Altitude Village
PCFCS
PCFCM
PCFCA
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Based on the above discussion, we therefore infer that seasonal modelling is more reflective of the actual watershed level fuelwood demand than the (averaged) annual data. Thus, we reinforced the path breaking seasonal model approach for precise future forecasts. 7. Conclusions The present paper successfully explores the variations in seasonal fuelwood consumption patterns in a rural watershed situated in the Himalayan region. The altitudinal influence in fuelwood consumption pattern is analysed and it is observed that the higher altitude village (approx 2003 m amsl) have much lower PCFC (532 168) than the lower altitude (approx 630 m amsl) village (918 560) with a minor altitudinal difference of approx 1300 m. Different authors [25, 27,30] observed that the firewood consumption increases with increasing altitude when studied at district/panchayat level but in our study, when taken at micro-watershed level, there is a decrease in increasing altitude. There are minor variations in PCFC among the different villages of different altitudinal categories. It is the size of the family that determines the quantity of food to cook and we analysed the relation of Household size with that of PCFC. The Highest family size (>9 TAE) households have 566 452 and the small family size (1e3 TAE) households have 794 431 as annual average. It is observed that the higher the household size there is a decrease in PCFC. The relation between the available alternative fuelwood sources (agricultural fallow land and non agricultural land) and PCFC was analysed. It is observed that the availability of these lands do not influence the PCFC value. In the present study the fuelwood consumption in the microwatershed is in the range of 0.455 to 2.388 kg/person/day which is lower than the values reported for the rural and tribal communities of Garhwal Himalaya (1.1e2.8 kg/capita/day) by Bhatt and Sachan [22], the Western Himalayas (1.49 kg/capita/day) by Bhatt et al. [31], for Northeast Himalaya in Arunachal Pradesh (3.1e10.4 kg/ capita/day) and Garos (5.0 kg/capita/day) by Maikhuri [32] and Shankar [33],for Karnataka state (2.01e2.32) by Ramachandra et al [30]., for Southern India (1.9e2.2 kg/capita/day) by Reddy [34] and Hegde [35], for South and South-East Asian countries (1.75e2.5 kg/ capita/day) by Donovan [36] and Wijesinghe [37], and Himalayan range of Nepal (1.23 kg/capita/day) by Mahat et al. [38]. This variation indicates the need to study at micro-watershed level for the proper policy and planning strategies. The total fuelwood consumption in the watershed is in the range of 124.42e4110.81 tons/annum with an average value of 879.14 tons/annum. Actual demand levels in the watershed are season dependent and seasonal modelling is more reflective of the actual watershed level fuelwood demand than the (averaged) annual data. Acknowledgement Special thanks to Dr. T. V. Ramachandra, Centre for Ecological Science, Indian Institute of Science, Bangalore for his valuable guidance and constant encouragement to Mr. Y. S. C. Khuman during the study period. NRDMS, DST, GOI and University of Delhi are acknowledged for partial financial support to Mr. Khuman. References [1] Domac J, Richards K, Risovic S. Socio-economic drivers in implementing bioenergy projects. Biomass and Bioenergy 2005;28:97e106. [2] Woods J, Hall DO. Bioenergy for development: technical and environmental dimensions. FAO Environment and Energy paper 13. Rome: FAO; 1994.
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[3] Kartha S, Leach G. Using modern bioenergy to reduce rural poverty. Report prepared to the shell foundation. Stockholm Environmental Institute; 2001. [4] Ravindranath NH, Hall DO. Biomass, energy, and environment: a developing country perspective from India. Oxford: Oxford University Press; 1995. [5] Nisanka SK, Misra MK. Ecological study of Indian village ecosystems: energetics. Biomass 1990;23:165e78. [6] Venkata RP, Joshi V. Use of biomass fuels in India- trends and issues. In: Venkata RP, editor. Rural and Renewable energy: perspectives from developing countries. New Delhi, India: TERI; 1997. p. 155e67. [7] Price MF, Thompson M. The complex life: human land uses in mountain ecosystems. Global Ecology and Biogeography Letters 1997;6:77e90. [8] Singh JS, Pandey U, Tiwari AK. Man and Forests: a central Himalayan case study. Ambio 1984;13(2):80e7. [9] Tiwari PC. Natural resources and sustainable development in Himalaya. Almora, U.P, India: Shree Almora Book Depot; 1995. pp. 147e68. 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People-forestinteraction: a neglected parameter in Himalayan forest management. Mountain Research and Development 1986;6:3e16. [16] Sen KK, Semwal RL, Rana U, Nautiyal S, Maikhuri RK, Rao KS, et al. Patterns and implications of land use/cover change- a case study in Pranmati (garhwal Himalya, India). Mountain Research and Development 2002;22(1):56e62. [17] Dhyani SK, Tripathi RS. Tree growth and crop yield under agri-silvi-cultural practices in north-east India. Agroforestry Systems 1999;44:1e12. [18] Semwal RL, Maikhuri RK, Rao KS, Singh K, Saxena KG. Crop productivity under differently lopped canopies of multipurpose trees in central Himalaya, India. Agroforestry Systems 2002;56:57e63. [19] Awasthi A, Uniyal SK, Rawat GS, Rajvanshi A. Forest resource availability ansd its use by the migratory villages of Uttarkashi, Garhwal Himalaya, India. Forest Ecology and Management 2003;174:13e24. [20] Martins PJ, Nautiyal JC. Population supporting capacities: in the central Himalayas. In: Khurana DK, Khosla PK, editors. Agroforestry for rural needs. ISTS, Dr. Y.S. Parmar University of Horticulture and Forestry, Solan, Himachal Pradesh, India; 1993. p. 439e62. [21] Tripathi RS, Sah VK. Material and energy Flows in high hill, mid hill and Valley farming systems of garhwal Himalaya. Agriculture. Ecosystems and Environment 2001;86:75e91. [22] Bhatt BP, Sachan MS. Firewood consumption along an altitudinal gradient in mountain villages of India. Biomass and Bioenergy 2004;27:69e75. [23] Bhatt BP, Sachan MS. Firewood consumption pattern of different tribal communities in Northeast India. Energy Policy 2004;32:1e6. [24] Sati VP. Traditional intramontane mobility in Garhwal Himalaya: a survey of subsistence practices in the Pindar basin, Uttaranchal. Singapore Journal of Tropical Geography 2008;29:173e85. [25] Prasad R, Maithel S, Mirza A. Renewable energy technologies for fuelwood conservation in the Indian Himalayan Region. Sustainable Development 2001; 9:103e8. 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Fuelwood consumption patterns in Fakot watershed, Garhwal Himalaya, Uttarakhand Y.S.C. Khuman a, *, Ranjita Pandey b, K.S. Rao a a b
Department of Botany, University of Delhi, Delhi 11007, India Department of Statistics, University of Delhi, Delhi 11007, India
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 October 2010 Received in revised form 7 April 2011 Accepted 11 May 2011 Available online 16 June 2011
Biomass is one of the key energy sources in rural India and constitutes 75% of the total energy consumption. In the tough mountain terrain, the consumption pattern depends on the availability of the resources as well as socio-economic conditions of the people. The present study is conducted at Fakot micro-watershed in Garhwal district of Uttarakhand, India. The present paper explores the variations in fuelwood consumption pattern in the watershed level at different altitudes with respect to family size. In the present study, the fuelwood consumption in the watershed is in the range of 455e2388 gm/person/ day. Absence of serial autocorrelation in the seasonal fuel consumption data is observed. Season specific models for fuel consumption are proposed. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Bioenergy Per capita fuelwood consumption Predictive seasonal model
1. Introduction Energy is one of the vital inputs for social and economic development of a country and its demand patterns vary from region to region depending upon their social and geographical conditions. The developing countries of the world vastly depend on biomass as primary energy source that covers all energy forms derived from organic fuels of biological origins. It comprises all purposely grown energy crops, multipurpose plantations and byproducts (residues/wastes). On the other hand, the capital intensiveness of fossil fuel and utility alternatives makes the biomass better for national and local economies. For a secure energy supply and energy independence, bio-energy and other renewable energy sources play important role in overcoming increasing external dependence [1]. Bioenergy can bring about other environmental benefits including the recovery of degraded land, reduction of soil erosion and protection of watersheds [2,3]. Bioenergy has the potential to provide a self reliant decentralized village level energy system to meet needs in a sustainable and environmentally sound way [4]. Amongst developing countries such as India, bio-energy is a source of fuel for people surviving at the subsistence level. But there is an acute shortage of this biomass fuel due to combined effects of diminishing supplies and growing demand in India [5].
* Corresponding author. Tel.: þ91 (0)9953172074; fax: þ91 11 29532374. E-mail address: [email protected] (Y.S.C. Khuman). 0360-5442/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2011.05.011
Continued growth of population without enhanced productivity of land, along with a lack of ecological conservation, may jeopardize the self sufficiency of the village ecosystems and as such careful planning and management of the ecosystem is necessary if standard of living to be maintained/improved [5]. Biomass consumption is dependent on the level of availability, access to the resource, local food and cooking practices among other factors and thus, likely to vary widely across small geographical units. Therefore, a careful study of the local factors influencing the supply and consumption pattern is imperative for designing effective intervention plans [6]. Factors influencing the variations in biomass consumption pattern at micro level are discussed in this paper. In the tough mountain terrain, accessibility is the limitation to the availability. Variations in biomass consumption pattern and its relationship to family size and accessibility of resources at micro level are discussed in the present paper. An attempt is made at seasonal modelling which is more precise in prediction as compared to the existing annual models. 1.1. The resource flow in Himalaya and its impact Mountain regions typically combine ecological heterogeneity and socio-cultural variety [7]. Forests and pastures, arable land, cattle and human population are the four basic components of hill agricultural system and all these components are linked in a series of dynamic relationships involving the production, transfer and consumption of energy [8e12]. Availability of fodder, fuel and litter
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Fig. 1. Study Area.
is important for the survival of the rural settlements since almost ninety percent of energy demand is met from the biomass in the Himalayan region [13]. The forest thinning, in government controlled forest and community forests, is largely the result of
increased demand for fuelwood, fodder and manure for subsistence [14,15]. Therefore, forest resource-based economic development opportunities for local people [16] with a biomass plan that integrate resource conservation [13] will mitigate biomass stress in the region. Several authors [17e19] have found seasonal variation in the nature of biofuel resource extraction from the forest ecosystems in the Himalayan region. Firewood, being the major source of fuel, was extracted throughout the year whereas lopping for leaffy fodder mostly occurred during the months of FebruaryeMay and leaf litter was collected during winter season. Thus, the
Table 1 Standard Adult Equivalents.
Fig. 2. Land use of Fakot watershed in 2004.
Family Size
Standard Adult Equivalent
Men 18e59 yr Women 18e59 yr Men >59 yr Women >59 yr Boys 5e18 yr Girls 5e18 yr Kids 1e5 yr Child <1 yr
1.0 0.8 0.8 0.8 0.5 0.5 0.35 0.25
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occurs during the monsoon months of June to September. The winter precipitation is mainly in the form of snow particularly at higher elevations.
Table 2 Land use statistics of Phakot Watershed (area in Ha). Village
Non-Agric Agric. Land
Naur 3.6 Katkor 3.5 Bhaintan 17.1 Malas 2.9 Tachhla 2.1 Ghadsera 8.0 Patta 1.4 Jangleth 2.2 Bhengarki 9.0 Agar 11.4 Chillogi 1.5 Lamyali 2.0 Total 64.8
Total Village Ratio Land (TVL) Non-Ag/ Crop/ Agr Fallow
Fallow Crop Land Land
Total
31.6 116.8 86.2 49.5 21.6 41.6 17.8 24.0 44.1 136.4 4.3 11.0 584.9
57.9 61.5 154.0 157.5 131.4 148.5 74.5 77.5 36.8 38.9 57.9 61.5 31.1 32.5 34.9 37.1 83.9 92.8 229.0 240.5 7.7 9.2 57.9 61.5 956.9 1018.9
26.3 37.2 45.2 25.0 15.2 26.3 13.3 11.0 39.8 92.6 3.4 26.3 361.4
1:16 1:43 1:7.5 1:25 1:17 1:7 1:22 1:16 1:9 1:20 1:5 1:28 1:15
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2.1. General resource use pattern
1:1 1:3 1:2 1:2 1:1.5 1:1.5 1:1 1:2 1:1 1:5 1:1 1:0.4 1:2.3
The main source of fuel and fodder in the watershed is forest and agricultural areas, mainly collected from the forests in the form of dry and fallen trees. Majority of the middle income group families own LPG but traditional chulha is popular for cooking purpose. The perception behind the use of wood as fuel is that it is available free. 2.2. Land use pattern Forest, fallow land and agriculture fields are the main land use categories in the study area. The land use pattern is influenced by the weather conditions of the year. During the study period i.e. 2006e2008, most of the agricultural fields are kept as temporary fallows. Most of the rain-fed fields in the higher elevation are left as fallow from the last three to four years. Using IKONOS data 29 land use/land cover classes belonging to six broad land use classes i.e. built up land, agricultural land, forests, wastelands, water bodies and others was identified by Kunwar et al. [29]. Fakot Watershed (Fig. 1) has a geographical area of 1466 ha. Fig. 2 gives the details of the land use in the study area during 2006e08. Sixty five percent of the watershed is under forest cover, while 29% is under agriculture and 3% are wastelands and others. Agar (240.476 ha), Bhentan (148.546 ha) and Katkor (157.486 ha) are the bigger villages and Chillogi (9.181 ha) is the smallest village. Village wise land use statistics shown in Table 2 is based on the information provided by village patwaris (Government official who maintains the records) of state revenue department. The Villagers are not allowed to access the reserved forest for fuelwood and fodder. As a result, the villagers have access to only 48% of the total forest area in the watershed. Fallow land also contributes to fuelwood supply. Village Agar has largest cropped area to fallow land (1:1.5) followed by Katkor (1:3) and Lamyali has the smallest (1:0.4). Fuel is extracting from the forest comprising of evergreen forest (293.68 ha) dominated by Oak (Quercus leucotriphora) and deciduous forest (654.3 ha) dominated by mixed Bakli (Anogeissus latifolia).
surrounding forest ecosystem provided a considerable amount of energy for sustenance of the human societies in terms of animal fodder and fuelwood [20] and is the most critical factor for the viability of the hill agro-ecosystems [21]. The fuelwood consumption pattern in the Himalayan regions such asmigratory villages in Uttarkashi district [19], along altitudinal gradients in Garhwal Himalaya [22], different tribal communities in Northeast India [23], Pindar basin [24], Himachal Pradesh [25,26], and Tehri district in Uttaranchal [27] were studied in detail by different authors. Spatio-temporal variation in resource extraction was observed among the migratory villages of Uttarkashi by Awasthi et al. [19]. Studies based on administrative boundary level data indicate that the firewood consumption is influenced by the climate and altitude of the place. On the other hand, studies based on the region level data indicate non-suitability of aggregated data in developing specific policies for the sustainable use of forest resources [26]. Rural energy planning is a subject of village development and should focus on study of the village as a management unit [28]. Thus, the main objective of the present paper is to find the variations in household energy consumption pattern in a small micro-watershed and develop specific seasonal consumption model applicable to the area and usable for similar areas in the Himalayan region. 2. Study area
2.3. Interventions initiated by state The Fakot watershed (in Tehri district) forms the parts of Huini watershed, a part of Alaknanda-Bhagirathi sub catchments of the upper Ganga Catchment. It lies at a distance of about 35 km from Rishikesh on Rishikesh-Tehri-Uttarkashi highway between the geographical range of 30190 5700 to 30 220 2000 N latitude and 78 160 5200 to 79 2105000 E longitude occupying an area of 1466 ha with an elevation range of 630e2015 m. The general climate is of sub-tropical monsoonal type. The winter is usually dry and mild while the summer is hot. The rugged topography and geographical position leads to a high variability in rainfall. It has an average rainfall of 1185 mm. Most of the rainfall
The watershed got much intervention under the Integrated Watershed Management Programme of United Nations Development Programme (UNDP) during 1970e1990s. 3. Methodology The initial phase of the research comprised of an exploratory survey in the watershed. Villages present in three altitudinal zones are identified. Socio economic data was collected from 246 households of nine villages (out of total 12 settlements) in the watershed.
Table 3 Average Seasonal Variations of fuelwood consumption in Fakot watershed. Fuelwood
g/day/person ton/annuma a
Summer
Winter
Monsoon
Annual
Max
Min
Avg.
Max
Min
Avg.
Max
Min
Avg.
Max
Min
Avg.
3030 3523.55
64 74.42
642 746.57
4545 5285.33
128 148.85
946 1100.09
3261 3792.18
101 117.45
680 790.76
3535 4110.81
107 124.42
756 879.14
It is calculated based on population figure of 2001 census (total population in the watershed ¼ 3186).
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Table 4 Fuelwood consumption in Fakot Watershed season wise and category-wise fuelwood (gm/person/day). Village Categories
Remote Lower Village Mid Easily Accessible Remote Higher Village
N
62 134 50
Summer
Winter
Monsoon
Annual
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
780 658 428
481 424 166
61.71 64.48 38.69
120 72 46
1122 969 666
481 638 219
42.9 65.9 32.9
120 108 61
851 668 501
564 438 169
66.21 65.5 33.82
140 74 47
918 765 532
560 492 168
61 64.3 31.6
139 83 47
3.1. Collection of primary data A questionnaire was prepared for the collection of household resource use pattern. Door to door survey was conducted for two years (2006e2008) in summer, rainy and winter seasons. The sum of seasonal data reflects the annual totals. Questionnaire used for survey data consisted of the following information: 1. Household information: Members details, Educational qualification, land holding, income and expenditure 2. Livestock: Number, fodder consumption 3. Fuelwood: Collection, quantity per day, season, Source and place of collection (actual measurement was conducted at the selected households) 4. Agriculture: Cropping pattern, input and production Data from 246 households from nine villages were collected during two years of field visit. Of the total 246 respondents, 25.2% represents Remote lower altitude villages, 54.5% represents Easily accessible Mid altitude villages and the rest 20.3% represents Remote higher altitude villages. 4. Data analysis The household population is converted into total adult equivalent (TAE), which is computed by using conversion factor provided by Ramachandra et al. [30]. During the study period, the Katkor village has the highest TAE (181.05) followed by Ghadsera (187.75), Bhengarki (174.1) and the Jangleth (24.6) has the lowest.
in the lower elevation and high elevation are accessible only through footpaths in varying lengths from this motorable highway. In order to see the impact of altitude and accessibility on the variations of fuelwood resource consumption in the watershed, the study area is segregated into three segments viz. Remote Lower altitude villages, Mid Easily Accessible Villages and Remote Higher altitude Villages. Remote Lower Altitude Villages are in the lower part of the state highway and majority of the households are in the foothill or between two hillocks. The Mid Easily Accessible Villages are near the state highway. The Remote Higher Altitude Villages are in upper part of the highway and most of the settlements are on the hill top with greater exposure to sunlight. 4.2. Statistical analysis MS Excel has been used for fitting the statistical distribution to data. Average values are represented using the arithmetic mean. Standard deviation (S. D.) represents degree of scatteredness around the respective arithmetic means, in order to assess the extent of probable dispersion on each side of arithmetic mean in the data set. The S. D. of the sampling distribution is given by standard error (S. E.). Coefficient of variation (C. V.) measures consistency of the values relative to arithmetic mean. Confidence Interval (C. I.) indicates the range inside which the true parameter value is likely to be found. Correlation measure studies the extent of degree and direction of linear relationship between the two involved factors. The autocorrelation function expresses how the correlation between any two values of the series changes as their extent of separation in time changes. This phenomenon is quantified using Durbin-watson statistic and is estimated using SPSS.
4.1. Per capita fuelwood consumption (PCFC) 5. Results and discussion To determine the fuelwood consumption pattern, we computed the Per Capita Fuelwood Consumption (PCFC) as: PCFC ¼ FC/p [30]. Where FC ¼ fuel consumed in kgs/day and p ¼ number of adult equivalents. The daily fuelwood consumption of each household is further converted to fuelwood consumption per adult using the adult equivalent of the number of household members (Table 1). The total annual income of the household (GRINCM) is converted into per capita income (PCINCM) for further analysis. The data are further analysed to see the role of income, household size and available village level fuelwood sources on per capita fuelwood consumption. It is observed in the study region that fuelwood is used for cooking and heating purpose only. The family size is categorised into small (1e3), medium (3.1e6), large (6.1e9) and very large (>9) classes. Of the total 246 respondents in the watershed, 23% represents the small, 61% medium, 12% large and 4% as very large. Based on the data collected during field observation, it is noticed that the watershed is dominated by medium size households. A state highway passes through the middle of the watershed which provides motorable vehicle accessibility to the habitations situated in the mid elevation in the watershed while the habitations
5.1. Fuelwood consumption 5.1.1. Total fuelwood consumption in the watershed The average seasonal variations of fuelwood consumption in Fakot watershed are shown in Table 3.
Fig. 3. Seasonal variations of Per Capita Fuelwood Consumption (Altitudinal).
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Table 5 Seasonal variations fuelwood consumption of different villages in Fakot Watershed (gm/person/day). Altitudinal Category
Village
Remote Lower altitude Higher altitude Mid-easily accessible
Chillogi Ghadsera Katkor Malas Agar Bhaintan Jangleth Bhengarki Patta
N
30 32 39 11 38 36 5 45 10
Summer
Winter
Monsoon
Annual
Avg.
SD
CV
CI
Avg.
SD
CV
CI
Avg.
SD
CV
CI
Avg.
SD
CV
CI
759 799 452 342 631 597 1936 642 409
579 376 158 169 226 311 1046 317 313
76 47 35 49 35 52 54 49 76
207 130 50 100 72 101 917 93 194
1063 1177 706 526 974 899 3063 869 610
828 523 201 232 399 486 1384 402 390
77 44 28 44 41 54 45 46 64
296 181 63 137 127 159 1213 118 242
796 903 502 497 636 598 2165 642 409
693 412 145 246 219 308 860 317 313
87 45 28 49 34 51 39 49 76
248 143 46 145 70 101 754 93 194
873 960 553 455 747 698 2388 718 476
679 426 152 206 264 365 1076 336 335
77 44 27 45 35 52 45 46 70
243 148 48 121 84 119 943 98 208
The per capita fuelwood consumption in the summer is in the range of 64e3030 gm/person/day with an average value of 642 g/ person/day in summer. It is in the range of 128e4545 g/person/day with an average value of 946 g/person/day in winter and 1013262 g/person/day with an average value of 680 g/person/day in monsoon. The annual average fuelwood consumption in the watershed is from a maximum value of 3535 g/person/day to a minimum 107 g/person/day with an average consumption of 756 g/person/day at the watershed level. 5.1.2. Altitudinal and seasonal variations in energy consumption The fuelwood consumption of different village categories is shown in Table 4. The average per capita fuelwood consumption in the watershed ranges from 428 to 780 g in summer, 666 to 1.122 g in winter, 501 to 851 g in monsoon. The annual average ranges between 532 and 918 gm per capita. The PCFC is higher in the remote lower altitude villages (780 481 in summer, 1122 481 in winters and 851 564 in monsoon) than other counterparts in mid easily accessible (658 424, 969 638 and 668 438, respectively, in summer, winter and monsoon seasons) and remote higher altitude villages (428 166, 666 219 and 501 169 in summer, winter and monsoon seasons, respectively). The Lower altitude has 1.73 times higher PCFC than the higher altitude villages and it is 1.44 times higher than that of mid-altitude villages. The lower altitude has more access to the available fuelwood. Altitudinal variation in PCFC is shown in Fig. 3. There is a decrease in PCFC with the increase in altitude. 5.1.3. Village wise variations in energy consumption with respect to altitude variation 5.1.3.1. Remote lower altitude village. Chillogi and Ghadsera are the villages in the lower altitude category. Chillogi is in the northern slope of the hill range with limited exposure to the sunlight and the Ghadsera is in the foothill between two hill ranges and on the bank of Huini stream, a tributary of Bhagirathi River. Chillogi is in the higher altitude than the Ghadsera. The seasonal variations in fuelwood consumption pattern between the two villages are shown in Table 5. Annually, Ghadsera have 1.09 times higher consumption
rate than that of Chillogi and it is 1.05, 1.10, and 1.13 in summer, winter and monsoon seasons, respectively. 5.1.3.2. Remote higher altitude village. Katkor, Malas and Naur are the remote higher altitude villages in the watershed. Katkor and Naur have some similar characteristics in terms of landscape, social structure and so of the three villages, we have taken Katkor and Malas as sample villages. Katkor is in the higher elevation with the highest point in the watershed and of flat land. The village is of Rawat and Harijan community. Malas is in the southern slope of the hill range with a greater availability of sunlight. Socially it is a Brahmin village. The seasonal variations in fuelwood consumption in the remote higher altitude villages are shown in Table 5. Katkor have higher PCFC (452 158, 706 201, 502 145 and 553 152 in summer, winter, monsoon seasons and annual average respectively) than Malas (342 169, 526 232, 497 246 and 455 206 in summer, winter, monsoon and annual average, respectively). 5.1.3.3. Easily accessible mid-altitude village. Easily Accessible Mid Altitute villages are those settlements near the state highway that passes through the middle of the watershed. Agar, Bhaintan, Jangleth, Bhengarki and Patta are the villages in this category. Agar which was once famous for Ginger production is one of the semiurban areas in the watershed. The State highway passes through this village. It is a Rawat community village. Most of the households are on the North facing slopes and a few in the east facing slope of the hill range. Bhaintan is in the south facing slope of the hill range and the highway passes through the village. It is also a Rawat community village. Jangleth is a small village with only a few households. It is in the east facing slope of the hill range. Bhengarki is a mixed community village of SC and general caste. Patta is a general community village. It is also named as Fakot. It is semiurban area and the surrounding villages are depending on this small market for their day to day commodities. Seasonal variations in fuelwood consumption in these villages are shown in Table 5. The higher PCFC is observed in Jangleth Village (1936 1046 in summer, 3063 1384 in winter, 2165 860 in Monsoon and
Table 6 Seasonal variations in Fuelwood consumption of different family size in Fakot Watershed (gm/person/day). TAECTR
1e3 (Small) 3.1e6 (Medium) 6.1e9 (Large) >9 (Very Large) Total
N
57 151 29 9 246
PCFCW
PCFCS
PCFCM
PCFCA
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
994 973 793 690 946
561 646 500 522 609
56 66 63 75 64
146 103 182 341 76
681 662 510 476 642
383 446 307 401 418
56 67 60 84 65
100 71 112 262 52
708 696 587 532 680
400 478 394 442 450
56 68 67 83 66
104 76 143 289 56
794 777 630 566 756
431 514 387 452 481
54 66 61 79 63
112 82 141 295 60
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Y.S.C. Khuman et al. / Energy 36 (2011) 4769e4776 Table 8 Statistical relationship. Variables
PCFCW PCFCS PCFCM PCFCA a b
Fig. 4. Seasonal variations in per capita fuelwood consumption by different household size.
2388 1076 as annual average) and lower value in Patta (409 313 in summer, 610 390 in winter, 409 313 in monsoon and 476 335 as annual average). 5.1.4. Family size The seasonal variations in fuelwood consumption of different family size classes are shown in Table 6. The size of household is found to be inversely proportional to per capita fuelwood consumption at micro level (Fig. 4). 5.1.5. Village level land use wise variations in energy consumption The PCFC level corresponding to altitudinal sectors is given in Table 7. There is a steady increase in village PCFC with the available non-agriculture village land under different altitudinal categories viz. Remote lower, easily accessible mid altitude and Remote higher villages. There is a steady increase in village PCFC with that of village agriculture fallow land at different altitudinal regions. A close perusal of Table 7, indicates the absence of causative relationship between PCFC and non-agriculture land as well as agriculture fallow land. 6. Model proposed It is observed that fuelwood consumption of a family diminishes as TAE increases though the income level of such families also increases marginally (Table 8).Thus, PCINCM is dropped as an explanatory variable in the further analysis. rTAE,GRINC ¼ 0.538, else problem of multicollinearity arises. It is observed that the two independents GRINC and TAE are moderately positively correlative and therefore it is inferred that due to existence of collinearity, TAE
r Value GRINC
PCINCM
TAE
0.183a 0.192a 0.158b 0.182a
0.127b 0.114 0.107 0.120
0.190a 0.199a 0.163b 0.188a
Durbin-Watson
Standard Error GRINC
TAE
1.522 1.604 1.545 1.534
0 0 0 0
0.02 0.014 0.015 0.016
Correlation is significant at the 0.01 level (2-tailed). Correlation is significant at the 0.05 level (2-tailed).
alone will be retained as the cause of fuel consumption in the subsequent analysis. The actual data is segregated on the basis of the season into 3 distinct groups and plotted on the graph in order to assess the presence of trend if any. Annual figure is a non-existent observation, since it is condensed as the mean value of the true seasonal data, which is the recorded value from the sample survey. Hence, the following predictive seasonal models are proposed for the specific watershed: 1. PCFCW ¼ 1.176e0.051 TAE 2. PCFCS ¼ 0.807e0.037 TAE 3. PCFCM ¼ 0.825e0.032 TAE The annual model is also constructed as: PCFCA ¼ 0.9360.040 TAE These seasonal models were fitted to the real data collected from the sample villages and the split half model validation has been conducted to authenticate proposed seasonal models. It is found that seasonal predictive errors are log normally distributed with E (ei)w ¼ 0. More specifically,
Eðeiw Þ ¼ 0:001; Eðeis Þ ¼ 0:001; Eðeim Þ ¼ 0:002; Eðeia Þ ¼ 0:000 Where eix ¼ component error for the ith case in the xth season. From the above, we notice that the prediction error is zero in the computed (data based) annual linear model. Analysts may therefore be tempted to adopt this model for the future fuel-demand forecasts. But doing so is incorrect as the annual values are arrived at by averaging over the recorded within-the-year observations, which in-effect cancels out the net fluctuations above and below the average level. Actual demand levels in the watershed are season dependent. We observe that:
tws ¼ 17:54; tsm ¼ 4:67; tmw ¼ 15:7; tcrit ð0:05Þ ¼ 1:96 Hence we conclude that the actual fuelwood demand varies significantly among the seasons.
Table 7 Seasonal statistics for per capita fuelwood consumption at village level available non-agriculture and agriculture fallow land. Altitude Code
Village name
Non Agric Land
AGRIC FALLOW
PCFCW Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
Avg
SD
CV
CI
Remote Lower Village
Chillogi Ghadsera Patta Bhengarki Agar Bhaintan Malas Katkor
1.49 8 1.42 8.95 11.4 17.1 2.93 3.53
4.29 41.6 17.8 44.1 136.4 86.23 49.5 117
1063 1177 610 869 974 899 526 706
828 523 390 402 399 486 232 201
77 44 63 46 41 54 44 28
296 181 242 118 127 159 137 63
759 799 409 642 631 597 342 452
579 376 313 317 226 311 169 158
76 47 76 49 36 52 49 35
207 130 194 93 72 101 100 50
796 903 409 642 636 598 497 502
693 412 313 317 219 308 246 145
87 45 76 49 34 51 49 29
248 143 194 93 70 101 145 46
873 960 476 718 747 698 455 553
679 426 335 336 264 365 206 152
77 44 70 46 35 52 45 27
243 148 208 98 84 119 121 48
Mid Easily Accessible Village
Remote Higher Altitude Village
PCFCS
PCFCM
PCFCA
Y.S.C. Khuman et al. / Energy 36 (2011) 4769e4776
Based on the above discussion, we therefore infer that seasonal modelling is more reflective of the actual watershed level fuelwood demand than the (averaged) annual data. Thus, we reinforced the path breaking seasonal model approach for precise future forecasts. 7. Conclusions The present paper successfully explores the variations in seasonal fuelwood consumption patterns in a rural watershed situated in the Himalayan region. The altitudinal influence in fuelwood consumption pattern is analysed and it is observed that the higher altitude village (approx 2003 m amsl) have much lower PCFC (532 168) than the lower altitude (approx 630 m amsl) village (918 560) with a minor altitudinal difference of approx 1300 m. Different authors [25, 27,30] observed that the firewood consumption increases with increasing altitude when studied at district/panchayat level but in our study, when taken at micro-watershed level, there is a decrease in increasing altitude. There are minor variations in PCFC among the different villages of different altitudinal categories. It is the size of the family that determines the quantity of food to cook and we analysed the relation of Household size with that of PCFC. The Highest family size (>9 TAE) households have 566 452 and the small family size (1e3 TAE) households have 794 431 as annual average. It is observed that the higher the household size there is a decrease in PCFC. The relation between the available alternative fuelwood sources (agricultural fallow land and non agricultural land) and PCFC was analysed. It is observed that the availability of these lands do not influence the PCFC value. In the present study the fuelwood consumption in the microwatershed is in the range of 0.455 to 2.388 kg/person/day which is lower than the values reported for the rural and tribal communities of Garhwal Himalaya (1.1e2.8 kg/capita/day) by Bhatt and Sachan [22], the Western Himalayas (1.49 kg/capita/day) by Bhatt et al. [31], for Northeast Himalaya in Arunachal Pradesh (3.1e10.4 kg/ capita/day) and Garos (5.0 kg/capita/day) by Maikhuri [32] and Shankar [33],for Karnataka state (2.01e2.32) by Ramachandra et al [30]., for Southern India (1.9e2.2 kg/capita/day) by Reddy [34] and Hegde [35], for South and South-East Asian countries (1.75e2.5 kg/ capita/day) by Donovan [36] and Wijesinghe [37], and Himalayan range of Nepal (1.23 kg/capita/day) by Mahat et al. [38]. This variation indicates the need to study at micro-watershed level for the proper policy and planning strategies. The total fuelwood consumption in the watershed is in the range of 124.42e4110.81 tons/annum with an average value of 879.14 tons/annum. Actual demand levels in the watershed are season dependent and seasonal modelling is more reflective of the actual watershed level fuelwood demand than the (averaged) annual data. Acknowledgement Special thanks to Dr. T. V. Ramachandra, Centre for Ecological Science, Indian Institute of Science, Bangalore for his valuable guidance and constant encouragement to Mr. Y. S. C. Khuman during the study period. NRDMS, DST, GOI and University of Delhi are acknowledged for partial financial support to Mr. Khuman. References [1] Domac J, Richards K, Risovic S. Socio-economic drivers in implementing bioenergy projects. Biomass and Bioenergy 2005;28:97e106. [2] Woods J, Hall DO. Bioenergy for development: technical and environmental dimensions. FAO Environment and Energy paper 13. Rome: FAO; 1994.
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