Aahen Chanu Waikhom?Arun Jyoti Nath?P.S.Yadava

Introduction

Sacred groves are patches of trees on forest land that have been protected through the ages by traditional societies and indigenous communities as part of their socio-cultural and religious practices(Kandari et al.2014).Around the world,sacred groves are areas that are often conserved by local residents for a variety of reasons ranging from belief in a forest deity to protection of a spring or sacred burial grounds(Lebbie and Freudenberger 1996;Ormsby 2012).Such natural forests are home to many indigenous people,and they harbour much of the world’s biological diversity(Binkley etal.1997).In India,there are about 100,000–150,000 sacred groves(Malhotra et al.2007),which are managed at a community level(Ormsby 2011).Since time immemorial,Manipuri people have worshiped forest patches(or sacred groves),and they believe these groves are the property of God/deities.Consequently,they have protected such forest patches against any anthropogenic disturbances,and the sacred groves have thus been important for maintaining the ecological integrity of forest and nature conservancy.The 166 sacred groves present in Manipur Valley range in size from 0.001 to 40 ha for a total area of 175.62 ha(Khumbongmayum et al.2004).

Forest ecosystems are the major biological scrubber of atmospheric CO2,and forest management strategies provide important tools to reduce net emissions of greenhouse gases(UNFCCC 1997).The most extensively forested areas with traditional associations have the highest carbon sink potential(He et al.2015).Therefore,such spiritually managed forests should be conserved for vegetative carbon stock management to better understand the feasibility of their use in a carbon credit mechanism.

After fossil fuel combustion,worldwide deforestation and forest degradation have been considered to be the second largest anthropogenic source of CO2to the atmosphere(Montagnini and Nair 2004;Van der Werf et al.2009).In international efforts,biodiversity and its relationship with the carbon cycle has become an important consideration formitigating climate change through reducing the conversion of natural ecosystems(Midgley et al.2010).To stabilize the increase in concentration of CO2in the atmosphere,managing carbon in the vegetative pool has been prioritized as a viable strategy(Pan et al.2011;Nath et al.2015).Therefore,it is essential to keep forests as intact as possible so they can continue to act as carbon stocks(Gibbs et al.2007).Since the sacred groves have been maintained traditionally for such a long time,it is essential to document the biomass carbon stock of such land uses.The speci fi c objective of the present research was to describe the best generalized biomass model to use to study biomass stock non-destructively and to assess the vegetative carbon stock of the largest sacred grove of Manipur.

Materials and methods

Study area

Manipur,a Northeast Indian state,lies between 23°80′N and 25°68′N latitudes and 93°03′E and 94°78′E longitude.The geographical area of the state is 22,327 km2.The state is bounded on the east by the Somra tract and the upper Chindwin areas of Myanmar,on the west by the Cachar Hills of Assam,on the north by the Naga Hills of Nagaland,and on the south by the Chin Hills of Myanmar.

The study site is located at 24°50′28′N latitude and 93°48′32′E longitude in Phayeng,Imphal west district Manipur,14 km from Imphal,the capital of Manipur,at an altitude from 790 to 870 m asl.The forest is the largest sacred grove of Manipur,covering an area of 40 ha.The people of Phayeng are indigenous people,the aboriginal inhabitants of Manipur before the arrival of Meiteis(Parratt 1980).

The climate of the area is dominated by monsoon,with warm moist summer and cool dry winter.Meteorological data from 2004 to 2013 were collected from the ICAR Research Complex for NEH Region,Manipur Centre,Lamphelpat,Imphal(Fig.1).The mean monthly maximum temperature varies from 21.77(January)to 29.57°C(August),and the mean monthly minimum temperature ranges from 4.94(January)to 22.38°C(July).The mean monthly rainfall ranges from 8.79(January)to 238.85 mm(July).The mean annual rainfall is 1430 mm.

Fig.1 Average monthly rainfall,temperature and relative humidity from 2004 to 2013.(Source:ICAR,Imphal)

The sacred forest of Phayeng represents an old growth,mature,natural forest protected by local people.Two forest sites of 1 ha each were selected from this sacred forest to represent the overall status of the sacred grove.Accordingly,site I was selected from the base of the forest,which was level to slightly sloped.Site II was situated toward the top of the hill and was steeply sloped.The elevation difference between the two sites was 80 m.The sacred grove of Phayeng is dominated byQuercus serrataMurray at both sites and co-dominated byRhus succidaneaL.at site I andLithocarpus fenestratus(Roxb.)Rehder at site II.Within each 1 ha area at both the sites,10 quadrates of 10×10 m were plotted in such a way to cover sparse to dense part of forest sites.The trees(＞10 cm DBH,diameter at breast height)within each sub quadrate at both sites were counted and DBH measured.DBH was measured because diameter describes most of the variability in aboveground biomassand carbon stock assessments(Brown et al.2004).For the total aboveground biomass estimation(AGB),models based on diameter have been recommended because of their simplicity,ease of accurate fi eld measurements and because DBH is the most common variable recorded in forest inventories(Segura and Kanninen 2005).The AGB was estimated using regression equations developed by Brown(1997),Brown et al.(1989),Chave et al.(2001)and Chambers et al.(2001).The best predictive model was determined by analyzingR2andFvalues.The aboveground carbon stock was calculated by assuming that carbon content is 50% of the total aboveground biomass(Brown et al.1989;Ravindranath et al.1997).Statistical software SPSS 19(IBM,Armonk,NY,USA)was used for analyzing the data.

Results and discussion

To estimate aboveground biomass,Chambers et al.(2001)model was found most suitable based on highR2(0.91 and 0.94)and highFvalues(1616.13 and 2070.72)for site I and II,respectively(Table 1).The same model was used by Baishya et al.(2009)while working with the distribution pattern ofAGB in humid tropicalforestsofMeghalaya,India.However,Borah et al.(2013)found that the model of Brown(1997)best fi tted for the estimation of AGB in tropical forests in Assam,Northeast India.In another study from tropical forests of the Kholahat Reserve forest in Assam,Borah et al.(2015)found Chave et al.(2001)model as best for biomass estimation.Thus,the best- fi tting generalized biomass equations differ for different forest types in Northeast India.

Table 1 Regression models used that were tested to obtain the best fi t model

The estimated AGB for site I was 1130.79 and 962.94 Mg ha-1for site II(Fig.2).Stand density for site I was 1240 and 1320 tree ha-1for site II.With respect to girth classes,stand density was highest in the 10–20 cm class for site I and the 20–30 cm class for site II.These tree densities are higher than the tree density values of 996 and 1028 trees ha-1reported for a natural and plantation forest in Meghalaya,India(Baishya et al.2009).The data in Fig.2 further reveals that the tree density distribution was highest in the 70–80 cm girth classes at site I,but highest in the 50–60 cm for site II.Basal area of the stand was 90.64 m2ha-1for site I and 79.43 m2ha-1for site II.However,basal area was highest for the 30–40 cm girth class in site I and 20–30 cm in site II.The 30–40 cm girth class for site I and site II contributed the highest proportion(23 and 34%,respectively)to the AGB(Fig.2).The estimated basal area was comparable with the 89.9 and 73.4 m2ha-1reported for the plantation and natural forests,respectively,of Shillong,Meghalaya of India by Baishya et al.(2009).However,the tree density and basal area found in the present study is much higher than reported for tropical forests in the Cachar district,Assam(Borah etal.2013).TheAGB carbon stock was 565 Mg ha-1for site I and 481 Mg ha-1for site II.

The AGB and carbon stock in the present study is comparable with reported values for the SemMukhem sacred forest,Garhwal Himalaya(Pala et al.2013),but much higher than reported for an evergreen forest in the Himalaya(Singh and Singh 1992),deciduous forest in Thailand(Ogawa et al.1965),evergreen forest in Venezuela(Klinge and Herrera 1978),and rainforest in Papua New Guinea(Edwards and Grub 1977).A regional comparison also showed that our estimated values are higher than in the tropical forests of the Cachar district,Assam,India(Borah et al.2013),tropical forests in Gibbon and Kholahat,Assam,India(Borah et al.2015)and a subtropical pine forest in Manipur(Yadava 2010).The high biomass carbon stock is attributed to the traditional conservation system that prevented forest from degradation and deforestation.Sacred forests and temple forests are one of the oldest forms of conserved natural forest(Pala et al.2013)and,therefore,contribute substantially to biomass carbon stock management.

Fig.2 Tree density,basal area and AGB with respect to DBH classes in the sacred grove of Manipur

Conclusions

The present study supports the following conclusions:(1)sacred groves are traditionally conserved forest patches with high stand density,(2)forest patches at the base of the foothill contains more biomass than that on steep slopes,(3)sacred groves can contribute signi fi cantly to biomass carbon stock management,and(4)further studies are required to explore how the traditional communities who manage these forests can bene fi t under clean development mechanism(CDM)and reduced emissions from deforestation and forest degradation(REDD)programmes.

AcknowledgementsThis study was funded by Department of Science and Technology(DST),New Delhi India.We express our sincere gratitude to DST,New Delhi India.We gratefully acknowledge the President of the Phayeng Forest Committee and local people of Phayeng,Manipu,Northeast India for their cooperation during our fi eldwork.

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