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Environmental Sustainability

, Volume 1, Issue 4, pp 383–392 | Cite as

Carbon sequestration potential of plantation forestry and improvements in soil nutrient status in a subtropical area of northern India

  • Mohd Baqir
  • Abdul Barey Shah
  • Richa Kothari
  • Rana Pratap SinghEmail author
Original Article
  • 261 Downloads

Abstract

Variability in carbon sequestration efficiency of different tree species in Kahinaur plantation forest of district Mau, Uttar Pradesh, India was evaluated. Moreover, improvement in nutrient status and other physicochemical characteristics of soil due to plantation forest was also taken into consideration. Soils in the plantation forest possessed higher soil organic carbon (SOC), nitrogen, phosphorus and potassium (NPK) than the adjacent waste land soil. However, nutrient status of both plantation and wasteland soil decreased with increasing soil depth and bulk density. The soil microbial biomass carbon (SMBC), soil enzyme activities like soil dehydrogenase activity, acidic and alkaline phosphates and soil respiration were higher in the plantation forest soil as compared to the waste land soil. The highest SMBC (114.47 μg g−1 soil 24 h−1) was reported in plantation forest soil, but only 56.65 μg g−1 soil 24 h−1 in waste land soil. In addition, the activity of soil dehydrogenase (2.74 μg g−1 soil h−1) was also highest in the plantation forest soil. Among the studied tree species, carbon accumulation was found maximum in Prosopis juliflora, Putranjiva roxburghii, Pithecellobium dulce and Artocarpus heterophyllus depicting that these can be recommended as atmospheric carbon reducers for their better potential to sequester and store carbon. The study indicated that afforestation or forest plantation improved SOC, nutrient stock and improved other important soil fertility parameters in the plantation forest soil as compared to the non-forest soil i.e., waste land.

Keywords

Carbon sequestration soil microbial biomass carbon Soil dehydrogenase activity Soil respiration Prosopis juliflora Putranjiva roxburghii 

Introduction

The concentration of carbon dioxide in the atmosphere has risen from 277 ppm in 1750 (Joos and Spahni 2008) to 406.42 ppm by 2017 (NOAA 2017), and it is predicted to continue to increase. Therefore, it is important to manage the carbon pool in soils and plants to reduce atmospheric concentration. This could be achieved by better management of plantation forests which acts as a sink for carbon (Vashum and Jayakumar 2012; Patil et al. 2015; Brahma et al. 2016). For mitigating the climate change, role of forest ecosystem in sequestering atmospheric carbon and also storing soil organic carbon (SOC) stocks is of prime significance. Therefore, development of knowledge databases of different tree species for their carbon stocks that can act as a potential measure for mitigation of atmospheric CO2 is essential (Jandl et al. 2007).

Developmental activities lead to deforestation and disturbance which in turn led to degradation of soil quality and reduction in terrestrial carbon stocks (Saxena and Choudhary 2015). Plantation forests are recognized as sequesters of greenhouse gases as well as enhancing terrestrial carbon stocks (Updegraff et al. 2004; Arora and Chaudhury 2014). It has been estimated that about two-third of the terrestrial sequestration of carbon is done by forests and forest soil (IPCC 2007; Kalies et al. 2016). Soil microbial biodiversity helps in maintenance of soil organic carbon (SOC) and nutrient recycling in soil (Shan et al. 2008; Song et al. 2016). Increased levels of soil microbial biomass carbon (SMBC) and SOC indicate sequestration of carbon into the soil (Shao et al. 2015). Soil enzyme activities like decomposition of organic matter; transfer of energy and recycling of soil nutrients reveal soil fertility characteristics (Ding et al. 2011; Sharma et al. 2015), that have been attributed to the dynamics of soil microorganisms (Das et al. 2014). Evaluation of SMBC in soil is an indicator of soil quality and productivity (Kara and Bolat 2007; Xiao et al. 2015; Shao et al. 2015).

Improving carbon storage capacity of forest land through afforestation is a vital aspect of carbon management (Wei et al. 2014). However, estimates of the effect of tree species on SOC stocks and other soil properties are very scarce and the scientific basis for targeted use of specific tree species to enhance SOC sequestration following afforestation also needs attention (Vesterdal et al. 2002; Srivastava et al. 2016). At present, plantation forests have a limited share in total pool of terrestrial carbon but their contribution is supposed to be bigger in future as various countries are developing new designated or planned forest on their waste land or unused lands (Payn et al. 2015). Reports on estimation of carbon pool in soils and plants of plantation forests are rarely available in Indian context (Ravindranath et al. 2008; Singh et al. 2015; Sharma et al. 2017). Certain plantation forests with mixed culture of tree species have been developed by forest department of India as part of plantation drives. Assessment of the potential of these plantation forests for their carbon sequestration and subsequent amelioration in soil physicochemical characteristics will help in developing a database of trees as best carbon sequesters, thereby providing environmental services. Keeping the above facts in consideration, the present study was aimed: (1) to assess the SOC from the mixed plantation forest in a semi-arid zone of India and impact of plantation forest, if any, on enhancement of SOC, soil microbial activity and other soil nutrients (2) interrelationship between soil carbon content, soil microbial biomass carbon, soil enzyme activity and soil respiration at different soil depths (3) quantify inter and intra specific variation in carbon concentration for 15 subtropical tree species to select better ones as atmospheric carbon sequesters.

Materials and methods

Study site

The present study was conducted in the Kahinaur plantation forest at village Kahinaur district Mau, Uttar Pradesh, India during the year 2017. The study area is a plantation developed by forest department of Uttar Pradesh under the plantation drive of mixed culture of plant species during the year 1983 for commercial production of wood, non-wood products and environmental services. The plantation forest is well managed with minimum disturbance of human intervention and only fuel wood collection is allowed to the poor rural households without harming the green plants. The total area of the plantation forest is about 118 hectares and is located between 25o52′274′′N and 83o30′578′′E, shown in (Fig. 1).
Fig. 1

Map showing the location of the study area

The selected plantation forest is a subtropical deciduous forest having 36 plant species monitored by the forest department of Uttar Pradesh government. However, for the present study we have selected 15 species that are dominant in the plantation forest. Climate of the study area is humid subtropical with dry winter. Average annual rainfall is 675 mm from May to October. Maximum temperature recorded in the area was 42 °C in June and minimum 9 °C in December.

Sample collection

Soil samples were collected by dividing the plantation forest into four sites and designated as PF1, PF2, PF3 and PF4 based on topographical feature of the study sites and physical feature of the soil. Four 30 × 30 m sampling plot were selected. From each sampling plot five sects of soil samples were collected at 0–10, 10–20 and 20–30 cm depth (IPCC 1997). Soil samples were collected with soil core (6 cm diameter) during September, 2017 from all the four compartments. Five sects of soil samples from each compartment were collected in depth of 0–10, 10–20 and 20–30 cm. All the four sects of soil sample with their respective soil depth were mixed together to form a composite soil sample and were brought to the laboratory for further analysis. Control soil samples were collected from the wasteland (WL) which is adjacent to the plantation forest. Stones, plant and root debris were removed from the collected soil and stored at 4 °C. After sieving (< 2 mm) the soil samples were analysed for SOC, soil microbial biomass carbon and soil enzyme activity. Collection of soil sample up to 30 cm depth for estimation of SOC and other soil properties was done in accordance with IPCC guideline (IPCC 1997).

Soil analysis

The pH was measured by the potentiometric method. Soil sample was taken in a 10 ml beaker to which 50 ml of distilled water was added. The suspension was stirred at regular intervals for 30 min and the pH was recorded using a pH meter (Gliessman 2000). For Electrical conductivity (EC), 20 g of soil sample was taken in a 10 ml beaker to which 50 ml of distilled water was added. The suspension was stirred at regular intervals for 30 min and the EC was recorded by dipping the electrode using potable digital EC meter. Bulk density (BD) was estimated according to Gupta (2004). BD was measured by taking an undisturbed block of soil (soil core). The soil was dried at 105 °C for 12 h and weighed. The exact volume of soil was determined by measuring the cylinder volume. SOC was estimated by rapid titration (Walkley 1947). Estimation of nitrogen was done by the Micro Kjeldahl procedure (Bremner and Mulvaney 1982). Available phosphorus was estimated by Olsen’s method and available potassium by flame photometry (Jackson 1973).

Soil microbial biomass carbon, soil respiration and soil enzyme activity

SMBC was estimated using the chloroform fumigation extraction method as per Vance et al. (1987). A 17 g of soil sample of equal particle size was taken. Each sample was duplicated and transferred to bottles. One was fumigated by chloroform in desiccator and the other was unfumigated. The chloroform was evaporated at 50 °C for 24 h, 70 ml K2SO4 was added and shaken for 30 min; extract was filtered through Whatman No 42 filter paper. Optical density (OD) was recorded at 280 nm. For estimation of soil respiration 50 g of soil samples were kept in 1 l glass bottles. 10 ml of NaOH (0.1 N) was kept in separate test tube lowered in the bottle with a thread. The bottle was tightly closed with a lid. The observation was taken on 1, 3, 5 days by removing the 0.1 N NaOH in test tube and titrating it with 0.1 N HCl (Li et al. 1996). For dehydrogenase activity twenty grams of air- dried soil (< 2 mm) and 0.2 g of CaCO3 were mixed thoroughly, and 6 g of this mixture was placed in each three test tubes. To each tube, 1 ml of 3% aqueous solution of triphenyl tetrazolium chloride (TTC) and 2.5 ml of distilled water were added. These samples were incubated at 37 °C for 24 h. Then, 10 ml of methanol was added. The solution was filtrated through a glass plugged with absorbent cotton. Volume was made to 100 ml with methanol. Optical density was recorded at 485 nm (Casida et al. 1964). For alkaline and acidic phosphates one gram of soil sample was placed in a 50 ml Erlenmeyer flask, 0.2 ml of toluene was added 1 ml of modified universal buffer (MUB) (pH 6.5 for the assay of acid phosphatase (ACP) or pH 11 for the essay of alkaline phosphatase (ALP)), 1 ml of p-nitrophenyl phosphate solution made in the same buffer, and swirl the flask for few seconds to mix the content. The sample was incubated at 37 °C for 1 h. Then 1 ml of 0.5 M CaCl2 and 4 ml of 0.5 M NaOH were added. The flask was swirled for a few seconds and soil suspension filtered through Whatman No. 2 filter paper. Optical density was recorded at 420 nm (Schneider et al. 2000).

Carbon content in different component of tree species

Samples of different tree components (stem, branch, leaf) were collected through an increment borer. For estimation of carbon content from stem, a wood sample was extracted from the place slightly above breast height; diameter at breast height (DBH). For estimation of carbon content from the branches, randomly two branches of each species where chosen while for estimation of carbon content from leaf, a quadrat of 1 m × 1 m was laid out on the surface and the leaves falling in the quadrat were collected and weighed. Each sample of different components from trees species were taken to the laboratory and dried in oven at 80 °C for 70 h to estimate dry weight and subsequently ground and passed through a 500 micron sieve. The carbon content in different tree components (stem, branch, leaf) was determined by Isotope Ratio Mass Spectrometer (IRMS) (Thermo Scientific Inc.) at CSIR-NBRI, Lucknow.

Statistical analysis

One Way Analysis of Variance (ANOVA) was used for statistical comparison of means using SPSS-20 software. Pearson Correlation coefficient was used to calculate the relationship between various soil physicochemical parameters.

Results

Physicochemical characteristics of soils from plantation forest and wasteland

The physicochemical characteristics of soils of plantation forest and its comparison with non-forest area i.e., wasteland are presented in (Fig. 2). Statistically significant differences were observed between the mean values of the parameters at selected sites (p ≤ 0.05). In the present study, pH of plantation forest (PF) of top soil (0–10 cm) ranged from 6.78 to 6.80 and in wasteland 7.68–8.85. However, pH increased with increasing soil depth in both the PF and in wasteland. In PF, pH at 10–30 cm depth ranged from 6.78 to 8.21, while in WL it ranged from 7.95 to 8.25. In the present study there is no consistent relationship between soil pH and EC with soil depth in both forest and wasteland area, however, variation in EC between forest and non-forest soil was reported. EC ranged from 0.86 to 0.31 mS/cm in PF and 0.44–0.58 mS/cm in WL. BD varied from 0.63 to 1.75 g cm−3 in PF and 1.78–1.98 g cm−3 in WL. BD increased with increasing soil depth. BD was observed to be higher in WL as compared to PF soil (Fig. 2). SOC was reported to show a decreasing trend with increasing soil depth in both PF and WL. However, the soil from PF showed higher SOC as compared to non-forest soil. In PF, SOC ranged from 0.91 to 2.48% and in WL it was 0.34–0.95% (Fig. 2). In the present study higher concentration of nitrogen, phosphorus, potassium (NPK) was found in PF as compared to WL with the levels of NPK decreasing with increasing soil depth in both PF and non-forest land (WL). NPK in the PF was higher as compared to WL with Nitrogen (N) ranging from 0.03 to 0.39 percent, Phosphorus 5.12–51.29 kg ha−1 and Potassium 125.56–248.90 kg ha−1, while in WL the values ranged from 0.01 to 0.04% N, 5.01–20.51 kg ha−1 P, 65.10–118.20 kg ha−1 K (Fig. 2).
Fig. 2

ag Physicochemical characteristics of soil at selected sites (values between brackets are standard deviations). One way ANOVA was performed to compare the mean of soil samples at different depths using Duncan Multiple Range Test (p ≤ 0.05). Different letters signify statistical differences among different soil parameter at the selected sites. EC electrical conductivity, BD bulk density, SOC soil organic carbon, TKN Total Kjeldahl nitrogen, P phosphorus, K potassium, PF plantation forest and WL waste land. Where as a = pH of soil sample, c = EC of soil sample, c = BD of soil sample, d = SOC soil sample, e = TKN of soil sample, f = Available P of soil sample, g = Available K of soil sample

Soil microbial biomass carbon (SMBC), Soil respiration (SR) and Soil enzyme activity

In the present study SMBC, soil dehydrogenase activity, acidic and alkaline phosphatase and soil respiration showed higher value in the plantation forest as compared to wasteland soil and the value decreased with increasing soil depth in both PF and WL (Fig. 3). The SMBC was significantly affected by plantation trees as PF showed higher SMBC as compared to non-forest sites. The value ranged from 96.65 to 114.47 μg g−1 soil per 24 h−1 SMBC in PF and 46.95 to 56.65 μg g−1 soil per 24 h−1 in WL.
Fig. 3

ae Soil microbial biomass carbon (SMBC), soil respiration (SR), dehydrogenase activity (DA), Acidic phosphatase (ACP) and Alkaline phosphatase (ALP) of soil samples at selected sites and the values between brackets are standard deviations. Different letters show significant differences among different soil parameters. One Way ANOVA was performed to compare the means at different depths using Duncan test (p < 0.05); whereas PF Plantation forest, WL Wasteland and PNPP = para-Nitro phenyl phosphate. Whereas, a = SMBM of soil sample, b = SR of soil sample, c = DA of soil sample, d = ACP of soil sample, e = ALP of soil sample

In the present study, SR showed higher value in PF as compared to WL ranging from 13.53 to 39.13 mg CO2 (100 g)−1 soil per 24−1 in PF and 10.79–14.42 mg CO2 (100 g)−1 soil per 24−1 in WL. SR also decreased with increasing soil depth in both PF and non-forest sites (Fig. 3). Soil dehydrogenase activity was also greater in PF and it ranged from 0.35 to 2.74 μg g−1 soil h−1 and in WL the value was 0.43 to 0.82 μg g−1 soil h−1. Acidic phosphates (ACP) and alkaline phosphates (ALP) varied significantly both in plantation forest and non-forest land. The values ranged from 16.76 to 36.01 μg para-Nitro phenyl phosphate (PNPP) g−1soil h−1 ACP and 25.26 to 68.42 μg PNPP g−1soil h−1 ALP in PF and 15.69–19.37 μg PNPP g−1 soil h−1ACP and 26.51–32.42 μg PNPP g−1 soil h−1 ALP in WL (Fig. 3).

Soil parametric correlation studies

Analysis of Pearson correlation coefficient between different soils parameters of the present study is shown in (Table 1). It is concluded that SOC showed a positive correlation with TKN (0.648), SMBC (0.675), SR (0.704), ACP (0.735), ALP (0.685); (p < 0.01), and with K (0.533) and DE (531); (p < 0.05). TKN in soil showed positive correlation with P (0.739), K (0.709), SR (0.733), ACP (0.735), ALP (0.685); (p < 0.01). Available P in soil showed a positive correlation with K (688); (p < 0.01). Available K showed positive correlation with SR (0.646); (p < 0.01). SMBC showed positive correlation with SR (0.751), DE (0.678), ALP (0.609); (p < 0.01) and with ACP (0.488); (p < 0.05). Among the soil enzyme activity, SR showed positive correlation with DE (0.566), ACP (0.655) and ALP (0.742); (p < 0.01). DE showed positive correlation with ALP (0.583); (p < 0.05) and ACP showed positive correlation with ALP (0.797); (p < 0.01).
Table 1

Pearson correlation coefficient matrix between soil physicochemical characteristics and enzyme activities of the selected sites

   

BD

SOC

TKN

P

K

SMBC

SR

DA

ACP

ALP

pH

1

           

EC

0.113

1

          

BD

0.127

− 0.031

1

         

SOC

− 0.388

0.363

0.188

1

        

TN

− 0.473*

0.012

− 0.257

0.648**

1

       

P

− 0.454

− 0.006

− 0.423

0.353

0.739**

1

      

K

− 0.492*

0.088

− 0.386

0.533*

0.709**

0.688**

1

     

SMBC

− 0.549*

0.335

− 0.363

0.675**

0.458

0.293

0.571*

1

    

SR

− 0.588*

− 0.055

− 0.110

0.704**

0.733**

0.385

0.646**

0.751**

1

   

DA

− 0.407

0.360

− 0.013

0.531*

0.297

0.046

0.015

0.678**

0.566*

1

  

ACP

− 0.242

0.157

− 0.039

0.735**

0.735**

0.423

0.570*

0.488*

0.655**

0.197

1

 

ALP

− 0.412

0.098

0.089

0.816**

0.685**

0.235

0.341

0.609**

0.742**

0.583*

0.797**

1

*Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed); whereas EC electrical conductivity, BD bulk density, SOC soil organic carbon, TKN Total Kjeldahl nitrogen, P phosphorus, K potassium, SMBC Soil microbial biomass carbon, SR soil respiration, DA dehydrogenase activity, ACP Acidic phosphatase, ALP Alkaline phosphatase

Carbon percentage in different tree species and its components

In the present study, percentage of carbon in different tree species and its components (branch, stem and leaf) is presented in (Table 2).
Table 2

Variation in carbon percentage (%) of stem, branch and leaf for the selected tree species

Name of species

Vernacular name

Carbon percent (%)

Stem

Branch

Leaf

Prosopis juliflora (Sw.) DC

Vilayati babul

52.01 ± 0.55ef

49.23 ± 1.58e

47.78 ± 0.89g

Putranjiva roxburghii Wall.

Putranjiva

50.48 ± 1.96g

47.43 ± 1.63cd

45.16 ± 0.72de

Pithecellobium dulce (Roxb.)

Jangle Jalebi

50.31 ± 0.69fg

48.04 ± 1.67e

46.72 ± 0.87e

Artocarpus heterophyllus Lam

Katahal

49.13 ± 0.24g

48.21 ± 1.59e

44.52 ± 1.24fg

Ficus benghalensis (L.)

Bargad

48.27 ± 2.44de

46.32 ± 1.85de

45.67 ± 0.38fg

Madhuca indica J.F. Gmel.

Mahva

47.86 ± 1.42cde

38.32 ± 0.94a

33.44 ± 1.70a

Mangifera indica (L.)

Mango

47.33 ± 2.22bc

45.32 ± 0.72cd

43.58 ± 2.57f

Alstonia Scholaris (L.) R. Br

Saptaparni

47.16 ± 3.68be

48.42 ± 1.70e

44.07 ± 0.52fg

Terminalia arjuna (Roxb. ex DC.)W& A

Arjun

45.95 ± 0.77bc

42.42 ± 1.54ab

35.59 ± 1.77ab

Tectona grandis (L. f.)

Teak

43.38 ± 0.97ab

45.24 ± 0.45e

40.03 ± 2.14de

Eucalyptus sp. (L’He´r.)

Safeda

43.17 ± 4.38ab

44.23 ± 2.01cd

51.44 ± 0.40i

Albizzia lebbek Benth.

Siris

43.05 ± 3.09ab

38.24 ± 0.73a

37.00 ± 0.50be

Alangium salvifolium (L. f.) W

Ankol

42.25 ± 4.85ab

41.24 ± 2.14ab

37.95 ± 1.97cd

Eugenia jambolana (Lam.)

Jamun

42.12 ± 1.80abc

38.32 ± 1.33a

34.54 ± 0.70ab

Holoptelea integreifolia (Planch.)

Chilbil

40.01 ± 0.65a

42.62 ± 0.74ab

38.34 ± 2.40cd

One way ANOVA was performed to compare the mean of using Duncan Multiple range tests (p < 0.05). Different letters shows significant differences among different fuel wood parameters of the selected tree species.

Inter and intra specific variation in carbon concentration showed significant difference (p ≤ 0.05). One way ANOVA showed significant differences among the carbon content of stem, branches and leaves of all studied plant species. It has been observed that carbon content showed increasing trend in different tree components (stem < branch < leaf) of the tree species. Individually, maximum carbon percent in different tree components was recorded in certain species like P. juliflora with stem showing (52.01%), branch (49.23%) and leaf (47.78%), P. roxburghii with stem (50.48%), branch (47.43%) and leaf (45.16%), P. dulce in stem (50.31%), branch (48.04%), leaf (46.72%) and A. heterophyllus in stem (49.13%), branch (48.21%) and leaf (44.52%).

Discussion

The current study was aimed to evaluate the prospective use of plantation forest as carbon sequesters and subsequently their impact on the soil nutrient status and other properties. Generally, soil pH of 5.9–7.2 is considered as good for providing nutrients and essential elements to the plants. In the present study, the lower value of pH observed in top soil layer may be due to addition of more organic matter, microbial activity and less disturbance (Gupta and Sharma 2009) and increase in pH with the increase in soil depth may be due to decrease in organic matter with soil depth (Sundarapandian et al. 2016). Contrary to this, other studies have reported that lower value of pH in PF compared with non-forest areas may be due to higher organic matter decomposition in the PF (Sariyildiz et al. 2005; Yaqoob et al. 2015). It has been reported that higher pH of soil may be attributed to the mixing of herbaceous litter with the soil (Xu et al. 2006). Electrical conductivity is directly related to the soluble salt concentration in the soil which effects the plant growth (Gupta et al. 2009; Wani et al. 2014). It has been reported that soil with high value of pH shows higher electrical conductivity (Goel and Behl 2008). The reason for increased bulk density in wasteland soil may be due to the lower amount of organic matter and the degradation of soil quality. Concurrently in other studies BD of 1.4–0.7 g cm−3 was observed in the plantation forest soil of Taiwan (Tsai et al. 2009). Decrease in BD of top soil of plantation forest as compared to the barren land may be due to higher accumulation of organic matter and with the increasing in soil depth, there is a stronger mixing of mineral material in the soil profile and therefore, higher bulk density (Schulp et al. 2008; Gupta and Sharma 2009; Dar and Somaiah 2015).

SOC pool plays an important role in soil quality and productivity which acts as strong indicator of soil quality in addition to other soil parameters (Gandhi and Sundarapandian 2017). The higher value of SOC under the plantation forest may be due to addition of more leaf litter fall on the surface of forest which keeps decomposing and adds more organic carbon into the soil (Wani et al. 2014). Variation in SOC stock within the forest may be due to composition of species, soil type and texture (Gandhi and Sundarapandian 2017). Higher SOC stock in top soil and its increase with increasing depth indicated that proper management and fewer disturbances could enhance the infiltration of SOC up to the deep soil layer (Rumpel 2014). Contradictory to this, decreasing levels of SOC with increasing soil depth have also been reported which may be attributed to slow cycling of nutrients and carbon pool and may also be due to compaction of soil (Semwal et al. 2009; Dar and Somaiah 2015). Therefore, top soil of the plantation forest should be protected to minimize the risk of large scale carbon release. Higher value of NPK in the top layer of the soil (0–10 cm) in PF may be attributed to heavy leaf litter and humus contents (Bharali et al. 2014). It has been reported that soil under plantation forest with minimum disturbance had improved considerably as compared to non-forest land (Dutta and Agarwal 2002; Haque and Barua 2013). Therefore, minimum disturbance of the surface soil and slow rate of decomposition may store maximum NPK and carbon into the soil (Paz et al. 2016). Soil under Gmelina arborea plantation in India showed significantly higher total nitrogen as compared to non-forest land with maximum concentration in the upper surface of 0–20 cm (Swamy and Puri 2005; Nath et al. 2015).

The SMBC is highly influenced by higher organic inputs than total change in soil organic matter (Chander et al. 1997). The higher SMBC in the plantation forest may reflect more addition of organic matter due to decomposition of leaf litter into the soil. Soil respiration (SR) signifies efflux of CO2 from the soil and is considered as a measure of total soil microbial activity of the soil system. It has been reported that the rate of SR was affected by tree species (Wang et al. 2013) and SR had a linear significant relationship with the forest canopy and soil temperature (Sun et al. 2009). Our study indicated that SR from the plantation forest was almost two-fold higher than the WL. The reason behind this may be due to higher availability of organic content due to leaf litter decomposition on the upper surface and maximum microbial population (Kara et al. 2016). Estimation of soil dehydrogenase activity depicts the involvement of soil microbes for their role in electron transport system (Kandeler and Dick 2007). It has been reported that dehydrogenase activity in natural forest showed higher value (0.42–1.02 μg g−1 soil h−1) than dump soil (0.05–0.163 μg g−1 soil h−1) (Verma et al. 2014). Enzyme activity such as ACP, ALP and dehydrogenase in soil is more affected by the biological activity of soil and population of soil microbes (Sharma et al. 2014). While comparing soil enzyme activity with soil depth, it has been observed that with the increase in soil depth, the enzyme activity decreases which corresponds to the microorganism distribution in soil profile (Khaziev and Burangulova 1965) and total organic matter (Arutyunyan and Simonyan 1975). Earlier studies reported that the soil enzyme activity and soil microbial population were higher in soil covered with broad leaf forest as compared to coniferous plantation (Xing et al. 2010). A positive correlation between SOC, total Kjeldahl nitrogen (TKN) and available K in soil has also been reported by Gupta and Sharma (2009) and Gairola et al. (2012). It was also reported that available P showed negative correlation with SOC and showed positive correlation with soil pH (Haque and Barua 2013). Positive correlation among these characteristics may be attributed with soil humus (Bharali et al. 2014).

Carbon percentage in different tree species and its components depends upon ash content and the ash content depends upon the structural components. Higher the structural tissue higher will be the ash content and lower will be the carbon percentage in wood sample (Negi et al. 2003). Thus, carbon percentage showed decreasing trend in different tree components (leaf < branch < stem). Several other studies have reported that variation of carbon content in intra and inter species is also influenced by stand characteristics, site condition and management practices (Guan et al. 2015). While comparing hard tissue and soft tissue, it has been observed that woody tissues like stem, root and branch show higher percentage of carbon stock than the soft tissues like leaf, flower and fine root (Kraenzel et al. 2003).

Conclusion

The current study revealed that forest plantations enhance soil organic carbon stock and improved the soil fertility in addition to providing a good sink for atmospheric carbon. Soil from plantation forest showed higher concentration of SOC, soil nutrient, soil microbial biomass carbon and soil enzyme activity as compared to the adjacent wasteland. Increase in soil microbial biomass carbon reflects an increase in soil microbial population which is essential for long term soil productivity and fertility. Improvement in SOC stock in the plantation forest soil reflects that afforestation or forest plantation is a viable option to increase SOC stock in soil. Variability in carbon percentage in different tree species within their components showed differential ability of these species. Species such as Prosopis juliflora, Putranjiva roxburghii, Pithecellobium dulce, Artocarpus heterophyllus have more potential to sequester atmospheric carbon over the other species and these species may be planted on priority basis for their better potential as atmospheric carbon sinks. Systematic plantation forests can thus be very important in sustaining the ecosystems and environment as compared to wastelands.

Notes

Acknowledgement

Authors are very thankful to Dr. U.S. Singh, IFS and ranger of the selected plantation forests for their support in providing necessary facilities during the sampling. Mohd Baqir is thankful to UGC- New Delhi for a fellowship.

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Copyright information

© Society for Environmental Sustainability 2018

Authors and Affiliations

  • Mohd Baqir
    • 1
  • Abdul Barey Shah
    • 1
  • Richa Kothari
    • 2
  • Rana Pratap Singh
    • 1
    Email author
  1. 1.Department of Environmental ScienceBabasaheb Bhimrao Ambedkar (Central) UniversityLucknowIndia
  2. 2.Department of Environmental ScienceCentral University of JammuSambaIndia

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