Effect of tree species on carbon and nitrogen stocks in mineral soil

Effects of tree species, stand age and land-use change on soil carbon and nitrogen stock rates were investigated in the northwest of Turkey using 4 common tree species as black pine (Pinus nigra Arnold.), scots pine (Pinus sylvestris L.), oriental beech (Fagus orientalis Lipsky) and uludağ fir (Abies nordmanniana ssp. Bornmuelleriana). Three tree species (black pine, scots pine and oriental beech stands) were used to investigate the differences in soil C and N among tree species. Old and young uludağ fir stands and adjacent grassland were used to study the differences in soil C and N with stand age and land-use change. Mineral soil samples were taken from 0–10 cm and 10–20 cm soil depths, and analyzed for pH, soil texture, bulk density, total soil carbon and total nitrogen. The total soil carbon and total nitrogen pools were then calculated by multiplying soil volume, soil bulk density, and the total soil carbon or total nitrogen content. Results showed that there were significant differences in soil carbon and nitrogen contents, carbon/nitrogen ratios and stock rates among three species, and between old and young fir stands and grassland. In general, when 0-20 cm soil depth was considered, mean soil carbon stock rate was the highest under black pine (79 Mg C ha-1) followed by Scots pine (73 Mg C ha-1) and beech (67 Mg C ha-1), whereas mean soil nitrogen stock rate was the highest under beech (9.57 Mg N ha-1) followed by Scots pine (5.77 Mg N ha-1) and black pine (4.20 Mg N ha-1). Young fir stands showed lower soil carbon stock, but higher soil nitrogen stock rates compared to old fir stands and grassland. These pioneer data from the northeast of Turkey has shown that tree species, stand tree age and land-use change can have significant effects on soil carbon and nitrogen content and stocks rates. Those data can be used to enhance forest management activities, such as selection of tree species for carbon sequestration in plantation systems, design of sustainable agroforestry systems, and improvement of biogeochemical models.