Considering Relationship between Temperature Sensitivity of Soil Organic Carbon Decomposition with some of the Soil Properties and Topographic Indices in Guilan Tea Gardens

Carbon dioxide is one of the main greenhouse gases that affect the world's air temperature. Small changes in the amount of carbon dioxide emissions from the soil have a significant effect on the concentration of this gas in the atmosphere. Soil respiration, the process that emits carbon dioxide from the soil to the atmosphere, is one of the most important carbon flows in the ecosystem and includes two components of heterotrophic respiration (microbial respiration) and autotrophic respiration (root respiration). Researchers measure the rate of soil respiration for every 10 degrees Celsius of temperature change with an index called temperature sensitivity of soil respiration (Q10). The evidence shows that the Q10 value of the soil is not constant and has a negative correlation with temperature and a positive correlation with soil moisture. Also, the amount of soil organic carbon, incubation temperature and the interaction of these two have a significant effect on soil organic carbon decomposition. Accordingly, this research measures the temperature sensitivity (Q10) in soil under tea cultivation and investigates its relationship with some soil chemical characteristics and topographic indices.

After surveying the east and west tea gardens in Guilan province in the north of Iran, 200 samples were taken at a depth of 0 to 40 cm. The experiments were conducted to determine Organic Carbon, Labile carbon, Bulk density, PH, Cation Exchange Capacity, Microbial Biomass and soil microbial respiration. To measure Q10, two temperature treatments of 25 and 35 °C were used. Elevation, slope and aspect were obtained using a DEM map in ArcGIS 10.5 and other topographical indicators such as wetness index, slope length, relative slope position, catchment area, channel network base level, vertical distance to channel network, convergence index, profile curvature and plan curvature were extracted from DEM map in Saga GIS 2.1.0. Pearson correlation was used to investigate whether there is any relationship between soil temperature sensitivity with other soil properties. Then, principal component analysis (PCA) was performed to determine a minimal data set. All the statistical analyses were done with SPSS 24. Regression charts were also drawn using Excel software.

The Q10 values varied from 1.19 to 1.58. This index has the most negative correlation with organic carbon (-0.863), Labile carbon (-0.863), microbial biomass (-0.837), respiration at 25 °C (-0.831) and 35 °C (-0.8) at 1% level and negative correlation with elevation at 5% (-0.159). The principal component analysis showed that the first six components (PC1, PC2, PC3, PC4, PC5 and PC6) have special values of more than one and were able to describe 73% of the total variance. The first main component (PC1) describes 23.125% of the total variance and includes soil organic carbon, labile carbon, microbial biomass and Q10 which have the highest factor loading in this component. The second one (PC2), which explains about 12.99% of the total variance, has the highest factor loading with the vertical distance to the channel network (0.880). The third component (PC3) explains about 12.22% of the total variance. In PC3, clay has the highest factor loading. In the fourth component catchment area, convergence profile and slope length have the highest factor loading, respectively. Finally, the fifth and the sixth components are related to the elevation, slope and plan curvature.

The highest positive factor loading is related to soil organic carbon (0.981). Therefore, the first main component can be "part of the role of organic carbon in microbial biomass, labile carbon and temperature sensitivity". The results showed that Q10 has the highest negative correlation with soil microbial biomass and organic and labile carbon. In other words, the higher the soil organic and microbial biomass carbon, the lower the amount of Q10. Also, the second component can be considered as topographic indicators related to the channel network. Topographic indices can be used very strongly to model making soil organic carbon. The third component is related to clay properties. Several studies have indicated that the amount of clay has a high relationship with cation exchange capacity and it is a good indicator to determine the quality of soil. According to the results, although the correlation between some characteristics obtained from soil topographical analysis can prove the possibility of using them as auxiliary variables in predicting soil organic carbon, this point should be taken into account that other factors also play a role in the process of soil formation and development.