Inclusive Indian Central Himalayan soil carbon estimates underscores significant inorganic carbon contribution and temporal dynamics: Implications for carbon sequestration

J Environ Manage. 2024 Nov 16:372:123312. doi: 10.1016/j.jenvman.2024.123312. Online ahead of print.

Abstract

Soil carbon estimates in the Indian Himalayan region-a global climate change hotspot-primarily rely on the lossy wet oxidation method and predominantly focus on soil organic carbon (SOC), neglecting the soil inorganic carbon (SIC) component. Sensitive and holistic soil carbon estimates are crucial for effective policy planning. By incorporating eight major Central Himalayan forest types along a 3000 m elevational gradient, we report that the acidic Himalayan soil (surface soil pH: 4.74-6.84) of the selected forest types hold up to 31% of the total soil carbon stock as SIC stock. Using soil carbon and soil organic matter assays based on elemental analyzer and the loss-on-ignition method, we established that these Himalayan soils store less than 50% of SOC in SOM, challenging the use of universal factors in the region. The amount of SOC in SOM also showed temporal variability. The machine learning Random Forest algorithm highlighted the influence of SOM and climate variables in regulating the distribution of SOC, microbial biomass carbon, and key carbon cycling soil enzymes. The vertical distribution of SOC was more uniform than that of SIC. We found higher activity of soil carbon-cycling enzymes (dehydrogenase, beta-glucosidase, and phenol oxidase) in the forest types. Sensitive and higher soil carbon estimates substantiate a lower microbial quotient (0.17-1.23 %) than the regional trend. Notably, we explained how seasonal and temporal changes in soil carbon estimations hinder a constant positive soil carbon flux. Meanwhile, the mean surface SOC flux (4.63 Mg C ha-1 yr-1) and SIC flux (1.68 Mg C ha-1 yr-1) indicate that the Himalayan soils have significant potential for carbon sequestration. In conclusion, our research indicates substantial soil organic and inorganic carbon storage in major Central Himalayan forest types, with negative anthropogenic activities posing a clear and present threat to the soil carbon stocks.

Keywords: Machine learning algorithms; Microbial biomass carbon; Microbial quotient; Soil carbon sequestration; Soil enzymes; Soil organic matter.