Spatial Variability and Multivariate Assessment of Soil Physico-Chemical Properties in Kanger Valley National Park, India
Abstract
Soil physicochemical properties are basic soil fertility parameters and soil ecosystem function in tropical forests as they control soil nutrient cycling, soil organic matter decomposition and the productivity of vegetation. In the present study, spatial variations of soil physicochemical properties were explored in Kanger Valley National Park, Chhattisgarh, India, by Pearson correlation analysis and Principal Component Analysis (PCA). Nine representative sites were sampled and composite samples of the surface soil (0-10 cm and 10-20 cm) were taken for each sample site, and analyzed for pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), available phosphorus (P), available potassium (K), particle density (PD), and bulk density (BD). Soils were found to be non-saline (pH 5.03 to 6.05) and had low electrical conductivity (0.08 to 0.34 dS m⁻¹), which is good for forest vegetation. Organic carbon content ranged between 0.64–0.72%, available nitrogen (160.25–179.75 ppm), phosphorus (9.23–13.05 ppm), and potassium (153.50–178.75 ppm) showed moderate fertility of the soil. The Pearson correlation values showed that there were high positive correlations between the available K and available N with organic C, and soil pH with organic C and available N, suggesting that these are more influenced by organic C than by bulk density, while the bulk density had significant negative correlations with most of the fertility parameters, indicating adverse effects of soil compaction on nutrient availability. Based on the Principal Component Analysis, the total variance was explained by two principal components with eigenvalues larger than one, accounting for 63.9% of the total variance. The first principal component (47.3%) explained the soil fertility gradient, which was dominated by soil organic carbon, pH, available soil N and soil K, and second principal component (16.6%) reflected variations in soil physical properties, mainly particle density and bulk density. PCA site scores and the biplot were able to clearly separate nutrient-rich sites from relatively nutrient-poor sites, indicating that there was a high degree of soil quality variability across the study area. The results show that organic carbon is the most important factor for soil fertility, while bulk density and particle density are secondary factors for soil variation. The results present a good baseline data for monitoring soil quality and aid sustainable conservation and management of the tropical forest ecosystems in Kanger Valley National Park.
Keywords
Download Options
Introduction
Soil is one of the most vital components of terrestrial ecosystems, and offers many ecosystem services such as nutrient cycling, water regulation, carbon sequestration, conservation of biodiversity, maintenance of vegetation productivity etc. These ecological functions are largely dependent on the physical, chemical and biological properties of the soil, which all influence the availability of nutrients, the activity of soil microorganisms, organic matter breakdown and plant growth. Therefore, the evaluation of soil physicochemical properties is an essential part of soil quality assessment, monitoring of forest ecosystems and sustainable land management. Recent developments in Soil Science have further highlighted the concept of soil health as the continuous capability of the soil to perform its functions as a living ecosystem sustaining plants, animals and human beingsand also contributing to climate control and sustainability of the environment (Lehmann et al., 2020; Maurya et al., 2020; Lal et al., 2021).
Forest soils are one of the biggest terrestrial stores of organic carbon and are vital for ecosystem stability because of the constant breakdown of litter, the recycling of nutrients and the activity of living organisms. The inputs of organic matter from plant litter, fine roots and microbial biomass increase soil organic carbon (SOC) which has positive effects on soil aggregation, water holding capacity, cation exchange capacity, nutrient retention and soil fertility. SOC is recognized as one of the most significant indicators of soil quality and ecosystem function because it encompasses physical, chemical and biological processes in soils. Furthermore, the conservation and enhancement of SOC is becoming more and more crucial in the era of climate change mitigation through long-term carbon sequestration in addition to its role in enhancing productivity and ecosystem resilience within forest areas (Lal, 2020; Devi, 2021; Lal et al., 2021; Veldkamp et al., 2020).
Conclusion
In the present study, descriptive statistics, Pearson correlation and Principal Component Analysis (PCA) were used to determine the spatial variability of the soil physicochemical properties in Kanger Valley National Park, Chhattisgarh, India. Soils were slightly acidic, and had low electrical conductivity and moderate levels of available macronutrients and organic carbon, which are good for nutrient cycling and forest ecosystem functioning. Pearson correlation analysis showed that soil OC is the most influential soil property, and showed strong positive relationship with available N and K, and soil pH, thus indicating its key role in controlling nutrient availability and soil fertility. Conversely, bulk density had negative relationships with most fertility parameters, which implies that compaction of soils could reduce nutrient availability and impact on soil quality.
The Principal Component Analysis (PCA) was able to reduce this variability in soil properties to two principal components that accounted for 63.9% of the total variance. The first principal component was indicative of a gradient of soil fertility, mainly related to organic carbon, pH, available nitrogen and available potassium, while the second principal component was associated with soil physical properties including particle density and bulk density. Additionally, the PCA biplot showed clear spatial heterogeneity between different sampling sites, which could be separated into nutrient-rich and comparatively nutrient-poor sites.
References
[1] Anderson, J. M., & Ingram, J. S. I. (1993). Tropical soil biology and fertility: A handbook of methods (2nd ed.). CAB International.
[2] Black, C. A. (Ed.). (1965). Methods of soil analysis. Part I: Physical and mineralogical properties (Agronomy Monograph No. 9). American Society of Agronomy.
[3] Bünemann, E. K., Bongiorno, G., Bai, Z., Creamer, R. E., De Deyn, G., De Goede, R., & Brussaard, L. (2018). Soil quality–A critical
review. Soil Biology and Biochemistry, *120*, 105-125. https://doi.org/10.1016/j.soilbio.2018.01.030
[4] Crowther, T. W., Van den Hoogen, J., Wan, J., Mayes, M. A., Keiser, A. D., Mo, L., & Maynard, D. S. (2019). The global soil community and its influence on biogeochemistry. Science, *365*(6455), eaav0550. https://doi.org/10.1126/science.aav0550
[5] Devi, A. S. (2021). Influence of trees and associated variables on soil organic carbon: A review. Journal of Ecology and Environment, *45*. https://doi.org/10.1186/s41610-021-00180-3
[6] Evans, D. L., Janes-Bassett, V., Borrelli, P., Chenu, C., Ferreira, C. S., Griffiths, R. I., & Visser, S. M. (2022). Sustainable futures over the next decade are rooted in soil science. European Journal of Soil Science, *73*(1), e13145. https://doi.org/10.5194/soil-7-191-2021
[7] FAO. (2022). Status of the World's Soil Resources—Regional Assessment.
[8] IPCC. (2022). Climate Change 2022: Mitigation of Climate Change. Cambridge University Press.
[9] Jackson, M. L. (1973). Soil chemical analysis. Prentice Hall of India.
[10] Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). Springer.
[11] Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, *20*(1), 141-151. https://doi.org/10.1177/001316446002000116
[12] Lal, R. (2020). Soil organic matter and water retention. Agronomy Journal, *112*(5), 3265-3277. https://doi.org/10.1002/agj2.20282
[13] Lal, R., Monger, C., Nave, L., & Smith, P. (2021). The role of soil in regulation of climate. Philosophical Transactions of the Royal Society B: Biological Sciences, *376*(1834), Article 20210084. https://doi.org/10.1098/rstb.2021.0084
[14] Lehmann, J., Bossio, D. A., Kögel-Knabner, I., & Rillig, M. C. (2020). The concept and future prospects of soil health. Nature Reviews Earth & Environment, *1*(10), 544-553. https://doi.org/10.1038/s43017-020-0080-8
[15] Maurya, S., Abraham, J. S., Somasundaram, S., Toteja, R., Gupta, R., & Makhija, S. (2020). Indicators for assessment of soil quality: A mini-review. Environmental Monitoring and Assessment, *192*, 604. https://doi.org/10.1007/s10661-020-08556-z
[16] Olsen, S. R., Cole, C. V., Watanabe, F. S., & Dean, L. A. (1954). Estimation of available phosphorus in soils by extraction with sodium bicarbonate (USDA Circular No. 939). United States Department of Agriculture.
[17] Pachani, S., & Kashyap, N. (2020). Soil health and its quality: A review. International Journal of Chemical Studies, *8*(2), 1434-1436. https://doi.org/10.22271/chemi.2020.v8.i2v.8966
[18] Peng, Y., Schmidt, I. K., Zheng, H., Heděnec, P., Bachega, L. R., Yue, K., & Vesterdal, L. (2020). Tree species effects on topsoil carbon stock and concentration are mediated by tree species type, mycorrhizal association, and N-fixing ability at the global scale. Forest Ecology and Management, *478*, 118510. https://doi.org/10.1111/gcb.15436
[19] Subbiah, B. V., & Asija, G. L. (1956). A rapid procedure for the estimation of available nitrogen in soils. Current Science, *25*(8), 259-260.
[20] Valani, G. P., Martíni, A. F., da Silva, L. F. S., Bovi, R. C., & Cooper, M. (2021). Soil quality assessments in integrated crop–livestock–forest systems: A review. Soil Use and Management, *37*(1), 22-36. https://doi.org/10.1111/sum.12667
[21] Veldkamp, E., Schmidt, M., Powers, J. S., & Corre, M. D. (2020). Deforestation and reforestation impacts on soils in the tropics. Nature Reviews Earth & Environment, *1*, 590-605. https://doi.org/10.1038/s43017-020-0091-5
[22] Yang, K., Diao, M., Zhu, J., Lu, D., & Zhang, W. (2022). A global meta‐analysis of indicators for assessing forest soil quality through comparison between paired plantations versus natural forests. Land Degradation & Development, *33*(17), 3603-3616. https://doi.org/10.1002/ldr.4411.