Effect of Land Uses on Soil Microbial Community and Spatial Variability in Mirabad Lands, Naghadeh

Document Type : Research Article

Authors

• Sheyda Kaboodi ¹
• farzin shahbazi ¹
• Nasser Aliasgharzad
• nosratola najafi ¹
• naser davatgar ²

¹ University of Tabriz

² tabriz university

https://doi.org/10.22067/jsw.v31i6.33721

Abstract

Introduction: Understanding soil biology and ecology is increasingly important for renewing and sustainability of ecosystems. In all ecosystems, soil microbes play an important role in organic matter turnover, nutrient cycling and availability of nutrients for plants. Different scenarios of land use may affect soil biological properties. Advanced information technologies in modern software tools such as spatial geostatistics and geographical information system (GIS) enable the integration of large and complex databases, models, tools and techniques, and are proposed to improve the process of soil quality and sustainability. Spatial distribution of chemical and biological properties under three scenarios of land use was assessed.
Materials and Methods: This study was carried out in Mirabad area located in the western part of Souldoz plain surrounded by Urmieh, Miandoab, Piranshahr and Naghadeh cities in the west Azerbaijan province with latitude and longitude of 36°59′N and 45°18′E, respectively. The altitude varies from 1310 to 1345 with average of 1325 m above sea level. The monthly average temperature ranges from -1.4 °C in January to 24.6 °C in July and monthly precipitation ranges from 0.9 mm in July to 106.6 mm in March. Apple orchard, crop production field and rich pasture are three selected scenarios in this research work. Soil samples were systematically collected at 65 sampling points (0-30 cm) on mid July 2010. Soil chemical and biological properties i.e. microbial community, organic carbon and calcium carbonate equivalent were determined. The ArcGIS Geostatistical Analyst tool was applied for assessing and mapping the spatial variability of measured properties. The experimental design was randomized complete blocks design (RCBD) with five replications. Two widely applied methods i.e. Kriging and Inverse Distance Weighed (IDW) were employed for interpolation. According to the ratio of nugget variance to sill of the best variogram model three following spatial dependence conditions for the soil properties can be considered: (I) if this ratio is less than 25%, then the variable has strong spatial dependence; (II) if the ratio is between 25% and 75%, the variable has moderate spatial dependence; and (III) otherwise, the variable has weak spatial dependence. Data were also integrated with GIS for creating digital soil biological maps after testing analysis and interpolating the mentioned properties.
Results and Discussion: Spherical model was the best isotropic model fitted to variograms of all examined properties. The value of statistics (R2 and reduced sum of squares (RSS)) revealed that IDW method estimated calcium carbonate equivalent more reliably while organic carbon and microbial community was estimated more accurately by Kriging method. The minimum effective range (6110 m) was found for microbial community which had the strong spatial dependence [(Co/Co+C)

Keywords

• Kriging
• Soil microbial community
• Spatial variability
• zoning

20.1001.1.20084757.1396.31.6.7.4

References

1- Aliasgharzad N. 2011. Methods in Soil Biology. University of Tabriz, Iran. (Translated to Persian).

2- Aşkın T., and Kizilkaya R. 2006. Assessing spatial variability of soil enzyme activities in pasture topsoils using geostatistics. European Journal of Soil Biology, 42: 230-237.

3- Ayoubi S., Mohammadzamzni S., and Khormali F. 2007. Prediction total nitrogen by organic matter content using some geostatistic approaches in part of farm land of Sorkhankalateh, Golestan Province. Journal of Agricultural Science and Natural Resources, 14: 78-87. (in Persian with English abstract)

4- Bardgett R.D., and Cook R. 1998. Functional aspects of soil animal diversity in agricultural grasslands. Applied Soil Ecology, 10: 263-276.

5- Brejda J.J., Moorma T.B., Smith J.L., Karlen D.L., Allen D.L., and Dao T.H. 2000. Distribution and variability of surface soil properties at a regional scale. Journal of American Soil Science Society, 64: 974-982.

6- Burgess T.M., and Webster R. 1980. Optimal interpolation and is arithmic mapping of soil properties: I. The variogram and punctual kriging. Journal of Soil Science, 31: 315-331.

7- Einax J.W., and Soldt U. 1999. Geostatistical and multivariate statistical methods for the assessment of polluted soils-merits and limitations. Chemometrics and Intelligent Laboratory Systems, 46: 79-91.

8- Fliebbach A., and Mader P. 2000. Microbial biomass and size-density fractions differ between soils of organic and conventional agricultural systems. Soil Biology and Biochemistry, 32: 757–768.

9- Froughifar H., Jafarzadeh A.A., Torabi Gelsefidi H., and Aliasgharzad N. 2012. Effect of different landforms on spatial variability and frequency distribution of soil biological properties in Tabriz Plain. Water and Soil Science-University of Tabriz, 4: 35-52. (in Persian with English abstract)

10- Gao Y., Mao L., Miao C.Y., Zhou P., Cao J.J., Zhi Y.E., and Shi W.J. 2010. Spatial characteristics of soil enzyme activities and microbial community structure under different land uses in Chongming Island, China: Geostatistical modelling and PCR-RAPD method. Science of the Total Environment, 408: 3251–3260.

11- Islam K.R., and Weil R.R. 2000. Soil quality indicator properties in mid-Atlantic soils as influenced by conservation management. Journal of Soil and Water Conservation, 55: 69-78.

12- Johnson N.C., Zak D.R., Tilman D., and Pfleger F.L. 1991. Dynamics of vesicular–arbuscular mycorrhizae during old field succession. Oecologia, 86: 349–358.

13- Mahmoodi S., and Hakimian M. 1998. Fundamentals of Soil Science. Tehran University Publication. (Translated to Persian)

14- McGrath D., and Zhang C. 2003. Spatial distribution of soil organic carbon concentrations in grassland of Ireland. Applied Geochemistry, 18: 1629-1639.

15- McKenzie N.J., Grundy M.J., Webster R., and Ringrose -Voase A.J. 2008. Guidelines for Surveying Soil and Land Resources. CSIRO Publishing, Australia, 576.

16- Nelson D.W., and Sommers L.E. 1982. Total carbon and organic matter. In: Page, A. L et al. (Eds.), Methods of Soil Analysis. Part 2. Chemical and Microbiological Methods. Agronomy Monographs ASA and SSSA, Madison, USA. 539-580.

17- Paul E.A., and Clark F.E. 1996. Soil Microbiology and Biochemistry, Academic Press, San Diego, CA. 340.

18- Plante A.F. 2007. Soil biogeochemical cycling of inorganic nutrients and metals. In: E.A. Paul (ed.) Soil Microbioloy, Ecology and Biochemistry. Academic Press, Oxford. 389-432.

19- Quine T.A., and Zhang Y. 2002. An investigation of spatial variation in soil erosion, soil properties and crop production within an agricultural field in Devon, U.K. Journal of Soil and Water Conservation, 57: 50-60.

20- Shahbzi F., Aliasgharzad N., Ebrahimzad S. A., and Najafi N. 2013 Geostatistical analysis for predicting soil biological maps under different scenarios of land use. European Journal of Soil Biology, 55: 20-27.

21- Sheklabadi M., Khademi H., Karimian Eghbal M., and Nourbakhsh F. 2007. Effect of climate and long term interdiction on some soil biological quality indices in central Zagros. Journal of Water and Soil Science-Journal of Science and Technology of Agriculture and Natural Resources, 11(41): 103-115. (in Persian)

22- Soil Conservation Service. 1992. Soil Survey Laboratory Methods and Procedures for Collecting Soil Sample. USDA-SCS. Soil Survey. Invest. Ret. No. 2. U. S. Gov. Print. Office, Washington, DC.

23- Van Vliet P.C.J., Van der Stelt B., Rietberg P.I., and de Goede R.G.M. 2007. Effects of organic matter content on earthworms and nitrogen mineralization in grassland soils. European Journal of Soil Biology, 43: S222-S229.

24- Zhang R., Shouse P.J., Yates S.R., and Kravchenko A. 1997. Application of geostatistics in soil science. Trends in Soil Science, 2: 95-104.