Iranian Journal of Soil and Water Research

Iranian Journal of Soil and Water Research

Assessment of geostatistical models for zoning spatial distribution of some soil properties in Darengan region with different land uses, Fars province

Document Type : Research Paper

Authors
hamidreza owliaie 1
Alireza Salehi 2
Gholamreza Zareian 3
1 Department of Soil Science, Faculty of Agriculture, University of Yasouj, Yasouj, Iran
2 Department of Forestry, Range and Watershed Management, Agricultural and Natural Resources, Yasouj University, Yasouj, Iran
3 Department of Soil and Water Research, Fars Agricultural and Natural Resources Research and Education Center, AREEO, Shiraz, Iran.
10.22059/ijswr.2023.367197.669596
Abstract
Determination of the physico-chemical characteristics of soil for sustainable agriculture on large scales is an important factor in achieving a precision agriculture. Different land use and management practices greatly impact soil properties, and knowledge of the variation in soil properties within different land uses, is essential in determining production constraints related to soil characteristics. Laboratory analyses of the soil properties are usually expensive and time consuming. Surmounting these problems is possible using geostatistics. This study was conducted to assess geostatistical methods for the spatial distribution of some soil properties of Darengan region with different land uses in Fars province. 134 surface soil samples at an interval of 1.0 × 1.0 km on a grid design were taken from pasture and agricultural land uses. Physico-chemical characteristics of the soil samples were analyzed. According to the results, the best spatial structure model with the highest accuracy was exponential model for the variables of sand, EC, CCE, pH, and BD, rational quadratic model for silt, and spherical model for clay, OC, and CEC. The spatial structure was weak for CCE, medium for organic carbon, and strong for the other variables. Among the characteristics studied, the variables of silt, clay and cation exchange capacity have the lowest range, and EC has the highest range. Based on the zoning map of the studied properties, the areas with agricultural land use had greater OC, clay, CEC, EC and lower pH. Understanding soil properties with their spatial dependency is of crucial importance for understanding the behavior of soil and hence providing better soil management.
Keywords
Zoning
Spatial variability
Semi-variable
Soil properties
Subjects

Geostatistical assessment of the spatial distribution of some Soil properties of the soils of Darengan region with different land uses, Fars province

 

EXTENDED ABSTRACT

 

Introduction

Mapping of soil properties is an important operation as it plays an important role in the knowledge about soil properties and how it can be used sustainably. Soil properties vary in different spatial areas due to the
combined effect of biological, physical, and chemical processes over time, and can vary within farmland or at the landscape scale. Different land use and management practices greatly impact soil properties, and knowledge of the variation in soil properties within different land uses is essential in determining production constraints related to soil characteristics. A tool often used to analyze how soil properties are spatially distributed in an area is geostatistics. It is effective for understanding the magnitude and structure of the spatial variability of the physical and chemical properties. Darengan region, located in the southwest of Shiraz city, is an important agricultural region with pasture, agricultural and garden uses, which has developed a lot in recent years, and a large area of pasture has been changed to agricultural land use. Therefore, this study was conducted to assess geostatistical methods for the spatial distribution of some soil properties of the soils of this region.

Material and Methods

The study area, with an area of about 20000 ha, is located 40 km southwest of Shiraz city, in the center of Fars province. The annual rainfall and temperature of the region are about 340 mm and 17.1°C, respectively. A total of 134 soil samples (from pasture and agricultural land uses) were collected from the surface (0–30 cm) at an approximate interval of 1.0 × 1.0 km on a regular grid design. Routine soil analysis includes soil texture, pH, EC, CCE, BD, OC, and CEC were measured in the laboratory. Descriptive statistical analysis was carried out and in geostatistical analysis, the semivariogram was calculated for each soil variable. Different models of two deterministic and geostatistical methods were used to estimate the soil characteristics in unsampled points in the study area. Deterministic methods include global polynomial interpolation, local polynomial interpolation, inverse distance weighting and Radial Basis Function. Geostatistical methods include Kriging, Cokriging and Empirical Bayesian Kriging. In all three mentioned geostatistical methods, three types of simple prediction; ordinary prediction and universal prediction, were used. In this study, 104 models, including 5 deterministic models and 99 geostatistical models, were used to select the most suitable model with the strongest spatial structure. All geostatistical and deterministic studies were carried out in ArcGIS 10.7.1 to achieve the most suitable interpolation model in terms of accuracy and precision.

Results and Discussion

The results revealed that, based on precision criteria, exponential co-kriging was the best method for interpolating sand, EC, CCE, pH, BD, spherical co-kriging for clay, OC., and CEC, and co-kriging rational quadratic for silt. The spatial structure was obtained for CCE, weak, for organic carbon, medium, and for the other variables, strong. Among the characteristics studied, the variables of silt, clay and CEC have the lowest range, and EC has the highest range. Variography analysis indicated that the ranges of influence for sand, silt, clay, EC, CCE, OC, pH, BD, and CEC, were 2733, 2000, 2004, 10553, 2290, 2584, 3448, 2361 m, respectively, and the RSME varied between 0.017 (for BD) and 5.75 (for CCE). For geostatistical analysis of soil variables, the value of the nugget: sill ratio ranges from 0% (sand, clay, BD, and CEC) to 175.5% (CCE), which indicates that internal (e.g., the soil-forming processes) factors were dominant over external (e.g., human activities) factors. However, the soil sand, silt, clay, EC, pH, BD, and CEC had a strong spatial dependency with a nugget: sill ratio of <25% since it was induced by structural factors. Meanwhile, OC had moderate spatial dependency with a nugget: sill ratio of 25–75% since this variable was mostly determined by both internal and external factors and CCE had weak spatial dependency with a nugget: sill ratio of >75%. Based on the zoning map of the studied properties, the areas with agricultural land use had greater organic carbon, clay, CEC, EC and lower pH. The results of this study showed the effectiveness of geostatistics and GIS techniques as powerful tools for spatial management of soil characteristics. In general, it seems that the studied properties were mainly influenced by factors such as topography, parent material and land use. Considering the variability of soil characteristics as well as the different influential ranges of these variables, it is suggested that for reducing costs, the sampling intervals of the soils be based on the influential range.

Ahmadi, A., Toranjzar, H. & Kazemi, A. (2019). Mapping Soil Salinity in Boulagh (Saveh) Saline Lands Using Geostatistical Methods. Journal of Natural Environmental Hazards, 8 (19): 1-14. (In Persian)
Ahmadivand, M., Zamani, J., Shekofteh, H. & Abbaszadeh Afshar, F. (2023). The effect of land use change on some physical and chemical properties of soil. Environmental Erosion Research, 13 (2): 210-234. (In Persian)
Alizadeh, A., Sheykheslami, A., Kiadaliri, H., Khazaei Poul, S., Salmanian, M. & Ramezani Poul, M. (2019). Achieving optimal dimensions for systematic sampling of forest using variography method in geostatistics (case study: series 5 of Safarood Ramsar forestry plan). Forest Research and Development, 5(4): 645-656. (In Persian)
Asadzadeh, F., Khosraviaqdam, K. Yaghmaeian Mahabadi, N. & Ramezanpour, H. (2019). Spatial variation of mineral particles of the soil using remote sensing data and geostatistics to the soil texture interpolation. Journal of Water and Soil, 32 (6): 1207-1222. (In Persian)
Askari, S., Owliaie, H.R., Safari, Y. Sedghi Asl, M. (2019). Spatial variability of some soil fertility characteristics as affected by land use change, Yasouj region. Journal of Soil Management and Sustainable Production, 9(1): 65-81. (In Persian)
Ayoubi, Sh., Mohammad Zamani, S. & Khormali, F. (2007). Spatial variability of some soil properties for site specific farming in northern Iran. International Journal of Plant Production 1(2): 225-236
Bangroo, S.A., Sofi, J., Bhat, M., Mir, Sh., Mubarak, T. & Bashir, O. (2021). Quantifying spatial variability of soil properties in apple orchards of Kashmir, India, using geospatial techniques. Arabian Journal of Geosciences, 14: 2047. https://doi.org/10.1007/s12517-021-08457-6
Behnam, V., Gholamalizadeh Ahangar, A., Rahmanian, M. & Bameri A. (2019). Spatial distribution of some physical and chemical properties of soil using geostatistic methods (Case study: Zabol to Zahedan route). Journal of Environment and Water Engineering, 5(3): 251-263. (In Persian) DOI: 10.22034/ jewe.2019.200821.1330.
Bijanzadeh, E., Mokarram, M. & Naderi, R. (2014). Applying spatial geostatistical analysis models for evaluating variability of soil properties in Eastern Shiraz, Iran. Iran Agricultural Research, 33, 35-46.
Blake, G.R. & Hartge, K. (1986). Bulk density. P. 363-375. A. Klute (ed.) Methods of Soil Analysis. Physical and Mineralogical Methods. Part 1. Agron. Monogr. 9. ASA and SSSA, Madison, WI.
Cao, C., Jiang, S., Ying, Z., Zhang, F. & Han, X. (2011). Spatial variability of soil nutrients and microbiological properties after the establishment of leguminous shrub Caragana microphylla Lam. plantation on sand dune in the Horqin sandy land of Northeast China. Ecological Engineering, 37.10. 1467-1475.
Chapman, HD. (1965). Cation exchange capacity. In: Black CA, editor. Methods of soil
analysis. Part 2. Madison (WI): American Society of Agronomy. p. 891–901.
Cambardella, C.A., Moorman, T.B., Novak, J.M., Parkin, T.B., Karlen, D.L., Turco, R.F. & Konopka, A.E. (1994). Field-Scale variability of soil properties in central Iowa soils. Journal of Soil Science Society of America, 58 (3): 1501-1511. https://doi.org/ 10.2136/sssaj1994. 03615995005800050033x
Chen, H., Shen, Z., Liu, G., & Tong, Z. (2009). Spatial variability of soil fertility factors in the Xiangcheng tobacco planting region, China. Frontiers of Biology in China, 4:3. 350-357.
Chen, S., Arrouays, D., Leatitia Mulder, V., Poggio, L. & et al. (2022). Digital mapping of GlobalSoilMap soil properties at a broad scale: A review. Geoderma, 409: 116957. https://doi.org/10.1016/j.geoderma.2021.115567
Danesh, M., Taghipour, F., Emadi, M. & Ghajar Sepanlou, M. (2022). The interpolation methods and neural network to estimate the spatial variability of soil organic matter affected by land use type. Geocarto International, 37, 11306-11315. doi.org/10.1080/10106049.2022.2048905
Foroughifar, H., Jafarzadah, A.A., Torabi, H. Gelsefidi, H. Aliasgharzadah, N. Toomanian, N. & Davatgar, N. (2011) Spatial variations of surface soil physical and chemical properties on different landforms of Tabriz Plain. Water and Soil Science, 21(3): 1-21. (In Persian)
Gee, G.W., & Bauder, J.W. (1986). Particle-size analysis. In ‘Methods of soil analysis, Part 1. Agronomy Monograph, Vol. 9’. 2nd ed. (Ed. A. Klute) pp. 383–411. (American Society of Agronomy: Madison, WI, USA) Geocarto International 37 (26). tps://doi.org/ 10.1080/ 10106049. 2022.2048905
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, 483p.
 Gunarathna, M. H. J. P., Nirmanee, K. G. S., & Kumari, M. K. N. (2016). Are geostatistical interpolation methods better than deterministic interpolation methods in mapping salinity of groundwater? International Journal Research Innovation Earth Science, 3(3): 59-64.
Habashi, H., Hosseini, Mohammadi, J. & Rahmani, R. (2007). Geostatistic applied in forest soil studying processes. Journal of Agricultural Science and Natural Science, 14: 18-27. (In Persian)
Hasani Pak, A.A. (2011). Geostatistics. Tehran University Press, 314 P. (In Persian)
Jalali, Gh., Tehrani, M.M., Broomand, N. & Sanjari, S. (2013). Comparison of geostatistical methods for mapping the spatial distribution of some nnutrients in the East of Mazandaran Province.  Soil Researches, 27(2): 195-204. (In Persian)
Johnson, K., Ver Hoef, J. M., Krivoruchko, K. & Lucas, N. (2001). Using ArcGIS Geostatistical Analyst. GIS by ESRI. Redlands, USA. 306 p.
Kazemi Poshtmasari, H., Tahmasebi, Z., Kamkar, B., Shataei, Sh. & Sadeghi, S. (2012). Evaluation of geostatistical methods for estimating and zoning of macronutrients in agricultural lands of Golestan Province.  Water and Soil Science, 22(1): 201-220. (In Persian)
Minasny, B., & McBratney, A. B. (2016). Digital soil mapping: A brief history and some lessons. Geoderma, 264: 301-311.
Mehmandoost, F., Owliaie, H.R., Adhami, E. & Naghiha, R. (2018). Effect of land use change on some physicochemical and biological properties of the soils of Servak Plain, Yasouj region. Soil and Water, 32(3): 587-599. (In Persian)
Nasiri, E., Owliaie, H.R., Safari, Y. & Sedghi Asl, M. (2019). Geostatistical assessing of some soil properties variability due to the oak land deforesting in Mokhtar plain, Yasouj. Applied Soil Research, 7 (3): 83-97. (In Persian)
Nelson, D.W., & Sommers, L.E. (1982). Total carbon, organic carbon, and matter. In ‘Methods of soil analysis. Part 2. Agronomy Monograph, Vol. 9’. 2nd ed. (Eds. AL Page, RH Miller, DR Keeney) pp. 539–577. (American Society of Agronomy: Madison, WI, USA)
Nelson, R.E. (1982) Carbonate and gypsum. In ‘Methods of soil analysis. Part 2’. (Eds AL Page, RH Miller, DR Keeney) pp. 181–197. (American Society of Agronomy: Madison, WI, USA).
Owliaie, H. R., Adhami, E. & Najafi Ghiri, M. (2022). Changes in soil magnetic properties and iron oxides following land use change (Case study: Mokhtar Plain, Kohgilouye Province). Journal of Water and Soil, 36(2): 267-282. (In Persian)
Qu, L., Lu, H., Tian, Z., Schoorl, J.M., Huang, B., Liang, Y., Qiu, D. & Liang, Y. (2024). Spatial prediction of soil sand content at various sampling density based on geostatistical and machine learning algorithms in plain areas. Catena, 234: 107572. https://doi.org/10.1016/j.catena. 2023.107572
Quine, T. A. & Zhang, Y. (2002). An investigation of spatial variation in soil erosion, soil properties and crop production within an agricultural field in Devon, U.K. J. Soil and Water Conservation. 57: 50-60.
Santra, P., Chopra, U. K. & Chakraborty, D. (2008). Spatial variability of soil properties and its application in predicting surface map of hydraulic parameters in an agricultural farm. Current Science, 95:7. 937-945.
Sarangi, A., Madramootoo, C.A., & Enright, P. (2006). Comparison of spatial variability techniques for runoff estimation from a Canadian Watershed. Biosystems Engineering, 95:2. 295-308.
Savage, M.J. (1993). Statistical aspects of model validation. Presented at a workshop on the field water balance in the modeling of cropping systems, University of Pretoria, South Africa.
Shabani, H., Delavar, M.A., Safari, Y. & Alamdari, P. (2018). Spatial variability of some soil characteristics in lands of Zanjan University. Applied Soil Researcg, 7(4): 164-178. (In Persian)
Soil Survey Staff. (2022). Keys to Soil Taxonomy, 13th edition. USDA Natural Resources. Conservation Service. USA.
Taati, A., Sarmadian, F., Motaghian, H. & Mousavi, R. (2020). Mapping features of surface and depth, soil profiles by using geostatistical techniques in part of Qazvin Plain. Human and Environment, 52: 67-81. (n Persian) 
Utset, A., T. Lopez, & M. Dıaz, (2000). A comparison of soil maps, kriging and a combined method for spatially predicting bulk density and field capacity of Ferralsols in the Havana–Matanzas Plain. Geoderma, 96(3): 199-213.
Wackernagel, H. (2003). Geostatistical models and kriging, IFAC Proceedings, 36: 543-548. 
Webster, R. & Oliver, M. A. (2007). Geostatistics for environmental scientists (Second Edition ed.). The Atrium, Southern Gate, Chichester, England: John Wiley and Sons. 330 p.
Wu, W., Xiu, D. T. & Liu, H. B. (2008). Spatial variability of soil heavy metals in the three gorges area, Multivariate and Geostatistical analysis. Journal of Environmental Monitoring Assessment, 157, 63-71.
Xing-Yi, Z., Yue-Yu', S., Xu Dong, Z., Kai, M. & Herbert, S.J. (2007). Spatial Variability of Nutrient Properties in Black Soil of Northeast China. Pedosphere, 17(1), 19-29.
Zareian, Gh. & Hasanshahi, M.H. (2004). Revision of semi-detailed studies of soil science and suitability of lands for major crops in Darengoon plain (Fars province). Final report of the research project. Soil and Water Research Institute. (In Persian)
Zareian, F., Mahmoudi, J. & Javadi, M.R. (2014). Predicating the spatial variability of some soil properties by using geostatistic methods in Darreh Viseh, Karaj. Soil Researches, 28(3): 511-520. (In Persian)
 Zeraatpisheh, M., Bottega, E.L. Bakhshandeh, E., Owliaie, H.R., Taghizadeh-Mehrjardi, Kerry, R., Scholten, T., & Xu, M. (2022). Spatial variability of soil quality within management zones: homogeneity and purity of delineated zones. Catena, 209 (1) 105835 10.1016/j. catena. 2021.105835
Zhang, X., Chen, S., Xue, J., Wang, N., Xiao, Y., Chen, Q., & Shi, Z. (2023). Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping. Geoderma, 432: 116383.
Zhang, X., Lin, F., Jiang, Y., Wang, K., & Feng, X. L. (2009). Variability of total and available copper concentrations in relation to land use and soil properties in Yangtze River Delta of China. Environmental monitoring and assessment, 155(1), 205-213.
Iranian Journal of Soil and Water Research
Volume 55, Issue 1
April 2024
Pages 97-116