Spatial distribution of some soil physico-chemical properties in agricultural soils of Isfahan province

Document Type : Research Paper

Authors

1 Soil and Water Research Department, Isfahan Agricultural and Natural Resources Research and Education Center. Agricultural Research, Education and Extension organization (AREEO), Isfahan, Iran.

2 Soil and Water Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran

Abstract

Knowing about the spatial dependence of different soil characteristics in farms is important to achieve more production and better management. The aim of this study was to evaluate the spatial variability and frequency distribution of some physical and chemical properties, including pH, EC, organic carbon, phosphorus and potassium that can be used by plants, texture and cation exchangble capacity, within the various landforms of Isfahan province. The study was conducted on 118 soil samples. The spatial correlation of each variable with a specific semi-variable and the best fitting model for each variable were selected using GS+ version 9 software. Interpolation was done using normal Kriging, Cokriging, and Inverse Distance Weighting with powers of 1 to 3. The accuracy of the distribution maps of these variables were evaluated by the mean deviation of error (MBE) and the root mean square error (RMSE). The results of the geostatistical analysis showed that potassium, sand percentage and pH had strong spatial dependent and the other characteristics had moderate spatial dependent. The exponential model was the most accurate to predict phosphorus, EC, CEC, clay and silt variables while potassium, pH, sand and organic carbon percentage were best fitted with an sphericalmodel. Also, EC had the smallest effective range (14.86 km) and pH had the largest effective range (around 71 km). For potassium, pH and EC variables, the Inverse Distance Weighting with the power of 1 (IDW-1) and for other variables the normal kriging method were recognized as the best interpolation methods.

Keywords

Main Subjects


EXTENDED ABSTRACT

Introduction

Knowing about the spatial dependence of different soil characteristics in farms is important to achieve more production and better management. Soil pH, Electrical Conductivity (EC), organic matter, available phosphorus, potassium, Cation Exchange Capacity (CEC), and soil structure are some of the most important indicators of soil fertility. These soil parameters are highly variable in space and time, especially in agricultural areas, with implications for crop production.The aim of this study was to evaluate the spatial variability and frequency distribution of some physical and chemical properties, including pH, EC, organic carbon, soil available phosphorus and potassium, texture and cation exchange capacity.

Methods

The study was conducted on 118 soil samples, in agricultural lands of different regions of Isfahan province in 2016. The spatial correlation of each variable with a specific semi-variable and the best fitting model for each variable were selected using GS+ version 9 software. Interpolation was done using normal Kriging, Cokriging, and Inverse Distance Weighting with powers of 1 to 3 methods. The accuracy of the distribution maps of these variables were evaluated by the Mean bias error (MBE) and the root mean square error (RMSE).

Results and Discussion

The results of the geostatistical analysis showed that potassium, sand percentage and pH had strong spatial dependent and the other characteristics had moderate spatial dependent. The strong spatial dependence due to the effect of intrinsic factors such as parent material, relief and soil types. The moderate spatial dependence could result from variation in environmental factors such as flood water, irrigation, fertilizeir addition, high water table level or agriculture practices and different agricultural managements. The exponential model was the most accurate to predict phosphorus, EC, CEC, clay and silt variables while potassium, pH, sand and organic carbon percentage were best fitted with an spherical model. Also, EC had the smallest effective range (14.86 km) and pH had the largest effective range (around 71 km). For potassium, pH and EC variables, the Inverse Distance Weighting with the power of 1 (IDW-1), RMSE equal to 0.171, 0.152, and 0.171 respectively, and for organic carbon, Phosohorus, texture and CEC, RMSE equal to 0.11, 0.199, 0.155, and 0.156, respectively,  the normal kriging method were recognized as the best interpolation methods. Spatial distribution maps according to the most accurate techniques showed that the greatest soil salinity and the  sand percentage were measured in north and especially the northeast parts (in the cities of Ardestan and Aran and Bidgol in Isfahan province), also the amount of  nutrient elements and soil organic carbon reduced in these areas. The soil fertility was better in the southern and western parts of the province. In addition to management factors such as fertilization and irrigation, this is due to the climate and more rainfall in these areas, as well as the higher quality of irrigation water in these areas. In general, according to the obtained results, it was found that the geostatistical modeling can show continuous changes of soil parameters with acceptable accuracy.

 

Afzali, A., Varwani, V. & Jafarinia, R. (2018). Application of geostatistics technique in predicting spatial changes of soil texture (Case study: Farahan Plain, Central Province) Geographical Quarterly, 15(58), 1-16. (In Persian)
Bogunovic, I., Paulo Pereira, P. & Brevik, C. (2017). Spatial distribution of soil chemical properties in an organic farm in Croatia. Science of the Total Environment, 584–58, 535-545.
Brejda, J. I., Moorman, T. B., Karlen, D. L. & Dao, T. H. 2000. Identification of Regional Soil Quality Factors and Indicators. I. Central and Southern High Plains. Soil Science Society of America Journal, 64, 2115-2124.
Cahn, M. D., Hummel J.W.  & Brouer, B. H. (1994). Spatial analysis of soil fertility for site-specific crop management. Soil Science Society of America Journal, 58, 1240-1248.
Dalchiavon, F. C., Carvalho, M. P., Andreotti, M. & Montanari, R. (2012). Spatial variability of the fertility attributes of dystropheric red latosol under a no-tillage system. Journal of Revista Ciencia Agronomice. 43, 453-461.
Delbari, M. & Jahani, S. (2014). Investigating the spatial changes of salinity and sodium characteristics of the soils of Chat region in Golestan province. Soil Research (Soil and Water Sciences), 28 (2), 433-446. (In persian).
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 lowa soils. Soil Science Society of America Journal, 58, 1501-1511.
Fatehi, Sh. 2011. Spatial variability of organic carbon, absorbable potassium and phosphorus in the fields of Islamabad West Agricultural Research Station, Kermanshah Province. Agronomy, 97,29-38. (In Persian).
Foroughifar, H., Jafarzadah, A. A., Torabi Gelsefidi, H., Aliasgharzadah, N., Toomanian, N. & Davatgar, N. (2011). Spatial Variations of Surface Soil Physical and Chemical Properties on Different Landforms of Tabriz Plain. Soil and water science, 21 (3), 1-21. (In persian).
Gotway, C. A., Ferguson, R. B., Herget, G. W. & Peterson, T. A. (1996). Comparison of kriging and Inverse- Distance methods for mapping soil parameters. Soil Science Society of America Journal, 60, 1237-1247.
Habashi, H., Hoseini, M., Mohammadi, J. and Rahmani, R. 2007. Application of geostatistics technique in soil studies of forest areas. Agricultural sciences and natural resources, 14, 17-28. (In Persian).
Isaaks, E.H. & Srivastava, R.M. (1989). An introduction to applied geostatistics. Oxford University Press. NewYork. P. 561.
Jaiver, D., Sanchez, T., Gustavo, A., Ligarreto, M. & Fabio, R. L. (2011). Spatial variability of soil chemical properties and its effect on crop yield. A case study in maize (zea mays L.) on the Bogota plateau. Journal of Agronomia colombiana. 29, 265- 274.
Jalali, Gh., Tehrani, M. M., Broumand, N. & Senjari, S. (2016). Comparison of geostatistical methods in preparing the spatial distribution map of some food elements in the east of Mazandaran province. Soil Research Journal (Soil and Water Sciences), 27(2), 195-204. (In Persian)
Jianbing, W., Boucher, A. & Zhang, T. (2008). A SGeMS code for pattern simulation of continuous and categorical variables: FILTERSIM. Computers & Geosciences, 4(12), 1863-1876.
Jordán, M.M., Navarro–Pedreňo, J., García – sánchez, E., Mateu, J. & Juan. P. (2004). Spatial dynamics of soil salinity under arid and semi-arid conditions: geological and environmental. Environmental Geology, 45,448–456.
Kilic, K. & Kilic, S. (2007). Spatial variability of salinity and alkalinity of a field having salination risk in semi –arid climate in northern Turkey. Environmental Monitoring Assessment, 127, 55–65.
Klute, A. (1986). Methods of Soil Analysis. Part 1, Physical and Mineralogical Methods. 2nd American Society of Agronomy, Agronomy Monographs 9(1), Madison, Wisconsin, 1188 pp
Kravchenko, A. & Bullock, D.G. (1999). A comparative study of interpolation methods for mapping soil properties. Agronomy Journal, 91, 393–400.
Liu, R., Xu, F., Yu, W., Shi, J., Zhang, P. & Shen, Z. (2016). Analysis of field-scale spatial correlations and variations of soil nutrients using geostatistics. Journal of Environmental Monitoring and Assessment. 188 (2):1–10.
Mulder, V.L., Lacoste, M., Richer-de-Forxges, A.C. & Arrouays, D. (2016). Global Soil Map.France: high resolution spatial modelling the soils of France up to two meter depth Advances in Soil Science, 573 (1),1352–1369.
Najafian, A., Dayani, M., Motaghian, H. & Nadian, H. (2012). Geostatistical assessment of the spatial distribution of some chemical properties in calcareous soils. Journal of Integrative Agriculture, 11, 1729-1737.
Nourzadeh, M., Mahdian, M. H. & Malakouti, M. J. (2010). Investigation and prediction spatial variability in chemical of agricultural soil using geostatistics. Archives of Agronomy and Soil Science, 1-15.
Olsen, S. R. & Sommers, L.E. (1982). Phosphorus. PP. 403-430. In: A. L. Page (Ed.), Methods of Soil Analysis, Agron. No. 9, Part 2, Chemical and microbiological properties, 2nd ed., American Society of Agronomy, Soil Science Society of America, Madison, 403-430.
 Page, A.L., R. H. Miller & M. Keeney. (1992). Methods of Soil Analysis. Part 2, Chemical and Microbiological Properties. American Society of Agronomy. In Soil Science Society of America, Vol. 1159.
Piri sahragard, H. & Zare Chahouki, M.A. (2015). An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province. Ecological Modelling, 309,310: 64-71.
Piri Sahragord, H. & Piri, J. (2016). Analysis of the spatial structure of some soil properties using the geostatistical method (Case study: West Taftan pastures, Khash city). Pasture Scientific Research Journal, 10 (2), 224-236. (In Persian).
Qiu, W., Curtin, D. & Beare, M. (2011). Spatial variability of available nutrients and soil carbon under arable cropping in Canterbury. The New Zealand Institute for plant and food research limited. 7 pp.
Rhodes, J.D. (1996). Salinity: electrical conductivity and total dissolved solids. PP: 417 – 435. In: Sparks, D.L. (Ed.), Methods of Soil Analysis. Part 3, Chemical Methods. SSSA Book Series No. 5. ASA, Madison, WI.
Seyed Jalali, S.A.R., Sarmadian, F., Mohammad Esmaielc, Z. & Navidia, V. (2019).  Assessment of spatial variability of cation exchange capacity with kriging and cokriging.  Desert, 24(1), 99-108.
Shaabani, H. & Delavar, M. A. (2016). Evaluation of the spatial changes of highly consumed food elements in the lands of Zanjan University. Journal of agriculture, 110, 75-83. (In Persian)
Sharma, B. D., Aggarwal, V. K., Mukhopadhayay, S. S.  & Arora, H. (2002). Micronutrient distribution and their association whit soil properties in Entisol of Punjab, India. Journal of Agricultural, 7, 315-322.
Utset, A., Lopez, T. & Diaz, M. (2000). A comparison of soil maps, kriging and a combined method for spatially prediction bulk density and field capacity of Ferralsols in the Havana-Matanaz Plain. Geoderma, 96, 199-213.
Vieira, S.R. & Gonzalez, A. (2003). Analysis of the spatial variability of crop yield and soil properties in small agricultural plots. Bragantia, 62,127-138.
Walkley, A.J. & Black, I.A. (1934). Estimation of soil organic carbon by the chromic acid titration method. Soil Science, 37, 29-38.
Wang, Y., Zhang, X. & Huang, C. (2009). Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China. Geoderma, 150, 141-149.
Weindorf, D. C. & Zhu, Y. (2010). Spatial Variability of Soil Properties at Capulin Volcano, New Mexico. USA. Journal of Soil Science Society of China, 20, 185–197.
Wollenhaupt, N. C., Wolkowski, R. P. & Clayton, M. K. (1994). Mapping soil test phosphorus and potassium for variable rate fertilizer application. Journal of Production Agriculture, 7, 441-448.
Wu, W., Xiu, D. T. & Liu, H. B. (2008). Spatial variability of soil heavy metals in the three gorges area, Multivariate and Geostatistical analysi. Journal of Environmental Mointoring Assessment, 157, 63-71.
Yong, J., Liang, W., Wen, D., Zhang, Y., & Chen, W. (2005). Spatial heterogeneity of DTPA-extractable zinc in cultivated soils induced by city pollution and land use. Science in China Series C: Life Sciences, 48(1), 82-91.
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.
Zare Chahouki, M.A., Zare Ernani, M., Zare Chahouki, A. & Khalasi Ahvazi, L. (2010). Application of spatial statistical methods in predictive models of plant species habitat. Journal of Arid Biom Scientific and Research, 1(1), 13-24. (In Persian)
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.