Spatial variability of some soil fertility characteristics as affected by land use change, Yasouj region

Document Type : Complete scientific research article


1 Soil Sci Dept. yasouj Univ

2 Soil Sci Dept Shahrood Univ

3 Soil Sci Dept Yasouj Univ


Introduction: In recent decades, site-specific management (SSM) has been specifically considered to achieve to increased input efficiency, improved economic margins of crop production and reduced environmental risks. Short-scale spatial variability of soil properties caused more necessity of SSM techniques. Natural variability of soil results from complex interactions between geology, topography and climatic factors, as well as land use change and land management strategies. Deforesting and vast land use changes are considered as the important land management strategies that have been extensively used in recent decades. Therefore, determining the effects of land use change on soil properties in conjunction with local environmental conditions and on the spatial variability of soil properties may drastically help the land use planners. Therefore, the present study was done aimed to explore the effects of oak trees deforesting in Shah-Mokhtar region in Yasouj and land use changes to dry farming on the spatial variability of nutritional elements using geostatistical techniques.
Materials and Methods: The present study was conducted in a Shah-Mokhtar, north, and northwest of Yasouj, in Kohgiluyeh Province, southern Iran. Spatial variability of seven soil fertility properties, including N, P, K, Fe, Cu, Zn, and Mn concentration in soil, were examined in three land uses, including dense forest, degraded (semi-dense) forest and rain-fed lands. A total of 100 surface (0-30 cm) soil samples were collected and analyzed for the nutritional elements after preparing in the laboratory. Data were analyzed statistically and their normal distribution pattern was examined using the Kolmogrov-Smirnov test. In the geostatistical analyses step, the spatial structure of studied variables was analyzed by fitting the suitable authorized models on calculated experimental semi-variograms. The concentration of studied elements was interpolated using ordinary kriging (OK) and inverse distance weighting (IDW) estimators and finally, the spatial distribution map of each soil nutrient was prepared using ArcGIS 10.3.
Results: The results showed that the highest mean soil concentration of almost all of selected nutrients belonged to the dense forest lands and due to the land use changes to the degraded forest and then dry farming, average values of selected elements significantly decreased. Among the studied properties, soil N concentration with an average of 0.34%, 0.17% and 0.08% in dense forest, degraded forest, and rain-fed soil samples, had the most decrease caused by deforesting. Semi-variogram analyses showed that spherical model had the best performance. For interpolating the soil K and Mn concentration, IDW method and for other studied elements, OK method was efficiently used. The spatial correlation class was strong for N, Cu, and Fe; whereas a moderate class was calculated for soil P and Zn concentration. The spatial distribution maps of selected nutrients revealed that the dense forest and dry farming soils had the highest and lowest contents of soil nutritional elements.
Conclusion: According to the findings of the present study, it can be stated that degradation of oak forest may lead to the significant decrease of soil nutritional elements, specifically soil N concentration as one of the main soil fertility quality. Therefore, it seems that not only the significant soil quality decline but also the extensive degradation of whole ecosystem and unfavorable climatic consequences will be the unavoidable results of deforesting in Shah-Mokhtar region.


 1.Aishah, A.W., Zauyah, S., Anuar, A.R., and Fauziah, C.I. 2010. Spatial variability of selected chemical characteristics of paddy soils in sawah sempadan, Selangor, Malaysia. Malaysi. J. Soil Sci. 14: 27-39.
2.Bremner, J.M., and Mulvaney, C.S. 1982. Nitrogen-Total. P 595-624, In: Page, A.L., Miller, R.H. and Keeney, D.R. (Eds.), Methods of soil analysis. Part 2. Chemical and microbiological properties, American Society of Agronomy, Soil Science Society of America, Madison, Wisconsin.
3.Cahn, M.D., Hummel, J.W., and Brouer, B.H. 1994. Spatial analysis of soil fertility for site-specific crop management. Soil Sci. Soc. Amer. J. 58: 4. 1240-1248.
4.Cao, C., Jiang, S., Ying, Z., Zhang, F., and 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.
5.Cetin, M., and Kirda, C. 2003. Spatial and temporal changes of soil salinity in a cotton field irrigated with low-quality water. J. Hydrol. 272: 1. 238-249.
6.Chen, H., Shen, Z., Liu, G., and 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.
7.David, A.A., and Auwal, M. 2015. Assessment of nutrient distribution as affected by land use pattern in Allahabad Region. Inter. J. Geol. Earth Environ. Sci. 5: 2. 26-31.
8.Dawson, J.J., and Smith, P. 2007. Carbon losses from soil and its consequences for land-use management. Science of the Total Environment, 382: 2. 165-190.
9.Doran, J.W., Sarrantonio, M., and Liebig, M., 1996. Soil health and sustainability. In: Sparks, D.L. (Ed.), Advances in Agronomy, Vol. 56. Academic Press, San Diego, Pp: 1-54.
10.Havlin, J., Beaton, J.D., Tisdale, S., and Nelson, W. 2007. Soil fertility and fertilizers. 7th Ed. MacMillan Publishing Co., N.Y. 528p.
11.Huang, S.W., Jin, J.Y., Yang, L.P., and Bai, Y.L. 2006. Spatial variability of soil nutrients and influencing factors in a vegetable production area of HebeiProvince in China. Nutrient Cycling in Agroecosystems, 75: 1-3. 201-212.
12.Kazemi, H., Tahmasebi, Z., Kamkar, B., Shataie, Sh., and Sadeghi, S. 2012. Evaluation of geostatistical methods for estimation and zonation of macronutrient elements in some agricultural lands of Golestan province. J. Water Soil Sci. 22: 1. 201-219. (In Persian) 
13.Li, X.G., Wang, Z.F., Ma, Q.F., and Li, F.M. 2007. Crop cultivation and intensive grazing affect organic C
pools and aggregate stability in arid grassland soil. Soil and Tillage Research, 95: 1. 172-181.
14.Lindsay, W.L., and Norvell, W.A. 1978. Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil Sci. Soc. Amer. J. 42: 3. 421-428.
15.Malakouti, M.J., and Gheibi, M.N. 2000. Determination of critical levels of nutrients in soil, plant, and fruit for the quality and yield improvements in strategic crops of Iran. 2nd Ed. High Council for Appropriate Use of Pesticides and Chemical Fertilizers, Ministry of Agriculture, Karaj, Iran. 92p. (In Persian)
16.Matijevic, L., Romic, D., and Romic, M. 2014. Soil organic matter and salinity affect copper bioavailability in root zone and uptake by Vicia faba L. plants. Environmental Geochemical Health, 36: 5. 883-96.
17.Meng, Q., Fu, B., Tang, X., and Ren, H. 2008. Effects of land use on phosphorus loss in the hilly area of the Loess Plateau, China. Environmental monitoring and assessment, 139: 1. 195-204.
18.Najafi-Ghiri, M., and Owliaie. H.R. 2014. Effect of vermicompost and zeolite applications on potassium transformation in calcareous soils of FarsProvince. J. Water Soil Sci. 69: 61-72. (In Persian)
19.Noorzadeh Hadad, M., and Baybordi, A. 2014. The zonation of micronutrient concentrations for fertilization management in some agricultural lands of northwest of Iran using geostatistics. J. Soil Manage. 3: 1. 11-19. (In Persian)
20.Olsen, S.R. 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. United States Department of Agriculture; Washington. 39p.
21.Olsen, S.R., Sommers, L.E., and Page, A.L. 1982. Methods of soil analysis. Part 2. Chemical and microbiological properties of phosphorus. ASA Monograph. 1143p.
22.Pratt, P.F. 1965. Potassium. Methods of soil analysis. Part 2. Chemical and microbiological properties, (Methods of Soils): Pp: 1022-1030.
23.Sadri, N., Owliaie, H.R., Adhami, E. and Najafi Ghiri, M. 2016. Effect of organic acids and vermicompost on potassium transformations in calcareous soils of Southern Iran. J. Water Soil. 30: 4. 1270-1281. (In Persian)
24.Saffari, M., Yasrebi, J., Saffari, V.R., Emadi, M., Moazallahi, M., and Fathi, H. 2009. Geostatistical investigation of sequentially extracted Zn forms at field scale in highly calcareous soils. Res. J. Biol. Sci. 4: 7. 866-873.
25.Santra, P., Chopra, U.K., and 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.
26.Sarangi, A., Madramootoo, C.A., and Enright, P. 2006. Comparison of spatial variability techniques for runoff estimation from a Canadian Watershed. Biosystems engineering, 95: 2. 295-308.
27.Sepaskhah, A.R., Ahmadi, S.H., and Shahbazi, A.N. 2005. Geostatistical analysis of sorptivity for a soil under tilled and no-tilled conditions. Soil and Tillage Research, 83: 2. 237-245.
28.Sharma, P., Shukla, M.K., and Mexal, J.G. 2011. Spatial variability of soil properties in agricultural fields of Southern New Mexico. Soil Science, 176: 6. 288-302.
29.Yasrebi, J., Saffari, M., Fathi, H., Karimian, N., Moazallahi, M., and Gazni, R. 2009. Evaluation and comparison of ordinary kriging and inverse distance weighting methods for prediction of spatial variability of some soil chemical parameters. Res. J. Biol. Sci. 4: 1. 93-102.
30.Yazdaninejhad, F., Torabi, H., and Davatgar, N. 2013. Mapping of available Fe, Zn, Cu and Mn in soils of Southern Tehran lands by Geostatistical and GIS techniques. Iran. J. Soil Water Res. 44: 4. 383-395. (In Persian) 
31.Zhang, X.Y., Yue-Yu, S.U.I., Zhang, X.D., Kai, M.E.N.G., and Herbert, S.J. 2007. Spatial variability of nutrient properties in Black soil of Northeast China. Pedosphere, 17: 1. 19-29.