Determination of Minimum Data Set for Assessment of Soil Quality:A Case Study in Choghakhur Lake Basin

Document Type : Research Article

Authors

• parvane mohaghegh ¹
• Mahdi Naderi ²
• jahangard mohammadi ³

¹ shahrekord university

² Shahrekord University

³ isfahan university

https://doi.org/10.22067/jsw.v30i4.47770

Abstract

Introduction: The mismanagement of natural resources has led to low soil quality and high vulnerability to soil erosion in most parts of Iran. To have a sustainable soil quality, the assessment of effective soil quality indicators are required. The soil quality is defined as the capacity of a soil to function within natural and/or managed ecosystem boundaries. Among approaches which are suggested for soil quality assessment like soil card design, test kits, geostatistical methods and soil quality indices (SQIs), SQIs are formed by combination of soil indicators which resulted from integration evaluation of soil physical, chemical and/or biological properties and processes complement by existing/measureable data, sensitive to land use changes, management practices and human activities and could be applied in different ecosystems. As the measurement and monitoring of all soil quality indicators is laborious and costly, many researchers focused on limited soil quality indicators. There are many methods for identification and determination of minimum data set that influence on soil quality such as linear and multiple regression analysis, pedotransfer functions, scoring functions, principle component analysis and discriminant analysis. Among these methods, principle component analysis is commonly used because it is able to group related soil properties into small set of independent factors and to reduce redundant information in original data set. The objective of this research was to investigate the effects of land use change on soil quality indicators and also the determination of minimum effective soil quality indicators for assessment of soil quality in Choghakhor Lake basin, Chaharmahal and Bakhtiari province, Iran.
Materials and Methods: To meet the goal, Latin hypercube sampling method was applied by using slope, land use and geological maps and 125 composite soil samples were collected from soil surface (0-20 cm). After pretreatments, 27 physical and chemical soil properties like clay, sand and silt content, bulk density (ρb), porosity, organic carbon (OC), particulate organic carbon in macro aggregate (POCmac), particulate organic carbon in micro aggregates (POCmic), proportion of particulate organic carbon in macro aggregates to micro aggregates (POCmac/mic), mean weight diameter (MWD), macro porosity (Mac pore), air content, available water content (AWC), relative water content (RWC), effective porosity (Feff), Dexter index (S), porosity, acidity (pH), electrical conductivity (EC), Nitrogen (N), Phosphorous (P), Iron (Fe), manganese (Mn), Zinc (Zn), Cadmium (Cd), lead (Pb), Copper (Cu) and Cobalt (Co) were measured using appropriate methods.
Results and Discussion: The impact of different land use types on soil quality was evaluated by measuring several soil properties that are sensitive to stress or disturbance and comparison of them. The results showed that measured values of OC, POCmac, POCmic, POCmac/mic, P, Fe, Zn, Mn, Cu, ρb, MWD, AWC, air content and S were in order orchards > crop land > good rangelands > dry lands > weak rangelands. In this region, land use changes have different effects on soil quality. The alternation of good pasture lands to orchard and crop lands caused to enhancement of soil quality parameters. The variation of good pasture to dry land and degradation of good pasture in this area led to decreasing of soil quality. The principle component analysis (PCA) was employed as a data reduction tool to select the most appropriate indicators of site potential for the study area from the list of indicators. Based on PCA, 8 components with eigenvalues ≥ 1 were selected that explained 99.96 percent of variance. The prominent eigenvectors in components were selected using Selection Criterion (SC). The results revealed that the most important component, was the first component with the most dominant measured soil property of Cu. 12 soil quality parameters base on SC were determined in the first component. Stepwise discriminate analysis also was applied for determination significant soil quality indicators from 12 soil parameters. Our result showed that the minimum data set influencing on soil quality were Zn followed by POCmac/mic, clay %, Cu, Mn and P, respectively.
Conclusion: The results suggested that the permanent crop management (Orchard and crop land) had generally a positive impact on soil quality, while dry land and degradation of good pasture had a negative impact on soil quality. Our study suggested that the PCA method and stepwise discriminant analysis for determination of minimum data set can be used in Chughakhur lake basin. In this study from27 of physical and chemical soil properties, the fertility factors such as the content of Zn, Cu, Mn and P and the proportion of particle organic carbon in macro aggregate to micro aggregate and also soil texture components can be used to the minimum data set that evaluates soil quality. These parameters mostly depend on soil management system.

Keywords

• Chughakhur Lake
• Discriminant analysis
• Principle component analysis
• Soil quality

20.1001.1.20084757.1395.30.4.9.5

References

1- Ayoubi Sh., Khormali F., Sharawat K.L., and Rodrigues de Lima A.C. 2011. Assessing of Land use Change on soil quality indicators in Loessial soil in Golestan Province, Iran. Journal of Agriculture Science Technique, 13: 727-742.

2- Bini D., Alcantara C., Banhos K., Kishino N., Andrade G., Zangaro W., and Nogueira M. 2013. Effects of land use on soil organic carbon and microbial processes associated with soil health in southern Brazil. European Journal of Soil Biology, 55: 117-123.

3- Cambardella C.A., and Elliott E.T. 1993. Carbon and nitrogen distributions in aggregates from cultivated and grassland soils. Soil Science Society and American Journal, 57: 1071-1076.

4- Cox M.S., Gerard P.D., Wardlaw M.C., and Abshire M.J. 2003. Variability of selected soil properties and their relationships with soybean yield. Soil Science Society and American Journal, 67: 1296-1302.

5- Dexter A.R. 2006. Applications of S-theory in tillage research. p. 429–442. Proceedings of the 17th Triennial Conference, August 3- 28. 2006. Kiel, Germany.

6- Doran J.W., and Parkin T.B. 1996. Quantitative indicators of soil quality: a minimum data set. p. 25-37. In: J.W. Doran and A.J. Jones (ed.), Methods for assessing soil quality. Soil Science Society of America, Special Publication.

7- Emami H., Neyshabouri M.R., and Shorafa M. 2012. Relationships between Some Soil Quality Indicators in Different Agricultural Soils from Varamin, Iran. Agriculture science and technology. 14: 951-959.

8- Fathallahi H., and Jalaliyan A. 2000. The effect of land use changes during different years on sediment yield, physical properties and soil erodibility in the Bazoft Wwatershed. pp 62-53.

9- Fox G.A., and Metla R. 2005. Soil property analysis using principle component analysis, soil line and regression models. Soil Science Society and American Journal, 69: 1782-1788.

10- Godwin R. J., and Miller P.C.H. 2003. A review of the technologies for mapping within- field variability, Biosystem Engineering, 84: 393-407.

11- Govaerts B., Sayre K.D., and Deckers J. 2006. A minimum data set for soil quality assessment of wheat and maize cropping in the highlands of Mexico. Soil and Tillage Research, 81: 163–174.

12- Imaz M.J., Virto I., Bescansa P., Enrique A., Fernandez-ugalde O., and Karlen D.L. 2010. Soil quality indicator response to tillage and residue management on semi-arid Mediterranean cropland. Soil and Tillage Research, 107: 17–25.

13- Information of meteorology department in Chaharmahal va Bakhtiari province. 2012.

14- Information of natural resource and watershed organization in Chaharmahal va Bakhtiari province. 2012.

15- Jiang P., and Telen K.D. 2004. Effect of soil and topographic properties on crop yield in a north central corn- soybean cropping system. Agronomy Journal, 96: 252-258.

16- Jolliffe I.T. 1986. Principle component analysis. Springer-Verlag.

17- Khaledian Y., Kiani F., and Ebrahimi S. 2012. The effect of land use change on soil and water quality in northern Iran. Journal of mountain science. 9: 798-816.

18- Kibena I., Nhapi W., and Gumindoga C. 2014. Assessing the relationship between water quality parameters and changes in land use patterns in the Upper Manyame River, Zimbabwe. Original Research Article Physics and Chemistry of the Earth, Parts A/B/C. 67–69: 153-163.

19- Miller R.H., and Keeney D.R. 1992. Methods of Soil Analysis, In: I,II. Physical, Chemical and mineralogical properties. SSSA Pub., Madison.

20- Olsen S.R., and Sommers L.E. 1982. Phosphorus. p. 403- 430. In: A. L. Page (ed.). Methods of soil analysis, Part 2. 2nd ed. Am. Sco. Agron. Madison, WI, USA.

21- Ovalles F.A., and Collins M.E. 1988. Variability of northwest Florida soils by principle component analysis. Soil Science Society and American Journal, 52: 1430-1435.

22- Podmanicky L., Balazs K., Belenyesi M., Centeri C., Kristof D., and Kohlheb N. 2011. Modeling soil quality changes in Europe. An impact assessment of soil quality in Europe. Ecological Indicators, 11(1): 4-15.

23- Reynolds W.D., Drury C.F., Tan C.S., Fox C.A. and Yang X.M. 2009. Use of indicators and pore volume function characteristics to quantify soil physical quality. Geoderma. 152: 252- 263.

24- Singh M.J., and Khera K.L. 2009. Physical indicators of soil quality in relation to soil erodibility under different land uses. Arid Land and Management. 23: 152–159.

25- Shukla M.K., Lal R., and Ebinger M. 2004. Soil quality indicators for the North Appalachian experimental watersheds in Coshocton, Ohio. Soil Science, 169:195–205.

26- Shukla M.K., Lal R., and Ebinger M. 2006. Determining soil quality indicators by factor analysis. Soil and Tillage Research, 87: 194–204.

27- SPSS for windows. 1999. Release. 7 (Nov 141996), Copyright SPSS, Inc.

28- Vaezi A.R., and Bahram H.A. 2014. Relationship between Soil Productivity and Erodibility in Rainfed Wheat Lands in Northwestern Iran. Journal of Agriculture Science Technique. 16: 1455-1466.

29- Van Genuchten M.T., Leij F.J., and Yates S.R. 1991. The RETC Code for Quantifying the Hydraulic Functions of Unsaturated Soils, Version 6.0. EPA Report 600/2-91/065, U.S. Salinity Laboratory, USDA-ARS, Riverside, California.

30- Van Genuchten M.T. 1980. A closed-form equation for predicting the hydraulic conductivity of saturated soils. Soil Science Society of America Journal 44: 892-898.

31- Walkley A., and Black I.A. 1934. An examination of Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science, 37: 29-37.

32- Wang Q., Liu J., Wang Y., Guan J., Liu Q., and Lv D. 2012. Land use effects on soil quality along a native wetland to cropland chronosequence Journal of Soil Biology, 53: 114-120.

33- Wilding L. 1985. Spatial variability. Its documentation, accommodation, and implication to soil surveys. In: D. R. Nielson and Bouma J. (ed). Soil Variability, Pudo, Wagenigen, the Netherlands.

34- Yao R.J., Yang J.S., Zhao X.F., Li X.M., Liu M.X. 2013. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area Soil and Tillage Research, 128: 137–148.

35- Yemefack M., Jetten V.G., Rossiter D.G. 2006. Developing a minimum data set for characterizing soil dynamics in shifting cultivation systems. Soil and Tillage Research, 86: 84–98.