K. Kamali; Gh. Zehtabian; tayybe Mesbahzadeh; M. Arabkhedri; Hossain Shohab Arkhazloo; A. Moghadamnia
Abstract
Introduction: Soil quality is an essential indicator for sustainable land management that generally depends on soil physical, chemical and biological properties. Due to the multiplicity of soil properties, the number of variables is usually reduced to a minimum set by statistical methods, which reduces ...
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Introduction: Soil quality is an essential indicator for sustainable land management that generally depends on soil physical, chemical and biological properties. Due to the multiplicity of soil properties, the number of variables is usually reduced to a minimum set by statistical methods, which reduces study time, decreases monitoring cost for sustainable use of agricultural lands. The aim of this study was to introduce the most effective soil characteristics of agricultural lands in Mohammadshahr plain, Karaj, to prevent the descending trend of soil quality.
Materials and Methods: In this study, four farms and orchards which were different in terms of crop type and irrigation system were selected and evaluated with Integrated Quality Index (IQI) and Nemero Quality Index (NQI). In both indicators, the characteristics affecting soil quality are combined in the form of a mathematical model and presented as a numerical quantity. For this purpose, first 12 soil profiles were described, followed by sampling from topsoil (surface layer) and sublayers (weighting average for the depths) and testing 17 soil characteristics affecting its quality. In the next step, both indicators were calculated using two different sets of soil properties. The first category, the Total Data Set (TDS), included all measured soil characteristics, and the second group, the Minimum Data Set (MDS), included the most important properties affecting soil quality. The Principle Component Analysis was implemented to select the MDS. Soil properties were scored to calculate IQI and NQI. For this purpose, a function was defined for each soil feature to standardize all scores between zero and one. Weighting various soil quality properties was also performed by calculating the common variance of the variables, which was obtained by factor analysis method.
Results and Discussion: Calculation of IQI and NQI indices showed that the topsoil samples were in grade III and sublayer samples belonged to grade IV with major limitations due to lack of profile development, organic carbon deficiency, salinity and high gravel. Four and six items out of 16 variables were identified effective for topsoil and sublayers, respectively. The IQI index based on TDS was more accurate and sensitive than the NQI index for soil quality assessment, as more features are considered for TDS. In the IQI index, both the weight of attributes and their scores are effective, while in the NQI index, only the attribute score is considered. On the other hand, the coefficient of determination between the TDS and MDS for topsoil and sublayer samples was 0.55 and 0.56% for IQI model, respectively, and 0.48 and 0.16% for NQI model, respectively. In other words, the determination coefficients showed the reliability of using the MDS instead of TDS in both IQI and NQI models. In the MDS, mean weight diameter (MWD) showed the highest effect on the surface layer and percentage of gravel had the greatest impact on the soil quality of the sublayer.
Conclusion: Although TDS took into account all soil properties and showed a slightly higher coefficient of determinations with both soil quality indicators, the MDS obtained similar results to the TDS with only about half of the properties. In the MDS, the features with an internal correlation is eliminated rendering it more cost effective. The results of this study assist decision-makers to choose better quality management and soil sustainability strategies while decreasing the monitoring cost.
parvane mohaghegh; Mahdi Naderi; jahangard mohammadi
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 ...
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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.
