earlier CRP upper limit
DIFFERENT GREEK SOIL TYPES
5. DEVELOPMENT OF APPROACH FOR CLASSIFICATION OF RUSSIAN SOIL ON THE BASIS OF RADIONUCLIDE TRANSFER FACTORS FROM SOIL TO CROPS
5.1. Identification of soil characteristics, which can be used for soil radioecological classification Correlation analysis was used to estimate the extent to which each soil characteristic is related to other
soil properties and radionuclide transfer factor to plants (estimation of significance of individual relations).
Factor analysis was used to consider a whole range of relations between soil characteristics and transfer factors in order to identify of significance factors. Its main objective was to detect latent general factors that explained relations between the observed variables.
For radioecological classification the experimental data were divided into two groups, those for mineral soils and those for organic soils (peat). Organic soils (peat) were identified as an independent group, because they have some peculiarities and show rather high mobility of radionuclides.
A set of standard soil parameters is not sufficient to describe differences in organic and mineral soils in terms of influence on the radionuclide behaviour. Neglecting some factors may result in errors in developing classification systems.
5.1.1. Correlation analysis
5.1.1.1. The relationships between soil properties
The first stage was to use correlation analysis to identify the relationship between soil properties.
Results for mineral soils are shown in Table 11.
TABLE 11. CORRELATION COEFFICIENT MATRIX FOR SOIL PROPERTIES
Exch. K Exch. Ca CEC pHKCl OM Clay
Exch. K 1.00 0.54 0.55 0.64 0.33 0.56
Exch. Ca 0.54 1.00 0.97 0.49 0.69 0.82
CEC 0.55 0.97 1.00 0.50 0.70 0.82
pHKCl 0.64 0.49 0.50 1.00 0.33 0.61
OM 0.33 0.69 0.70 0.33 1.00 0.58
Clay 0.56 0.82 0.82 0.61 0.58 1.00
The results from the correlation analysis can be used to identify the most significant (in terms of soil radioecological classification) variables. Soil characteristics that closely correlate with the largest number of other properties have been identified. On the other hand, attention should be paid to the soil characteristics that poorly correlate with the other properties.
Analysis of the information presented in the correlation matrix offers the following conclusions.
(i) Clay, Exch. Ca and CEC show the highest correlation among each other. These can be interchanged in soil classification.
(ii) The highest correlation coefficent (0.97) was obtained for Exch. Ca and CEC. Either of the two can be used in soil classification.
Parameters such as pHKCl, Exch. K, ОМ do not correlate as well with other variables. These variables can be defined as most “independent” among all the soil properties.
5.1.1.2. The relationship between radionuclide TFs and soil properties
A relation was found between 137Cs and 90Sr transfer factors to crops and soil properties (Tables 12, 13). Table 14 shows a close correlation between TF 137Cs in barley and clay content, exchangeable cations (Exch. Ca and Exch. K) and CEC. Very low correlation coefficients between TF 137Сs for cabbage and soil characteristics exclude the use of the “137Cs cabbage” data sample for further processing and analysis. The highest correlation factors were obtained between 90Sr TF for barley and soil properties such as CEC, Exch. Ca and Clay (Table 12).
The correlation coefficient between 90Sr TF for cabbage and soil properties are similar. This is consistent with correlations coefficients reported for barley. However, the use of the “90Sr-cabbage”
sample for factor analysis seems less effective then that the use of the “90Sr-barley” sample.
TABLE 12. CORRELATION COEFFICIENTS BETWEEN 137Cs TFs IN PLANTS AND SOIL PROPERTIES
Crop Exch. K Exch. Ca CEC pHKCl OM Clay
Barley -0.60 -0.62 -0.62 -0.40 0.01 -0.71
Cabbage 0.16 -0.17 -0.09 0.17 -0.19 -0.11
TABLE 13. CORRELATION FACTORS BETWEEN 90Sr TFs IN PLANTS AND SOIL PROPERTIES
Crop Exch. K Exch. Ca CEC pHKCl OM Clay
Barley -0.57 -0.79 -0.81 -0.49 -0.5 -0.74
Cabbage -0.23 -0.65 -0.55 -0.30 -0.19 -0.58
The information presented in the correlation matrix (Tables 11, 12) shows that correlation coefficients between TFs and some soil parameters have similar values. Thus other statistical procedures need to be invoked. Also it is advisable to use only values derived for barley for further processing.
objective is to detect latent general factors that explain relations between the observed variables. At the first stage, a set of initial data included only agrochemical soil properties. The choice of a number of factors was based on the “screen criterion”. To make use of this criterion, a plot of eigenvalues was constructed as a function of their numbers. The co-ordinate of the point at which reduction of eigenvalues is maximally retarded determines the number of factors. The factors were identified by the method of principal components. Factor rotation was performed by the Varimax method. Factor loads for two datasets incorporating soil agrochemical parameters are summarized in Table 14.
TABLE 14. FACTOR LOAD FOR A DATASET INCORPORATING AGROCHEMICAL VARIABLES OF MINERAL SOILS
Variables Factor 1 Factor 2 Factor 3 Factor 4
Exch. K 0.288 0.321 0.100 0.895
Exch. Ca 0.870 0.147 0.344 0.249
CEC 0.860 0.164 0.362 0.249
pHKCl 0.252 0.901 0.113 0.308
OM 0.400 0.118 0.902 0.091
Clay 0.805 0.418 0.191 0.178
The share of variance
explained by Factor 0.408 0.192 0.187 0.177
The factor analysis made it possible to identify four factors. The maximum factor loads fall at the main factor, Factor 1, due to parameters Clay, Exch. Ca and CEC. Factor 2 is determined by the pHKCl
value. The maximum factor loads on Factor 3 are caused by the influence of the OM variable and on Factor 4 by the Exch. K variable.
Factor 1 is difficult to interpret because of the high factor loads of three variables. The correlation matrix (Table 11) reveals a high correlation coefficient between the variables Exch. Ca and CEC (0.97). This gives grounds to exclude one of the factors from consideration. Subsequent analysis excluded the CEC variable. This allowed unloading of Factor 1. Table 15 summarizes results of the factor analysis (factor loads) for a range of data including soil parameters and TFs of 137Cs in barley.
TABLE 15. FACTOR LOAD FOR A DATASET INCORPORATING AGROCHEMICAL VARIABLES OF MINERAL SOILS AND 137Cs TFs TO BARLEY
Variables Factor 1 Factor 2 Factor 3 Factor 4
TF -0.923 0.078 -0.167 -0.252
Exch. K 0.336 0.068 0.251 0.897
Exch. Ca 0.592 0.464 0.307 0.465
pHKCl 0.189 0.065 0.950 0.200
OM -0.001 0.979 0.066 0.058
Clay 0.616 0.226 0.577 0.398
The share of variance
explained by Factor 0.288 0.207 0.237 0.214
The factor analysis identified four factors, with the maximum factor loads due to variables Clay, OM, pHKCl, Exch. K. The factors can be ranked by values of factor loads due to the TF value as follows:
Factor 1>Factor 4> Factor 3>Factor 2.
5.1.3. Classification systems on the basis of factor analysis
The soil variables for soil radioecological classification using 137Cs as a marker radionuclide can be ranked as follows: content of clay; content of exchangeable K; рH value; and content of organic matter.
When classifying the available experimental data, it is expedient to use two variables, as using more requires a larger datasets. The factor loads due to TF on Factor 3 and Factor 4 (Table 14) differ insignificantly. Therefore, for soil radioecological classification with 137Cs as a marker radionuclide, two systems of variables can be used.
Classification system 1:
soil group (organic–peat, mineral),
mechanical composition (physical clay content), рH value.
Classification system 2:
soil group (organic–peat, mineral),
mechanical composition (physical clay content), content of exchangeable K.
Table 16 summarizes results of the factor analysis (factor loads) for a dataset incorporating soil parameters of mineral soils and 90Sr transfer factors to barley.
TABLE 16. FACTOR LOADS FOR A DATASET INCORPORATING AGROCHEMICAL VARIABLES OF MINERAL SOILS AND 90Sr TFs TO BARLEY
Variables Factor 1 Factor 2 Factor 3 Factor 3
TF -0.831 -0.327 -0.188 -0.203
Exch. K 0.382 0.021 0.325 0.864
Exch. Ca 0.838 0.223 0.227 0.317
pHH2O 0.269 0.050 0.920 0.257
OM 0.238 0.968 0.037 0.018
Clay 0.740 0.048 0.546 0.291
The share of variance
explained by Factor 0.369 0.183 0.223 0.173
The factor analysis identified for factors, with the maximum factor loads due to the variables Clay, OM, pHH2O, Exch. K. The factors can be ranked by values of factor loads due to the TF parameter as follows: Factor 1>Factor 2>Factor 4>Factor 3.
Significant factor loads on Factor 1 are from two variables - Exch. Ca and Clay. There is a high correlation coefficient between these variables (0.83) which suggests that the two variables are interchangeable. On this basis two systems of variables can be employed for soil radioecological classification with 90Sr as a representative radionuclide: classification system 1 using soil group (organic–peat, mineral), content of exchangeable Ca, content of organic matter; and classification system 2 using soil group (organic–peat, mineral), particle size composition (physical clay content), content of organic matter.
However, there may be another interpretation of the factor analysis results (Table 15). For soil classification, soil properties can be used with the maximum factor loads on the most significant factor (Factor 1). In this case the classification system will include the following parameters: soil group (organic–peat, mineral); particle size composition (physical clay content); content of exchangeable Ca.
A larger set of variables could be used for soil classification but broader classification systems require larger bodies of experimental data.
6.1. Characteristics of data samples for 137Cs and 90Sr transfer factors in the classification