松嫩平原苏打盐渍化旱田土壤表观电导率空间变异

Spatial variations of apparent soil electrical conductivity in the saline-sodic upland soil of the Songnen Plain

在松嫩平原西部吉林省大安市乐胜乡,于2013年4月20日选择盐碱程度不均一的典型盐渍化旱田地块,面积为4.8 hm^2作为研究样地。利用EM38大地电导率仪测定结合田间定点采样,并通过经典统计和地统计相结合的方法研究了盐渍化旱田土壤表观电导率空间变异特征,分析了土壤表观电导率与土壤盐碱指标之间的关系。结果表明,盐渍化旱田土壤水平方向表观电导率(EC_h)经对数转换后具有强空间自相关,其变异特征主要是与地形地貌和水文状况等结构性因素有关。垂直方向表观电导率(EC_v)经对数转换后具有中等空间自相关性,其变异特征受结构性因素和随机因素共同作用。EC_h和EC_v半方差模拟的最优模型分别为球状模型和指数模型。Pearson分析表明土壤表观电导率(EC_h和EC_v)与土壤盐碱指标EC_(1∶5)、pH_(1∶5)、SAR、SC、Na^+、CO_3^(2-)、HCO_3^-呈正相关关系(P<0.05),EC_h与土壤盐碱指标相关系数均大于EC_v。在实际应用中可以用EC_h来指示土壤的盐碱程度。回归分析表明土壤表观电导率(EC_h和EC_v)与土壤盐碱指标呈线性相关,且EC_h回归模型的决定系数均大于EC_v回归模型的决定系数,可用水平方向土壤表观电导率(EC_h)来计算土壤盐碱指标,进行土壤盐渍化的快速评估。

A representative saline-sodic area measuring 300 m×160 m was investigated to determine the salinity and sodicity levels in Lesheng Town, Da＇an City, Jilin Province, northeast China on April 20, 2013. Apparent soil electrical conductivity was measured using Em^38 （electromagnetic induction） and GPS, and its spatial variability was studied by using classical statistics and geostatistics. Correlations between apparent soil electrical conductivity, including apparent soil electrical conductivity in horizontal direction（ECh） and apparent soil electrical conductivity in vertical direction （ECv）, and salinity-sodicity parameters, including soil ions, EC1：5, pH1：5, sodium adsorption ratio （SAR）, and soil salinity content （SC） were analyzed in the saline-sodic upland soil of the Songnen Plain. The results indicated a moderate spatial variability for ECh and ECv in classical statistics. Frequency distributions and the Kolmogorv-Smirov test for normality showed that ECh and ECv were not normally distributed （P 〈 0.05）. Therefore, mathematical transformations were performed to convert the data to fit the normal distribution, which is a prerequisite for calibration of the theoretical model and generation of semivariogram parameters and kriged maps. After log-transformation, ECh and ECv showed normal distributions. The results of geostatistical analyses indicated that ECh has a strong spatial variability and dependence, and the spatial distribution of ECh is affected by structural factors, which might include topography, hydrology, and climatic factors. However, ECv had a moderate spatial variability and dependence, and the spatial distribution of ECv was jointly affected by structural and random factors. Empirical semivariograms for ECh were simulated by spherical models, but those of ECv were simulated by exponential models. The results of a Pearson correlation showed five indexes of salinity-sodicity parameters （pH1：5, EC1：5, SAR, SC, Na＋） were significantly correlated with ECh and ECv （P 〈 0.05）. The correlation coefficients between ECh and five salinity-sodicity parameters were higher than that between ECv and five salinity-sodicity parameters. A stepwise regression analysis revealed that the regression prediction models with ECh and ECv could explain most of the variations of the soil salinity-sodicity parameters. The regression prediction models between apparent soil electrical conductivity and five salinity-sodicity parameters were linear. The determination coefficient of ECh was higher than that of ECv. Therefore, the apparent soil electrical conductivity in a horizontal direction could be used to calculate the index of the parameters of soil salinity-sodicity and to indicate soil salinity-sodicity in practical applications.