Ecological stoichiometry and spatial variation characteristics of soil nutrients in a cultivation area of Zhangjiakou City, Hebei Province
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摘要:
应用地统计学、生态化学计量学和GIS技术,对河北省张家口市万全区某种植区的表层土壤N、P、K2O、CaO、MgO、S、Mo、Mn、Fe2O3、B、Corg、pH等参数含量特征、趋势分布、空间变异性及生源要素的分布特征进行研究。结果表明,研究区Corg属于较强分异型,变异系数为71.54%,其余元素均属于弱或均匀分异型。N、P、K2O和pH整体呈现极丰富和丰富等级,S、Mo、Mn、Fe2O3、B和Corg则处于缺乏和极缺乏等级。通过不同趋势阶数中误差的比较叠加块金系数分析,初步确定研究区可分为3种预测类型,即无趋势预测、一阶趋势预测和二阶趋势预测。K2O、CaO、MgO、S、Mn和Corg主要受结构型影响,N、P、Mo、Fe2O3、B和pH主要受结构型和随机性混合影响,其中S和Mo的变异函数表面具有较弱的方向性。生态化学计量学表明,生源要素平均含量K2O>Corg>N>P>S,土壤CNR和NPR主要受控于土壤Corg和N,垂向上表现为以表聚型和平稳型分布为主,以突变型和锯齿型分布为辅。最后通过普通克里格插值清晰可见区域范围内N、P、K2O、CaO、MgO、Mn均表现为由东北部至西南部流域范围内明显异常区,主要受控于河流迁移搬运作用,重点区中N和P属于梯型,K2O、CaO和pH属于内凹型,S和Mo属于外凸型,MgO、Fe2O3、Mn、B和Corg属于跳跃型。
Abstract:By applying geostatistics, ecological chemometrics and GIS techniques, the characteristics of the content, trend distribution, spatial variability and distribution of biogenic elements of N, P, K2O, CaO, MgO, S, Mo, Mn, Fe2O3, B, Corg, pH and other parameters in the surface soil of a planting area in Wanquan District, Zhangjiakou City, Hebei Province were studied.The results showed that Corg belonged to the strongly differentiated variation type with a coefficient of variation of 71.54%, while the rest of the elements belonged to the weak or uniform differentiation type.N, P, K2O and pH were in the very abundant and abundant classes, while S, Mo, Mn, Fe2O3, B and Corg were in the deficient and extremely deficient classes.By comparing the errors in different trend orders superimposed on the block gold coefficient analysis, it was tentatively determined that the study area could be classified into three prediction types, namely, no trend prediction, first-order trend prediction and second-order trend prediction, with K2O, CaO, MgO, S, Mn and Corg mainly influenced by the structural type, and N, P, Mo, Fe2O3, B and pH mainly influenced by a mixture of structural and stochastic properties, among which S and Mo variance functions surface with weak directionality.Ecological chemometrics showed that the average content of biogenic elements K2O > Corg > N > P > S.Soil CNR and NPR were mainly controlled by soil Corg and N, and the vertical direction showed a predominantly epistatic and smooth distribution, supplemented by mutational and sawtooth distribution.Finally, ordinary kriging interpolation clearly shows that N, P, K2O, CaO, MgO, and Mn all show obvious anomalous areas in the watershed range from northeast to southwest, mainly controlled by river migration and transport, with N and P in the key area being of the trapezoidal type, K2O, CaO, and pH of the inner concave type, S and Mo of the outer convex type, and MgO, Fe2O3, Mn, B, and Corg belong to the jump type.
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表 1 132件样品中土壤营养元素描述性统计
Table 1. Descriptive statistics of soil nutrient elements in the 132 samples
指标 最小值 最大值 中位数 平均值 标准差 变异系数(Cv)/% 偏度 峰度 黄淮海平原(孙厚云等,2022) 全国土壤背景(孙厚云等,2022) N/10-6 93 3296 1075 1223 721 58.95 0.80 0.24 381 707 P/10-6 259 1937 836 878 366 41.69 0.47 -0.36 517 570 K2O/% 0.94 4.48 2.70 2.80 0.48 17.14 0.83 3.14 2.34 2.36 CaO/% 0.39 11.09 2.26 2.24 1.28 57.14 3.31 19.5 4.10 2.74 MgO/% 0.04 4.83 1.42 1.34 0.55 41.04 1.57 11.64 1.88 1.43 S/10-6 51 988 232 245 137 55.92 1.79 6.48 142 245 Mo/10-6 0.43 1.55 0.74 0.79 0.21 26.58 1.75 3.59 0.52 0.7 Mn/10-6 149 1020 518 491 134 27.29 -0.04 1.64 705 569 Fe2O3/% 1.67 9.33 3.96 4.01 0.90 22.44 1.43 8.64 3.71 2.8 B/10-6 3.26 65.51 27.29 26.67 9.40 35.25 0.18 1.40 52 43 Corg/% 0.03 4.33 1.05 1.23 0.88 71.54 1.51 2.42 0.26 0.6 pH 6.38 8.78 8.22 8.09 0.44 5.44 -1.41 2.20 8.61 8 CNR 1.58 112.59 9.30 10.41 9.26 88.95 10.39 115.26 6.82 8.49 CPR 0.34 62.26 11.08 15.76 13.40 85.03 1.89 2.92 5.03 10.53 NPR 0.05 4.55 1.18 1.49 0.94 63.09 1.68 2.45 0.74 1.24 CSR 6.60 107.30 45.84 48.69 19.03 39.08 0.59 0.57 18.31 24.49 表 2 研究区土壤营养元素及pH值相关性分析
Table 2. Correlation analysis of soil nutrients and pH in the study area
指标 N P K2O CaO MgO S Mo Mn Fe2O3 B Corg pH N 1 P 0.375** 1 K2O -0.170 -0.570** 1 CaO -0.025 0.386** -0.711** 1 MgO 0.153 0.660** -0.808** 0.663** 1 S 0.837** 0.531** -0.303** 0.145 0.281** 1 Mo 0.025 0.046 0.330** -0.111 -0.027 0.068 1 Mn 0.255** 0.654** -0.683** 0.476** 0.724** 0.349** -0.054 1 Fe2O3 0.150 0.506** -0.554** 0.415** 0.763** 0.202* 0.279** 0.646** 1 B 0.414** 0.463** -0.545** 0.366** 0.429** 0.500** -0.080 0.510** 0.260** 1 Corg 0.863** 0.115 -0.036 -0.114 0.003 0.709** -0.020 0.111 0.034 0.291** 1 pH -0.318** 0.077 -0.233** 0.370** 0.202* -0.275** -0.245** 0.271** -0.040 0.171 -0.341** 1 注:*表示在0.05水平(双侧)上显著相关(双尾),**表示在0.01水平(双侧)上显著相关(双尾),n=132 表 3 研究区土壤营养元素分级统计
Table 3. Classification statistics of soil nutrients in the study area
指标 一级 二级 三级 四级 五级 分级标准 数量 分级标准 数量 分级标准 数量 分级标准 数量 分级标准 数量 N/10-6 >2000 19 1500~2000 19 1000~1500 35 750~1000 22 ≤750 37 P/10-6 >1000 45 800~1000 24 600~800 28 400~600 25 ≤400 10 K2O/% >3.01 29 2.41~3.01 89 1.81~2.41 13 1.21~1.81 0 ≤1.21 1 CaO/% >7.32 2 4.23~7.32 2 1.30~4.23 105 0.50~1.30 20 ≤0.50 3 MgO/% >2.45 1 1.87~2.45 10 1.23~1.87 73 0.75~1.23 29 ≤0.75 19 S/10-6 >757 1 430~757 9 245~430 49 156~245 38 ≤156 35 Mo/10-6 >5.0 0 2.3~5.0 0 1.1~2.3 11 0.6~1.1 106 ≤0.6 15 Mn/10-6 >967 1 711~967 3 540~711 51 342~540 58 ≤342 19 Fe2O3/% >6.23 1 5.04~6.23 12 4.24~5.04 31 3.46~4.24 61 ≤3.46 27 B/10-6 >82.3 0 58.6~82.3 1 41~58.6 5 25.9~41 65 ≤25.9 61 Corg/% >4.0 3 3.0~4.0 4 2.0~3.0 12 1.0~2.0 50 ≤1.0 63 pH >8.6 5 8.1~8.6 76 6.8~8.1 49 5.3~6.8 2 ≤5.3 0 表 4 土壤元素含量变异函数理论模型及其相关参数
Table 4. Soil elements content and variogram model parameters
元素指标 变程/m 块金值(C0) 基台值(C0+C) 块金系数C0/(C0+C) 决定系数R2 残差RSS 拟合模型 N 1191.65 194000 537600 0.361 0.889 2.04×1010 Gaussian P 1091.19 47300 142500 0.332 0.808 2.79×109 Gaussian K2O 2522 0.0336 0.2722 0.123 0.913 7.927×10-3 Spherical CaO 833 0.001 1.623 0.001 0.763 0.601 Spherical MgO 2157 0.0609 0.3408 0.179 0.802 0.0261 Spherical S 348 3000 19570 0.153 0.146 7.12×107 Exponential Mo 5222 0.02808 0.05626 0.499 0.808 1.563×10-4 Spherical Mn 1848 4240 19540 0.217 0.835 5.72×107 Spherical Fe2O3 2047 0.342 0.88 0.389 0.715 0.151 Spherical B 2704 48.7 97.5 0.499 0.87 445 Spherical Corg 3153 0.14 0.89 0.157 0.933 0.0372 Exponential pH 1001.13 0.0621 0.1932 0.321 0.884 2.74×10-3 Gaussian -
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