不同成土母质条件下土壤养分空间变异研究
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摘要
本文在地理信息系统(Geographical Information System, GIS)技术支持下,以重庆市梁平县的粮食主产区作为研究对象,结合成土母质因素(新冲积物,沙溪庙组和遂宁组),分析土壤养分的空间变异性特征,利用平均预测误差MPE (Mean prediction error)和均方根预测误差RMSPE (root mean squared prediction error)两个指标对土壤养分空间分布插值结果进行精度比较,探讨在考虑土壤母质条件下与不考虑土壤母质条件下,土壤养分空间插值精度,为研究土壤养分空间分布提供依据。主要研究结果如下：
(1)在不考虑土壤母质情况下,对研究区730个土壤样品养分进行统计分析,结果表明：各养分变异系数的大小顺序为土壤磷>氮>钾>有机质。在各土壤养分含量的空间变异性结构中,土壤有机质的变程最大,为50940m,其次为土壤氮、钾和磷,其变程分别为21480m、2910m和1710m。土壤养分中土壤磷的块金值最小;土壤氮的块金值较大。从块金系数来看,有机质、土壤氮、土壤磷和土壤钾的比值在25%到75%之间,说明四种均具有中等的空间相关性,其空间变异是由结构性因素和人为因素共同影响的。
(2)在考虑土壤母质影响区域,土壤养分变异特征分析。
①新冲积物母质区域。各养分变异系数的大小顺序为土壤磷>土壤钾>土壤氮>有机质。在各土壤养分含量的空间变异性结构中,土壤钾含量的变程最大,为57240m,其次为土壤氮、磷和有机质,其变程分别为1320m、1520m和3240m。有机质、土壤氮、土壤磷和土壤钾的块金系数分别为43.7%、35.4%、26.5%和29.8%,均在25%-75%之间,表明其空间变异是由结构性因素和人为因素共同影响的。
②沙溪庙组母质区域。各养分变异系数的大小顺序为土壤磷>土壤钾>土壤氮>有机质。在各土壤养分含量的空间变异性结构中,土壤有机质含量的变程最大,为4560m,其次为土壤磷、钾和氮,其变程分别为1080m、850m和730m。有机质、土壤氮、土壤磷和土壤钾的块金系数分别为35.7%、36.1%、34.9%和30.8%,均在25%到75%之间,表明具有中等的空间相关性,其空间变异是由结构性和随机性因素共同作用的结果。
③遂宁组母质区域。各养分变异系数的大小顺序为土壤磷>土壤氮>土壤钾>有机质。在各土壤养分含量的空间变异性结构中,土壤有机质含量的变程最大,为27660m,其次为土壤氮、钾和磷,其变程分别为2370m、1430m和1100m。从Co/(C0+C)来看,土壤OM和土壤氮的块金系数分别为17.2%和20.2%,小于25%,表明土壤属性具有强烈的相关性；土壤磷和钾的比值分别为41.7%和31%,在25%-75%之间,表明具有中等的空间相关性。
(3)预测精度分析。当不考虑母质因素时,土壤有机质、氮、磷和钾含量的平均预测误差MPE为0.001424、0.149、0.1701和0.19968,标准化均方根预测误差RMSPE分别是0.9108、0.8054、0.8603和0.8811。在不同的成土母质区,各养分的平均预测误差MPE值要更小,标准化均方根预测误差RMSPE指标RMSPE更接近于1。其中,在新冲积物母质区域,土壤有机质、氮、磷和钾含量的MPE分别是-0.000941、0.01799、-0.1466和-0.1712,RMSPE分别是0.987、0.9712、1.048和0.9343；沙溪庙组母质区域,土壤有机质、氮、磷和钾含量的MPE分别0.0009544、-0.02857、-0.1682和-0.1741,RMSPE分别是1.057、0.9168、0.9837和1.028；遂宁组母质区域,土壤有机质、氮、磷和钾含量的MPE是0.001404、0.02087、-0.1533和-0.1735,RMSPE分别是1.037、1.004、1.114、和1.023。因此,在该研究区域进行土壤养分空间预测插值时应考虑土壤母质因素的影响。
(4)分别对研究区三种母质土壤养分进行空间插值,将其结果进行拼接。结果表明,研究区土壤有机质比较缺乏,其含量介于0.6-1.3%之间；土壤氮含量很低,含量范围在10.9-33.9mg／L之间,均低于临界值50mg/L；土壤磷含量介于2.9-19.0mg／L之间,低于临界值(12mg／L)的区域占研究区的94.7%；土壤钾含量介于37.2-90.6mg／L之间,低于临界值(78.2mg／L)的区域占95.5%。
Spatial variability of soil nutrients under different parent materials (New Alluvium, Shaximiaozu, and Suiningzu) was conducted in the main grain production areas of Liangping, Chongqing. Two indicators, namely, Mean Prediction Error (MPE) and Root Mean Squared Prediction Error (RMSPE), were used to evaluate the accuracy of spatial interpolation of soil nutrients. The main results were as follows:
(1) On average, the order of coefficients of variation of soil nutrients was soil P> soil N> soil K> organic matter (OM). The spatial variability structure of soil nutrients was explored using GS+. The range of OM was 50940m, followed by soil N (21480m), K (2910m) and P (1710m). Soil P had lowest nugget value while OM had largest one. In terms of nugget to sill ratio, OM, soil N, and K showed moderate spatial autocorrelation suggesting that their spatial variability were affected by natural factors as well as human activities, whereas soil P presented weak spatial autocorrelation indicating that P was mainly associated with human activities.
(2) The variability of soil nutrients under different parent materials.
①New Alluvium region. The coefficients of variation of soil nutrients originated from New Alluvium showed in the order of soil P> soil K>soil N>OM. Soil K had the largest range of 57240m followed by OM (3240m), P (1520m), and soil N (1320m). In terms of nugget to sill ratio, soil P presented weak spatial autocorrelation indicating that P was mainly associated with human activities, whereas OM, soil N, and K showed moderate spatial autocorrelation suggesting that their spatial variability were affected by natural factors as well as human activities.
②Shaximiaozu region. The coefficients of variation of soil nutrients developed from Shaximiaozu hold the rank of soil P> soil K>soil N>OM. Organic matter had the largest range of 4560m followed by soil P (1080m), K (850m), and N (730m). In terms of nugget to sill ratio, soil N presented weak spatial autocorrelation indicating that N was mainly associated with human activities, whereas OM, soil P, and K showed moderate spatial autocorrelation suggesting that their spatial variability were affected by natural factors as well as human activities.
③Suiningzu region. The coefficients of variation of soil nutrients originated from Suiningzu were in the order of soil P>soil K>soil N>OM. Organic matter had the largest range of 27660m followed by soil N (2370m), K (1430m), and P (1100m). In terms of nugget to sill ratio, soil N, P, and K presented weak spatial autocorrelation indicating that their spatial variability were mainly associated with human activities, whereas OM showed moderate spatial autocorrelation suggesting that OM was affected by natural factors as well as human activities.
(3) Analysis of prediction accuracy. In the case of eliminating the effect of parent materials, MPEs were 0.001424,0.149,0.1701 and 0.19968 and RMSPEs were 0.9108,0.8054,0.8603 and 0.8811 for OM, soil N, P and K, respectively.
Nevertheless, MPE was lower and RMSPE was much closer to 1 with respect to the effect of parent material. In New Alluvium region, MPEs were-0.000941,0.01799,-0.1466 and-0.1712 and RMSPEs were 0.987,0.9712,1.048 and 0.9343 for OM, soil N, P and K, respectively. In the area of Shaximiaozu, MPEs were 0.0009544,-0.02857,-0.1682 and-0.1741, RMSPEs were 1.057,0.9168, 0.9837 and 1.028 for OM, soil N, P and K, respectively. In Suiningzu region, MPEs were 0.001404, 0.02087,-0.1533 and-0.1735, and RMSPEs were 1.037,1.004,1.114 and 1.023, respectively. Consequently, it was of great necessity to take into account the effects of parent materials to study the spatial interpolation of soil nutrients.
(4) Through spatial interpolate and splice the soil nutrients under three parent materials, we could come to the conclusion that OM ranged between 0.6-1.3%, the content of OM was deficient in the study area. Content of soil N was below the critical value (50mg/L), changed between 10.9-33.9%. Soil P ranged between 2.9-19.0%, there were approximately 94.7% of study area in which the content of soil P were below the critical value (12mg/L). Soil K changed between 37.2-90.6%, and about 95.5% of study area where the content were below the critical value (78.2mg/L).
(1)在不考虑土壤母质情况下,对研究区730个土壤样品养分进行统计分析,结果表明：各养分变异系数的大小顺序为土壤磷>氮>钾>有机质。在各土壤养分含量的空间变异性结构中,土壤有机质的变程最大,为50940m,其次为土壤氮、钾和磷,其变程分别为21480m、2910m和1710m。土壤养分中土壤磷的块金值最小;土壤氮的块金值较大。从块金系数来看,有机质、土壤氮、土壤磷和土壤钾的比值在25%到75%之间,说明四种均具有中等的空间相关性,其空间变异是由结构性因素和人为因素共同影响的。
(2)在考虑土壤母质影响区域,土壤养分变异特征分析。
①新冲积物母质区域。各养分变异系数的大小顺序为土壤磷>土壤钾>土壤氮>有机质。在各土壤养分含量的空间变异性结构中,土壤钾含量的变程最大,为57240m,其次为土壤氮、磷和有机质,其变程分别为1320m、1520m和3240m。有机质、土壤氮、土壤磷和土壤钾的块金系数分别为43.7%、35.4%、26.5%和29.8%,均在25%-75%之间,表明其空间变异是由结构性因素和人为因素共同影响的。
②沙溪庙组母质区域。各养分变异系数的大小顺序为土壤磷>土壤钾>土壤氮>有机质。在各土壤养分含量的空间变异性结构中,土壤有机质含量的变程最大,为4560m,其次为土壤磷、钾和氮,其变程分别为1080m、850m和730m。有机质、土壤氮、土壤磷和土壤钾的块金系数分别为35.7%、36.1%、34.9%和30.8%,均在25%到75%之间,表明具有中等的空间相关性,其空间变异是由结构性和随机性因素共同作用的结果。
③遂宁组母质区域。各养分变异系数的大小顺序为土壤磷>土壤氮>土壤钾>有机质。在各土壤养分含量的空间变异性结构中,土壤有机质含量的变程最大,为27660m,其次为土壤氮、钾和磷,其变程分别为2370m、1430m和1100m。从Co/(C0+C)来看,土壤OM和土壤氮的块金系数分别为17.2%和20.2%,小于25%,表明土壤属性具有强烈的相关性；土壤磷和钾的比值分别为41.7%和31%,在25%-75%之间,表明具有中等的空间相关性。
(3)预测精度分析。当不考虑母质因素时,土壤有机质、氮、磷和钾含量的平均预测误差MPE为0.001424、0.149、0.1701和0.19968,标准化均方根预测误差RMSPE分别是0.9108、0.8054、0.8603和0.8811。在不同的成土母质区,各养分的平均预测误差MPE值要更小,标准化均方根预测误差RMSPE指标RMSPE更接近于1。其中,在新冲积物母质区域,土壤有机质、氮、磷和钾含量的MPE分别是-0.000941、0.01799、-0.1466和-0.1712,RMSPE分别是0.987、0.9712、1.048和0.9343；沙溪庙组母质区域,土壤有机质、氮、磷和钾含量的MPE分别0.0009544、-0.02857、-0.1682和-0.1741,RMSPE分别是1.057、0.9168、0.9837和1.028；遂宁组母质区域,土壤有机质、氮、磷和钾含量的MPE是0.001404、0.02087、-0.1533和-0.1735,RMSPE分别是1.037、1.004、1.114、和1.023。因此,在该研究区域进行土壤养分空间预测插值时应考虑土壤母质因素的影响。
(4)分别对研究区三种母质土壤养分进行空间插值,将其结果进行拼接。结果表明,研究区土壤有机质比较缺乏,其含量介于0.6-1.3%之间；土壤氮含量很低,含量范围在10.9-33.9mg／L之间,均低于临界值50mg/L；土壤磷含量介于2.9-19.0mg／L之间,低于临界值(12mg／L)的区域占研究区的94.7%；土壤钾含量介于37.2-90.6mg／L之间,低于临界值(78.2mg／L)的区域占95.5%。
Spatial variability of soil nutrients under different parent materials (New Alluvium, Shaximiaozu, and Suiningzu) was conducted in the main grain production areas of Liangping, Chongqing. Two indicators, namely, Mean Prediction Error (MPE) and Root Mean Squared Prediction Error (RMSPE), were used to evaluate the accuracy of spatial interpolation of soil nutrients. The main results were as follows:
(1) On average, the order of coefficients of variation of soil nutrients was soil P> soil N> soil K> organic matter (OM). The spatial variability structure of soil nutrients was explored using GS+. The range of OM was 50940m, followed by soil N (21480m), K (2910m) and P (1710m). Soil P had lowest nugget value while OM had largest one. In terms of nugget to sill ratio, OM, soil N, and K showed moderate spatial autocorrelation suggesting that their spatial variability were affected by natural factors as well as human activities, whereas soil P presented weak spatial autocorrelation indicating that P was mainly associated with human activities.
(2) The variability of soil nutrients under different parent materials.
①New Alluvium region. The coefficients of variation of soil nutrients originated from New Alluvium showed in the order of soil P> soil K>soil N>OM. Soil K had the largest range of 57240m followed by OM (3240m), P (1520m), and soil N (1320m). In terms of nugget to sill ratio, soil P presented weak spatial autocorrelation indicating that P was mainly associated with human activities, whereas OM, soil N, and K showed moderate spatial autocorrelation suggesting that their spatial variability were affected by natural factors as well as human activities.
②Shaximiaozu region. The coefficients of variation of soil nutrients developed from Shaximiaozu hold the rank of soil P> soil K>soil N>OM. Organic matter had the largest range of 4560m followed by soil P (1080m), K (850m), and N (730m). In terms of nugget to sill ratio, soil N presented weak spatial autocorrelation indicating that N was mainly associated with human activities, whereas OM, soil P, and K showed moderate spatial autocorrelation suggesting that their spatial variability were affected by natural factors as well as human activities.
③Suiningzu region. The coefficients of variation of soil nutrients originated from Suiningzu were in the order of soil P>soil K>soil N>OM. Organic matter had the largest range of 27660m followed by soil N (2370m), K (1430m), and P (1100m). In terms of nugget to sill ratio, soil N, P, and K presented weak spatial autocorrelation indicating that their spatial variability were mainly associated with human activities, whereas OM showed moderate spatial autocorrelation suggesting that OM was affected by natural factors as well as human activities.
(3) Analysis of prediction accuracy. In the case of eliminating the effect of parent materials, MPEs were 0.001424,0.149,0.1701 and 0.19968 and RMSPEs were 0.9108,0.8054,0.8603 and 0.8811 for OM, soil N, P and K, respectively.
Nevertheless, MPE was lower and RMSPE was much closer to 1 with respect to the effect of parent material. In New Alluvium region, MPEs were-0.000941,0.01799,-0.1466 and-0.1712 and RMSPEs were 0.987,0.9712,1.048 and 0.9343 for OM, soil N, P and K, respectively. In the area of Shaximiaozu, MPEs were 0.0009544,-0.02857,-0.1682 and-0.1741, RMSPEs were 1.057,0.9168, 0.9837 and 1.028 for OM, soil N, P and K, respectively. In Suiningzu region, MPEs were 0.001404, 0.02087,-0.1533 and-0.1735, and RMSPEs were 1.037,1.004,1.114 and 1.023, respectively. Consequently, it was of great necessity to take into account the effects of parent materials to study the spatial interpolation of soil nutrients.
(4) Through spatial interpolate and splice the soil nutrients under three parent materials, we could come to the conclusion that OM ranged between 0.6-1.3%, the content of OM was deficient in the study area. Content of soil N was below the critical value (50mg/L), changed between 10.9-33.9%. Soil P ranged between 2.9-19.0%, there were approximately 94.7% of study area in which the content of soil P were below the critical value (12mg/L). Soil K changed between 37.2-90.6%, and about 95.5% of study area where the content were below the critical value (78.2mg/L).
引文
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