北京土壤肥力及其关键要素空间变异与尺度效应研究
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摘要
本文以北京为研究区域,通过引入粗糙集理论,探讨了土壤肥力评价过程中指标权重的确定方法,并运用传统统计学和地统计学分析了土壤综合肥力及有机碳(SOC)的空间变异规律和驱动因素,初步探讨了SOC的空间变异随采样密度变化的响应,研究结果为区域土壤肥力评价和养分资源管理提供理论基础。主要研究结果与结论如下：
(1)粗糙集在土壤肥力评价中的赋权步骤为数据的离散化、土壤肥力综合等级的初步确定、属性值约简、等价类划分、属性重要度的计算和指标权重计算等。通过实例产量数据验证,特尔斐法权重的评价结果与产量显著线性相关,R2为0.77,均方根误差(RMSE)为1.25thm-2；而粗糙集理论权重的评价结果与产量也显著线性相关,R2达0.83,RMSE为1.09thm-2,RMSE相对精度提高值达12.80%。因此,粗糙集理论相对于特尔斐法确定的权重更为合理、准确。
(2)在常规养分指标的基础上引入微量元素养分指标,采用粗糙集理论赋权法、隶属度函数和地统计学方法对北京延庆盆地土壤养分综合状况及其空间变异的研究表明,研究区土壤常规养分和微量元素养分的肥力贡献率分别为0.66和0.34。菜地土壤单一有效微量元素指标的肥力水平和养分综合水平总体上均要高于果园和粮田。土壤综合肥力指数的空间异质性同时受随机部分和结构性部分的共同作用；空间分布总体上主要受有机质和全氮的作用,但局部地区分布还受有效铜、有效锌等微量元素养分的影响。因此,将土壤常规养分和微量元素养分同时纳入土壤养分评价指标体系是必要的,并较好地揭示了土壤养分的空间变异规律。
(3)对北京1985年和2009年时隔25年的SOC的时空变异及其影响因素分析表明,1985年和2009年SOC平均含量分别为9.97g kg-1和10.46g kg-1,均在东—西向呈现全局性的二阶趋势效应；空间分布总体研究区的西部高于东部,北部高于南部；期间,大部分地区SOC含量有所增加,增幅最大的区域主要为房山和门头沟西南部,降幅最大的区域主要分布在怀柔和密云。长期定位监测数据表明,1987-2009年期间北京SOC随时间的动态变化总体处于先增加后下降再增加趋势。地形、土壤质地、土壤类型、成土母质和土地利用类型显著影响SOC含量的空间分布。而土地利用变化直接影响SOC的动态平衡,与维持原利用类型相比,其它用地转旱地的SOC相对下降,转果园的则有所增加；早地转其它用地,SOC相对增加；而果园转其它用地,SOC相对下降。这与早地耕作的粗放管理,果园的较高水肥投入有关。
(4)对四种不同采样密度下SOC的空间变异的结构变化以及空间预测的不确定性方面的初步探讨表明,SOC关于地形因子的趋势属于宏观趋势,以低采样密度的趋势拟合效果最优；随着采样密度的增加,SOC及其去趋势后残差的系统内部随机变异逐渐增强,结构变异逐渐减弱,变异函数的分布也越平稳,空间预测的不确定性也逐渐减小,另外,变异函数的变程可能也影响空间预测的不确定性；增加采样密度和引入地形因子辅助的回归克里格法均可以提高北京SOC的预测精度；在预测精度不降低的情况下,引入地形因子辅助一定程度上可以减少采样的数量。
This paper took Beijing as a research area, rough set theory were introduced to to investigate the weight determination method in soil fertility evaluation. Traditional statistics and geostatistical method were used to analyze spatial variation and driving factors of soil fertility and soil organic carbon (SOC) and to discuss preliminarily the spatial variability response of SOC to sampling scale changes. The results can provide a theoretical basis for soil fertility quality assessment and nutrient management. The main results and conclusions are as follows:
(1) The determination of weight using rough set theory for soil fertility evaluation involve several steps:data discretization, preliminary determination of soil fertility grade, attribute value reduction, equivalence partitioning, attribute significance calculation, and index weights calculation. Through the analysis of actual case, there was a significant linear correlation between crop yield and soil integrated fertility index (IFI) obtained by Delphi weighting method. The determination coefficient R1was0.77and root mean square error (RMSE) was1.25t hm-2. Again, a significant linear correlation between IFI (obtained by rough set theory weighting method) and crop yield was observed. The later method has higher accuracy, as indicated by higher values of R2(0.83) and lower value of RMSE (1.06t hm-2). Therefore, results of this study indicates that it is feasible to adopt rough set theory for determining the index weights of soil fertility.
(2) Based on introducing soil microelements as soil evaluation indexes as well as soil macroelements, rough set weighting method and membership function were used for assessing soil fertility and its spatial variability was analyzed by using geostatistics method. The results showed that the contribution rates for integrated nutrient of soil conventional nutrient indexes and microelement indexes were0.66and0.34, respectively. The semi-variance analysis of the residuals showed medium degree of spatial autocorrelation. The overall spatial distribution trends of soil integrated nutrient in Yanqing Basin was mainly affected by organic matter and total N. But in the local area, the micronutrients such as available Cu and available Zn played a leading role. Both fertility levels for single available microelement and soil integrated nutrient of vegetable fields were higher than orchards and grain crop fields. Therefore, bringing microelement indexes into evaluation index system as same as conventional nutrient indexes is practicable and necessary, which can reveal satisfactorily the spatial variation pattern of soil fertility.
(3) Spatial variation of SOC from1985to2009and its influencing factors were analysed. The results showed that the SOC contents in1985and2009were9.97g kg-1and10.46g kg-1, respectively. The long-term monitoring data showed the variation tendency of SOC was " increasing, decreasing and increasing". In both two periods, the SOC contents presented a2nd-order global trend from east to west and their spatial distribution was higher in north lower in south and higher in west lower in east. During 25years, the SOC of most area has increased with the largest increase for Fangshan and southwestern Mentougou area and the largest drop for Huairou and Miyun District. The terrain, soil textures, soil types, parent materials and land use were the significant influence factors for the spatial distribution of SOC. However, the land use changes directly affected the dynamic balance of SOC. Compared with maintained land, the SOC of the other lands transformed to dry lands was relatively small and to orchards was relatively increase. The SOC of dry lands transformed to other lands was relatively increase while orchards transformed to other lands was relatively decrease. This reason was extensive management for dry lands and high water and fertilizer inputs for orchards.
(4) We designed four different sampling densities to investigate preliminarily the structural changes of the variogram and uncertainty of spatial prediction with the study scale changes. The results showed that the SOC was macroscopically related to terrain factor and low sampling density data were the most optimal for use in fitting the trend value. As sampling density increasing, the distribution of variogram of SOC and its residuals flattened out gradually, the random variation were growing strongly, and structural variation and uncertainty of spatial prediction decreased gradually. In addition, range of variogram may also affect the uncertainty of spatial prediction. Increasing sampling density and regression kiging method aided by terrain factors can improve prediction accuracy of SOC. Therefore, soil monitoring and management introducing auxiliary variable can to some extent cut the number of sampling points without reducing prediction accuracy.
(1)粗糙集在土壤肥力评价中的赋权步骤为数据的离散化、土壤肥力综合等级的初步确定、属性值约简、等价类划分、属性重要度的计算和指标权重计算等。通过实例产量数据验证,特尔斐法权重的评价结果与产量显著线性相关,R2为0.77,均方根误差(RMSE)为1.25thm-2；而粗糙集理论权重的评价结果与产量也显著线性相关,R2达0.83,RMSE为1.09thm-2,RMSE相对精度提高值达12.80%。因此,粗糙集理论相对于特尔斐法确定的权重更为合理、准确。
(2)在常规养分指标的基础上引入微量元素养分指标,采用粗糙集理论赋权法、隶属度函数和地统计学方法对北京延庆盆地土壤养分综合状况及其空间变异的研究表明,研究区土壤常规养分和微量元素养分的肥力贡献率分别为0.66和0.34。菜地土壤单一有效微量元素指标的肥力水平和养分综合水平总体上均要高于果园和粮田。土壤综合肥力指数的空间异质性同时受随机部分和结构性部分的共同作用；空间分布总体上主要受有机质和全氮的作用,但局部地区分布还受有效铜、有效锌等微量元素养分的影响。因此,将土壤常规养分和微量元素养分同时纳入土壤养分评价指标体系是必要的,并较好地揭示了土壤养分的空间变异规律。
(3)对北京1985年和2009年时隔25年的SOC的时空变异及其影响因素分析表明,1985年和2009年SOC平均含量分别为9.97g kg-1和10.46g kg-1,均在东—西向呈现全局性的二阶趋势效应；空间分布总体研究区的西部高于东部,北部高于南部；期间,大部分地区SOC含量有所增加,增幅最大的区域主要为房山和门头沟西南部,降幅最大的区域主要分布在怀柔和密云。长期定位监测数据表明,1987-2009年期间北京SOC随时间的动态变化总体处于先增加后下降再增加趋势。地形、土壤质地、土壤类型、成土母质和土地利用类型显著影响SOC含量的空间分布。而土地利用变化直接影响SOC的动态平衡,与维持原利用类型相比,其它用地转旱地的SOC相对下降,转果园的则有所增加；早地转其它用地,SOC相对增加；而果园转其它用地,SOC相对下降。这与早地耕作的粗放管理,果园的较高水肥投入有关。
(4)对四种不同采样密度下SOC的空间变异的结构变化以及空间预测的不确定性方面的初步探讨表明,SOC关于地形因子的趋势属于宏观趋势,以低采样密度的趋势拟合效果最优；随着采样密度的增加,SOC及其去趋势后残差的系统内部随机变异逐渐增强,结构变异逐渐减弱,变异函数的分布也越平稳,空间预测的不确定性也逐渐减小,另外,变异函数的变程可能也影响空间预测的不确定性；增加采样密度和引入地形因子辅助的回归克里格法均可以提高北京SOC的预测精度；在预测精度不降低的情况下,引入地形因子辅助一定程度上可以减少采样的数量。
This paper took Beijing as a research area, rough set theory were introduced to to investigate the weight determination method in soil fertility evaluation. Traditional statistics and geostatistical method were used to analyze spatial variation and driving factors of soil fertility and soil organic carbon (SOC) and to discuss preliminarily the spatial variability response of SOC to sampling scale changes. The results can provide a theoretical basis for soil fertility quality assessment and nutrient management. The main results and conclusions are as follows:
(1) The determination of weight using rough set theory for soil fertility evaluation involve several steps:data discretization, preliminary determination of soil fertility grade, attribute value reduction, equivalence partitioning, attribute significance calculation, and index weights calculation. Through the analysis of actual case, there was a significant linear correlation between crop yield and soil integrated fertility index (IFI) obtained by Delphi weighting method. The determination coefficient R1was0.77and root mean square error (RMSE) was1.25t hm-2. Again, a significant linear correlation between IFI (obtained by rough set theory weighting method) and crop yield was observed. The later method has higher accuracy, as indicated by higher values of R2(0.83) and lower value of RMSE (1.06t hm-2). Therefore, results of this study indicates that it is feasible to adopt rough set theory for determining the index weights of soil fertility.
(2) Based on introducing soil microelements as soil evaluation indexes as well as soil macroelements, rough set weighting method and membership function were used for assessing soil fertility and its spatial variability was analyzed by using geostatistics method. The results showed that the contribution rates for integrated nutrient of soil conventional nutrient indexes and microelement indexes were0.66and0.34, respectively. The semi-variance analysis of the residuals showed medium degree of spatial autocorrelation. The overall spatial distribution trends of soil integrated nutrient in Yanqing Basin was mainly affected by organic matter and total N. But in the local area, the micronutrients such as available Cu and available Zn played a leading role. Both fertility levels for single available microelement and soil integrated nutrient of vegetable fields were higher than orchards and grain crop fields. Therefore, bringing microelement indexes into evaluation index system as same as conventional nutrient indexes is practicable and necessary, which can reveal satisfactorily the spatial variation pattern of soil fertility.
(3) Spatial variation of SOC from1985to2009and its influencing factors were analysed. The results showed that the SOC contents in1985and2009were9.97g kg-1and10.46g kg-1, respectively. The long-term monitoring data showed the variation tendency of SOC was " increasing, decreasing and increasing". In both two periods, the SOC contents presented a2nd-order global trend from east to west and their spatial distribution was higher in north lower in south and higher in west lower in east. During 25years, the SOC of most area has increased with the largest increase for Fangshan and southwestern Mentougou area and the largest drop for Huairou and Miyun District. The terrain, soil textures, soil types, parent materials and land use were the significant influence factors for the spatial distribution of SOC. However, the land use changes directly affected the dynamic balance of SOC. Compared with maintained land, the SOC of the other lands transformed to dry lands was relatively small and to orchards was relatively increase. The SOC of dry lands transformed to other lands was relatively increase while orchards transformed to other lands was relatively decrease. This reason was extensive management for dry lands and high water and fertilizer inputs for orchards.
(4) We designed four different sampling densities to investigate preliminarily the structural changes of the variogram and uncertainty of spatial prediction with the study scale changes. The results showed that the SOC was macroscopically related to terrain factor and low sampling density data were the most optimal for use in fitting the trend value. As sampling density increasing, the distribution of variogram of SOC and its residuals flattened out gradually, the random variation were growing strongly, and structural variation and uncertainty of spatial prediction decreased gradually. In addition, range of variogram may also affect the uncertainty of spatial prediction. Increasing sampling density and regression kiging method aided by terrain factors can improve prediction accuracy of SOC. Therefore, soil monitoring and management introducing auxiliary variable can to some extent cut the number of sampling points without reducing prediction accuracy.
引文
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