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杭锦旗黄河南岸灌区土壤质量指数计算方法比较研究
周文涛1,潘岳1,乔冠宇1,朱金籴2,佟长福3,王贵平4,杨智4,李品芳1*
0
(1.中国农业大学 土地科学与技术学院,北京 100193;2.开封市农林科学研究院,河南 开封 475004;3.水利部 牧区水利科学研究所,呼和浩特 010020;4.杭锦旗黄河灌排服务中心,内蒙古 鄂尔多斯 017400)
摘要:
为研究土壤质量评价过程中不同质量指数计算方法的差异与最优组合,采集并测定了内蒙古杭锦旗黄河南岸灌区79个土壤样本的11项理化指标,并基于主成分分析法构建最小数据集,对比分析了主成分分析法和层次分析法2种权重确定方法与隶属度函数、线性评分函数和非线性评分函数3种指标评分方法组合下计算的土壤质量指数,最后通过与全体数据集的自相关精度检验获取最优方法组合。结果表明:1)层次分析法确定的物理指标权重高于主成分分析法,速效养分指标权重低于主成分分析法,隶属度函数、线性评分函数、非线性评分函数对指标分值计算结果的分布趋势基本保持一致,但隶属度函数对物理指标进行评分时具有更高的区分度;2)采用层次分析法确定指标权重,隶属度函数确定指标分值计算的最小数据集土壤质量指数的自相关检验精度最高(R2=0.85),纳什效率系数为0.81,相对偏差系数为0.04;3)最优方法组合下全体数据集和最小数据集的土壤质量指数变化分别是0.25~0.71和0.16~0.78,平均值为0.43和0.45,表明研究区域土壤处于中等质量水平。综上,本研究确定了准确评价杭锦旗黄河南岸灌区土壤质量的最优方法组合,可为今后土壤质量评价研究中指标权重和分值计算方法的选择提供参考。
关键词:  土壤质量指数  主成分分析法  层次分析法  最小数据集  黄河南岸灌区
DOI:10.11841/j.issn.1007-4333.2024.04.22
投稿时间:2023-12-05
基金项目:鄂尔多斯市科技重大专项项目(2021ZD社17-18)
Comparative study on the calculation methods of soil quality index in the south bank of Yellow River irrigation area of Hangjin Banner
ZHOU Wentao1, PAN Yue1, QIAO Guanyu1, ZHU Jindi2, TONG Changfu3, WANG Guiping4, YANG Zhi4, LI Pinfang1*
(1.College of Land Science and Technology, China Agricultural University, Beijing 100193, China;2.Kaifeng Academy of Agriculture and Forestry, Kaifeng 475004, China;3.Pastoral Water Conservancy Science Research Institute of the Ministry of Water Resources, Hohhot 010020, China;4.Hangjin Banner Yellow River Irrigation and Drainage Service Center, Ordos 017400, China)
Abstract:
The purpose of this study is to investigate the differences and optimal combinations of different quality index calculation methods in the soil quality evaluation process. A total of 11 physical and chemical indicators from 79 soil samples in the south bank of Yellow River irrigation area of Hangjin Banner, Inner Mongolia were collected and measured, and a minimum data set based on the principal component analysis was constructed. Moreover, this study compared the soil quality indices calculated under the combination of two weighting methods (principal component analysis and analytic hierarchy process) and three index scoring methods (membership function, linear scoring function, and nonlinear scoring function), and obtained the optimal combination of methods through the correlation accuracy test with the total data set. The results showed that: 1) The analytic hierarchy process determined higher weights for physical indicators and lower weights for available nutrient indicators than the principal component analysis. The distribution trend of index score calculation results by membership function, linear scoring function, and nonlinear scoring function was basically consistent, but the membership function had a higher degree of differentiation when scoring the physical indicators. 2) The highest accuracy of correlation test for the minimum data set soil quality index was calculated using the analytic hierarchy process to determine the weights, and the membership function to determine the index scores, with R2=0.85, the Nash-Sutcliffe efficiency coefficient of 0.81, and the relative deviation coefficient of 0.04. 3) The range of soil quality indices calculated for two datasets under the optimal combination of methods was 0.25-0.71 and 0.16-0.78, respectively, with mean values of 0.43 and 0.45, indicating that the soil quality in the study area was at a moderate level. In summary, this study determined the optimal combination of methods to accurately evaluate the soil quality in the south bank of Yellow River irrigation area in Hangjin Banner, and provided reference for the selection of index weights and score calculation methods in further studies.
Key words:  soil quality index  principal component analysis  analytical hierarchy process  minimum data set  south bank of Yellow River irrigation area