基于最小数据集的黄土丘陵区耕地土壤质量评价

Evaluation of Soil Quality of Cropland in Loess Hilly Area Based on Minimum Data Set

摘要:

土壤质量评价是精细化农业生产和土地科学管理的关键依据，对保障国家粮食安全具有重要意义。为明确黄土丘陵区耕地土壤质量，以黄土高原南缘韩城市为研究区，采集土壤表层（0～20cm) 134个土壤样品，测定了涵盖土壤物理、养分和环境特征的27项指标，基于主成分分析和Norm原则构建最小数据集（Minimum data set，MDS），同时结合土壤质量指数（Soil quality index，SQI）法和地统计分析，对研究区土壤质量进行评价。结果表明：研究区土壤偏碱性（pH平均值为8.31），质地属于粘壤土，土壤环境处于轻度生态风险，环境质量良好，土壤养分中碱解氮含量较缺乏，有机碳和有效磷含量处于适中水平，全磷和速效钾含量较为丰富。黄土高原南缘韩城地区土壤质量评价最小数据集由土壤含水率、比重、毛管孔隙度、有机碳含量、锌含量、镍含量和粗砂粒含量7项指标构成，其中有机碳含量在土壤质量评价指标中权重最大，即有机碳含量为控制该区域土壤质量的关键因子。最小数据集土壤质量指数（SQI-MDS）均值（0.522）与全量数据集土壤质量指数（SQI-TDS）均值（0.537）相差较小，在土壤质量分级上均属于同一等级。SQI-MDS的变化区间和变异系数均高于SQI-TDS，且SQI-MDS与SQI-TDS拟合决定系数R2为0.812。因此，基于最小数据集的土壤质量评价法在该区域具有更好的适用性，且评价准确度较高。半变异函数为高斯函数时，预测精度最高，土壤质量在空间上呈现一定的分布规律，靠近河流区域，土壤质量指数越高，土壤质量越好。最小数据集和土壤质量指数评价法相结合可以准确高效全面反地映土壤质量，为解决土壤质量评价过程中土壤指标多、测试成本高和计算复杂等问题提供了新方法。

Abstract:

Soil quality evaluation is a key basis for refined agricultural production and scientific land management, which is of great significance in guaranteeing national food security. To clarify the soil quality of arable land in loess hilly areas, Hancheng on the southern edge of the Loess Plateau was taken as the target area, totally 134 soil samples were collected from the soil surface layer (0～20cm), and 27 indexes covering soil physical, nutrient and environmental characteristics were measured, and then a minimum data set was constructed based on principal component analysis and Norm principle. The results showed that the soil in the study area was slightly alkaline (with a average pH value of 8.31), and its texture belonged to clay loam. The soil environment was at a mild ecological risk, with good environmental quality. The content of alkali-hydrolyzable nitrogen in soil nutrients was relatively deficient, the contents of organic carbon and available phosphorus were at a moderate level, and the contents of total phosphorus and available potassium were relatively rich. The minimum data set for soil quality evaluation in the Hancheng area on the southern edge of the Loess Plateau consisted of seven indicators: soil moisture content, specific gravity, capillary porosity, organic carbon content, zinc content, nickel content and coarse sand content. Among them, the organic carbon content had the largest weight in the soil quality evaluation indicators, that was, the organic carbon content was the key factor controlling the soil quality in this area. The mean value of the soil quality index (SQI-MDS) in the minimum dataset (0.522) and the mean value of the soil quality index (SQI-TDS) in the full dataset (0.537) differed slightly, and both belonged to the same grade in soil quality classification. The variation range and coefficient of variation of SQI-MDS were both higher than those of SQI-TDS, and the determination coefficient R2 of the fitting result between SQI-MDS and SQI-TDS were 0.812. Therefore, the soil quality assessment method based on the minimum data set had better applicability in this area and higher evaluation accuracy. When the semi-variogram was a Gaussian function, the prediction accuracy was the highest. The soil quality showed a certain distribution pattern in space. In the area close to the river, the higher the soil quality index was, the better the soil quality would be. The combination of the minimum data set and the soil index evaluation method can accurately, efficiently and comprehensively reflected soil quality, providing an approach to solve problems such as numerous soil indicators, high testing costs and complex calculations in the process of soil quality evaluation.