耕地质量监测中合理样本量配置及不确定性分析——以陕西省宝鸡市为例

耕地质量监测样本量的优化配置对降低成本,实现可持续土地资源监测与利用有重要指导价值。以陕西省宝鸡市为例,采用2018年耕地质量监测数据,运用传统统计学和地统计学方法,探讨耕地质量监测中土壤监测指标的合理样本量及不确定性。研究表明：样本变异性大小与样本量的关系并不是绝对的。样本代表性评价方法和地统计学方法较Cochran方法能有效降低样本的不确定性;样本代表性评价法适用性更广,但研究结果缺乏稳定性;地统计学方法的适用性存在局限性,可降低中等变异性指标的不确定性,但对低、高变异性指标适用性较低。中等变异性指标宜采用地统计学方法确定合理样本量;低、高变异性指标宜采用代表性评价方法确定合理样本量。

Reasonable sample allocation and uncertainty analysis in cultivated land quality monitoring: A case study in Baoji city,Shaanxi province

The optimal allocation of sample size of cultivated land quality monitoring has important guiding value for reducing cost and realizing sustainable monitoring and utilization of land resources. Taking Baoji city of Shaanxi province as an example, this study, based on uses cultivated land quality monitoring data in 2018, uses traditional statistics and geostatistical methods to explore the reasonable sample size and uncertainty of soil monitoring indicators in cultivated land quality monitoring. The results show that the relationship between sample variability and sample size is not absolute. The sample representative evaluation method and geostatistical method can effectively reduce the sample uncertainty compared with the Cochran method. The sample representative evaluation method is more applicable, but the research results are lack of stability. The applicability of geostatistical methods has limitations, which can reduce the uncertainty of medium variability indicators, but less applicable to low and high variability indicators. The reasonable sample size should be determined by geostatistics method for medium variability indicators and representative evaluation method for low and high variability indicators.