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

doi:10.31497/zrzyxb.20210817

摘要/Abstract

摘要：

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。

关键词: 土壤监测指标, 合理样本量, 不确定性, 传统统计学, 地统计学

Abstract:

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.

Key words: soil monitoring indicator, reasonable sample size, uncertainty, traditional statistics, geostatistics

张万涛, 吉静怡, 许明祥, 李彬彬. 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[J]. 自然资源学报, 2021, 36(8): 2139-2151.

ZHANG Wan-tao, GI Jing-yi, XU Ming-xiang, LI Bin-bin. Reasonable sample allocation and uncertainty analysis in cultivated land quality monitoring: A case study in Baoji city, Shaanxi province[J]. JOURNAL OF NATURAL RESOURCES, 2021, 36(8): 2139-2151.

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监测指标	样本数/个	最小值	最大值	平均值	标准偏差	变异系数/%	偏度	峰度
AP/(mg/kg)	512	1.35	116.20	23.08	17.92	77.63	2.14	5.67
AK/(mg/kg)	523	62.32	725.26	223.99	109.81	49.03	1.39	1.74
SOM/(g/kg)	521	3.00	34.23	18.23	4.69	25.73	0.05	0.55
TN/(g/kg)	524	0.28	1.97	1.15	0.27	23.41	-0.13	0.25
SAK/(mg/kg)	527	605.82	1675.18	1151.52	173.23	15.04	0.09	0.55
pH	518	7.24	8.81	8.24	0.19	2.25	-1.81	6.06

数据集	AP		AK		SOM		TN		SAK		pH值
数据集	样本量	合理样本量	样本量	合理样本量	样本量	合理样本量	样本量	合理样本量	样本量	合理样本量	样本量	合理样本量
A	512	926	523	369	521	102	524	84	527	35	518	1
A₁	384	991	392	376	391	105	393	80	395	34	389	1
A₂	288	967	294	397	293	102	295	81	296	34	292	1
A₃	216	968	221	375	220	96	221	77	222	33	219	1
A₄	162	897	166	372	165	102	166	78	167	33	164	1
A₅	122	817	125	364	124	99	125	85	125	34	123	1
A₆	92	899	94	337	93	104	94	85	94	31	92	1
A₇	69	981	71	343	70	93	71	90	71	30	69	1
A₈	52	1021	53	305	53	105	53	90	53	35	52	1
A₉	39	971	40	339	40	114	40	98	40	40	39	1
A₁₀	29	1117	30	225	30	101	30	75	30	37	29	1

监测指标	样本量/个	理论模型	块金值	基台值	块基比	变程/km	R²	RSS
SOM	521	Exponential	16.38	40.39	0.41	211.00	0.86	13.10
TN	524	Exponential	0.04	0.08	0.50	54.90	0.92	1.16
SAK	527	Exponential	17280.00	34570.00	0.50	89.70	0.96	1.14
AP	512	Exponential	200.50	490.70	0.41	320.70	0.85	5618
AK	523	Exponential	910.00	11510.00	0.08	3.90	0.17	3×10⁶
pH	518	Gaussian	0.02	0.08	0.23	183.25	0.98	2.56

	AP	AK	SOM	TN	SAK	pH值
AP		0.364^**	0.224^**	0.319^**	0.265^**	-0.224^**
AK	0.453^**		0.361^**	0.477^**	0.408^**	0.038
SOM	0.177^**	0.201^**		0.734^**	0.175^**	-0.080
TN	0.266^**	0.299^**	0.669^**		0.231^**	-0.124^**
SAK	0.294^**	0.280^**	0.186^**	0.279^**		-0.203^**
pH值	-0.298^**	0.011	-0.197^**	-0.316^**	-0.242^**

土壤指标	Cochran方法	样本代表性评价方法	普通克里格方法	协同克里格方法
AP	无法确定	288	无法确定	无法确定
AK	392	392	无法确定	无法确定
SOM	124	93	293	293
TN	94	53	295	295
SAK	40	53	222	222
pH值	无法确定	518	无法确定	无法确定