基于距离聚类与K-means动态聚类的棉田土壤养分评价研究

doi:10.13866/j.azr.2021.04.09

干旱区研究 ›› 2021, Vol. 38 ›› Issue (4): 980-989.doi: 10.13866/j.azr.2021.04.09

基于距离聚类与K-means动态聚类的棉田土壤养分评价研究

范向龙^1,²(),吕新^1,²(),张泽^1,²,高攀^2,³,张强^1,²,印彩霞^1,²,易翔^1,²

1.石河子大学农学院,新疆生产建设兵团绿洲生态农业重点实验室,新疆石河子 832003
2.新疆兵团农业大数据国家地方联合工程研究中心,新疆石河子 832003
3.石河子大学信息科学与技术学院,新疆石河子 832003

收稿日期:2020-08-28 修回日期:2020-10-02 出版日期:2021-07-15 发布日期:2021-08-03
通讯作者: 吕新
作者简介:范向龙(1992-),男,博士研究生,研究方向为农业信息技术. E-mail: 1574468658@qq.com
基金资助:
兵团重点领域创新团队项目(2018CB004);兵团国际合作计划项目(2018BC009);国家博士后面上项目(2017M623282);石河子大学创新发展专项(CXFZ201903);新疆兵团棉花生产大数据关键技术及农业大数据平台研发应用(2018AA004)

Soil nutrient evaluation of cotton field based on distance clustering and K-means dynamic clustering

FAN Xianglong^1,²(),LYU Xin^1,²(),ZHANG Ze^1,²,GAO Pan^2,³,ZHANG Qiang^1,²,YIN Caixia^1,²,YI Xiang^1,²

1. Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Group, Agriculture College of Shihezi Universit, Shihezi 832003, Xinjiang, China
2. National and Local Joint Engineering Research Center for Agricultural Big Data of Xinjiang Production and Construction Corps, Shihezi 832003, Xinjiang
3. School of Information Science and Technology, Shihezi University, Shihezi 832003, China

Received:2020-08-28 Revised:2020-10-02 Online:2021-07-15 Published:2021-08-03
Contact: Xin LYU

摘要/Abstract

摘要：

为了解棉田土壤养分状况及确定养分丰缺程度,研究采用主成分结合距离聚类、K-means动态聚类以及土壤养分综合评价方法对新疆棉田土壤进行分析评价。结果表明:(1) 在主成分分析中,有效铜、有效锰和碱解氮起主要作用,有效铜含量为1.82 mg·kg^-1,丰缺评价状况属于高等水平,有效锰含量为11.36 mg·kg^-1,属于较低水平,碱解氮含量为122.07 mg·kg^-1,属于高等水平,土壤速效磷、速效钾、有效锌和有效铁含量较低,土壤养分含量分布不均匀。在距离聚类和K-means动态聚类中,有机质、碱解氮、有效锰含量较低,其余养分含量较高。在距离聚类中,土壤各类养分可表示为:第Ⅰ类>第Ⅴ类>第Ⅳ类>第Ⅱ类>第Ⅲ类,而在K-means动态聚类中可以表示为:第Ⅲ类>第Ⅰ类>第Ⅴ类>第Ⅱ类>第Ⅳ类。(2) 在土壤综合肥力指数评价值中(IFI),1连和16连的等级高;2连、3连、4连、6连、15连、19连和二监区的等级较高;8连、9连、10连、11连、12连、17连、18连和20连在中等水平。5连、7连、一监区和农市站的等级较低。K-means动态聚类比距离聚类分类效果好,可以更加科学合理、准确有效地对土壤养分进行综合评价。

关键词: 土壤养分, 距离聚类, K-means动态聚类, 综合评价值

Abstract:

This study used principal component analysis combined with distance clustering, K-means dynamic clustering, and comprehensive soil nutrient evaluation methods to analyze and evaluate the soil nutrient status of cotton field soil in Xinjiang, China. The results showed that the available copper, manganese, and alkali hydrolyzable nitrogen played a major role principal component analysis: The available copper content was high (1.82 mg·kg^-1); the available manganese content was low (11.36 mg·kg^-1); and the alkali hydrolyzable nitrogen content was 122.07 mg·kg^-1. The available phosphorus, potassium, zinc and iron contents were low. Thus, the distribution of soil nutrients was uneven. In the distance clustering and K-means dynamic clustering, the organic matter, available nitrogen, and available manganese contents were lower, whereas the other nutrient contents were higher. In the distance cluster, the soil nutrients could be expressed as: class I > class V > class IV > class II > class III, whereas the soil nutrients could be expressed in the K-means dynamic cluster as: class III > class I > class V > class II > class IV. In the comprehensive evaluation of soil nutrients (IFI), the grades of 1 and 16 are higher. The second company, third company, fourth company, sixth company, 15th company, 19th company, and the second prison area had higher levels, whereas the 8th, 9th, 10th, 11th, 12th, 17th, 18th and 20th companies were in the middle level. The grades of companies 5, 7, and 1 supervision district and agricultural city station were lower. Thus, K-means dynamic clustering is better than distance clustering, as it can be more scientific, reasonable, accurate, and effective for the comprehensive evaluation of soil nutrients.

Key words: soil nutrients, distance clustering, K-means dynamic clustering, comprehensive evaluation value

范向龙,吕新,张泽,高攀,张强,印彩霞,易翔. 基于距离聚类与K-means动态聚类的棉田土壤养分评价研究[J]. 干旱区研究, 2021, 38(4): 980-989.

FAN Xianglong,LYU Xin,ZHANG Ze,GAO Pan,ZHANG Qiang,YIN Caixia,YI Xiang. Soil nutrient evaluation of cotton field based on distance clustering and K-means dynamic clustering[J]. Arid Zone Research, 2021, 38(4): 980-989.

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指标/(mg·kg^-1)	最大值	最小值	均值	极差	标准差	变异系数
OM	11220	6310	9370	4910	1510	0.16
AN	158.32	88.63	122.07	69.69	19.77	0.16
AP	26.39	10	20.36	16.39	4.44	0.21
AK	192.13	143.6	149.77	48.53	29.97	0.20
AFe	15.63	10.79	9.25	4.84	3.29	0.35
AMn	15.18	3.65	11.36	11.53	3.81	0.33
ACu	2.41	1.55	1.82	0.86	0.61	0.33
AZn	3.45	1.97	1.07	1.48	0.651	0.61

主成分数	特征值	累计方差贡献率/%
1	4.260	53.252
2	1.757	75.212
3	1.078	88.690
4	0.578	95.914
5	0.174	98.084
6	0.117	99.553
7	0.032	99.956
8	0.003	100.000

指标	PC1	PC2	PC3
OM	0.693	0.670	0.147
AN	-0.157	0.038	0.964
AP	0.319	0.809	0.261
AK	0.517	0.719	0.377
AFe	0.593	0.164	0.747
AMn	-0.220	0.812	-0.268
ACu	0.966	0.084	0.028
AZn	0.538	0.025	0.700

类别	OM	AN	AP	AK	AFe	AMn	ACu	AZn
Ⅰ	1.03a	86.37a	20.08b	192.31a	15.72a	3.45ab	2.42a	3.21a
Ⅱ	0.67b	73.77b	17.87b	170.96a	13.96a	2.43b	1.40b	1.36b
Ⅲ	0.76b	61.38c	11.18c	111.26b	5.91c	4.57a	1.79b	1.66b
Ⅳ	0.98a	57.85c	28.68a	176.75a	14.5a	2.88b	1.86b	2.68a
Ⅴ	1.02a	70.18b	20.89a	192.43a	8.88b	4.44a	1.60b	1.77b

类别	OM	AN	AP	AK	AFe	AMn	ACu	AZn
Ⅰ	1.03ab	62.18a	27.64a	166.49b	12.32a	2.96b	1.85b	2.02b
Ⅱ	1.00b	61.75a	17.96b	160.62b	7.67b	4.54a	1.63b	1.55c
Ⅲ	1.11a	71.87a	19.35b	192.69a	12.43a	4.05a	2.92a	3.01a
Ⅳ	0.70c	63.48a	10.46c	110.42c	6.76b	4.23a	1.73b	1.71ab
Ⅴ	0.65c	74.43a	18.36b	166.33b	13.5a	2.58b	1.34b	1.37c

基于距离聚类与K-means动态聚类的棉田土壤养分评价研究

Soil nutrient evaluation of cotton field based on distance clustering and K-means dynamic clustering

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	OM	AN	AP	AK	AFe	AMn	ACu	AZn
公因子方差	0.8943	0.9383	0.7335	0.9184	0.9971	0.9565	0.7483	0.8275
权重	0.0217	0.1348	0.0003	0.0722	0.5327	0.2196	0.0039	0.0146

指标/(g·kg^-1)	最大值	最小值	均值	标准差	变异系数
OM	0.991	0.100	0.648	0.270	0.417
AN	0.992	0.098	0.549	0.243	0.442
AP	0.994	0.148	0.583	0.238	0.409
AK	0.921	0.098	0.619	0.189	0.305
AFe	0.994	0.097	0.472	0.232	0.491
AMn	0.974	0.102	0.549	0.257	0.468
ACu	0.836	0.065	0.328	0.167	0.511
AZn	0.921	0.100	0.366	0.135	0.371

区域	IFI	等级	区域	IFI	等级	区域	IFI	等级
1连	1.596	高	8连	0.473	中等	17连	0.533	中等
2连	0.628	较高	9连	0.483	中等	18连	0.591	中等
3连	0.733	较高	10连	0.513	中等	19连	0.618	较高
4连	0.704	较高	11连	0.513	中等	20连	0.552	中等
5连	0.225	较低	12连	0.451	中等	一监区	0.383	较低
6连	0.634	较高	15连	0.705	较高	二监区	0.765	较高
7连	0.356	较低	16连	0.922	高	农市站	0.292	较低

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