Spatial Distribution Characteristics and Fertility Evaluation of Soil Nutrients Based on GIS

doi:10.11924/j.issn.1000-6850.casb2022-0730

Abstract

Abstract:

To understand the soil nutrient status of 18 villages in the north of Ligezhuang Town of Jiaozhou City, so as to provide reference for scientific setting of fertilization methods and soil improvement in the construction of high-standard farmland. Soil nutrient content was detected in the study area. A total of 43 sampling points were set up to determine soil pH, organic matter, total nitrogen, alkali-hydrolyzed nitrogen, available phosphorus and available potassium. The general Kriging interpolation method of ArcGIS was applied to analyze the spatial distribution characteristics of soil nutrients in the study area. The index weights were determined by principal component analysis, and the soil fertility grades were divided by index sum method and the results were visualized. The results showed that: (1) the soil in the study area was mainly acidic and neutral. The content of organic matter, total nitrogen and alkali-hydrolyzed nitrogen was insufficient, and the content of available phosphorus was rich with medium available potassium. (2) Generally, pH values were low in the north and south, high in the middle, while low in the east but high in the west. The contents of organic matter, total nitrogen and alkali-hydrolyzed nitrogen were higher in the east but lower in the west, indicating that they were significantly correlated. The distribution of available phosphorus was low in the east while high in the west, and that of available potassium was high in the east but low in the west. (3) The land areas of class Ⅰ-Ⅴ were 365.11, 545.80, 803.71, 547.93 and 390.85 hm², respectively, accounting for 13.76%, 20.57%, 30.29%, 20.65% and 14.73% of the total studied area. The overall soil fertility of the 18 villages in the north of Ligezhuang Town in Jiaozhou City was at a medium level, and it was abundant in the east but poor in the west. To control soil acidification and increase contents of organic matter, total nitrogen and alkali-hydrolyzed nitrogen were of great significance for the next step of building high standard farmland.

Key words: soil nutrients, distribution characteristics, index content, soil fertility level, Kriging interpolation, principal component analysis

LI Jiaxin, GAI Weiling, SUN Xianmin, ZHAO Xin, ZHANG Xiaoguang, CUI Dejie. Spatial Distribution Characteristics and Fertility Evaluation of Soil Nutrients Based on GIS[J]. Chinese Agricultural Science Bulletin, 2023, 39(25): 94-101.

Figures/Tables 10

References 33

[1]	徐明岗, 卢昌艾, 张文菊, 等. 我国耕地质量状况与提升对策[J]. 中国农业资源与区划, 2016, 37(7):8-14.
[2]	郭安廷, 崔锦霞, 许鑫, 等. 基于GIS与地统计的土壤养分空间变异研究[J]. 中国农学通报, 2018, 34(23):72-79. doi: 10.11924/j.issn.1000-6850.casb17070049
[3]	KRAL F, CORSTANJE R, WHITE J R, et al. A Geostatistical analysis of soil properties in the Davis pond Mississippi freshwater diversion[J]. Soil science society of America journal, 2012, 76(3):1107-1118. doi: 10.2136/sssaj2011.0206 URL
[4]	厉雅华, 张向前, 安祺, 等. 耕地地力评价方法及实践应用研究进展[J]. 中国农学通报, 2022, 38(15):60-68. doi: 10.11924/j.issn.1000-6850.casb2021-0610
[5]	耿庆龙, 李娜, 赖宁, 等. 基于修正内梅罗指数法的果园土壤肥力评价[J]. 新疆农业科学, 2019, 56(11):2104.
[6]	HAN C, LIANG C, WU K, et al. Comprehensive evaluation of soil fertility based on GIS technology and grey correlative degree method[J]. Transactions of the Chinese society of agricultural engineering, 2008, 24:53-56.
[7]	DENG F W, REN Z Z, CHUN H G. Evaluation of soil fertility quality based on fuzzy mathematical model[J]. Water conservancy science and technology and economy, 2019.
[8]	赖宁, 陈署晃, 付彦博, 等. 基于GIS的南疆果园土壤肥力评价[J]. 新疆农业科学, 2019, 56(8):1476-1486. doi: 10.6048/j.issn.1001-4330.2019.08.012
[9]	孟博, 周一帆, 杨林生, 等. 勐海县甘蔗土壤养分空间分布特征及肥力评价[J]. 土壤, 2022, 54(2):277-284.
[10]	孙宇, 高明, 王丹, 等. 基于GIS和改进灰色关联模型的岩溶区土壤肥力评价——以重庆市丰都县岩溶区为例[J]. 中国岩溶, 2014, 33(3):347-355.
[11]	王珊珊. 山东省青岛市胶州市李哥庄镇特色小镇建设纪实——空港小镇生态新城[J]. 小城镇建设, 2016(11):83-86.
[12]	楼晨. 新中国农田建设历程刍议[J]. 农村工作通讯, 2021(20):4.
[13]	邓亚中. 《全国高标准农田建设规划(2021—2030年)》解读——提高国家粮食综合生产能力[J]. 河北农机, 2022(6):157-159.
[14]	鲍士旦. 土壤农化分析(3版)[M]. 北京: 中国农业出版社, 2000.
[15]	石媛媛, 郑党斌, 岑祖明, 等. 广西雅长林场林地养分空间分布及肥力评价[J]. 福建林业科技, 2022, 49(1):21-29.
[16]	冯卓亚. 基于GIS的耕地质量评价研究[D]. 北京: 华北水利水电大学, 2021.
[17]	赵秀红. 基于主成分分析的特征提取的研究[D]. 西安: 西安电子科技大学, 2016.
[18]	聂群海. 基于GIS技术和主成分分析的苏州土地适宜性评价与应用[D]. 苏州: 苏州大学, 2017.
[19]	赵慧琴, 石立, 刘金山, 等. SPSS软件计算主成分分析的缺陷与纠正[J]. 统计与决策, 2020, 36(15):56-59.
[20]	曹文涛. 基于野外实测光谱监测的潍北地区土壤盐分含量研究[D]. 济南: 山东师范大学, 2017.
[21]	WALLACE A. Soil acidification from use of too much fertilizer[J]. Communications in soil science and plant analysis, 1994, 25(1-2):87-92. doi: 10.1080/00103629409369010 URL
[22]	肖特, 崔阔澍, 黄文娟, 等. 玉米/大豆不同种植模式对土壤有机质和全氮空间分布的影响[J]. 草地学报, 2022, 30(7):1801-1810. doi: 10.11733/j.issn.1007-0435.2022.07.022
[23]	王风芹, 田丽青, 宋安东, 等. 华北刺槐林与自然恢复植被土壤微生物量碳、氮含量四季动态[J]. 林业科学, 2015(3):16-24.
[24]	FU X, SHAO M, WEI X, et al. Soil organic carbon and total nitrogen as affected by vegetation types in Northern Loess Plateau of China[J]. Geoderma, 2010, 155(1-2):31-35. doi: 10.1016/j.geoderma.2009.11.020 URL
[25]	崔帅, 刘烁然, 王寅, 等. 吉林省旱地土壤有效硫含量及其与土壤有机质和全氮的关系[J]. 中国农业科学, 2022, 55(12):12.
[26]	王慧, 刘金山, 惠晓丽, 等. 旱地土壤有机碳氮和供氮能力对长期不同氮肥用量的响应[J]. 中国农业科学, 2016, 49(15):2988-2998. doi: 10.3864/j.issn.0578-1752.2016.15.013
[27]	朱梓弘, 朱同彬, 杨霖, 等. 中国土壤碱解氮含量与影响因子的空间关系研究[J]. 生态环境学报, 2019, 28(11):2199-2207. doi: 10.16258/j.cnki.1674-5906.2019.11.008
[28]	尹嘉德, 侯慧芝, 张绪成, 等. 化肥与有机肥结合对旱地覆膜春小麦产量形成和土壤碱解氮累积影响[J]. 水土保持学报, 2021, 35(2):279-287.
[29]	潘根兴, 丁元君, 陈硕桐, 等. 从土壤腐殖质分组到分子有机质组学认识土壤有机质本质[J]. 地球科学进展, 2019, 34(5):451-470. doi: 10.11867/j.issn.1001-8166.2019.05.0451
[30]	江叶枫, 叶英聪, 郭熙, 等. 南方红壤区不同侵蚀程度下耕地土壤有效磷空间分布特征及其驱动因素[J]. 土壤, 2018, 50(5):1013-1021.
[31]	曹婧, 陈怡平, 江瑶, 等. 陕西省农田土壤速效钾时空变化及其影响因素[J]. 水土保持学报, 2021, 35(5):296-302,311.
[32]	赵秀东, 陈晓芳, 袁自然, 等. 有机肥替代对土壤养分的影响[J]. 中国农学通报, 2022, 38(16):74-80. doi: 10.11924/j.issn.1000-6850.casb2021-0611
[33]	郭军玲, 王永亮, 郭彩霞, 等. 基于GIS和测土配方数据的晋北县域春玉米专用肥配方筛选[J]. 农业工程学报, 2016, 34(7):158-164.

丰缺状况	有机质/(g/kg)	全氮/(g/kg)	碱解氮/(mg/kg)	有效磷/(mg/kg)	速效钾/(mg/kg)
极丰富	> 40.00	> 2.00	> 150.00	> 40.00	> 200.00
丰富	30.00~40.00	1.50~2.00	120.00~150.00	20.00~40.00	150.00~200.00
中等	10.00~30.00	0.75~1.50	60.00~120.00	5.00~20.00	50.00~150.00
缺乏	6.00~10.00	0.50~0.75	30.00~60.00	3.00~5.00	30.00~50.00
极缺乏	< 6.00	< 0.50	< 30.00	< 3.00	< 30.00

丰缺状况	有机质/(g/kg)	全氮/(g/kg)	碱解氮/(mg/kg)	有效磷/(mg/kg)	速效钾/(mg/kg)
极丰富	> 40.00	> 2.00	> 150.00	> 40.00	> 200.00
丰富	30.00~40.00	1.50~2.00	120.00~150.00	20.00~40.00	150.00~200.00
中等	10.00~30.00	0.75~1.50	60.00~120.00	5.00~20.00	50.00~150.00
缺乏	6.00~10.00	0.50~0.75	30.00~60.00	3.00~5.00	30.00~50.00
极缺乏	< 6.00	< 0.50	< 30.00	< 3.00	< 30.00

养分指标	范围	平均值	变异系数/%	样品分布/%
养分指标	范围	平均值	变异系数/%	极缺乏	缺乏	中等	丰富	极丰富
有机质	1.45~24.72 g/kg	(9.63±4.30) g/kg	44.67	23.26	32.56	44.19	—	—
全氮	0.27~1.85 g/kg	(0.71±0.30) g/kg	42.71	18.60	39.53	39.54	2.33	—
碱解氮	29.13~227.40 mg/kg	(93.47±46.14) mg/kg	49.37	2.33	25.58	48.84	11.63	11.63
有效磷	5.70~193.50 mg/kg	(63.42±34.96) mg/kg	55.13	—	—	4.66	20.93	74.42
速效钾	52.76~305.70 mg/kg	(137.99±47.23) mg/kg	34.23	—	—	67.44	23.26	9.30

养分指标	范围	平均值	变异系数/%	样品分布/%
养分指标	范围	平均值	变异系数/%	极缺乏	缺乏	中等	丰富	极丰富
有机质	1.45~24.72 g/kg	(9.63±4.30) g/kg	44.67	23.26	32.56	44.19	—	—
全氮	0.27~1.85 g/kg	(0.71±0.30) g/kg	42.71	18.60	39.53	39.54	2.33	—
碱解氮	29.13~227.40 mg/kg	(93.47±46.14) mg/kg	49.37	2.33	25.58	48.84	11.63	11.63
有效磷	5.70~193.50 mg/kg	(63.42±34.96) mg/kg	55.13	—	—	4.66	20.93	74.42
速效钾	52.76~305.70 mg/kg	(137.99±47.23) mg/kg	34.23	—	—	67.44	23.26	9.30

等级	土壤肥力水平	综合指数
Ⅰ级	极丰富	0.0274≤X≤0.0302
Ⅱ级	丰富	0.0246≤X≤0.0274
Ⅲ级	中等	0.0218≤X≤0.0246
Ⅳ级	缺乏	0.0190≤X≤0.0218
Ⅴ级	极缺乏	0.0162≤X≤0.0190