Spatial differentiation and trade-off /synergy relationship of soil fertility in typical karst peak-cluster depressions
-
摘要: 科学评价网格尺度下的岩溶峰丛洼地土壤肥力水平是实现区域土壤精细化施肥保肥的重要基础。文章基于平果市典型峰丛洼地实测采样与网格预测的土壤数据，通过半方差函数模型、空间集聚模型以及斯皮尔曼相关系数等方法探析肥力指标的空间分布特征与权衡/协同关系。结果表明：研究区全氮（TN）、全磷（TP）、有机质三项养分指标的空间分布总体呈自东北向西南降低的变化特点，且其空间分异主要受人为活动等随机因素影响；研究区土壤综合肥力为较缺乏水平，三（中等）~五等（缺乏）肥力水平的土壤面积占比之和高达86.88%，肥力空间大致呈东北高—西南低的分异格局；五项肥力指标对土壤综合肥力的协同贡献大小为TP＞TN＞有机质＞pH＞土地利用，TK对于其余五项指标均表现出权衡损益效应，土壤有机质、TN协同/权衡的主要空间类型为高—高协同增强型、低—低协同减弱型。平果市典型岩溶峰丛洼地土壤肥力状况总体一般，各肥力元素权衡/协同效应显著，可根据土壤肥力现状和农林生产需要，通过水土保持、配方施肥、土地利用布局优化调整等方式改善岩溶峰丛洼地土壤肥力条件。Abstract:
A scientific evaluation of soil fertility levels in karst peak-cluster depressions at a grid scale is essential for implementing precise fertilization and conservation strategies at the regional level. To provide targeted support for the effective management and cultivation of soil fertility in these depressions, this study selected typical peak-cluster depressions in Pingguo City, Guangxi, as the research object. A 500 m×500 m grid was employed as the evaluation unit, and soil data were collected through a combination of field sampling and Kriging interpolation. An index system was developed, incorporating pH, organic matter (OM), total nitrogen (TN), total phosphorus (TP), total potassium (TK) and land use types. The Analytic Hierarchy Process (AHP) was utilized to determine the weights of these indicators, and a multi-factor comprehensive evaluation method was applied to assess the overall soil fertility. Furthermore, semi-variance functions, spatial clustering models (including global and local Moran’s I), and Spearman’s correlation coefficients were employed to systematically analyze the spatial differentiation characteristics of soil fertility and the trade-off/synergy relationships among the indicators. The results indicate that: (1) spatial differentiation is significant: TN, TP and OM contents generally exhibit a decreasing pattern from the northeast to the southwest, with weak spatial correlation (nugget coefficient>75%). This pattern is mainly influenced by anthropogenic stochastic activities, such as fertilizer application and planting methods. In contrast, pH value demonstrates the strongest spatial correlation with a nugget coefficient of 17%, which is predominantly governed by the structural factors of the karst geological background. Additionally, the spatial distribution of TK is largely opposite to that of TN, TP and OM. (2) The overall soil fertility is characterized by a relatively deficient level, with an average value of 2.93. Grid cells classified as moderate (Grade 3), relatively deficient (Grade 4), and deficient (Grade 5) account for 86.88% of the total area. Spatially, soil fertility exhibits a differentiation pattern of being higher in the northeast and lower in the southwest. High-fertility zones are concentrated in low-slope forestlands and agricultural lands in the northeastern region, while low-fertility zones are found in sloping farmlands, construction sites, and steep-slope forestlands in the northwestern area. (3) Significant trade-off/synergistic effects exist among indicators: TP, TN and OM show significant synergy effects in enhancing comprehensive fertility (Spearman’s correlation coefficient>0.389), with contribution rankings as TP>TN>OM. However, TK exhibits a significant trade-off relationship (negative correlation) with the other four indicators (TN, TP, OM and pH), particularly showing the strongest trade-off effect with TN. Spatial agglomeration analysis indicates that OM, TN, TP, and TK exhibit strong spatial autocorrelation (Moran’s I>0.47), with predominant agglomeration type being “low-low” synergy-weakening patterns, while “high-high” synergy-enhancing patterns are mainly distributed in the northeastern agricultural land area. The relationship between land use and spatial synergy/trade-off effects of OM and TN is generally moderate. In conclusion, soil fertility in typical karst peak-cluster depressions is relatively low and exhibits significant spatial heterogeneity, with complex trade-offs and synergies among nutrients. Accordingly, it is proposed that differentiated measures targeting different fertility grades and spatial areas should be implemented, including soil and water conservation, optimization of fertilization structures (with a focus on phosphorus and nitrogen synergy supplementation, as well as potassium balance), and adjustments to land-use planning to achieve effective enhancement and sustainable management of soil fertility. -
图 1 研究区土壤样品采集预测与网格化处理
Figure 1. Prediction and grid processing of soil sampling in the study area
图 2 土壤各项养分指标空间分布特征图
Figure 2. Spatial distribution characteristics of soil nutrient indexes
图 3 土壤肥力综合评价结果空间分布图
Figure 3. Spatial distribution of comprehensive evaluation of soil fertility
表 1 岩溶区土壤肥力综合评价指标体系及量化
Table 1. Index system and quantification of comprehensive evaluation of soil fertility in karst areas
一级指标 二级指标 评分分值 权重 5 4 3 2 1 土壤养分 pH (6.5，7.5] (5.5，6.5]或(7.5，8.0] (4.8，5.5]或(8.0，8.5] (4.5，5.0] (0，4.5] 0.030 有机质 (30，48] (25，30] (20，25] (15，20] (10，15] 0.073 全氮 (2.2，3.0] (1.9，2.2] (1.6，1.9] (1.3，1.6] (1.0，1.3] 0.439 全磷 (1.2，2.5] (0.8，1.2] (0.4，0.8] (0.2，0.4] (0，0.2] 0.291 全钾 (20，35] (15，20] (10，15] (5，10] (0，5] 0.152 立地条件 土地利用类型 耕地 草地 林地 其他土地 建设用地 0.016 
下载: 导出CSV
表 2 土壤综合肥力等级划分表
Table 2. Grading of comprehensive soil fertility
等级 一等（丰富） 二等（较丰富） 三等（中等） 四等（较缺乏） 五等（缺乏） f综肥 ≥4.0 4.0~3.5 3.5~3.0 3.0~2.5 <2.5
下载: 导出CSV
表 3 土壤养分指标描述性统计分析表
Table 3. Descriptive statistical analysis of soil nutrient index
指标 极小值 极大值 中值 均值 标准差 偏度 峰度 离散系数/% pH 4.00 8.14 6.38 6.37 0.91 −0.18 −0.37 14.44 有机质/×10−3 10.60 46.90 20.40 22.52 7.06 0.98 0.69 31.56 TN/×10−3 1.00 2.97 1.79 1.80 0.28 0.382 1.708 15.82 TP/×10−3 0.13 2.43 0.43 0.54 0.38 2.117 5.286 69.83 TK/×10−3 1.30 42.60 7.82 10.00 7.02 0.22 5.50 70.00
下载: 导出CSV
表 4 土壤养分指标半方差统计结果表
Table 4. Semi-variance statistical results of soil nutrient indexes
指标 理论模型 块金值 基台值 块金系数 可决系数 残差平方 pH Exponential 0.15 0.86 0.17 0.79 0.00 有机质 Gaussian 1.09 0.46 2.37 0.84 0.00 TN Gaussian 1.13 0.19 5.95 0.87 0.00 TP Gaussian 0.98 0.03 32.67 0.74 0.00 TK Gaussian 0.85 0.14 6.07 0.68 0.00
下载: 导出CSV
表 5 土壤肥力指标斯皮尔曼相关系数
Table 5. Spearman correlation coefficient of soil fertility indexes
指标 TN TP TK 有机质 pH 土地利用 综合肥力 TN 1.00 —— —— —— —— —— —— TP 0.459** 1.00 —— —— —— —— —— TK −0.426** −0.332** 1.00 —— —— —— —— 有机质 0.613** 0.389** −0.299** 1.00 —— —— —— pH 0.249 −0.252 −0.208** −0.02 1.00 —— —— 土地利用 −0.127 0.227** 0.344 0.249 −0.163* 1.00 ——
下载: 导出CSV
-
[1] Creamer R E, Barel J M, Bongiorno G, Zwetsloot M J. The life of soils: Integrating the who and how of multifunctionality[J]. Soil Biology and Biochemistry, 2022, 166: 19-27. [2] 汤颖颖, 吴秀芹. 广西岩溶碳汇对气候变化和石漠化治理措施的响应[J]. 北京大学学报(自然科学版), 2023, 59(2): 189-196.TANG Yingying, WU Xiuqin. Response of Karst Carbon Sink to Climate Change and Rocky Desertification Control Measures in Guangxi Zhuang Autonomous Region[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2023, 59(2): 189-196. [3] JIANG Zhongcheng, LIANG Yanqing, QIN Xiaoqun. Rocky desertification in South west China: Impacts, causes, and restoration[J]. Earth-Science Reviews, 2014, 132: 1-12. doi: 10.1016/j.earscirev.2014.01.005 [4] CUI Naxin, CAI Min, ZHANG Xu, Ahmed A Abdelhafez, ZHOU Li, SUN Huifeng, CHEN Guifa, ZOU Guoyan, ZHOU Sheng. Run off loss of nitrogen and phosphorus from a rice paddy field in the east of China: Effects of long-term chemical N fertilizer and organic manure applications[J]. Global Ecology and Conservation, 2020, 22: e01011. doi: 10.1016/j.gecco.2020.e01011 [5] 姜冰, 王松涛, 孙增兵, 张海瑞, 王建, 刘阳. 基于隶属度函数和主成分分析的耕地土壤肥力评价[J]. 中国农学通报, 2023, 39(2): 22-27.JIANG Bing, WANG Songtao, SUN Zengbing, ZHANG Hairui, WANG Jian, LIU Yang. Evaluation of Cultivated Land Soil Fertility Based on Membership Function and Principal Component Analysis[J]. China Agricultural Science Bulletin, 2023, 39(2): 22-27. [6] LIU Jianguo, GOU Xiaohua, ZHANG Fen, BIAN Rui, YIN Dingcai. Spatial patterns in the C: N: P stoichiometry in Qinghai spruce and the soil across the Qilian Mountains, China[J]. Catena, 2021, 196: 104814-104829. doi: 10.1016/j.catena.2020.104814 [7] 裴小龙, 韩小龙, 钱建利, 陈文, 秦天, 李翾. 自然资源综合观测视角下的土壤肥力评价指标[J]. 资源科学, 2020, 42(10): 1953-1964.PEI Xiaolong, HAN Xiaolong, QIAN Jianli, CHEN Wen, QIN Tian, LI Xuan. Soil fertility assessment indicators from the perspective of natural resources comprehensive observation[J]. Resource Science, 2020, 42(10): 1953-1964. [8] 张江周, 刘亚男, 高伟, 王蓓蓓, 阮云泽. 我国香蕉园土壤肥力现状的整合分析[J]. 热带作物学报, 2022, 43(7): 1401-1410.ZHANG Jiangzhou, LIU Yanan, GAO Wei, WANG Beibei, YUAN Yunzhe. Integrated analysis of soil fertility status in banana orchards in China[J]. Chinese Journal of Tropical Crops, 2022, 43(7): 1401-1410. [9] 赵刚, 吴会军, 张永清, 武雪萍, 宋霄君, 姚宇卿, 吕军杰, 李生平, 刘晓彤, 韩紫璇. 豫西长期不同耕作下土壤肥力质量评价[J]. 中国土壤与肥料, 2023(6): 1-11.ZHAO Gang, WU Huijun, ZHANG Yongqing,WU Xueping, SUN Xiaojun, YAO Yuqing, LV Junjie,LI Shenping, LIU Xiaotong, HAN Zixuan. Evaluation of soil fertility quality under long-term different tillage measures in western Henan[J]. China Soil and Fertilizer Sciences in China, 2023(6): 1-11. [10] 彭健健, 徐坚, 王晓晓, 傅伟军, 张申, 彭欣怡, 徐懿, 姜霓雯, 方嘉, 吴家森. 杨梅主产区土壤肥力空间异质性及其影响因素:以浙江仙居和临海为例[J]. 果树学报, 2023, 40(7): 1421-1433.PENG Jianjian, XU Jian, WANG Xiaoxiao, FU Weijun, ZHANG Shen, PENG Xinyi, XU Yi, JIANG Niwen, FANG Jia, WU Jiasen. Spatial variation of soil fertility and its influencing factors in Myrica rubra region: A case study in Xianju county and Linhai city[J]. Journal of Fruit Science, 2023, 40(7): 1421-1433. [11] 陆欣春, 范欣欣, 邹文秀, 严君, 陈旭, 韩晓增, 邓维娜. 肥沃耕层构建对白浆土土壤肥力和玉米产量的影响[J]. 应用生态学报, 2023, 34(4): 883-891.LU Xinchun, FAN Xinxin, ZOU Wenxiu, YAN Jun, HAN Xiaozeng, DENG Weina. Effects of the construction of fertile and cultivated soil layer on soil fertility and maize yield in Albic Journal of Applied Ecology, 2023, 34(4): 883-891. [12] 王潇璇, 何诗杨, 叶子豪, 胡颖槟, 傅伟军, 吴家森. 村域尺度山核桃林地土壤肥力空间变异特征及影响因素[J]. 浙江农林大学学报, 2023, 40(4): 811-819. doi: 10.11833/j.issn.2095-0756.20220544WANG Xiaoxuan, HE Shiyang, YE Zihao, HU Yingbing, FU Weijun, WU Jiasen. Spatial variability and affecting factors of soil fertility in Chinese hickory stands at village scale[J]. Journal of Zhejiang A & F University, 2023, 40(4): 811-819. doi: 10.11833/j.issn.2095-0756.20220544 [13] 唐佐芯, 冯俊娜, 阮亚男, 陆庆华, 吴 甜, 夏体渊, 李柘锦. 红河州植烟土壤养分和土壤肥力变化特征研究[J]. 西南农业学报, 2022, 35(8): 1862-1869.TANG Zuoxin, FENG Junna, RUAN Yanan, LU Qinghua, WU Tian, XIATiyuan, LI Zhemian. Change characteristics of soil nutrient and fertility in tobacco-growing toil of Honghe prefecture[J]. Southwest China Journal of Agricultural Sciences, 2022, 35(8): 1862-1869. [14] 刘娟, 张乃明, 邓洪. 勐海县茶园土壤养分状况及肥力质量评价[J]. 农业资源与环境学报, 2021, 38(1): 79-86.LIU Juan, ZHANG Naiming, DENG Hong. Soil nutrient status and fertility evaluation of tea gardens in Menghai County[J]. Journal of Agricultural Resources and Environment, 2021, 38(1): 79-86. [15] 叶少萍, 李铤, 张俊涛, 曹芳怡. 基于主成分分析的古树土壤肥力综合评价[J]. 生态科学, 2022, 41(1): 196-205.YE Shaoping, LI Ting, ZHANG Juntao, WU Fangyi. Comprehensive evaluation of soil fertility for ancient trees based on principal component analysis[J]. Ecological Science, 2022, 41(1): 196-205. [16] 闫素云, 周先艳, 杜玉霞, 赖新朴, 李丹萍, 李进学, 岳建强. 云南省柠檬主产区土壤肥力特征与评价[J]. 中国南方果树, 2022, 51(1): 28-33.YAN Suyun, ZHOU Xianyan, DU Yuxia, LAI Xinpu, LI Danping, LI Jinxue, YU Jianqiang. Characteristics and Evaluation of the Soil Fertility in Main Lemon Production Areas of Yunnan Province[J]. South China Fruit, 2022, 51(1): 28-33. [17] 姜霓雯, 童根平, 叶正钱, 程樟峰, 吕永强, 傅伟军. 浙江清凉峰自然保护区土壤肥力指标空间变异及其影响因素[J]. 生态学报, 2022, 42(6): 2430-2441.JIANG Niwen, TONG Genping, YE Zhengqian, CHEN Zhangfeng, LV Yungqiang, FU Weijun. Spatial variability of soil fertility properties and its affecting factors of Qingliangfeng Nature Reserve, Zhejiang[J]. Acta Ecologica Sinica, 2022, 42(6): 2430-2441. [18] 中国地质调查局. DZ/T0258-2014多目标区域地球化学调查规范(1: 250 000)[S]. 北京: 中国标准出版社, 2015. [19] 鲍士旦. 土壤农化分析[M]. 北京: 中国农业出版社, 2000. [20] 中国科学院南京土壤研究所. 土壤理化分析[M]. 上海: 上海科学技术出版社, 1978. [21] FU Weijun , ZHAO Keli, JIANG Peikun, YE Zhengqian, Hubert Tunney, ZHANG Chaosheng. Field-scale variability of soil test phosphorus and other nutrients in grasslands under long-term agricultural managements[J]. Soil Research, 2013, 51(6): 503-511. doi: 10.1071/SR13027 [22] Hair J F, Black W C, Babin B J, et al. Multivariate Data Analysis[M]. London: Prentice Hall, 2008. [23] 叶云, 赵小娟, 胡月明. 基于GA-BP神经网络的珠三角耕地质量评价[J]. 生态环境学报, 2018, 27(5): 964-973.YE Yun, ZHAO Xiaojuan, HU Yueming. Evaluation of Cultivated Land Quality in Pearl River Delta Based on GA-BP Neural Network[J]. Ecology and Environmental Sciences, 2018, 27(5): 964-973. [24] LIU Yuanyuan, CHEN Guangjie, Carsten Meyer-Jacob, HUANG Linpei, LIU Xiaolong, HUANG Guangcai, Anna-Marie Klamt, John P Smol. Land-use and climate controls on aquatic carbon cycling and photo-trophs in karst lakes of southwest China[J]. Science of the Total Environment, 2020, 751(18): 141738-141749. [25] 钱凤魁, 王秋兵, 边振兴, 董秀茹, 郑刘平. 凌源市耕地质量评价与立地条件分析[J]. 农业工程学报, 2011, 27(11): 325-329.QIAN Fengkui, WANG Qiubing, BIAN Zhenxing, DONG Xiuru, ZHENG Liuping. Farmland quality evaluation and site assessment in Lingyuan city[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(11): 325-329. [26] 熊昌盛, 谭荣, 岳文泽. 基于局部空间自相关的高标准基本农田建设分区[J]. 农业工程学报, 2015, 31(22): 276-284.XIONG Changsheng, TAN Rong, YUE Wenze. Zoning of high standard farmland construction based on local indicators of spatial association[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(22): 276-284. [27] Spearman C. The proof and measurement of association between two things[J]. The American Journal of Psychology, 1904, 15(1): 72-101. doi: 10.2307/1412159 [28] 覃星铭, 马国斌, 蒋忠诚, 胡宝清, 谢薇薇, 谭帅, 曹雨薇. 典型石漠化峰丛洼地土壤重金属的空间分异特征及其影响因素[J]. 地质科技通报, 2022, 41(5): 283-292.QIN Xingming, MA Guobin, JIANG Zhongxin, HU Baoqing, XIE Weiwei,TANG Suai, CAO Yuwei. Spatial variations and influencing factors analysis of heavy metals in the soil of typical rocky desertification peak cluster depression[J]. Bulletin of Geological Science and Technology, 2022, 41(5): 283-292. [29] DAI Zhaohua, LIU Yunxia, WANG Xingjun, ZHAO Dianwu. Changes in pH, CEC and exchangeable acidity of some forest soils in Southern China during the last 32−35 years[J]. Water, Air, and Soil Pollution, 1998, 108(3- 4): 377-390. [30] 王杰, 张春燕, 卢加文,陈书, 王棋. 广安区柑橘土壤养分状况及综合肥力评价[J]. 土壤通报, 2021, 52(6): 1360-1367.WANG Jie, ZHANG Chunyan, LU Jiawen, CHEN Shu, WANG Qi. Nutrient Status and Comprehensive Frilityt Evaluation of the Citrus Soil in Guang' an Disrict[J]. Chinese Journal of Soil Science, 2021, 52(6): 1360-1367. [31] 于航, 冯天骄, 卫伟, 王平. 晋西黄土区土壤理化特征对长期植被恢复的响应[J]. 生态学报, 2024, 44(7): 2873-2885.YU Hang, FENG Tianjiao, WEI Wei, WANG Ping. Response of soil physicochemical characteristics to long-term vegetation restoration inloess region of western Shanxi province[J]. Acta Ecologica Sinica, 2024, 44(7): 2873-2885. [32] 赵小娟, 叶云, 周晋皓, 刘洛, 戴文举, 王秋香, 胡月明. 珠三角丘陵区耕地质量综合评价及指标权重敏感性分析[J]. 农业工程学报, 2017, 33(8): 226-235.ZHAO Xiaojuan, YE Yun, ZHOU Jinhao, LIU Luo, DAI Wenju, WANG Qiuxiang, HU Yueming. Comprehensive evaluation of cultivated land quality and sensitivity analysis of index weight in hilly region of Pearl River Delta[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(8): 226-235. [33] Ohana-Levi N, Ben-Gal A, Peeters A, Termin D, Paz-Kagan T. A comparison between spatial clustering models for determining N-fertilization management zones in orchards[J]. Precision Agriculture, 2020, 79(3): 105-118. [34] 李颖慧, 姜小三. 黄淮海平原农区农用地土壤肥力评价及时空变化特征: 以山东省博兴县为例[J]. 农业资源与环境学报, 2022, 39(3): 602-612.LIYinghui, JIANG Xiaosan. Spatiotemporal characteristics of soil nutrients and fertility evaluation of agricultural land in the HuangHuai-Hai Plain agricultural area: A case study of Boxing County, Shandong Province[J]. Journal of Agricultural Resources and Environment, 2022, 39(3): 602-612. [35] LIU Yu, ZHANG Ruiqi, HUANG Ze, CHENG Zhen, Manuel López-Vicente, MA XiaoRong, WU GaoLin. Solar photovoltaic panels significantly promote vegetation recovery by modifying the soil surface microhabitats in an arid sandy ecosystem[J]. Land Degradation & Development, 2019, 30(8): 2177-2186. [36] 张海琳, 张雨, 王顶, 谢军, 张跃强, 张宇亭, 王洁, 石孝均. 西南不同类型紫色土pH变化、重金属累积与潜在生态风险评估[J]. 环境科学, 2024, 45(4): 2440-2449.ZHANG Hailin, ZHANG Yu, WANG Ding, XIE Jun, ZHANG Yueqiang, ZHANG Yuting, WANG Jie, SHI Xiaojun. Heavy Metal Accumulation and Assessment of Potential Ecological Risk Caused by Soil pH Changes in Different Types of Purple Soils in Southwest China[J]. Environmental Science, 2024, 45(4): 2440-2449. [37] Gustavo Pereira Valani, Fabiane Machado Vezzani, Karina Maria Vieira Cavalieri-Polizeli. Soil quality: Evaluation of on-farm assessments in relation to analytical index[J]. Soil and Tillage Research, 2020, 198: 104565-104577. doi: 10.1016/j.still.2019.104565 [38] 陈亚男, 庄媛, 闫瑞瑞, 秦琪, 金晶炜, 杨培志, 刘洋, 熊军波, 辛晓平. 南方长期不同土地利用方式下土壤肥力变化特征: 以湖北省长阳县火烧坪乡为例[J]. 植物营养与肥料学报, 2023, 29(1): 188-200.CHEN Yanan, ZHUANG Yuan, YAN Ruirui, QIN Qi, JIN Jingwei, YANG Peizhi, LIU Yang, XIONG Junbo, XIN Xiaoping. Characteristics of soil fertility under different long-term land-use patterns in south China: A case study in Huoshaoping Township, Changyang County, Hubei Province[J]. Journal of Plant Nutrition and Fertilizer, 2023, 29(1): 188-200. -
点击查看大图
图(4) / 表(5)
计量
- 文章访问数: 26
- HTML浏览量: 5
- PDF下载量: 2
- 被引次数: 0
