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Assessment of water-soil-vegetation coupling characteristics in the central farming areas of the Sanjiang Plain |
HE Jin-Bao1,2( ), KONG Fan-Peng1( ), ZHAO Jian1, LIU Bo-Wen1, LIU Hong-Bo1 |
1. Mudanjiang Natural Resources Comprehensive Survey Center, China Geological Survey, Changchun 130000, China 2. Northeast Geological S&T Innovation Center, Shenyang 110034, China |
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Abstract Soils and water emerge as important natural resources for plant growth and development. Understanding the responses of vegetation to water and soil characteristics is crucial to the scientific management of agricultural production. However, there is a lack of studies on the quantification of coupling characteristics of these factors. To reveal the coupling characteristics and responses among water, soils, and vegetation, this study investigated the farming areas in Huachuan, Jixian, and Youyi counties in the hinterland of the Sanjiang Plain. Using a survey of vegetation and the analysis of soil and water samples, this study established an index system for the assessment of the water-soil-vegetation coupling. The weights of the assessment indices were determined using principal component analysis, and a water-soil-vegetation coupling coordination model was constructed for the farming areas. Additionally, the primary factors influencing plant growth were analyzed using gray correlation analysis. Results indicate that vegetation, water, and soils in the farming areas are strongly coupled. Primary factors influencing vegetation growth and development include soil bulk density; sand content; zinc, boron, and copper contents, and the calcium ion concentration and hardness of water bodies. Notably, the coupling coordination degree is not consistent with the coupling degree. Specifically, water, soils, and vegetation in the farming areas exhibit strong coupling, characterized mainly by sound coordination. In contrast, some areas of Huachuan and Jixian counties exhibit poor coupling among water, soils, and vegetation. This is primarily due to water pollution, soil texture, and deficiencies in trace elements. Therefore, it is necessary to improve the groundwater ecosystem and implement protective farming of cultivated land. The coupling model of water, soil and vegetation established in this paper provides and important basis for ecological environment protection and restoration.
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Received: 26 February 2024
Published: 22 July 2025
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Location of the study area and distribution of sampling points
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指标 | w(Se)/ 10-6 | w(I)/ 10-6 | w(F)/ 10-6 | 容重/ (g·cm-3) | pH | 黏粒 /% | 粉粒 /% | 砂粒 /% | 上限值 | 0.175 | 1.5 | 500 | 1.2 | 6.5 | 40 | 40 | 20 | 下限值 | 0.4 | 5 | 550 | 1.4 | 7.5 | 55 | 55 | 50 |
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Upper and lower limits of suitability indicators
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指标 | F1 | F2 | F3 | 综合得分系数 | 权重 | Ca2+ | 0.361 | -0.104 | -0.038 | 0.217 | 0.096 | K+ | -0.046 | -0.114 | 0.900 | 0.039 | 0.017 | S | 0.249 | 0.435 | 0.086 | 0.273 | 0.121 | Cl- | 0.261 | 0.445 | 0.137 | 0.288 | 0.128 | Mg2+ | 0.362 | -0.049 | 0.015 | 0.235 | 0.104 | Na+ | 0.334 | -0.064 | 0.192 | 0.231 | 0.102 | TDS | 0.372 | -0.007 | 0.064 | 0.256 | 0.113 | N | 0.286 | 0.174 | 0.044 | 0.236 | 0.104 | fCO2 | 0.285 | -0.059 | -0.337 | 0.144 | 0.064 | TH | 0.366 | -0.090 | -0.021 | 0.224 | 0.099 | HC | 0.233 | -0.472 | -0.037 | 0.049 | 0.022 | pH | -0.073 | 0.564 | -0.059 | 0.069 | 0.030 |
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Matrix of principal component score coefficients and indicator weights for water body indicators
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指标 | F1 | F2 | F3 | F4 | F5 | 综合得分系数 | 权重 | B | 0.261 | 0.016 | 0.210 | 0.311 | 0.005 | 0.174 | 0.046 | Mo | 0.331 | 0.092 | 0.088 | 0.022 | 0.166 | 0.198 | 0.052 | Mn | 0.054 | 0.176 | 0.480 | 0.128 | 0.365 | 0.173 | 0.046 | S | 0.242 | 0.344 | 0.017 | 0.003 | 0.070 | 0.203 | 0.054 | Cu | 0.254 | 0.125 | 0.100 | 0.365 | 0.125 | 0.200 | 0.053 | Zn | 0.319 | 0.138 | 0.057 | 0.166 | 0.062 | 0.203 | 0.054 | P | 0.101 | 0.313 | 0.374 | 0.155 | 0.271 | 0.209 | 0.056 | N | 0.219 | 0.364 | 0.026 | 0.103 | 0.091 | 0.210 | 0.056 | K | 0.308 | 0.114 | 0.092 | 0.237 | 0.121 | 0.208 | 0.055 | SOM | 0.211 | 0.361 | 0.045 | 0.115 | 0.114 | 0.212 | 0.056 | CaO | 0.226 | 0.321 | 0.023 | 0.103 | 0.055 | 0.199 | 0.053 | MgO | 0.338 | 0.016 | 0.065 | 0.074 | 0.001 | 0.168 | 0.045 | 砂粒 | 0.083 | 0.166 | 0.268 | 0.148 | 0.622 | 0.181 | 0.048 | 黏粒 | 0.263 | 0.113 | 0.221 | 0.129 | 0.415 | 0.220 | 0.058 | 粉粒 | 0.217 | 0.306 | 0.055 | 0.138 | 0.111 | 0.203 | 0.054 | pH | 0.013 | 0.056 | 0.002 | 0.588 | 0.077 | 0.079 | 0.021 | 容重 | 0.282 | 0.165 | 0.098 | 0.214 | 0.091 | 0.206 | 0.055 | F | 0.126 | 0.269 | 0.174 | 0.061 | 0.309 | 0.179 | 0.047 | I | 0.082 | 0.068 | 0.495 | 0.321 | 0.050 | 0.149 | 0.040 | Se | 0.086 | 0.298 | 0.374 | 0.225 | 0.139 | 0.194 | 0.051 |
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Principal component score coefficient matrix and index weight of soil indicators
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Box plot of distribution of water evaluation indicators
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Box plot of distribution of soil evaluation indicators
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Gray correlation between NDVI and water and soil evaluation indicators
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耦合协调度D | 耦合协调特征 | 水—土—植被系统 耦合模式 | 0<D≤0.2 | 水土植被耦合关系极度失调 | 低级协调发展模式 | 0.2<D≤0.4 | 水土植被耦合关系严重失调 | 初级协调发展模式 | 0.4<D≤0.6 | 水土植被耦合关系勉强协调 | 中级协调发展模式 | 0.6<D≤0.8 | 水土植被耦合关系良好协调 | 良好协调发展模式 | 0.8<D≤1.0 | 水土植被耦合关系同步发展 | 优质协调发展模式 |
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Classification and evaluation criteria for the coup-ling coordination degree of water soil vegetation system
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点位 编号 | f(x) | g(y) | h(z) | C值 | D值 | 水—土—植被 系统耦合模式 | 1 | 0.82 | 0.45 | 0.68 | 0.97 | 0.63 | 良好协调发展模式 | 2 | 0.94 | 0.30 | 0.64 | 0.90 | 0.56 | 中级协调发展模式 | 3 | 0.93 | 0.41 | 0.61 | 0.94 | 0.61 | 良好协调发展模式 | 4 | 0.91 | 0.36 | 0.60 | 0.93 | 0.58 | 中级协调发展模式 | 5 | 0.62 | 0.44 | 0.63 | 0.99 | 0.56 | 中级协调发展模式 | 6 | 0.82 | 0.47 | 0.63 | 0.97 | 0.62 | 良好协调发展模式 | 7 | 0.80 | 0.41 | 0.45 | 0.96 | 0.53 | 中级协调发展模式 | 8 | 0.86 | 0.56 | 0.63 | 0.98 | 0.67 | 良好协调发展模式 | 9 | 0.07 | 0.42 | 0.54 | 0.73 | 0.25 | 初级协调发展模式 | 10 | 0.43 | 0.46 | 0.72 | 0.97 | 0.52 | 中级协调发展模式 | 11 | 0.86 | 0.39 | 0.68 | 0.95 | 0.61 | 良好协调发展模式 | 12 | 0.84 | 0.56 | 0.65 | 0.99 | 0.68 | 良好协调发展模式 | 13 | 0.65 | 0.61 | 0.66 | 1.00 | 0.64 | 良好协调发展模式 | 14 | 0.65 | 0.66 | 0.64 | 1.00 | 0.65 | 良好协调发展模式 | 15 | 0.79 | 0.45 | 0.64 | 0.98 | 0.61 | 良好协调发展模式 |
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Evaluation results of the coupling and coordination status of water soil vegetation system
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[1] |
李小雁, 马育军. 地球关键带科学与水文土壤学研究进展[J]. 北京师范大学学报:自然科学版, 2016, 52(6):731-737.
|
[1] |
Li X Y, Ma Y J. Advances in earth's critical zone science and hydropedology[J]. Journal of Beijing Normal University:Natural Science Edition, 2016, 52(6):731-737.
|
[2] |
杨安乐, 张小平, 李宗省, 等. 气候变化和人类活动对祁连山国家公园植被净初级生产力的定量影响[J]. 生态学报, 2023, 43(5):1784-1792.
|
[2] |
Yang A L, Zhang X P, Li Z X, et al. Quantitative analysis of the impacts of climate change and human activities on vegetation NPP in the Qilian Mountain National Park[J]. Acta Ecologica Sinica, 2023, 43(5):1784-1792.
|
[3] |
Prashar P, Shah S. Impact of fertilizers and pesticides on soil microflora in agriculture[J]. Agricultural and Food Sciences,Environmental Scienc, 2016,19:331-361.
|
[4] |
高奇, 师学义, 张琛, 等. 县域农业生态环境质量动态评价及预测[J]. 农业工程学报, 2014, 30(5):228-237,293.
|
[4] |
Gao Q, Shi X Y, Zhang C, et al. Dynamic assessment and prediction on quality of agricultural eco-environment in county area[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(5):228-237,293.
|
[5] |
宋进喜, 高隽清, 李晓鑫, 等. 近20年来黄土高原蒸散发变化规律及其驱动因素[J]. 西北大学学报:自然科学版, 2023, 53(6):974-990.
|
[5] |
Song J X, Gao J Q, Li X X, et al. Changes of evapotranspiration and its driving factors in the Loess Plateau in recent 20 years[J]. Journal of Northwest University:Natural Science Edition, 2023, 53(6):974-990.
|
[6] |
袁毓婕, 高学睿, 黄可静, 等. 基于RHESSys模型的延河流域水文要素定量模拟[J]. 南水北调与水利科技, 2023, 21(1):116-126.
|
[6] |
Yuan M J, Gao X R, Huang K J, et al. Quantitative simulation of hydrological elements based on RHESSys model in Yanhe River Basin[J]. South-to-North Water Transfers and Water Science & Technology, 2023, 21(1):116-126.
|
[7] |
黄星怡, 张佳乐, 杨肖丽, 等. 黄河流域水文干旱时空特征研究[J]. 华北水利水电大学学报:自然科学版, 2023, 44(3):25-34.
|
[7] |
Huang X Y, Zhang J L, Yang X L, et al. Spatial and temporal characteristics of hydrological drought in the Yellow River Basin[J]. Journal of North China University of Water Resources and Electric Power:Natural Science Edition, 2023, 44(3):25-34.
|
[8] |
罗爽, 许有鹏, 王强, 等. 城市化背景下年最大日径流演变及影响因素研究[J]. 湖泊科学, 2023, 35(6):2123-2132.
|
[8] |
Luo S, Xu Y P, Wang Q, et al. The evolution of annual maximum daily runoff and its influencing factors under the back-ground of urbanization:An example of Qinhuai River Basin in the lower reaches of the Yangtze River[J]. Journal of Lake Sciences, 2023, 35(6):2123-2132.
|
[9] |
刘子玥, 王祎宸, 骆丕昭, 等. 湘西石漠化地区植物多样性与土壤因子的耦合关系[J]. 森林与环境学报, 2021, 41(5):471-477.
|
[9] |
Liu Z Y, Wang Y C, Luo P Z, et al. Coupling relationships between plant diversity and soil characteristics in rocky desertification areas of western Hunan[J]. Journal of Forest and Environment, 2021, 41(5):471-477.
|
[10] |
濮阳雪华, 王月玲, 赵志杰, 等. 陕北黄土区不同植被恢复模式植被与土壤耦合关系研究[J]. 草业学报, 2021, 30(5):13-24.
|
[10] |
Puyang X H, Wang Y L, Zhao Z J, et al. Coupling relationships between vegetation and soil in different vegetation restoration models in the Loess region of Northern Shaanxi Province[J]. Acta Prataculturae Sinica, 2021, 30(5):13-24.
|
[11] |
彭晚霞, 宋同清, 曾馥平, 等. 喀斯特峰丛洼地退耕还林还草工程的植被土壤耦合协调度模型[J]. 农业工程学报, 2011, 27(9):305-310.
|
[11] |
Peng W X, Song T Q, Zeng F P, et al. Models of vegetation and soil coupling coordinative degree in grain for green project in depressions between Karst hills[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(9):305-310.
|
[12] |
南国卫, 赵满兴, 王月月, 等. 不同退耕类型土壤—植被系统耦合协调关系评价[J]. 干旱区资源与环境, 2021, 35(5):157-162.
|
[12] |
Nan G W, Zhao M X, Wang Y Y, et al. Evaluation of coupling coordination relationship between soil and vegetation systems in different afforestation types[J]. Journal of Arid Land Resources and Environment, 2021, 35(5):157-162.
|
[13] |
杨新国, 赵伟, 陈林, 等. 荒漠草原人工柠条林土壤与植被的演变特征[J]. 生态环境学报, 2015, 24(4):590-594.
|
[13] |
Yang X G, Zhao W, Chen L, et al. Antidromal succession between soil and plant in the Caragana intermedia shrubland in the desert steppe[J]. Ecology and Environmental Sciences, 2015, 24(4):590-594.
|
[14] |
白一茹, 阮晓晗, 包维斌, 等. 宁南山区坡面不同土地利用方式下植被—土壤耦合关系评价[J]. 水土保持研究, 2021, 28(4):251-258.
|
[14] |
Bai Y R, Ruan X H, Bao W B, et al. Evaluation on coupling of vegetation and soil on slopes of mountain area in southern Ningxia[J]. Research of Soil and Water Conservation, 2021, 28(4):251-258.
|
[15] |
杨梅焕, 朱志梅, 曹明明, 等. 毛乌素沙地东南缘不同沙漠化阶段土壤—植被关系研究[J]. 西北农林科技大学学报:自然科学版, 2010, 38(5):181-187,192.
|
[15] |
Yang M H, Zhu Z M, Cao M M, et al. Study on the correlation of soil-vegetation in different desertification stages on the southeastern edge of Mu Us Sandy Land[J]. Journal of Northwest A & F University:Natural Science Edition, 2010, 38(5):181-187,192.
|
[16] |
刘大刚, 王少丽, 许迪, 等. 农田排水资源灌溉利用适宜性评价指标体系研究[J]. 灌溉排水学报, 2013, 32(2):93-96.
|
[16] |
Liu D G, Wang S L, Xu D, et al. Evaluation index system of the suitability of drainage water for irrigation in agriculture[J]. Journal of Irrigation and Drainage, 2013, 32(2):93-96.
|
[17] |
王莺, 王静, 姚玉璧, 等. 基于主成分分析的中国南方干旱脆弱性评价[J]. 生态环境学报, 2014, 23(12):1897-1904.
|
[17] |
Wang Y, Wang J, Yao Y B, et al. Evaluation of drought vulnerability in Southern China based on principal component analysis[J]. Ecology and Environmental Sciences, 2014, 23(12):1897-1904.
|
[18] |
Shammi S A, Meng Q M. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling[J]. Ecological Indicators, 2021,121:107124.
|
[19] |
Roznik M, Boyd M, Porth L. Improving crop yield estimation by applying higher resolution satellite NDVI imagery and high-resolution cropland masks[J]. Remote Sensing Applications:Society and Environment, 2022,25:100693.
|
[20] |
Huang J, Wang H M, Dai Q, et al. Analysis of NDVI data for crop identification and yield estimation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(11):4374-4384.
|
[21] |
李鑫, 张文菊, 邬磊, 等. 土壤质量评价指标体系的构建及评价方法[J]. 中国农业科学, 2021, 54(14):3043-3056.
|
[21] |
Li X, Zhang W J, Wu L, et al. Advance in indicator screening and methodologies of soil quality evaluation[J]. Scientia Agricultura Sinica, 2021, 54(14):3043-3056.
|
[22] |
吴海燕, 金荣德, 范作伟, 等. 基于主成分和聚类分析的黑土肥力质量评价[J]. 植物营养与肥料学报, 2018, 24(2):325-334.
|
[22] |
Wu H Y, Jin R D, Fan Z W, et al. Assessment of fertility quality of black soil based on principal component and cluster analysis[J]. Journal of Plant Nutrition and Fertilizers, 2018, 24(2):325-334.
|
[23] |
胡星星, 牧振伟. 基于主成分分析法对乌鲁木齐河水质的综合评价[J]. 节水灌溉, 2012(4):37-39.
|
[23] |
Hu X X, Mu Z W. Comprehensive evaluation of water quality of Urumqi River based on principal component analysis[J]. Water Saving Irrigation, 2012(4):37-39.
|
[24] |
王皓月, 郭月峰, 徐雅洁, 等. 九峰山不同林分类型生态恢复植被—土壤系统耦合关系评价[J]. 生态环境学报, 2021, 30(12):2309-2316.
|
[24] |
Wang H Y, Guo Y F, Xu Y J, et al. Coupling relationship between vegetation and soil system in ecological restoration of different stand types in Jiufeng Mountain[J]. Ecology and Environmental Sciences, 2021, 30(12):2309-2316.
|
[25] |
Brindha K, Kavitha R. Hydrochemical assessment of surface water and groundwater quality along Uyyakondan channel,south India[J]. Environmental Earth Sciences, 2015, 73(9):5383-5393.
|
[26] |
赵可英, 牟凯. 基于灰色关联度分析法和主成分分析法对泥页岩储层评价方法的探讨[J]. 地质与勘探, 2023, 59(2):443-450.
|
[26] |
Zhao K Y, Mou K. Evaluation of shale reservoirs based on grey relation analysis and principal component analysis[J]. Geology and Exploration, 2023, 59(2):443-450.
|
[27] |
张磊, 苏芳莉, 郭成久, 等. 灰色关联分析在不同生态修复模式土壤质量评价中的应用[J]. 沈阳农业大学学报, 2009, 40(6):703-707.
|
[27] |
Zhang L, Su F L, Guo C J, et al. Application of grey correlation analysis in different models of ecological restoration in soil quality evaluation[J]. Journal of Shenyang Agricultural University, 2009, 40(6):703-707.
|
[28] |
龚尧, 杜文, 王宇寰, 等. 横坡耕作与优化施肥对缓坡地氮磷流失特征和土壤肥力的影响[J]. 水土保持通报, 2023, 43(5):53-61,68.
|
[28] |
Gong Y, Du W, Wang Y H, et al. Effects of cross-slope tillage and increasing organic fertilizer on soil nitrogen and phosphorus loss characteristics and soil fertility on gentle slope[J]. Bulletin of Soil and Water Conservation, 2023, 43(5):53-61,68.
|
[29] |
薛鸥, 魏天兴, 刘飞, 等. 公路边坡植物群落多样性与土壤因子耦合关系[J]. 北京林业大学学报, 2016, 38(1):91-100.
|
[29] |
Xue O, Wei T X, Liu F, et al. Modeling the degree of coupling and interaction between plant community diversity and soil properties on highway slope[J]. Journal of Beijing Forestry University, 2016, 38(1):91-100.
|
[30] |
张艳, 赵廷宁, 史常青, 等. 坡面植被恢复过程中植被与土壤特征评价[J]. 农业工程学报, 2013, 29(3):124-131.
|
[30] |
Zhang Y, Zhao T N, Shi C Q, et al. Evaluation of vegetation and soil characteristics during slope vegetation recovery procedure[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(3):124-131.
|
[31] |
秦文婧, 宋献方, 谷洪彪. 基于层次聚类法的柳江煤矿对地下水水质影响分析[J]. 水文地质工程地质, 2018, 45(3):30-39.
|
[31] |
Qin W J, Song X F, Gu H B. Impacts of the Liujiang coal mine on groundwater quality based on hierarchical cluster analysis[J]. Hydrogeology & Engineering Geology, 2018, 45(3):30-39.
|
[32] |
郭二果, 蔡煜, 马静, 等. 草原区露天煤矿开发地下水水质影响评价[J]. 中国煤炭, 2015, 41(1):123-130.
|
[32] |
Guo E G, Cai Y, Ma J, et al. Assessment of the impact on groundwater quality of the open-pit coal mine in prairie region[J]. China Coal, 2015, 41(1):123-130.
|
[33] |
王雪梅, 闫帮国, 王梓丞, 等. 不同土壤和微量元素对车桑子幼苗生长的影响[J]. 热带亚热带植物学报, 2023, 31(5):695-704.
|
[33] |
Wang X M, Yan B G, Wang Z C, et al. Effects of soil types and microelements on growth and physiological characteristics of dodonaea viscosa seedlings[J]. Journal of Tropical and Subtropical Botany, 2023, 31(5):695-704.
|
[34] |
周俊, 杨子凡, 董博, 等. 张掖地区土壤微量元素空间分布及其对农产品质量的影响[J]. 中国农学通报, 2014, 30(33):219-224.
|
[34] |
Zhou J, Yang Z F, Dong B, et al. Spatial distribution of soil trace elements and its effect on the quality of agricultural products of Zhangye Region[J]. Chinese Agricultural Science Bulletin, 2014, 30(33):219-224.
|
[35] |
Kumar M, Singh S, Singh V, et al. Effect of zinc and boron on growth and yield of maize (Zea mays L.)[J]. Progressive Research-An International Journal, 2019, 14(3):215-221.
|
[36] |
杨忠生, 赵丽岩, 刘君阁. 桦川县土壤肥力现状及有机肥、化肥施用调查与思考[J]. 黑龙江农业科学, 2005(4):39-49.
|
[36] |
Yang Z S, Zhao L Y, Liu J G. The precent situation of soil fertility,orgaric and inorganic fertilizer application and suggestion of fertilizer application[J]. Heilongjiang Agricultural Sciences, 2005(4):39-49.
|
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