自然资源遥感, 2023, 35(4): 178-185 doi: 10.6046/zrzyyg.2022294

技术应用

基于多源环境变量和随机森林模型的江西省耕地土壤pH值空间预测

钟骁勇,1,2, 李洪义3,4, 郭冬艳2, 谢模典3,4, 赵婉如3,4, 胡碧峰,3,4

1.江西财经大学财税与公共管理学院,南昌 330013

2.中国自然资源经济研究院,北京 101149

3.江西财经大学旅游与城市管理学院,南昌 330013

4.江西财经大学财经数据科学重点实验室,南昌 330013

Spatial distribution prediction of soil pH in arable land of Jiangxi Province based on multi-source environmental variables and the random forest model

ZHONG Xiaoyong,1,2, LI Hongyi3,4, GUO Dongyan2, XIE Modian3,4, ZHAO Wanru3,4, HU Bifeng,3,4

1. School of Finance and Public Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China

2. Chinese Academy of Natural Resources Economics, Beijing 101149, China

3. School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China

4. Key Laboratory of Data Science in Finance and Economics, Jiangxi University of Finance and Economics, Nanchang 330013, China

通讯作者: 胡碧峰(1992-),男,博士,副教授,主要从事土地生态与环境、资源环境遥感与信息技术、时空统计分析与建模研究。Email:hbfddmm297@163.com。

责任编辑: 陈理

收稿日期: 2022-07-27   修回日期: 2023-03-2  

基金资助: 国家自然科学基金项目“南疆滴灌棉田水盐四维时空变异性与管理风险评价研究”(42071068)
江西省高校人文社会科学研究项目“江西省自然资源生态系统服务价值评估”(GL21217)
江西省教育厅科技项目“近20年江西省耕地生态系统服务价值时空变化及驱动因子分析”(GJJ210541)
江西省社会科学基金项目“江西省耕地资源资产价值核算及时空变异特征研究”(21YJ43D)

Received: 2022-07-27   Revised: 2023-03-2  

作者简介 About authors

钟骁勇(1990-),男,博士,副研究员,主要从事自然资源权益管理和土地资源核算研究。Email: zhongxy0509@126.com。

摘要

研究多源环境变量条件下随机森林(random forest,RF)模型和普通克里格(ordinary Kriging,OK)模型对大尺度耕地土壤pH值空间预测的性能差异,研究分析RF模型对提升土壤pH值预测精度的参考价值。以中国江西省为研究区,基于气候、地形和植被等环境协变量信息,结合土壤属性和耕地利用条件,利用RF模型对江西省耕地土壤pH值进行空间预测,识别土壤pH值空间变异的影响因素,并与OK模型计算精度进行对比。结果表明,增加土壤属性和耕地利用条件作为环境变量的RF-A模型预测耕地土壤pH值的精度优于以地形、气候、植被属性作为环境变量的RF-B模型和OK模型的预测结果,气候因素是决定土壤pH值的最重要因素,地形地貌因子和人为因素对pH值变异也有重要影响。研究结果表明该方法对提升大尺度耕地土壤pH值预测制图精度具有一定的理论和现实意义。

关键词： 耕地; 土壤pH值; 随机森林; 江西省; 影响因素

Abstract

This study aims to compare the accuracy of random forest(RF) and ordinary kriging(OK) model for predicting spatial distribution of soil pH in arable land of Jiangxi Province using different covariates combination, and assess the feasibility and potential of RF method for improving the prediction accuracy of soil pH value. The RF algorithm is used to predict the pH value of cultivated soil in Jiangxi Province based on environmental covariate such as climate, topography and vegetation, combined with soil properties and cultivated land use conditions, identify the main influencing factors. The results produced by the RF was compared with the classical OK interpolation model. Our results showed that the accuracy of RF-A model with soil properties and cultivated land use conditions as environmental variables is better than that of RF-B model which only including terrain, climate and vegetation attributes as environmental variables. Climatic condition is the dominate factor which control the spatial variation of soil pH. the topographic factors and anthropogenic factors also have essential effect on spatial variability of soil pH. Thus, this study proved RF method has theoretical and practical significance for improving the accuracy of soil pH prediction at large-scale.

Keywords： arable land; soil pH; random forest; Jiangxi Province; influencing factor

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本文引用格式

钟骁勇, 李洪义, 郭冬艳, 谢模典, 赵婉如, 胡碧峰. 基于多源环境变量和随机森林模型的江西省耕地土壤pH值空间预测[J]. 自然资源遥感, 2023, 35(4): 178-185 doi:10.6046/zrzyyg.2022294

ZHONG Xiaoyong, LI Hongyi, GUO Dongyan, XIE Modian, ZHAO Wanru, HU Bifeng. Spatial distribution prediction of soil pH in arable land of Jiangxi Province based on multi-source environmental variables and the random forest model[J]. Remote Sensing for Land & Resources, 2023, 35(4): 178-185 doi:10.6046/zrzyyg.2022294

0 引言

耕地资源是人类赖以生存的重要物质基础和生产资料,土壤pH值作为耕地土壤关键属性之一,不仅是影响农作物生长发育的重要因素,也是影响土壤养分有效性和土壤重金属元素迁移转换的关键因子[1]。但是,随着人类社会工业化进程加快,酸性气体(SO2)、颗粒污染物排放以及氮肥的大量施用等,使得土壤酸化在许多地方成为一个普遍的趋势[2⇓-4]。张福锁院士团队在Science上发表的研究结果表明,从20世纪80年代—21世纪初,中国主要农作物产区土壤pH值明显下降[5]。土壤酸化会增加土壤重金属的活性,不利于农作物健康生长,进而威胁中国的粮食生产安全,这一问题在中国南方地区尤为突出[6-7]。因此,获得准确的土壤pH值空间分布信息对于遏制土壤酸化趋势、保护土壤环境质量和保障粮食生产安全具有十分重要的意义和价值。

国内外许多研究学者使用地统计学等空间插值方法用于土壤属性空间信息的预测,主要有普通克里格插值法(ordinary Kriging,OK)、反距离权重插值法(inverse distance weighted,IDW)、普通最小二乘法(ordinary least squares,OLS)、地理加权回归(geographically weighted regression,GWR)等[6,8⇓⇓⇓-12]。近年来,随着计算机技术、卫星遥感和土壤近地传感器等技术的快速发展,机器学习算法应用于数字土壤制图(digital soil mapping,DSM)成为一个新的研究热点[13⇓⇓⇓⇓⇓⇓⇓-21]。张甘霖等[22]指出: DSM未来有从地统计学走向机器学习为主导模型的趋势。随机森林(random forest,RF)作为一种以数据为驱动的机器学习方法[23],具有训练速度快、对多元线性不敏感、易于处理缺失数据等优点,还可避免对模型的过度拟合[24⇓-26]。RF能够获取和表达土壤属性空间变化与环境变量的复杂非线性关系,并根据这种关系推测土壤属性空间分布,还能以相对较少的样本量取得较好的预测精度[27]。目前已有研究将RF模型用于土壤有机碳、土壤类型和土壤质地等属性空间预测和制图[28⇓⇓-31,26]。但是目前将RF应用于土壤pH值空间预测制图的研究还较为少见,尤其是国内运用RF模型开展大尺度耕地pH值空间预测分析的研究鲜有报道。

因此,本文以土壤酸化现象显著的江西省为例,使用RF算法和环境协变量信息开展耕地土壤pH值的预测研究。同时将预测结果与使用OK方法插值结果进行对比,验证使用RF算法开展大尺度耕地土壤pH值预测研究的可行性和可靠性,为江西省土壤酸化问题的治理和农业生产管理提供数据基础和依据。

1 研究区概况及数据源

1.1 研究区概况

江西省位于中国东南部,长江中下游南岸,位于E113°34'36″~118°28'58″,N24°29'14″~30°04'41″之间(图1),属于亚热带季风性湿润气候,是我国重要的粮食、油料、棉花、蔬菜和水果生产基地。江西地貌类型以山地、丘陵为主,山地占全省面积的36%,丘陵占42%,平原占12%,水域占10%。主要土壤类型为偏酸性的红壤,面积达93 111.32 km2,约占全省总面积的56%,具有较为明显的地带性和地域分布规律。

图1

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图1   研究区土壤采样点分布

Fig.1   Sampling locations in study area


1.2 数据源及预处理

采集土壤样点16 582个,同时选取样点的土壤属性以及植被指数、地形变量、气候变量和耕地利用条件作为耕地土壤pH值空间预测的环境变量。其中土壤属性包括土壤类型、有机质、有效磷、速效钾、全氮、全磷、全钾、阳离子交换量、成土母质和耕层质地,数据来源于2018年江西省耕地质量等别数据库、1980年和2010年江西省农业测土配方项目数据。归一化植被指数、地形变量的地貌类型和高程以及气象因子的年均气温和年降水量均通过资源环境科学数据中心(http://www.resdc.cn/)网站下载获取,影像空间分辨率均为30 m。为了更好地反映出江西省地形条件对土壤属性变化的影响,选择高程、坡度、坡向、剖面曲率、平面曲率、沟谷深度、地形湿度指数、垂直到沟谷距离和多分辨率谷底平坦度等地形因子,分别借助ArcGIS 10.2和SAGA 7.6.2软件从数字高程模型(digital elevation model,DEM)数据中提取。耕地利用条件包括灌溉保证率、排水条件、氮肥用量、磷肥P2O5用量、钾肥K2O用量、秸秆还田比例和秸秆还田量,数据来源于2018年江西省耕地质量等别数据库(表1)。

表1   土壤pH值空间预测的环境变量及数据来源

Tab.1  Data sources of environmental covariates used for predicting pH

变量类别具体指标数据来源
地形变量地貌类型、高程、坡度、坡向、剖面曲率、平面曲率、沟谷深度、地形湿度指数、垂直到沟谷距离和多分辨率谷底平坦度中国科学院资源环境科学数据中心(http://www.resdc.cn/)
气候变量年均气温、年降水量中国科学院资源环境科学数据中心(http://www.resdc.cn/)
植被指数归一化植被指数中国科学院资源环境科学数据中心(http://www.resdc.cn/)
土壤属性土壤类型、有机质、有效磷、速效钾、全氮、全磷、全钾、阳离子交换量、成土母质和耕层质地2018年江西省耕地质量等别数据库、1980年和2010年江西省农业测土配方项目实测数据
耕地利用条件灌溉保证率、排水条件、氮肥用量、磷肥P2O5用量、钾肥K2O用量、秸秆还田比例和秸秆还田量2018年江西省耕地质量等别数据库

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2 研究方法

2.1 环境变量的组合和筛选

为全面分析土壤pH值空间变异规律以及探索不同环境变量对模型预测精度的贡献程度,将表1中30个环境变量分成2种不同的组合: ①以地形变量、气候变量和植被指数等遥感数据为基础,增加土壤属性和耕地利用条件等实测数据进行模型训练(RF-A); ②采用传统的地形变量、气候变量和植被指数等遥感数据训练模型(RF-B)。

2.2 RF模型构建

基于采集到的16 582个土壤样点数据,按照8∶2,7∶3和6∶4的比例分为训练集和验证集。RF模型采用bootstrap方法对样本进行放回抽样,没有被抽取的样本会自动生成一个对照集,因此不需要进行交叉验证。RF模型中2个可调参数决策树数量ntree和节点分裂次数mtry决定了模型的配置。

2.3 精度分析与比较

RF模型精度评价选用皮尔逊相关系数r、平均误差ME、绝对平均误差MAE和均方根误差RMSE等4个统计指标,其计算公式分别为:

r=∑i=1n(pi-pi¯)(oi-oi¯)∑i=1n(pi-pi¯)2　∑i=1n(oi-oi¯)2　
ME=1n∑i=1n(pi-oi)
MAE=1n∑i=1npi-oi
RMSE=1n∑i=1n(pi-oi)2　

式中: pi和oi分别为样点的预测值和观测值; pi¯和oi¯分别为样点预测值和观测值的平均值; n为样本总数。并将RF预测结果与常用的地统计插值方法OK模型结果进行对比,模型输出结果的ME,MAE和RMASE越接近0,r越接近1,表明模型的预测精度越高。

3 结果与分析

3.1 土壤pH值的描述性统计分析

通过江西省耕地土壤pH值描述性统计结果可知,土壤pH值范围在4.00~8.30之间,平均值为5.20,中位数为5.10,标准差为4.91,变异系数为9.43%,表明江西省耕地土壤pH值呈弱变异。参考通用标准将江西省土壤pH值划分为7个等级: 极强酸性(pH<4.5)、强酸性[4.5,5.5)、酸性[5.5,6.5)、中性[6.5,7.5)、碱性[7.5,8.5)、强碱性[8.5,9.5)和极强碱性(pH≥9.5)。结果显示,江西省耕地土壤pH值样点中,极强酸性样本数67个,占比0.43%; 强酸性样本数12 718个,占比76.69%; 酸性样本数3 301个,占比19.90%; 中性样本数435个,占比2.62%; 碱性样本数61个,占比0.36%; 没有强碱性样本。综上可知,江西省耕地土壤总体呈现明显的酸性特征。

3.2 RF模型最优参数分析

通过逐次试验,确定RF模型中mtry和ntree参数的最优值。固定mtry(分别设为2,4,6和8),逐次调整ntree(分别设为300,600,900,1 200和1 500),进行4组20次试验。为避免过拟合问题,通过比较训练集和验证集的相关系数(r值),根据两者最为接近的结果确定最优预测模型的参数。结果表明(表2): 当mtry=8,ntree=900时,pH值预测模型的训练集和验证集r值最为接近,表明此时的模型稳定性最佳。

表2   RF模型中节点分裂次数和决策树数量的筛选

Tab.2  Optimization of critical parameters for RF model

组别mtryntree训练集r验证集r
1组23000.9690.551
6000.9700.558
9000.9700.556
1 2000.9700.559
1 5000.9700.555
2组43000.9700.567
6000.9700.574
9000.9700.573
1 2000.9710.573
1 5000.9710.572
3组63000.9690.579
6000.9690.578
9000.9700.579
1 2000.9700.580
1 5000.9700.580
4组83000.9690.582
6000.9700.582
9000.9700.583
1 2000.9700.582
1 5000.9690.581

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3.3 不同训练集的RF和OK模型预测精度比较

基于不同训练集运行模型所获得的预测结果有所不同(表3)。从相关系数r看,不同训练集的RF-A和RF-B模型预测的土壤pH值与实测值的r较高(均大于0.96)且差别不大,而OK模型r处于0.65左右,预测精度明显低于RF模型; 从误差看,RF-A模型的ME,MAE和RMSE值最小,而OK模型的误差明显更高。3种模型的拟合能力随训练样本量呈现不同的变化趋势。总体上看,RF-A模型略微优于RF-B模型,OK模型明显弱于前2个模型,并且训练集和验证集抽样比在7∶3时RF-A模型精度最佳。在验证数据集中,除了抽样比7∶3时,OK模型比RF-A模型精度高外,其他情况下RF-A模型的r更高。这表明环境变量增加耕地土壤实测数据有利于提高模型预测精度。因此在土壤样点数据充足的情况下,应优先选择RF-A模型用于土壤pH值的建模预测制图。

表3   使用不同训练集时RF和OK模型预测精度比较

Tab.3  Comparison of model performance with different training and validation dataset ratios

预测模型抽样比训练集验证集
rMEMAERMSErMEMAERMSE
RF-A8∶20.970-0.0040.1210.1670.599-0.0090.2910.392
7∶30.970-0.0040.1210.1670.567-0.0140.2990.401
6∶40.970-0.0040.1220.1680.566-0.0180.3020.406
RF-B8∶20.966-0.0050.1270.1750.548-0.0230.3070.413
7∶30.967-0.0050.1270.1750.525-0.0190.3080.418
6∶40.967-0.0050.1270.1740.504-0.0140.3140.425
OK8∶20.6790.0020.2610.3640.5590.0090.3110.409
7∶30.6830.0010.2590.3570.5880.0070.2890.394
6∶40.6520.0020.2780.3790.5410.0100.3280.414

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3.4 环境变量重要性分析

使用RF模型定量计算出不同变量对于土壤pH值预测的重要性(图2)。由图2可知,气候因素如年均气温和年降水量是决定土壤pH值的最重要因素,这一结果与Chen等[32]、卢宏亮等[33]和王世航等[34]的研究结果基本一致。此外,地形地貌因子如沟谷深度、高程和多分辨率谷底平坦度也是决定土壤pH值的重要因子。说明江西省土壤pH值分布与地形地貌特征高度相关。土壤理化性质对于土壤pH值也有着不可忽视的影响。人为因素如化肥施用量、排水和灌溉条件等也对土壤pH值有较大影响。但是部分因子如土壤类型、秸秆还田比例的重要性低于预期,需要后续进一步研究加以分析。

图2

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图2   RF-A模型变量相对重要性

Fig.2   Relative importance of covariates for RF-A model


3.5 耕地土壤pH值空间预测制图

研究区耕地土壤pH值空间分布如图3所示,整体上使用OK模型、RF-A和RF-B模型获得的研究区土壤pH值空间分布特征大体相同,其中土壤pH低值区主要分布在江西省中部鄱阳湖平原的南昌市、抚州市和吉安市等地区,高值区域主要分布于江西省西部和东部山地丘陵区域,如九江市、鹰潭市、萍乡市和上饶市等地区。结合图1高程信息可知,这一分布特征与江西省地形地势较为相似,表明江西省土壤pH值分布与地形地貌因素存在较大的关联性,这一结果与图2结果相互印证。本文预测结果与周宏冀[35]采用OK模型、协同克里格和BP神经网络所获得的预测结果也基本一致。

图3

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图3   基于RF-A,RF-B和OK模型的耕地pH值空间分布

Fig.3   Distribution of soil pH produced by RF-A, RF-B and OK model


4 讨论

4.1 模型精度分析

根据表3评价结果所示,当训练集和验证集划分比例为8∶2时,RF-A模型对于pH值的预测结果精度最高,其r为0.599,RMSE为0.392,其预测值与实测值散点图见图4。模型精度略低于齐雁冰等[36]的研究结果。主要原因可能是江西省地貌类型复杂,山地丘陵地貌区所占比重较大,山区耕地分布面积小而破碎,土壤pH值预测难度相比地势平坦、地形地貌类型单一的小区域难度更大[37]。虽然本文使用RF模型预测精度仍需进一步提高,但是预测结果基本符合预期,仍然可以为江西省耕地资源管理提供宝贵的数据基础。

图4

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图4   RF-A模型预测值与实测值散点图

Fig.4   Scatter plot of soil pH predicted and measured by RF-A model


4.2 pH值空间变异的影响因素分析

通过运用R Studio软件开展pH值空间变异的影响因素分析,结果显示江西省耕地土壤pH值与年均气温和年降水量关系密切(图2),这一结论与Chen等[32]、卢宏亮等[33]和王世航等[34]的研究结果基本一致。此外,沟谷深度、多分辨率谷底平坦度、高程、坡度和坡向等地形地貌因子也可以通过影响温度、光照、水分和热量的再分配以及母岩形成分布等影响土壤的发育,从而间接影响土壤pH值[38]。因此,地形因子对于预测土壤pH值也具有很高的重要性(图2)。土壤pH值与土壤有机质含量呈现极其显著负相关关系,而土壤pH值与阳离子交换量呈现极其显著的正相关关系,这一结果与戴万宏等[39]、许亚琪[40]、康婷等[41]和王晖等[42]的研究结果一致。耕层质地和土壤类型对于土壤pH值也有明显的影响,轻壤pH值明显高于其他耕层质地类型,黏土次之,不同土壤类型pH值差异也十分显著,其中草洲沙泥土、鳝泥棕红壤和棕色石灰土土壤pH值相对最高,这一结果与前人研究基本一致[32]。需要注意的是,排水条件和灌溉保证率对于耕地土壤pH值也有不可忽视的影响,本研究结果表明,耕地排水能力不满足的情况下耕地土壤pH值最高,在排水能力充分满足和基本满足的耕地土壤pH值相对较低。同时灌溉能力不满足的耕地土壤pH值也高于灌溉能力基本满足和充分满足的耕地土壤,这一结果与朱丹妹等[43]和Ding等[44]的研究结果较为相符(图5)。

图5

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图5   pH值空间变异的影响因素关系

Fig.5   Relevance of pH and influencing factors


5 结论

综合使用地形、气候、植被指数、土壤属性和耕地利用条件等自然环境协变量和人为管理因素数据,基于大量土壤实测样点,分别训练使用2种不同变量组合、不同模型参数设置和不同训练抽样比数据集合的随机森林模型,并预测江西省耕地土壤pH值,再将预测结果与普通克里格方法进行对比。得到如下结论:

1)增加土壤属性和耕地利用条件作为环境变量的RF-A模型预测耕地土壤pH值的精度优于以地形、气候、植被属性作为环境变量的RF-B模型和普通克里格的预测结果。

2)环境变量重要性分析结果显示,气候因素如年均气温和年降水量是决定土壤pH的最重要因素,地形因子、化肥施用量、排水和灌溉条件对土壤pH值变异的影响次之,分析结果与前人研究基本一致,表明相关结论较为可靠,对开展大尺度耕地土壤pH值预测具有一定的理论借鉴和实际应用价值。

但是,对于土壤pH值空间预测精度仍有待提高,今后将考虑增加环境协变量因子,提高基础数据空间分辨率等,进一步提高随机森林模型预测土壤pH值的精度。

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Extending previous work on quantile classifiers (-classifiers) we propose the *-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 * 1, where * equals the unconditional probability of observing a minority class sample. The motivation for *-classification stems from a density-based approach and leads to the useful property that the *-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the *-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply *-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to -mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered.

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为探讨不同特征挖掘方法与广义提升回归模型相结合在数字土壤制图中的应用,本研究首先使用递归特征消除和过滤式两种特征筛选方法对环境协变量进行筛选,再分别使用原始环境协变量、筛选后的最优变量组合作为自变量,建立基于广义提升回归模型和随机森林模型的安徽省土壤pH预测模型并进行制图。结果表明: 引入两种特征挖掘方法均可有效提高广义提升回归模型和随机森林模型预测土壤pH的精度,并且可以起到降维的作用;相较于随机森林模型,广义提升回归模型的验证集预测精度略低,在训练集中,广义提升回归模型的精度却远高于随机森林模型,模型解释度高,整体效果较好;随机森林模型的主要参数ntree和mtry对于模型的影响程度较低,而不同参数对于广义提升回归模型的预测精度影响较大,不同参数组合模型精度不同,建模前需要进行调参。空间制图结果表明,安徽省土壤pH呈“南酸北碱”趋势。

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遥感数据作为反映土壤组成结构及植被生长状况的数据源,借助辅助环境因子的土壤属性预测在数字土壤制图中日益受到重视。论文运用随机森林（Random Forest,RF）算法,基于AWIFS（分辨率56 m）和MODIS（分辨率250 m）遥感数据及501个实测样点数据对陕西省土壤有机质空间分布状况进行预测,并对预测精度进行估算。结果表明陕西省土壤有机质含量以南部的秦岭山地区和大巴山区为最高,土壤有机质含量大于25 g&middot;kg<sup>-1</sup>,黄土高原南部处于中等水平,大部分在16~25 g&middot;kg<sup>-1</sup>之间,关中平原和汉中低山丘陵区含量偏低,大部分在13~25 g&middot;kg<sup>-1</sup>,而黄土高原北部和风沙滩区含量大部分低于10 g&middot;kg<sup>-1</sup>。基于AWIFS影像的预测效果要优于MODIS影像,成像日期对有机质预测的影响不大。基于RF模型的土壤有机质预测精度在设定的不同抽样百分比条件下,独立验证数据集的平均误差大部分不超过3 g&middot;kg<sup>-1</sup>,预测值与实测值的相关系数在0.7以上。高程是影响土壤有机质预测的最重要因子,当影像的分辨率降低时,样点分布的地理经纬度和坡度对土壤有机质预测的影响上升,植被因子的影响程度下降。

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研究江西省土壤阳离子交换量区域分布特征及其影响因素,为今后江西省土壤地力提升采取合理的培肥措施提供理论依据。采用乙酸铵交换法测定江西省土壤耕层367个土样的阳离子交换量,分析其区域分布特征。结果表明:（1）江西省旱地和稻田土壤阳离子交换量(CEC)含量变幅较大,土壤CEC含量小于10.5 cmol(+)/kg的占比高达82.02%,保肥性弱的稻田土壤比旱地高2.46%。（2）从各区域保肥力中等的土壤占比来看,江西省各区域旱地土壤和稻田土壤CEC含量分布高度一致,表现为赣西>赣中>赣北>赣东北>赣南。（3）江西省土壤阳离子交换量主要受有机质、全N显著(P<0.05)影响,其次是土壤pH、速效N、速效K、全P、全K的影响,而有效P的影响力最小。江西省耕地土壤保肥能力弱,旱地保肥能力略强于稻田。

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To improve soil fertility in Jiangxi Province, the regional distribution characteristics of soil cation exchange capacity and the influencing factors were studied. The ammonium acetate exchange method was used to determine the cation exchange capacity of 367 soil samples in surface soil and analyzed their regional distribution characteristics. The results showed that: (1) the content of soil cation exchange capacity in dry soil and paddy soil had a large variation, and the proportion of soil CEC content less than 10.5 cmol (+)/kg was as high as 82.02%, the paddy soil with low capability of conserving fertilizer was 2.46% higher than that of dry soil; (2) according to the proportion of soil with medium capability of conserving fertilizer, the distribution of CEC content in dry soil and paddy soil in Jiangxi was highly consistent, with the order of west Jiangxi >central Jiangxi > north Jiangxi > northeast Jiangxi > south Jiangxi; (3) the soil cation exchange capacity in Jiangxi was mainly affected by organic matter and total N (P<0.05), followed by soil pH, available N, available K, total P and total K, while available P had the least influence. In Jiangxi Province, the capability of conserving fertilizer of cultivated land is weak, and the capability of conserving fertilizer of dry soil is slightly stronger than that of paddy soil.

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