基于地统计学和GIS的通州区于家务乡土壤肥力综合评价
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摘要
【目的】本次研究对农业土壤肥力定量化管理、土地规划管理、土壤生态化建设等方面均有指导意义,已经建立的这套土壤肥力综合评价方法,为土地进一步分等定级和定量化研究提供数据支撑。【方法】选取北京市通州区于家务乡表层土壤8项常规肥力指标(全氮、全磷、全钾、碱解氮、有效磷、速效钾、有机质、pH值)作为评价指标,采用指标相关系数法和隶属度函数评价方法相结合计算出代表土壤肥力状况的IFI值对土壤肥力进行综合评价,并运用地统计学方法和GIS技术进行趋势和空间分析。【结果】研究区土壤中有效磷的变异系数最大为107.04%,pH值变异系数最小为2.80%,变异系数由大到小依次为有效磷>速效钾>碱解氮>有机质>全氮>全磷>全钾>pH值。经过不同趋势次数指标插值误差的综合比较下,初步确定速效钾和IFI选择一次,全氮、碱解氮和pH值选择常数,其余指标均选择二次,同时其具有良好的半方差结构,符合指数模型和高斯型,具有较强的空间相关性,结构性因素引起的变异占主导作用。【结论】根据IFI值将研究区土壤分为优质、良好、中等、差等、劣等5个等级,其中良好、中等和差等级别土壤面积占比高达94.86%。通过空间分布图可见,研究区土壤综合肥力分布较均匀,北部至南部高肥力地区与低肥力地区之间呈条带状交织分布,且高肥力地区与低肥力地区分布面积几乎相等。
【Objective】The research has a guiding significance in quantitative management of agricultural soil fertility, land planning management and ecological construction of soil. The comprehensive evaluation method for the soil fertility which has been figured out will provide data support for land grading and quantitative research. 【Method】8 fertility indexes(including total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus, available potassium, organic matter and pH value) of the surface soil located at Yujiawu Township, Tongzhou District, Beijing were selected as the indexes for evaluating the soil fertility. Depending on index correlation coefficient method and membership function evaluation method, this paper figured out IFI value(IFI represents the soil fertility), by which soil fertility could be evaluated comprehensively. Then this paper analyzed the trend and space by geostatistical method and GIS technology. 【Result】It was found that the variation coefficient of available phosphorus was the biggest, which was 107.04 % while the variation coefficient of pH was the lowest, which was 2.80 %. The sequence of variation coefficient is: available phosphorus>available potassium>available nitrogen>organic matter>total nitrogen>total phosphorus>total potassium>pH. Under a comprehensive comparison of the index interpolation error with different trends, linear should be selected for rapidly available potassium and IFI while constant should be selected for total nitrogen, available nitrogen and pH value, quadratic should be selected for other indexes. The semi-variance structure was good, which was consistent with the exponential model and Gaussian-type. Therefore, it had spatial correlation. The variation caused by structural factors played a leading role. 【Conclusion】According to IFI value, the soil at the research area is divided into 5 levels——excellent soil, good soil, medium quality soil, bad soil and inferior quality soil. The area of good soil, medium quality soil and inferior quality soil accounts for 94.86 %. From the special layout, we can know that the fertility of the soil at the research area is evenly distributed; from the north to the south, high fertility area and low fertility area are ribbon-like distributed, whose areas are almost the same.
【Objective】The research has a guiding significance in quantitative management of agricultural soil fertility, land planning management and ecological construction of soil. The comprehensive evaluation method for the soil fertility which has been figured out will provide data support for land grading and quantitative research. 【Method】8 fertility indexes(including total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus, available potassium, organic matter and pH value) of the surface soil located at Yujiawu Township, Tongzhou District, Beijing were selected as the indexes for evaluating the soil fertility. Depending on index correlation coefficient method and membership function evaluation method, this paper figured out IFI value(IFI represents the soil fertility), by which soil fertility could be evaluated comprehensively. Then this paper analyzed the trend and space by geostatistical method and GIS technology. 【Result】It was found that the variation coefficient of available phosphorus was the biggest, which was 107.04 % while the variation coefficient of pH was the lowest, which was 2.80 %. The sequence of variation coefficient is: available phosphorus>available potassium>available nitrogen>organic matter>total nitrogen>total phosphorus>total potassium>pH. Under a comprehensive comparison of the index interpolation error with different trends, linear should be selected for rapidly available potassium and IFI while constant should be selected for total nitrogen, available nitrogen and pH value, quadratic should be selected for other indexes. The semi-variance structure was good, which was consistent with the exponential model and Gaussian-type. Therefore, it had spatial correlation. The variation caused by structural factors played a leading role. 【Conclusion】According to IFI value, the soil at the research area is divided into 5 levels——excellent soil, good soil, medium quality soil, bad soil and inferior quality soil. The area of good soil, medium quality soil and inferior quality soil accounts for 94.86 %. From the special layout, we can know that the fertility of the soil at the research area is evenly distributed; from the north to the south, high fertility area and low fertility area are ribbon-like distributed, whose areas are almost the same.
引文
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[2]郑城,聂国辉.浙江省典型小流域不同土地利用类型土壤养分评价[J].浙江水利科技,2016,44(4):16-19.
[3]叶回春,张世文,黄元仿,等.北京延庆盆地农田表层土壤肥力评价及其空间变异[J].中国农业科学,2013,46(15):3151-3160.
[4]王妙星,冶军,孙建亭,等.哈密第十三师农田土壤养分变化分析与肥力评价[J].新疆农业科学,2017,54(3):528-537.
[5]Nie Y,Zhou Y,Yu J,et al.Farmland quality evaluation based on GIS and fuzzy matter 2 element proximity cluster analysis model[J].Acta Pedologica Sinica,2005,42(4):551-558.
[6]Luo B S,Zhong J H,Chen J J.Integrated digitization evaluation on soil fertility[J].Soils,2004,36(1):104-106.
[7]Zhang X Y,Sui Y Y,Zhang X D,et al.Spatial Variability of Nutrient Properties in black soil of northeast China[J].Pedosphere,2007,17(1):19-29.
[8]Darilek J L,Huang B,Wang Z G,et al.Changes in soil fertility parameters and the environmental effects in a rapidly developing region in China[J].Agriculture,Ecosystems and Environment,2009,129(1/2/3):286-292.
[9]安永龙,黄勇,孙朝,等.北京通州某改造区土壤中DDTs和HCHs的地球化学特征及风险评价[J].现代地质,2017,31(3):545-554.
[10]北京市通州区统计局.北京市通州区统计年鉴[M].北京:中国统计出版社,2007.
[11]安永龙,黄勇,孙朝,等.北京通州某改造区土壤中PAHs的来源分析及风险评价[J].水文地质工程地质,2017,44(5):112-120.
[12]Edward C J,Laura L L.A soil-quality index and its relationship to Efficiency and productivity growth measures:Two decompositions[J].American Journal of Agricultural Economics,1999,81(4):881-893.
[13]邓聚龙.灰色系统基本方法[M].武汉:华中理工大学出版社,1987.
[14]付强.投影寻踪模型及其在水文水资源系统分析中的应用[J].黑龙江水专学报,2008,35(4):80-85.
[15]曹志洪.解译土壤质量演变规律,确保土壤资源持续利用[J].世界科技研究与发展,2001(3):28-32.
[16]沈润平,丁国香,魏国栓,等.基于人工神经网络的土壤有机质含量高光谱反演[J].土壤学报,2009,46(3):391-397.
[17]颜雄,张杨珠,刘晶.土壤肥力质量评价的研究进展[J].湖南农业科学,2008(5):82-85.
[18]马芊红,张光辉,耿韧,等.我国水蚀区坡耕地土壤肥力现状分析[J].水土保持学报,2016,30(5):190-196,204.
[19]王军,丁效东,何振峰,等.广东南雄烟区植烟土壤肥力特征及综合评价[J].中国烟草科学,2015,36(6):30-36.
[20]李晓宁,高明,王子芳.重庆市植烟土壤肥力数值化综合评价[J].西南农业学报,2007(1):67-71.
[21]李国强,冯晓,郑国清,等.基于改进灰色关联模型的土壤肥力综合评价[J].河南农业科学,2013,42(4):71-74.
[22]郭鹏,徐丽萍,常存.北疆小尺度滴灌棉田土壤全氮半变异函数建模及空间变异特征[J].西北农业学报,2013,22(6):79-84.
[23]苏松锦,刘金福,何中声,等.格氏栲天然林土壤养分空间异质性[J].生态学报,2012,32(18):5673-5682.
[24]巫振富,赵彦锋,陈杰.土壤属性预测中Kriging参数对插值精度的影响研究[J].土壤通报,2012,43(3):551-558.
[25]阮起和,胡省英,周亚男,等.北京市平原区土壤环境地球化学调查成果报告[R].北京:北京市地质勘察技术院,2007.
[26]倪俊,杨良华,雷能忠.基于GIS的舒城县土壤养分综合评价[J].皖西学院学报,2008,24(5):153-156.
[27]岳子明,李晓秀,高晓晶.北京通州区土壤环境质量模糊综合评价[J].农业环境科学学报,2007,26(4):1402-1405.
[28]王琦.实用模糊数学[M].北京:科学技术文献出版社,1992.
[29]吕晓男,陆允甫,王人潮.土壤肥力综合评价初步研究[J].浙江大学学报(农业与生命科学版),1999,25(4):38-42.
[30]骆伯胜,钟继洪,陈俊坚.土壤肥力数值化综合评价研究[J].土壤,2004,36(1):104-106,111.
[31]Bekele A,Hudnall W H.Spatial variability of soil chemical properties of a prairie-forest transition in Louisiana[J].Plant and Soil,2006(280):7-21.
[32]王瑞,何中青,丁建方,等.洪泽湖农场土壤速效钾含量的地统计学和GIS分析[J].中国农学通报,2012,28(21):96-101.
[33]曾春阳,唐代生,唐嘉锴.森林立地指数的地统计学空间分析[J].生态学报,2010,30(13):3465-3471.
[34]王璐,王海燕,何丽鸿,等.基于GIS的土壤肥力质量综合评价——以天然云冷杉针阔混交林为例[J].土壤通报,2016,47(5):1223-1230.
[35]Han S,Schneider S M,Evans R G.Evaluating cokriging for improving soil nutrient sampling efficiency[J].Transaction of the ASAE,2003,46(3):845-849.
[36]石媛媛,邓明军,林北森,等.基于GIS和地统计学的百色植烟土壤养分空间分析[J].南方农业学报,2014,45(8):1403-1409.
[37]张彬,杨联安,杨粉莉,等.礼泉县苹果园土壤养分空间变异特征及综合评价[J].土壤通报,2016,47(4):860-867.
[38]周伟,马琨.旱作区耕地土壤养分空间变异及评价[J].宁夏农林科技,2015,56(4):19-23+2.
[39]张彬,杨联安,杨粉莉,等.苹果主产区土壤养分空间分布特征及其影响因素——以陕西省礼泉县为例[J].土壤,2016,48(4):777-784.
[40]余泓,潘剑君,李加加,等.长三角地区农用地土壤肥力特征及综合评价——以朱林镇为例[J].土壤通报,2017,48(2):372-379.
[41]周睿,潘贤章,王昌坤,等.上海市城郊土壤有机质的时空变异特征及其影响因素[J].土壤,2014,46(3):433-438.
[42]胡保文,赵文军,薛开政,等.有机肥与无机肥不同施用配比对烤烟“K326”产质量的影响[J].云南农业大学学报(自然科学),2016,31(2):316-321.
[43]黄安,杨联安,杜挺,等.基于主成分分析的土壤养分综合评价[J].干旱区研究,2014,31(5):819-825.
[2]郑城,聂国辉.浙江省典型小流域不同土地利用类型土壤养分评价[J].浙江水利科技,2016,44(4):16-19.
[3]叶回春,张世文,黄元仿,等.北京延庆盆地农田表层土壤肥力评价及其空间变异[J].中国农业科学,2013,46(15):3151-3160.
[4]王妙星,冶军,孙建亭,等.哈密第十三师农田土壤养分变化分析与肥力评价[J].新疆农业科学,2017,54(3):528-537.
[5]Nie Y,Zhou Y,Yu J,et al.Farmland quality evaluation based on GIS and fuzzy matter 2 element proximity cluster analysis model[J].Acta Pedologica Sinica,2005,42(4):551-558.
[6]Luo B S,Zhong J H,Chen J J.Integrated digitization evaluation on soil fertility[J].Soils,2004,36(1):104-106.
[7]Zhang X Y,Sui Y Y,Zhang X D,et al.Spatial Variability of Nutrient Properties in black soil of northeast China[J].Pedosphere,2007,17(1):19-29.
[8]Darilek J L,Huang B,Wang Z G,et al.Changes in soil fertility parameters and the environmental effects in a rapidly developing region in China[J].Agriculture,Ecosystems and Environment,2009,129(1/2/3):286-292.
[9]安永龙,黄勇,孙朝,等.北京通州某改造区土壤中DDTs和HCHs的地球化学特征及风险评价[J].现代地质,2017,31(3):545-554.
[10]北京市通州区统计局.北京市通州区统计年鉴[M].北京:中国统计出版社,2007.
[11]安永龙,黄勇,孙朝,等.北京通州某改造区土壤中PAHs的来源分析及风险评价[J].水文地质工程地质,2017,44(5):112-120.
[12]Edward C J,Laura L L.A soil-quality index and its relationship to Efficiency and productivity growth measures:Two decompositions[J].American Journal of Agricultural Economics,1999,81(4):881-893.
[13]邓聚龙.灰色系统基本方法[M].武汉:华中理工大学出版社,1987.
[14]付强.投影寻踪模型及其在水文水资源系统分析中的应用[J].黑龙江水专学报,2008,35(4):80-85.
[15]曹志洪.解译土壤质量演变规律,确保土壤资源持续利用[J].世界科技研究与发展,2001(3):28-32.
[16]沈润平,丁国香,魏国栓,等.基于人工神经网络的土壤有机质含量高光谱反演[J].土壤学报,2009,46(3):391-397.
[17]颜雄,张杨珠,刘晶.土壤肥力质量评价的研究进展[J].湖南农业科学,2008(5):82-85.
[18]马芊红,张光辉,耿韧,等.我国水蚀区坡耕地土壤肥力现状分析[J].水土保持学报,2016,30(5):190-196,204.
[19]王军,丁效东,何振峰,等.广东南雄烟区植烟土壤肥力特征及综合评价[J].中国烟草科学,2015,36(6):30-36.
[20]李晓宁,高明,王子芳.重庆市植烟土壤肥力数值化综合评价[J].西南农业学报,2007(1):67-71.
[21]李国强,冯晓,郑国清,等.基于改进灰色关联模型的土壤肥力综合评价[J].河南农业科学,2013,42(4):71-74.
[22]郭鹏,徐丽萍,常存.北疆小尺度滴灌棉田土壤全氮半变异函数建模及空间变异特征[J].西北农业学报,2013,22(6):79-84.
[23]苏松锦,刘金福,何中声,等.格氏栲天然林土壤养分空间异质性[J].生态学报,2012,32(18):5673-5682.
[24]巫振富,赵彦锋,陈杰.土壤属性预测中Kriging参数对插值精度的影响研究[J].土壤通报,2012,43(3):551-558.
[25]阮起和,胡省英,周亚男,等.北京市平原区土壤环境地球化学调查成果报告[R].北京:北京市地质勘察技术院,2007.
[26]倪俊,杨良华,雷能忠.基于GIS的舒城县土壤养分综合评价[J].皖西学院学报,2008,24(5):153-156.
[27]岳子明,李晓秀,高晓晶.北京通州区土壤环境质量模糊综合评价[J].农业环境科学学报,2007,26(4):1402-1405.
[28]王琦.实用模糊数学[M].北京:科学技术文献出版社,1992.
[29]吕晓男,陆允甫,王人潮.土壤肥力综合评价初步研究[J].浙江大学学报(农业与生命科学版),1999,25(4):38-42.
[30]骆伯胜,钟继洪,陈俊坚.土壤肥力数值化综合评价研究[J].土壤,2004,36(1):104-106,111.
[31]Bekele A,Hudnall W H.Spatial variability of soil chemical properties of a prairie-forest transition in Louisiana[J].Plant and Soil,2006(280):7-21.
[32]王瑞,何中青,丁建方,等.洪泽湖农场土壤速效钾含量的地统计学和GIS分析[J].中国农学通报,2012,28(21):96-101.
[33]曾春阳,唐代生,唐嘉锴.森林立地指数的地统计学空间分析[J].生态学报,2010,30(13):3465-3471.
[34]王璐,王海燕,何丽鸿,等.基于GIS的土壤肥力质量综合评价——以天然云冷杉针阔混交林为例[J].土壤通报,2016,47(5):1223-1230.
[35]Han S,Schneider S M,Evans R G.Evaluating cokriging for improving soil nutrient sampling efficiency[J].Transaction of the ASAE,2003,46(3):845-849.
[36]石媛媛,邓明军,林北森,等.基于GIS和地统计学的百色植烟土壤养分空间分析[J].南方农业学报,2014,45(8):1403-1409.
[37]张彬,杨联安,杨粉莉,等.礼泉县苹果园土壤养分空间变异特征及综合评价[J].土壤通报,2016,47(4):860-867.
[38]周伟,马琨.旱作区耕地土壤养分空间变异及评价[J].宁夏农林科技,2015,56(4):19-23+2.
[39]张彬,杨联安,杨粉莉,等.苹果主产区土壤养分空间分布特征及其影响因素——以陕西省礼泉县为例[J].土壤,2016,48(4):777-784.
[40]余泓,潘剑君,李加加,等.长三角地区农用地土壤肥力特征及综合评价——以朱林镇为例[J].土壤通报,2017,48(2):372-379.
[41]周睿,潘贤章,王昌坤,等.上海市城郊土壤有机质的时空变异特征及其影响因素[J].土壤,2014,46(3):433-438.
[42]胡保文,赵文军,薛开政,等.有机肥与无机肥不同施用配比对烤烟“K326”产质量的影响[J].云南农业大学学报(自然科学),2016,31(2):316-321.
[43]黄安,杨联安,杜挺,等.基于主成分分析的土壤养分综合评价[J].干旱区研究,2014,31(5):819-825.