九台市土壤养分空间分布预测研究
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摘要
21世纪,保障粮食安全是我国农业现代化的首要任务,而实现粮食安全的关键和前提是摸清我国耕地资源的数据和质量。土壤养分是耕地地力的重要标志,研究土壤养分的空间分布是调整管理措施和各种物质投入量、获得最大效益的基础。
本文的研究目的是对九台市土壤养分的空间分布进行预测,以便于有效地指导农业生产,为未来推广测土配方施肥,实现精准农业提供一个基本保障;为实现农业可持续发展提供重要的理论和实践意义。
在研究中,选择了以往常用的普通克吕格法和反距离权重法以及近年来比较流行的回归克吕格法三种方法。通过研究发现,回归克吕格法虽然近年来比较流行,但由于九台市土壤养分的影响因素过于复杂,并不适合九台市土壤养分的空间分布预测研究。普通克吕格法和反距离权重法对于土壤养分分布趋势的反映上基本是一致的,反距离权重插值的精度要高于普通克吕格插值的精度,说明九台市土壤养分的空间分布预测研究更适合采用反距离权重方法。
最终的研究结果表明,有机质和速效钾的分布规律比较相似,呈现出由西南方向向东北方向逐渐减少的趋势。但是有机质的较高值主要出现在作物为玉米的黑土上,而速效钾的高值主要出现在作物为水稻的草甸土上。速效磷的分布是由南向北逐渐递减的,高值主要出现在台地地区的草甸土上,耕地类型为旱田,作物类型主要为玉米。全氮在整个研究区的分布普遍较低,最高值零落的出现在中南部,西南部等附近台地地区的黑土和草甸土上,主要粮食作物有玉米、蔬菜和水稻。
In recent years, the spatial variability of soil nutrients have been widespread concern and attention with the protection of food security and the rapid development of precision agriculture. According to the research status at home and abroad, the paper chose the Ordinary Kriging, Regression Kriging and Inverse Distance Weighted Interpolation methods to analysis soil nutrients spatial variability characteristics and distribution pattern in Jiutai County. Studies had shown that because of soil nutrient factors that affected differences in the larger, different nutrients were required in different directions. Study results had a great significance to reveal the spatial variation of soil nutrients in the law, as well as the precise and accurate adjustment of soil management measures to optimize the use of nutrients to maximize resources, to obtain the maximum yield and maximum economic efficiency and to reduce the negative impact on the environment, toprotect the land and other agricultural natural resources. The main conclusions were as follows:
The variation coefficient of Four types of soil nutrients (Total N, Organic matter, Available P and Available K) was between 10-100%, belonging to the middle variation. These changes were as a result of structural factors and random factors. The normal distribution of data found that the four original data did not comply with normal distribution. The only Total N was not in line with the normal distribution after log transfermation, So it can not use Ordinary Kriging.
To choose some representative environmental factors (terrain factor and NDVI) analysised their correlation with soil nutrients in Jiutai City by ArcGIS, four types of soil nutrients at the level of 0.01 showed negative correlation; Organic matter and Available K at the level of 0.01 showed negative correlation with slope and terrain relief; Organic matter and Available P at the level of 0.01 showed negative correlation with surface roughness; four types of soil nutrients related to vegetation (NDVI) were positive correlated. Although the soil nutrients and environmental factors demonstrated a certain degree of relevance, by fitting multiple linear regression equation found that the agreed coefficient was too low that it lost the significance. Regression Kriging method is more popular now as a prediction of soil nutrients, but the factors which impact it were too complex. However it was not fit to use Regression Kriging fou soil nutrients in Jiutai City.
The ratio of nugget value and base value about Organic matter and Available K was about 20%, showing a strong spatial correlation,that noted structural factors (soil parent material, topography, climate and other natural factors) played a major role to the spatial variability of this two types of soil nutrients . The ratio of nugget value and base value about Available P wsa about 70%, showing a medium spatial correlation, that noted random factors (such as cropping systems, fertilization, cultivation system) played a major role to the spatial variability of available P.
The prediction map was basically consistent of Ordinary Kriging and Inverse Distance Weighted Interpolation methods to soil nutrients-Organic matter, Available P and Available K. The distribution of Organic matter and Available K were basically the same, showing a direction from southwest to northeast gradually decreasing trend. However, the higher data of Organic matter mainly was in the black soil of corn crops, but the higher data of Available K occurred mainly in the rice crop for the meadow territories. Jiutai City, northeast of the high-lying, southwest low-lying, topographic factors on the distribution of organic matter and potassium played an important role in the control. The distribution of Available P was gradually descending from south to north, and high-value mainly occurred in the tableland areas meadow soil above of which crops mainly maize and arable land types is glebe. Inverse Distance Weighted Interpolation diagram showed that Total N in the distribution of the entire study area was generally low and the maximum value emerged in the lower of part of the region and the southwest and other areas near the mesa and meadow black earth soil above which the main food crops of corn, vegetables and rice grew. This may be the common results of intrinsic factors such as the topography and external factors such as man-made systems, fertilization in Jiutai, however human economic activity made the distribution of nutrients tend homogenization.
To comparison Kriging interpolation and Inverse Distance Weighted Interpolation found that Inverse Distance Weighted Interpolation was more suitable to forecast the spatial distribution pattern of soil nutrients in Jiutai City, and its accuracy was higher than Kriging interpolation, especially Available P. This might be related to the environment of Jiutai City itself. So it was more suitable for using Inverse Distance Weighted Interpolation to forecast spatial variability of soil nutrients in Jiutai City.
本文的研究目的是对九台市土壤养分的空间分布进行预测,以便于有效地指导农业生产,为未来推广测土配方施肥,实现精准农业提供一个基本保障;为实现农业可持续发展提供重要的理论和实践意义。
在研究中,选择了以往常用的普通克吕格法和反距离权重法以及近年来比较流行的回归克吕格法三种方法。通过研究发现,回归克吕格法虽然近年来比较流行,但由于九台市土壤养分的影响因素过于复杂,并不适合九台市土壤养分的空间分布预测研究。普通克吕格法和反距离权重法对于土壤养分分布趋势的反映上基本是一致的,反距离权重插值的精度要高于普通克吕格插值的精度,说明九台市土壤养分的空间分布预测研究更适合采用反距离权重方法。
最终的研究结果表明,有机质和速效钾的分布规律比较相似,呈现出由西南方向向东北方向逐渐减少的趋势。但是有机质的较高值主要出现在作物为玉米的黑土上,而速效钾的高值主要出现在作物为水稻的草甸土上。速效磷的分布是由南向北逐渐递减的,高值主要出现在台地地区的草甸土上,耕地类型为旱田,作物类型主要为玉米。全氮在整个研究区的分布普遍较低,最高值零落的出现在中南部,西南部等附近台地地区的黑土和草甸土上,主要粮食作物有玉米、蔬菜和水稻。
In recent years, the spatial variability of soil nutrients have been widespread concern and attention with the protection of food security and the rapid development of precision agriculture. According to the research status at home and abroad, the paper chose the Ordinary Kriging, Regression Kriging and Inverse Distance Weighted Interpolation methods to analysis soil nutrients spatial variability characteristics and distribution pattern in Jiutai County. Studies had shown that because of soil nutrient factors that affected differences in the larger, different nutrients were required in different directions. Study results had a great significance to reveal the spatial variation of soil nutrients in the law, as well as the precise and accurate adjustment of soil management measures to optimize the use of nutrients to maximize resources, to obtain the maximum yield and maximum economic efficiency and to reduce the negative impact on the environment, toprotect the land and other agricultural natural resources. The main conclusions were as follows:
The variation coefficient of Four types of soil nutrients (Total N, Organic matter, Available P and Available K) was between 10-100%, belonging to the middle variation. These changes were as a result of structural factors and random factors. The normal distribution of data found that the four original data did not comply with normal distribution. The only Total N was not in line with the normal distribution after log transfermation, So it can not use Ordinary Kriging.
To choose some representative environmental factors (terrain factor and NDVI) analysised their correlation with soil nutrients in Jiutai City by ArcGIS, four types of soil nutrients at the level of 0.01 showed negative correlation; Organic matter and Available K at the level of 0.01 showed negative correlation with slope and terrain relief; Organic matter and Available P at the level of 0.01 showed negative correlation with surface roughness; four types of soil nutrients related to vegetation (NDVI) were positive correlated. Although the soil nutrients and environmental factors demonstrated a certain degree of relevance, by fitting multiple linear regression equation found that the agreed coefficient was too low that it lost the significance. Regression Kriging method is more popular now as a prediction of soil nutrients, but the factors which impact it were too complex. However it was not fit to use Regression Kriging fou soil nutrients in Jiutai City.
The ratio of nugget value and base value about Organic matter and Available K was about 20%, showing a strong spatial correlation,that noted structural factors (soil parent material, topography, climate and other natural factors) played a major role to the spatial variability of this two types of soil nutrients . The ratio of nugget value and base value about Available P wsa about 70%, showing a medium spatial correlation, that noted random factors (such as cropping systems, fertilization, cultivation system) played a major role to the spatial variability of available P.
The prediction map was basically consistent of Ordinary Kriging and Inverse Distance Weighted Interpolation methods to soil nutrients-Organic matter, Available P and Available K. The distribution of Organic matter and Available K were basically the same, showing a direction from southwest to northeast gradually decreasing trend. However, the higher data of Organic matter mainly was in the black soil of corn crops, but the higher data of Available K occurred mainly in the rice crop for the meadow territories. Jiutai City, northeast of the high-lying, southwest low-lying, topographic factors on the distribution of organic matter and potassium played an important role in the control. The distribution of Available P was gradually descending from south to north, and high-value mainly occurred in the tableland areas meadow soil above of which crops mainly maize and arable land types is glebe. Inverse Distance Weighted Interpolation diagram showed that Total N in the distribution of the entire study area was generally low and the maximum value emerged in the lower of part of the region and the southwest and other areas near the mesa and meadow black earth soil above which the main food crops of corn, vegetables and rice grew. This may be the common results of intrinsic factors such as the topography and external factors such as man-made systems, fertilization in Jiutai, however human economic activity made the distribution of nutrients tend homogenization.
To comparison Kriging interpolation and Inverse Distance Weighted Interpolation found that Inverse Distance Weighted Interpolation was more suitable to forecast the spatial distribution pattern of soil nutrients in Jiutai City, and its accuracy was higher than Kriging interpolation, especially Available P. This might be related to the environment of Jiutai City itself. So it was more suitable for using Inverse Distance Weighted Interpolation to forecast spatial variability of soil nutrients in Jiutai City.
引文
[1]刘士奇.耕地安全是粮食安全的重要保障和基础[J].福建农业,2005,(01):5-8.
[2]全国土壤普查办公室.中国土壤[M].北京:中国农业出版社,2001.
[3]Alex B McBratney, Inakwu O A Odeh, Thomas F A Bishop, et al. An overview of pedometric techniques for use in soil survey [J].Geoderma,2000,97:293-327.
[4]Kay Sum?eth, Rainer Duttmann. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators [J].Ecological Indicators,2007,28(3):205-218.
[5]李晓燕,张树文,王宗明等.吉林省德惠市土壤特性空间变异特征与格局[J].地理学报,2004, 59(6):989-997.
[6]杨玉玲,文启凯,田长彦等.土壤空间变异研究现状及展望[J].干旱区研究,2001,18(2):50-55.
[7]王晓芳.东北地区县域经济发展的地域类型与演进机理研究[D].长春:东北师范大学,2008.
[8]程先富,史学正等.江西省兴国县土壤全氮和有机质的空间变异及其分布格局[J].应用与环境生物学报,2004,10(1):64-73.
[9]Franzen DW, Cihacek LJ, Hofman VL. Variability of soil nitrate and phosphate under different landscapes [A]. Proceedings of the 3th in international conference on precision agriculture [C]. Minneapolis, Minnesota, ASA, CSSA, SSSA, 1996, 521-529.
[10]王珂,许红卫,史舟,Junta Yanai.土壤钾素空间变异性和空间插值方法的比较研究.植物营养与肥料学报,2000,6(3):318-322.
[11]Jose AA. Appication of geostatistics to spatial studies of soil properties [J].Soil Sci.2000, 3(8):45-94.
[12]David Pullar and Webster R. Improved estimation of micro-nutrients in hectare plots of the sonning series [J].SoilSci.2000,5(4):667-672.
[13]鲁如坤,史陶.土壤磷素在利用过程中的消耗和积累[J].土壤通报,1980,42(4):134-139.
[14]S.B.Mohamed, Berndtsson R and Jinno K. Spatial dependence of geochemical elements in a semiarid agricultural field:Ⅱ[J].Geostatistical properties. Soil Sci. Soc. Am. J.1993, 57:1323-1329.
[15]Campbell JB. Spatial variation of sand content and PH within single contiguous delineation of two soil map-ping units [J].Soil Sci. Soc. Am. J.1994, 58:1531-1538.
[16]ShufengHan, A.Gallardo. Spatial Variability of soil properties in a floodplain forest in northwest spain [J].Ecosystems,1995,6:564-576.
[17]Richard B. FerguSon. Geostatistical tools for characterzing the spatial variability of microbiological and physico-chemical soil properties [J].Biol Fertil Soils,1993, 27(1): 315-334.
[18]Alex B. McBratney, Inakwu O.A. OdehBogunovic. Inventory of soils in croatia [J].Agriculturae Conspectus Scientificus,1990,63(3):105-112.
[19]Hammond P, Webster R. Soil variability: A review [J].Soil and Fertilizers, 1988, 34:1-15.
[20]Gebruder Wollenhaupt and Michele Ceccarelli. Interpolation processes using multivariate geostatistics for mapping of climatological precipitation mean in the Sannio Mountains[J].Earth Surface Processes and Landforms,1994, 30: 259–268.
[21]Carol A.Gotway, S.Grunwald, et al. Spatial variability, distribution and uncertainty assessment of soil phosphorus in a south Florida wetland [J].Environmetrics,1996,15:811–825.
[22]A.B.McBratney, Ovalles FA. Soil-landscape relationships and soil variability in north central Florida [J]. SoilSci. Soc. Am. J,1996,50:401-408.
[23]徐吉炎,webster.土壤调查数据地域统计的最佳估值研究-章武县表层土全氮量的半方差图和块状Kriging估植[J].土壤学报,1983,20(4):419-430.
[24]郭旭东,傅伯杰,陈利顶等.河北省遵化平原土壤养分的时空变异特征—变异函数与Kriging插值分析[J].地理学报,2000,14(05):54-67.
[25]李世清,高亚军,李生秀.土壤养分的空间变异性及确定样本容量的研究[J].土壤与环境,2000,10(01):112-119.
[26]张素梅,王宗明.利用回归克里金方法研究吉林省农安县土壤养分空间分布[J].土壤学报,2008,4(12):67-72.
[27]王学锋,章衡.土壤有机质的空间变异性[J].土壤学报,1995,27(2):59-85.
[28]周慧珍,龚子同,Lamp J.土壤空间变异性研究[J].土壤学报,1996,33(3):232-241.
[29]李菊梅,李生秀.几种营养元素在土壤中的空间变异[J].干旱地区农业研究,1998,16(2): 58-64.
[30]胡克林,李报国,林启美等.农田土壤养分的空间变异性特征[J].农业工程学报,1999,15(3): 33-38.
[31]白由路,金继运,杨俐苹等.基于GIS的土壤养分分区管理模型研究[J].中国农业科学,2001, 34(l):46-50.
[32]黄绍文,金继运,杨俐苹等.县级区域粮田土壤养分空间变异与分区管理技术研究[J].土壤学报,2003,3(01):94-102.
[33]Trianfilis J, Odeh I O A, McBratney A B. Five geostatistical models to predict soil salinity from electromagnetic introduction data across irrigated cotton [J].Soil Sci. Soc. Am. J,2001,65:869-878.
[34]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.
[35]王红,宫鹏,刘高焕.黄河三角洲多尺度土壤盐分的空间分异[J].地理研究,2006, 25(4): 649-658.
[36]Baxter S J, Oliver M A. The spatial prediction of soil mineral N and potentially available N using elevation [J].Geoderma,2005,128:325-339.
[37]Hengl T, Heuvelink G B M, Stein A. A generic framework for spatial prediction of soil variables based on regression kriging[J].Geoderma,2004,120:75-93.
[38]K.F.Stacey, R.M.Lark, A.P.Whitmore, et al. Using a process model and regression kriging to improve predictions of nitrous oxide emissions from soil [J].Geoderma, 2006(135):107-117.
[39]闰德智,王德建.土壤供氮能力研究方法进展[J].土壤学报,2005,37(l):20-24.
[40]陈新萍.土壤中全磷测定方法的改进试验[J].塔里木大学学报,2005,17(2):96-98.
[41]吕英华,秦双月.测土与施肥[M].北京:中国农业出版社,2002.
[42]鲍士旦.土壤农化分析[M],北京:中国农业出版社,2000.
[43]Cahn MD, Hummel JW, Brouer BH. Spatial analysis of soil fertility for site-specific crop management [J].Soil Sci. Soc. Am. J,1994,58:1240-1248.
[44]李菊梅,李生秀.几种营养元素在土壤中的空间变异[J].干旱区农业研究,1998, 16(2):58-64.
[45]Liang Wenju, Li Qi, Jiang Yong, et al. Effect of cultivation on spatial distribution of nematode trophic groups in black soil [J].Pedosphere,2003,13(2):97–102.
[46]魏孝荣,邵明安.黄土高原沟壑区小流域不同地形下土壤性质分布特征[J].自然资源学报,2007,22(6):946-953.
[47]Liu D W, Wang Z M, Zhang B. Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China [J].Agriculture Ecosystems and Environment,2006,113:73-81.
[48]柳云龙,胡宏涛.红壤地区地形位置和利用方式对土壤物理性质的影响[J].土壤学报,2004,18(1):22-26.
[49]郭胜利,刘文兆,史竹叶等.半千旱区流域土壤养分分布特征及其与地形,植被的关系[J].干旱地区农业研究,2003,21(4):40-43.
[50]郑顺安,常庆瑞,齐雁冰.黄土高原不同树龄土壤质地和矿质元素差异研究[J].干旱地区农业研究,2006,24(6):94 -97.
[51]马黎春,盛建东,蒋平安等.克拉玛依干旱生态农业区土壤质地的空间异质性研究[J].干旱区地理,2006,29(1):109-114.
[52]陈玉福,董鸣.毛乌素沙地景观的植被与土壤特征空间格局及其相关分析[J].植物生态学报,2001,25(3):265-269.
[53]I.Numata, J.V.Soares, D.A.Roberts et al. Relationships among soil fertility dynamics and remotely sensed measures across pasture chronosequences in Rond?nia, Brazil [J].Remote Sensing of Environment,2003(87):446-455.
[54]Moore I D, GesslerP E,Nieslen G A, et al. Soil attribute prediction using terrain analysis [J].SoilSci. Soc. Am. J,1993,57:443-452.
[55]Gessler P E, Moore I D, Mckenzie N J, et al. Soil-landscape modeling and spatial prediction of soil attributes [J].Geographical Information Systems,1995,4:421-432.
[56]McKenzie N J, Ryan P J. Spatial prediction of soil properties using environmental correlation [J].Geoderma,1999,89:67-94.
[57]Kay Sum?eth, Rainer Duttmann. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators [J]. Ecological Indicators,2007,14:35-51.
[58]I.V. Florinsky, R.G.Eilers, G.R. Manning etal. Prediction of soil properties by digital terrain modelling [J].Environmental Modelling&Software,2002(17):295-311.
[59]张彩霞,杨勤科,李锐.基于DEM的地形湿度指数及其应用研究进展[J].地理科学进展,2005,24(6):116-123.
[2]全国土壤普查办公室.中国土壤[M].北京:中国农业出版社,2001.
[3]Alex B McBratney, Inakwu O A Odeh, Thomas F A Bishop, et al. An overview of pedometric techniques for use in soil survey [J].Geoderma,2000,97:293-327.
[4]Kay Sum?eth, Rainer Duttmann. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators [J].Ecological Indicators,2007,28(3):205-218.
[5]李晓燕,张树文,王宗明等.吉林省德惠市土壤特性空间变异特征与格局[J].地理学报,2004, 59(6):989-997.
[6]杨玉玲,文启凯,田长彦等.土壤空间变异研究现状及展望[J].干旱区研究,2001,18(2):50-55.
[7]王晓芳.东北地区县域经济发展的地域类型与演进机理研究[D].长春:东北师范大学,2008.
[8]程先富,史学正等.江西省兴国县土壤全氮和有机质的空间变异及其分布格局[J].应用与环境生物学报,2004,10(1):64-73.
[9]Franzen DW, Cihacek LJ, Hofman VL. Variability of soil nitrate and phosphate under different landscapes [A]. Proceedings of the 3th in international conference on precision agriculture [C]. Minneapolis, Minnesota, ASA, CSSA, SSSA, 1996, 521-529.
[10]王珂,许红卫,史舟,Junta Yanai.土壤钾素空间变异性和空间插值方法的比较研究.植物营养与肥料学报,2000,6(3):318-322.
[11]Jose AA. Appication of geostatistics to spatial studies of soil properties [J].Soil Sci.2000, 3(8):45-94.
[12]David Pullar and Webster R. Improved estimation of micro-nutrients in hectare plots of the sonning series [J].SoilSci.2000,5(4):667-672.
[13]鲁如坤,史陶.土壤磷素在利用过程中的消耗和积累[J].土壤通报,1980,42(4):134-139.
[14]S.B.Mohamed, Berndtsson R and Jinno K. Spatial dependence of geochemical elements in a semiarid agricultural field:Ⅱ[J].Geostatistical properties. Soil Sci. Soc. Am. J.1993, 57:1323-1329.
[15]Campbell JB. Spatial variation of sand content and PH within single contiguous delineation of two soil map-ping units [J].Soil Sci. Soc. Am. J.1994, 58:1531-1538.
[16]ShufengHan, A.Gallardo. Spatial Variability of soil properties in a floodplain forest in northwest spain [J].Ecosystems,1995,6:564-576.
[17]Richard B. FerguSon. Geostatistical tools for characterzing the spatial variability of microbiological and physico-chemical soil properties [J].Biol Fertil Soils,1993, 27(1): 315-334.
[18]Alex B. McBratney, Inakwu O.A. OdehBogunovic. Inventory of soils in croatia [J].Agriculturae Conspectus Scientificus,1990,63(3):105-112.
[19]Hammond P, Webster R. Soil variability: A review [J].Soil and Fertilizers, 1988, 34:1-15.
[20]Gebruder Wollenhaupt and Michele Ceccarelli. Interpolation processes using multivariate geostatistics for mapping of climatological precipitation mean in the Sannio Mountains[J].Earth Surface Processes and Landforms,1994, 30: 259–268.
[21]Carol A.Gotway, S.Grunwald, et al. Spatial variability, distribution and uncertainty assessment of soil phosphorus in a south Florida wetland [J].Environmetrics,1996,15:811–825.
[22]A.B.McBratney, Ovalles FA. Soil-landscape relationships and soil variability in north central Florida [J]. SoilSci. Soc. Am. J,1996,50:401-408.
[23]徐吉炎,webster.土壤调查数据地域统计的最佳估值研究-章武县表层土全氮量的半方差图和块状Kriging估植[J].土壤学报,1983,20(4):419-430.
[24]郭旭东,傅伯杰,陈利顶等.河北省遵化平原土壤养分的时空变异特征—变异函数与Kriging插值分析[J].地理学报,2000,14(05):54-67.
[25]李世清,高亚军,李生秀.土壤养分的空间变异性及确定样本容量的研究[J].土壤与环境,2000,10(01):112-119.
[26]张素梅,王宗明.利用回归克里金方法研究吉林省农安县土壤养分空间分布[J].土壤学报,2008,4(12):67-72.
[27]王学锋,章衡.土壤有机质的空间变异性[J].土壤学报,1995,27(2):59-85.
[28]周慧珍,龚子同,Lamp J.土壤空间变异性研究[J].土壤学报,1996,33(3):232-241.
[29]李菊梅,李生秀.几种营养元素在土壤中的空间变异[J].干旱地区农业研究,1998,16(2): 58-64.
[30]胡克林,李报国,林启美等.农田土壤养分的空间变异性特征[J].农业工程学报,1999,15(3): 33-38.
[31]白由路,金继运,杨俐苹等.基于GIS的土壤养分分区管理模型研究[J].中国农业科学,2001, 34(l):46-50.
[32]黄绍文,金继运,杨俐苹等.县级区域粮田土壤养分空间变异与分区管理技术研究[J].土壤学报,2003,3(01):94-102.
[33]Trianfilis J, Odeh I O A, McBratney A B. Five geostatistical models to predict soil salinity from electromagnetic introduction data across irrigated cotton [J].Soil Sci. Soc. Am. J,2001,65:869-878.
[34]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.
[35]王红,宫鹏,刘高焕.黄河三角洲多尺度土壤盐分的空间分异[J].地理研究,2006, 25(4): 649-658.
[36]Baxter S J, Oliver M A. The spatial prediction of soil mineral N and potentially available N using elevation [J].Geoderma,2005,128:325-339.
[37]Hengl T, Heuvelink G B M, Stein A. A generic framework for spatial prediction of soil variables based on regression kriging[J].Geoderma,2004,120:75-93.
[38]K.F.Stacey, R.M.Lark, A.P.Whitmore, et al. Using a process model and regression kriging to improve predictions of nitrous oxide emissions from soil [J].Geoderma, 2006(135):107-117.
[39]闰德智,王德建.土壤供氮能力研究方法进展[J].土壤学报,2005,37(l):20-24.
[40]陈新萍.土壤中全磷测定方法的改进试验[J].塔里木大学学报,2005,17(2):96-98.
[41]吕英华,秦双月.测土与施肥[M].北京:中国农业出版社,2002.
[42]鲍士旦.土壤农化分析[M],北京:中国农业出版社,2000.
[43]Cahn MD, Hummel JW, Brouer BH. Spatial analysis of soil fertility for site-specific crop management [J].Soil Sci. Soc. Am. J,1994,58:1240-1248.
[44]李菊梅,李生秀.几种营养元素在土壤中的空间变异[J].干旱区农业研究,1998, 16(2):58-64.
[45]Liang Wenju, Li Qi, Jiang Yong, et al. Effect of cultivation on spatial distribution of nematode trophic groups in black soil [J].Pedosphere,2003,13(2):97–102.
[46]魏孝荣,邵明安.黄土高原沟壑区小流域不同地形下土壤性质分布特征[J].自然资源学报,2007,22(6):946-953.
[47]Liu D W, Wang Z M, Zhang B. Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China [J].Agriculture Ecosystems and Environment,2006,113:73-81.
[48]柳云龙,胡宏涛.红壤地区地形位置和利用方式对土壤物理性质的影响[J].土壤学报,2004,18(1):22-26.
[49]郭胜利,刘文兆,史竹叶等.半千旱区流域土壤养分分布特征及其与地形,植被的关系[J].干旱地区农业研究,2003,21(4):40-43.
[50]郑顺安,常庆瑞,齐雁冰.黄土高原不同树龄土壤质地和矿质元素差异研究[J].干旱地区农业研究,2006,24(6):94 -97.
[51]马黎春,盛建东,蒋平安等.克拉玛依干旱生态农业区土壤质地的空间异质性研究[J].干旱区地理,2006,29(1):109-114.
[52]陈玉福,董鸣.毛乌素沙地景观的植被与土壤特征空间格局及其相关分析[J].植物生态学报,2001,25(3):265-269.
[53]I.Numata, J.V.Soares, D.A.Roberts et al. Relationships among soil fertility dynamics and remotely sensed measures across pasture chronosequences in Rond?nia, Brazil [J].Remote Sensing of Environment,2003(87):446-455.
[54]Moore I D, GesslerP E,Nieslen G A, et al. Soil attribute prediction using terrain analysis [J].SoilSci. Soc. Am. J,1993,57:443-452.
[55]Gessler P E, Moore I D, Mckenzie N J, et al. Soil-landscape modeling and spatial prediction of soil attributes [J].Geographical Information Systems,1995,4:421-432.
[56]McKenzie N J, Ryan P J. Spatial prediction of soil properties using environmental correlation [J].Geoderma,1999,89:67-94.
[57]Kay Sum?eth, Rainer Duttmann. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators [J]. Ecological Indicators,2007,14:35-51.
[58]I.V. Florinsky, R.G.Eilers, G.R. Manning etal. Prediction of soil properties by digital terrain modelling [J].Environmental Modelling&Software,2002(17):295-311.
[59]张彩霞,杨勤科,李锐.基于DEM的地形湿度指数及其应用研究进展[J].地理科学进展,2005,24(6):116-123.