基于GIS的水稻施肥决策研究与应用
|
摘要
农田养分的精确管理就是应用先进的信息技术手段,提高对农田养分资源的监测、评估和管理水平,同时提高农业生产技术的智能化水平。施肥模型是施肥技术的重要基石,是农田养分精准管理重要的一方面。本研究在不同条件下不同种类的水稻当季肥料利用率的研究基础上,构建了改进型养分平衡法施肥模型、神经网络施肥模型,并以GIS为工具建立县域水稻分区的肥料效应函数模型,在此基础上研发基于GIS平台和施肥推荐模型有机集成的农田养分信息管理系统。
1.以江西省17个县217个试验点265次“3414”试验数据,分析得到不同土壤类型、不同地形地貌条件下江西省早、中、晚稻养分平衡施肥模型中的相关参数。研究结果表明：江西省水稻氮肥肥料利用率平均值为32.04%,P2O5肥料利用率为27.96%,K20的肥料利用率为50.69%。不同土壤类型对肥料利用率及其产量有着不同程度的影响。不同土壤类型中NPK当季肥料利用率的高低基本上表现为潴育型潮沙泥田>潴育型黄泥田＞潴育型麻沙泥田>潴育型鳝泥田＞潴育型黄沙泥田＞潴育型红沙泥田>淹育型红沙泥田>淹育型黄泥田>潜育型潮沙泥田>潜育型鳝泥田。不同地貌类型对肥料利用率和水稻产量也有一定的影响,氮、磷、钾当季利用率的相对高低顺序为平原>丘陵>山地。肥料当季养分利用率的高低与土壤肥力水平成反比,即高肥力田块由于供肥能力强,肥料利用率低,而低肥力田块由于供肥能力弱,肥料利用率反而高。不同水稻品种对肥料利用率也要一定影响,本研究表明中稻的肥料利用率大于晚稻、早稻,但差异不显著。
2.针对传统的目标产量法施肥模型的缺陷,尤其是在县域推广进行测土配方施肥时,无法一一做试验确定其参数的不足,利用已进行的试验计算相关参数并添加相关调整系数对传统的养分平衡法水稻施肥模型进行改进。构建的改进型养分平衡法施肥模型为FQ=Np×(G-B/Al)/(Fn×FUR×Fl×F2×F3)。研究结果表明：利用全省不同土壤类型、不同地貌类型、不同水稻类型肥料利用率修正系数的改进型养分平衡法施肥模型,可以在全省进行推广使用。在奉新的试验表明改进型实际产量最高,传统型次之,习惯施肥再次,空白处理最低；改进型养分平衡法实际产量平均值比习惯施肥处理高135kg/hm2,增幅达1.9%,比传统养分平衡法处理高77.81 kg/hm2,增幅为1.1%。增产节支效益方面,改进型增产节支效益最高,传统养分平衡法次之,习惯施肥最小。改进型比传统型高出367.91元／公顷,增幅达20.63%。改进型比习惯施肥处理高出499.15元／公顷,增幅达30.21%。同时,可以看出,改进型推荐施肥的处理更接近目标产量。
3.以BP算法为工具,利用试验结果,构建了江西省基于人工神经网络的水稻施肥模型。研究结果表明：构建的"4-4-10-3”的水稻施肥神经网络结构,有较好的训练精度和测试精度。施NPK肥模型结果与实际值的相关性为0.997,、0.982,,0.972。误差率均控制在1%以内,具有较好的泛化性功能。在奉新县选择了六个试验点进行了不同处理的产量试验表明神经网络模型实际产量最高为6846.75kg/hm2,改进型次之为6813.75kg/hm2,习惯施肥再次之为6600.5kg/hm2,空白处理最低为3967.95kg/hm2。同时对处理间差异显著性进行分析,结果表明改进型养分平衡法施肥以及神经网络模型施肥的产量差异不显著,习惯施肥与改进型养分平衡法、神经网络模型法处理的产量差异显著。
4.结合传统试验数据,综合GIS和建模技术,从大尺度县域入手解决小尺度区域施肥问题；以弋阳县为例探讨分析丘陵县域土壤养分区域化分布的状况,实现丘陵耕地地力综合分区,得到各区水稻最佳经济效益施肥方案。研究结果表明：弋阳县土壤养分综合分区,共九个区,V区面积最大,达8005.22公顷,占全县耕地面积的34.02%,这个区域也是弋阳县最重要的粮食生产区域；面积大小依次是Ⅳ区、Ⅲ区、Ⅶ区、Ⅱ区、Ⅷ区、Ⅵ区、Ⅸ区,面积最小的是Ⅰ区,仅36.54公顷,仅占全县耕地面积的0.16%。所形成的具有代表性的各分区肥料效应函数方程分别代表各区肥力等别土壤施肥量与产量的关系,针对性强,解决了以往单个肥料效应方程难以代表不同地块及不同肥力水平的问题；相对以往的数据显著地增加了代表性。
5.以C#为平台,利用ArcEngine的二次开发功能,在构建施肥模型的基础上,建立了施肥的实体-关系型数据库,形成施肥子模块,并形成农田养分管理信息系统。研究结果表明：系统采用可视化开发语言C#和地理信息系统二次开发平台ArcEngine,可以脱离GIS工具软件的运行环境、功能完全可以不逊于通用型GIS软件。农田养分管理信息系统的功能分为文件管理、信息查询、属性编辑、施肥推荐等相关模块,每个模块中又分若干个子功能,同时可以最大限度的满足农业部门土肥业务管理的需求,系统达到了既提高办事效率和减轻业务人员的劳动强度的要求。
Accurate management of paddy nutrient is to improve the level of surveillance、evaluation and management, at the same time to improve the intelligence of agricultural techniques by advanced information technology, fertilization model is an important cornerstone for Fertilizer Technique, and is most one for Accurate management of paddy nutrient. Based on the research of fertilizer apparent recovery fraction at current season with different species of rice at different conditions, this research forms modified nutrient balance model and neural network model, establishes regionalized fertilization model of rice sativa by GIS. In conclusion, the organic integrated research and development of paddy nutrient information management is formed based on GIS and fertilizer recommendation model.
1.Based on 265 dates from 217 test points of "3414" in 17 rational countries of Jiangxi Province, at last comes to a relative parameters conclusion in nutrient balance model of early-season rice、mid-season rice and late-season rice with different soil types and topography. Research results show:At Jiangxi Province the average for Nitrogen fertilizer utilization ratio is 32.04%, for P2O5 fertilizer utilization ratio is 27.96%, and for K20 fertilizer utilization ratio is 50.69%. Different soil types have different affection degree of fertilizer utilization and production. In different soil types NPK apparent fertilizer utilization at current season is different, hydromorphic tidal field sand clayey soil is the highest, then are hydromorphic yellow clayey soil、hydromorphic Ma sand clayey soil,hydromorphic eel clayey soil,hydromorphic yellow sand clay soil.hydromorphic red sand clay soil,submergenic red sand clay soil.submergenic yellow clayey soil,gleyic tidal field sand clayey soil and gleyic eel clayey soil. Different topographies affect fertilizer utilization and production, NPK apparent fertilizer utilization at current season is highest in flats, then is hilly land and mountainous area. Fertilizer apparent recovery fraction at current season is in inverse proportion of soil fertility, because the nutrient-supply capacity of high productivity fields are stronger, fertilizer utilization is lower; low productivity fields go by contraries. Different rice varieties affect fertilizer utilization, this research shows that fertilizer utilization of mid-season rice is higher than early-season rice and late-season rice, but diversity is not so remarkable.
2.In allusion to fertilization model defects of the traditional target yield method; Especially, when conducted in the county to promote soil testing and fertilizer, we are unable to do testing in order to determine the lacks of parameters; Using tests which have been carried out, calculating the parameters and adding a related adjustment factor, this research improves the traditional model of nutrient balance method for Rice Fertilization, and the improved nutrient balance model is FQ=Np×(G-B/Al)/ (Fn×FUR×Fl×F2×F3).The research results show:The improved nutrient balance model with fixed coefficients of fertilizer utilization ratio for different soil types, different topographies, and different rice varieties, can be promoted and used in the province. The tests at Fengxin show:The highest actual production is the improved model, the second is the traditional model, the third is the farmers'fertilizer practice, the lowest is without any action; With disposing actual production averages the improved nutrient balance model is 135kg/hm2 (Growth rate reached 1.9%) higher than the farmers'fertilizer practice, and is77.81 kg/hm2 (Growth rate reached 1.1%) higher than the traditional nutrient balance model. With the yield efficiency saving the improved model is highest, the traditional nutrient balance model is second, the farmers'fertilizer practice is least; the improved model is 367.91 yuan/ha. (Growth rate reached 20.63%) higher than the traditional model, and is 499.15yuan/ha. (Growth rate reached 30.21%) higher than the farmers'fertilizer practice. At the same time, disposing fertilize of the improved nutrient balance model is Closer to the target output.
3.With BP algorithm as a tool, using the test results, we construct the model of rice fertilization based on Jiangxi Province artificial neural network. And the results show that: Construction of the "4-4-10-3"neural network structure of rice fertilization has better training accuracy and testing accuracy. The correlation coefficient between the model results of NPK fertilizer applied and the actual value are 0.984,0.966,0.987; error rate are less than 1%with in the control; these have good generalization of sexual function. In Fengxin county, we select six pilot sites to fertilize with different ways, the research results show:The neural network model's actual production is 6846.75 kg/hm2 with the highest, the improved model's is 6813.75 kg/hm2 with the second, the farmers'fertilizer practice's is 6600.5 kg/hm2 with the third, and without any action's is 3967.95 kg/hm2 with the lowest. At the same time, we analyze variations between the different ways with significance analysis, the results show:the variations for production between the improved nutrient balance model and the neural network model are not obvious, but the variations for production between the farmers'fertilizer practice, the improved model and the neural network model are obvious.
4.Union tradition empirical datum, Synthesizes GIS and the modeling technology, starts from the county with large-scale problem of fertilizer to solve small-scale regional. This research analysed soil nutrient regionalizing distributed condition for hill county territory at Yiyang County, obtained the hill farming soil fertility comprehensive district, then got best economic value paddy fertilization program in all areas of the district. The findings indicated:The soil Nutrient at Yiyang County can be colligating zoned nine districts, the largest district is V which is 8005.22 ha., counting for 34.02%of the total farmland, and is the most district of grain production area at Yiyang County; The districts from large to small are IV III VII. II VIII VI IX; The smallest district is I which is 36.54 ha., only counting for 0.16%of the total farmland. The representative district fertilizer effect function equations represent the districts such as soil fertility, and relationship between yield fertilizer and output; These equations have strong pointed, solve the problem that the equation of a single fertilizer effect is difficult to represent different plots and different levels of fertility; and obviously increase data representation than before.
5.Take C# as the platform, using the secondary development function of ArcEngine. basing on building a fertilization module, we establish entity-relational database for fertilization, form the fertilizer submodule and paddy nutrient information management system. The research results show:The system uses the non-visual development languages, C# and the secondary GIS development function of ArcEngine, then it's operating environment can be divorced from GIS, it's functions like the general-purpose GIS software. The functions of paddy nutrient information management system divide into File management, information query, attribute editing, fertilizer recommend, and others relevant modules, every modules can divide into several functions. The system can make the best of fertile business management needs for the agricultural sector, and achieve business personnel requests at both improving efficiency and reducing work intensity.
1.以江西省17个县217个试验点265次“3414”试验数据,分析得到不同土壤类型、不同地形地貌条件下江西省早、中、晚稻养分平衡施肥模型中的相关参数。研究结果表明：江西省水稻氮肥肥料利用率平均值为32.04%,P2O5肥料利用率为27.96%,K20的肥料利用率为50.69%。不同土壤类型对肥料利用率及其产量有着不同程度的影响。不同土壤类型中NPK当季肥料利用率的高低基本上表现为潴育型潮沙泥田>潴育型黄泥田＞潴育型麻沙泥田>潴育型鳝泥田＞潴育型黄沙泥田＞潴育型红沙泥田>淹育型红沙泥田>淹育型黄泥田>潜育型潮沙泥田>潜育型鳝泥田。不同地貌类型对肥料利用率和水稻产量也有一定的影响,氮、磷、钾当季利用率的相对高低顺序为平原>丘陵>山地。肥料当季养分利用率的高低与土壤肥力水平成反比,即高肥力田块由于供肥能力强,肥料利用率低,而低肥力田块由于供肥能力弱,肥料利用率反而高。不同水稻品种对肥料利用率也要一定影响,本研究表明中稻的肥料利用率大于晚稻、早稻,但差异不显著。
2.针对传统的目标产量法施肥模型的缺陷,尤其是在县域推广进行测土配方施肥时,无法一一做试验确定其参数的不足,利用已进行的试验计算相关参数并添加相关调整系数对传统的养分平衡法水稻施肥模型进行改进。构建的改进型养分平衡法施肥模型为FQ=Np×(G-B/Al)/(Fn×FUR×Fl×F2×F3)。研究结果表明：利用全省不同土壤类型、不同地貌类型、不同水稻类型肥料利用率修正系数的改进型养分平衡法施肥模型,可以在全省进行推广使用。在奉新的试验表明改进型实际产量最高,传统型次之,习惯施肥再次,空白处理最低；改进型养分平衡法实际产量平均值比习惯施肥处理高135kg/hm2,增幅达1.9%,比传统养分平衡法处理高77.81 kg/hm2,增幅为1.1%。增产节支效益方面,改进型增产节支效益最高,传统养分平衡法次之,习惯施肥最小。改进型比传统型高出367.91元／公顷,增幅达20.63%。改进型比习惯施肥处理高出499.15元／公顷,增幅达30.21%。同时,可以看出,改进型推荐施肥的处理更接近目标产量。
3.以BP算法为工具,利用试验结果,构建了江西省基于人工神经网络的水稻施肥模型。研究结果表明：构建的"4-4-10-3”的水稻施肥神经网络结构,有较好的训练精度和测试精度。施NPK肥模型结果与实际值的相关性为0.997,、0.982,,0.972。误差率均控制在1%以内,具有较好的泛化性功能。在奉新县选择了六个试验点进行了不同处理的产量试验表明神经网络模型实际产量最高为6846.75kg/hm2,改进型次之为6813.75kg/hm2,习惯施肥再次之为6600.5kg/hm2,空白处理最低为3967.95kg/hm2。同时对处理间差异显著性进行分析,结果表明改进型养分平衡法施肥以及神经网络模型施肥的产量差异不显著,习惯施肥与改进型养分平衡法、神经网络模型法处理的产量差异显著。
4.结合传统试验数据,综合GIS和建模技术,从大尺度县域入手解决小尺度区域施肥问题；以弋阳县为例探讨分析丘陵县域土壤养分区域化分布的状况,实现丘陵耕地地力综合分区,得到各区水稻最佳经济效益施肥方案。研究结果表明：弋阳县土壤养分综合分区,共九个区,V区面积最大,达8005.22公顷,占全县耕地面积的34.02%,这个区域也是弋阳县最重要的粮食生产区域；面积大小依次是Ⅳ区、Ⅲ区、Ⅶ区、Ⅱ区、Ⅷ区、Ⅵ区、Ⅸ区,面积最小的是Ⅰ区,仅36.54公顷,仅占全县耕地面积的0.16%。所形成的具有代表性的各分区肥料效应函数方程分别代表各区肥力等别土壤施肥量与产量的关系,针对性强,解决了以往单个肥料效应方程难以代表不同地块及不同肥力水平的问题；相对以往的数据显著地增加了代表性。
5.以C#为平台,利用ArcEngine的二次开发功能,在构建施肥模型的基础上,建立了施肥的实体-关系型数据库,形成施肥子模块,并形成农田养分管理信息系统。研究结果表明：系统采用可视化开发语言C#和地理信息系统二次开发平台ArcEngine,可以脱离GIS工具软件的运行环境、功能完全可以不逊于通用型GIS软件。农田养分管理信息系统的功能分为文件管理、信息查询、属性编辑、施肥推荐等相关模块,每个模块中又分若干个子功能,同时可以最大限度的满足农业部门土肥业务管理的需求,系统达到了既提高办事效率和减轻业务人员的劳动强度的要求。
Accurate management of paddy nutrient is to improve the level of surveillance、evaluation and management, at the same time to improve the intelligence of agricultural techniques by advanced information technology, fertilization model is an important cornerstone for Fertilizer Technique, and is most one for Accurate management of paddy nutrient. Based on the research of fertilizer apparent recovery fraction at current season with different species of rice at different conditions, this research forms modified nutrient balance model and neural network model, establishes regionalized fertilization model of rice sativa by GIS. In conclusion, the organic integrated research and development of paddy nutrient information management is formed based on GIS and fertilizer recommendation model.
1.Based on 265 dates from 217 test points of "3414" in 17 rational countries of Jiangxi Province, at last comes to a relative parameters conclusion in nutrient balance model of early-season rice、mid-season rice and late-season rice with different soil types and topography. Research results show:At Jiangxi Province the average for Nitrogen fertilizer utilization ratio is 32.04%, for P2O5 fertilizer utilization ratio is 27.96%, and for K20 fertilizer utilization ratio is 50.69%. Different soil types have different affection degree of fertilizer utilization and production. In different soil types NPK apparent fertilizer utilization at current season is different, hydromorphic tidal field sand clayey soil is the highest, then are hydromorphic yellow clayey soil、hydromorphic Ma sand clayey soil,hydromorphic eel clayey soil,hydromorphic yellow sand clay soil.hydromorphic red sand clay soil,submergenic red sand clay soil.submergenic yellow clayey soil,gleyic tidal field sand clayey soil and gleyic eel clayey soil. Different topographies affect fertilizer utilization and production, NPK apparent fertilizer utilization at current season is highest in flats, then is hilly land and mountainous area. Fertilizer apparent recovery fraction at current season is in inverse proportion of soil fertility, because the nutrient-supply capacity of high productivity fields are stronger, fertilizer utilization is lower; low productivity fields go by contraries. Different rice varieties affect fertilizer utilization, this research shows that fertilizer utilization of mid-season rice is higher than early-season rice and late-season rice, but diversity is not so remarkable.
2.In allusion to fertilization model defects of the traditional target yield method; Especially, when conducted in the county to promote soil testing and fertilizer, we are unable to do testing in order to determine the lacks of parameters; Using tests which have been carried out, calculating the parameters and adding a related adjustment factor, this research improves the traditional model of nutrient balance method for Rice Fertilization, and the improved nutrient balance model is FQ=Np×(G-B/Al)/ (Fn×FUR×Fl×F2×F3).The research results show:The improved nutrient balance model with fixed coefficients of fertilizer utilization ratio for different soil types, different topographies, and different rice varieties, can be promoted and used in the province. The tests at Fengxin show:The highest actual production is the improved model, the second is the traditional model, the third is the farmers'fertilizer practice, the lowest is without any action; With disposing actual production averages the improved nutrient balance model is 135kg/hm2 (Growth rate reached 1.9%) higher than the farmers'fertilizer practice, and is77.81 kg/hm2 (Growth rate reached 1.1%) higher than the traditional nutrient balance model. With the yield efficiency saving the improved model is highest, the traditional nutrient balance model is second, the farmers'fertilizer practice is least; the improved model is 367.91 yuan/ha. (Growth rate reached 20.63%) higher than the traditional model, and is 499.15yuan/ha. (Growth rate reached 30.21%) higher than the farmers'fertilizer practice. At the same time, disposing fertilize of the improved nutrient balance model is Closer to the target output.
3.With BP algorithm as a tool, using the test results, we construct the model of rice fertilization based on Jiangxi Province artificial neural network. And the results show that: Construction of the "4-4-10-3"neural network structure of rice fertilization has better training accuracy and testing accuracy. The correlation coefficient between the model results of NPK fertilizer applied and the actual value are 0.984,0.966,0.987; error rate are less than 1%with in the control; these have good generalization of sexual function. In Fengxin county, we select six pilot sites to fertilize with different ways, the research results show:The neural network model's actual production is 6846.75 kg/hm2 with the highest, the improved model's is 6813.75 kg/hm2 with the second, the farmers'fertilizer practice's is 6600.5 kg/hm2 with the third, and without any action's is 3967.95 kg/hm2 with the lowest. At the same time, we analyze variations between the different ways with significance analysis, the results show:the variations for production between the improved nutrient balance model and the neural network model are not obvious, but the variations for production between the farmers'fertilizer practice, the improved model and the neural network model are obvious.
4.Union tradition empirical datum, Synthesizes GIS and the modeling technology, starts from the county with large-scale problem of fertilizer to solve small-scale regional. This research analysed soil nutrient regionalizing distributed condition for hill county territory at Yiyang County, obtained the hill farming soil fertility comprehensive district, then got best economic value paddy fertilization program in all areas of the district. The findings indicated:The soil Nutrient at Yiyang County can be colligating zoned nine districts, the largest district is V which is 8005.22 ha., counting for 34.02%of the total farmland, and is the most district of grain production area at Yiyang County; The districts from large to small are IV III VII. II VIII VI IX; The smallest district is I which is 36.54 ha., only counting for 0.16%of the total farmland. The representative district fertilizer effect function equations represent the districts such as soil fertility, and relationship between yield fertilizer and output; These equations have strong pointed, solve the problem that the equation of a single fertilizer effect is difficult to represent different plots and different levels of fertility; and obviously increase data representation than before.
5.Take C# as the platform, using the secondary development function of ArcEngine. basing on building a fertilization module, we establish entity-relational database for fertilization, form the fertilizer submodule and paddy nutrient information management system. The research results show:The system uses the non-visual development languages, C# and the secondary GIS development function of ArcEngine, then it's operating environment can be divorced from GIS, it's functions like the general-purpose GIS software. The functions of paddy nutrient information management system divide into File management, information query, attribute editing, fertilizer recommend, and others relevant modules, every modules can divide into several functions. The system can make the best of fertile business management needs for the agricultural sector, and achieve business personnel requests at both improving efficiency and reducing work intensity.
引文
[1]赵其国.现代土壤学与农业持续发展.土壤学报,1996.33(1)：1-12.
[2]Russell. E.J. Soil conditions and plant growth. John Wiley&Sons. New York.,1988.
[3]赵其国,叶方.信息化与农业现代化.土壤学报，2004.41)3)：449-4550
[4]李仁岗.肥料效应函数.农业出版社1987.1-87.
[5]Angus JF. Bowden JW and Keating BA. Modelling nutrient responses in the field. In:BarrowNJ (ed.)Plant nutrition from genetic engineering to field practice. Kluwer Publisher,1993,59～68.
[6]Black CA. Soil fertility evaluation and control. LEWIS Publishers, Bockaton,1992,1～69.
[7]Baum EL, Heady EO and BlackmoreJ. Economic analysis of fertilizer use data. The Iowa State College Press-Ames. Iowa, U. S. A. Pretace.1956.
[8]王兴仁,陈新平.张福锁.毛达如施肥模型在我国推荐施肥中的应用植物营养与肥料学报19984(1)67-74
[9]王兴仁、张福锁等编著.现代肥料试验设计.中国农业出版社,1996,164～185.
[10]高亮之等水稻计算机模拟模型及其应用之一：水稻钟模型-水稻发育的计算机模型,中国农业气象,1989.10(2)：3-10
[11]张宁陶灿炎.冬小麦生长发育的模拟研究南京气象学院学报,1991,14(1)：113-121.
[12]黄耀高亮之.水稻群体茎雍动态的计算机模型,生态学杂志,1994.13(4)27-32.
[13]赵春江,诸德辉,李鸿祥等小麦栽培管理计算机专家系统的研究与应用,中国农业科学,1997,30(5)：42-49
[14].曹卫星,潘洁等.基于生长模型与Web应用的小麦管理决策支持系统.农业工程学报2007.23(1)：133-138.
[15]Reetz HFJr. Site-specific nutrient management systems for the 1990s. Better crops with plant food(78). No.4:14-19.
[16]Atherton. B.C..M.T.Morgan,S.A.Shearere.T.S.Stombawgh,and A.D.Ward.Site-specific farming:A perspective on information needs. benefits,and limitations. J. Soil Water Conserv. 1999,54(2):455～61.
[17]Blackmore B S. Wheeler P N, Morris R M, et al. The Role of Precision Farming in Sustainable Agriculture;A European Perspective. Presented at the 2nd International Conference on Site Specific Management for Agriculture Systems. Minneapolis USA. March,1994.
[18]Earl R, Wheeler PN. Blackmore BS and Godwin RJ, Precision farming:the management of variability. Landwards,1996.51(4):18～23.
[19]Yao Chi Lu,Craig Daughtry,Galen Hart et al,The current state of precision farm ing. Food Rev.Int,1997,(2):141～162.
[20]Johnson, Ron C., Target Farming, A Brief Introduction to Precision Agriculture, Proceed ings-1998 Direct Seeding Conference, Saskatchewan Soil Conservation Association, Regina 1998,15-18.
[21]van Schilfgaarde, J. Is precision agriculture sustainable? Am. J. Altern. Agric.1999,14(1): 43～46.
[22]Bullock, D.G, D.S. Bullock, E.D. Nafzinger, T.A. Doerge,S.R.Paskiewicz, P.R. Carter, and T.A.Peterson. Does variable rate seeding of corn pay? Agron.J.1998,90:830-836.
[23]Bullock, D.S., and D.G. Bullock. Economic optimality of input application rates in precision farming. Precis. Agric.2000,2:71～101.
[24]JESS L D. A Bird's Eye View of Precision Ag [A]. Precision AG[C],2004.8-10.
[25]BOUMA J, FINKE P A. Origin and Nature of Soil Resource Variability. Soil Specific Crop Management [M]. ASA, CSSA,SSSA, Madison, WI,1993.3-14.
[26]DOBERMANN A, WITT C, DAWE D. Increasing the Productivity of Intensive Rice Systems Through Site-specific Nutrient Management[M]. Science Publishers, IRRI, Enfield& LosBanos. 2004
[27]TASNEE A, YOST R S. A Site-specific Nutrient Management Approach for Maize [J]. Better Crops International,2003,17(1):3-7.
[28]YADAV K S, HIRA N. Site-specific Nutrient Management for Optimal Foodgrain Production in Haryana [J]. Better Crops,2004,88(2):21-23.
[29]ARVIND K, SHUKLA V K, SINGH B S, et al. Site-specific Nutrient Management for Maximum Economic Yield of the Rice-Wheat Cropping System [J]. Better Crops,2004,88 (4):18-21.
[30]JOSE E.FRANCISCO M.SERGIO C.et al.GIS-based Site-Specific Management of Cocoa[J]. BetterCrpps.2006,90(1):36-39.
[31]徐吉炎和R.Webster,土壤调查数据地域统计的最佳估值研究---彰武县表层土全氮量的半方筹图和块状Kriging估值.土壤学报,1983.20(4)：419-430.
[32]李菊梅和李生秀,几种营养元素在土壤中的空间变异.干旱地区农业研究,1998；16(2)：58-64
[33]周慧珍,龚子同和LampJ,土壤空间变异性研究.土壤学报,199633(3)：232---2410
[34]张有山,林启美,秦耀东和李保国,大比例尺区域土壤养分空间变异定量分析.华北农学报,1998.，13(1)：122-128
[35]王学锋和章衡,1995.土壤有机质的空间变异性.土壤,27(2)：85-89
[36]姜城.不同经营体制下土壤养分空间变异规律及管理技术研究,中国农业科学院研究生院博士学位论文，北京.2000。
[37]胡克林,李保国.林启美,李贵铜和陈德立,农田土壤养分的空间变异性特征，农业工程学报,1999.15(3)：33-38
[38]杨俐苹,评价区域性土壤肥力的取样技术的回顾与展望,土壤肥料.20001：3-8
[39]郭旭东，傅伯杰,陈利顶.河北省遵化平原土壤养分的时空变异特征-变异函数与K riging插值分析.地理学报，200055(5)：555-566
[40]雷咏雯,危常州.李俊华,侯振安，冶军，鲍柏杨，不同尺度下土壤养分年空间变异特征的研究,土壤，2004,36(4)：376-381
[41]白由路,金继运.杨俐苹,不同尺度的土壤养分变异特征与管理,见：金继运,白由路主编,精准农业与十壤管理,北京：中国大地出版社,2001：51-59
[42]朱安宁,张佳宝.张玉铭，信秀丽.潮褐土土壤养分的空间分布特征，土壤通报.2004.35(2)：.97-101。
[43]白由路,金继运，杨俐苹，梁鸣早,基于GIS的土壤养分分管理模型研究,中国农业科学,2001,34(1)：46-50
[44]盛建东，蒋平安,文启凯，武红旗,杨玉玲.李荣,基于GIS的区域土壤养分管理与作物推荐施肥信息系统研究,土壤,2002,第2期：77-81
[45]黄绍文,金继运,杨俐苹,程明芳.县级区域粮田土壤养分空间变异与区管理技术研究,土壤学报200340(1)：79
[46]危常州,侯振安,雷咏雯,朱和明,张福锁,鲍柏杨,郭深,王桂花,不同地理尺度下综合肥模型的建模与验证.植物营养与肥料学报,2005.11(1)：13-20
[47]Suan-Pheng, K., Applying GIS technology to natural resource management in developing countries:The realities behind the promises. In The practice of GIS applications:Problems and challenges,Part 1. Proc. Seminar on GIS and Developing Countries,2nd. June 1993. GISDECO, Utrecht,Netherlands.1993. p.1-20.
[48]Hartkamp A. D, Jeffrey W. White and Gerrit Hoogenboom, Interfacing Geographic Information Systems with Agronomic Modeling---A Review, Agronomy Journal 1999.91:761-772.
[49]Campbell WG, Church M.R., Bishop GD., Mortenson D.C., Pierson S.M. The role for a geographical information system in a large environmental project. Int. J. Geogr.Inf.Syst.1989;3(4):349-362.
[50]Hartkamp A. D, Jeffrey W. White and Gerrit Hoogenboom, Interfacing Geographic Information Systems with Agronomic Modeling---A Review, tlgronomy Journal 1999.91:761-772.
[51]Tim U.S.Coupling Vadose zone models with GIS:Emerging trends and potential bottlenecks J.Environ.Qual.1996;25:535-544.
[52]Lam, D.C.L., D.A. Swayne, C.I. Mayfield, and D.D. Cowan.Integration of GIS with other software systems:Integration versus interconnection. In Proc. Int.Conf.Workshop on Integrating GIS and Environmental Modeling,3rd, Santa Fe, NM.21-25 Jan.1996. Natl. Ctr. for Geographic Information and Analysis, Santa Barbara, CA.1996.
[53]Burrough, P.A. Environmental modeling with geographical information systems. In A. Stein, et al.(ed.) Models in action. Proc. Seminar Ser., Quantitative Approaches in Systems Analysis. DLO Res.Inst. for Agrobiology and Soil Fertility and C.T. de Wit Graduate School for Production Ecology,Wageningen, Netherlands.6. June 1996. p.59-69.
[54]李生秀.植物营养与肥料学科的现状与望[J].植物养与肥料学报,1999,5(3)：193-205
[55]农牧渔业部农业局.配方施肥技术工作要点[J].土壤肥料,1987,(1)：6-12
[56]金耀青.配方施肥的方法及其功能(-对我国配方施肥工作的评述)[J].土壤通报,1989,20(1)：46-49
[57]周鸣铮.中国的测土施肥[J].土壤,1987,(1)：7-13
[58]侯彦林施肥模型研究综述[J].土壤通报,200435(4)：493-501
[59]刘文通,刘声元,郝景发.长春地区诊断施肥量计算公式中几个参数的探讨[J].土壤通报,1984.15(3)：117-120
[60]张宽,赵景云，王秀芳，等.黑土供磷能力与磷肥经济合理用量问题的初步研究[J].土壤通报，1984,(3)：120-123
[61]姜文彬.杨铁成,单文波.玉米诊断施肥技术的研究与应用.吉林农业大学学报[J].1986,8(4)：62-68
[62]张大光.刘武仁,边秀芝，等.玉米测土施肥中几个主要参数及其应用的研究[J].吉林农业科学.1987(1)：58-63
[63]周鸣铮，王竺美.浙江省水稻土“因产定氮”基本公式及其有关参数的探讨[J].土壤学报,1987,24(2)：127-133
[64]李伟波,李运东,王辉.用15N研究吉林黑土春玉米对氮肥的吸收利用[J].土壤学报,2001,38(4)：476-482
[65]侯彦林.“生态平衡施肥”的理论基础和技术体系[J].生态学报.2000.20(4)：653-658
[66]张绍林,朱兆良,徐银华,等.关于太湖地区稻麦上氮肥的适宜用量[J].土壤，1988.20(1)：5-9
[67]陈德立.朱兆良.稻田十壤供氮能力的解析研究[J].土壤学报,1988,25(3)：262-267
[68]朱兆良.关于稻田土壤供氮量的预测和平均适宜施氮量的应用[J].土壤，1988,20(2)：57-61
[69]Greenland D J and Watanabe I. The Continuing Nitrogen Enigma.Trans.12th Inter-congr [J]Soil Sci.,1982,5:123-137.
[70]刘文通,刘声元,郝景发.长春地区诊断施肥量计算公式中几个参数的探讨[J].土壤通报，1984，15(3)：117-120
[71]张宽,赵景云,王秀芳，等.黑十供磷能力与磷肥经济合理用量问题的初步研究[J].土壤通报，1984,(3)：120-123
[72]张大光,刘武仁：.边秀芝,等.玉米测十施肥中儿个主要参数及其应用的研究[J].吉林农业科学.1987(1)：58-63
[73]姜文彬,杨铁成,单文波.玉米诊断施肥技术的研究与应用.吉林农业大学学报[J].1986,8(4)：62-68.
[74]Andreas Gransee and Wolfgang Merbach. Phosphorus Dynamics in A Long-Term P Fertilization Trial on Luvic Phaeozem at Halle[J]. J.Plant Nutr.Soil Sci.,2000,163:353-357
[75]唐近春.中国十壤肥料工作的成就与任务[J].土壤学报，1994,31(4)：341-347
[76]Schroder J J, Neeteson J J, and Withagen J C M, et al. Effects of N Application on Agrono
--mic and Environmental Parameters in Silage Maize Production on Sandy Soils. Field Crops Research,1998,58:55-67.
[77]Bangar A R. Fertilization of Sorghum Based on Modified Mitscherlich-Bray Equation Under Semi-Arid Tropics. Journal of the Indian Society of Soil Science,1998,46 (3):383-391.
[78]Ghosh P C and Misra U K. Modified Mitscherlich-Bray Equation for Calculation of Crop Response to Applied Phosphate. Journal of the Indian Society of Soil Science,1996,44 (4):786-788
[79]Sonawane S S and Sonar K R Application of Mitscherlic-BrayEquation for Fertilizer Use in Peari Millet on Vertisol. Journalof the Indian Society of Soil Science,1995,43 (2):276-277
[80]Narendra K S.Identification and control of dynamic systems using neural networlcs,IEEE Trans. on Neural Networks.1990,1 (1):4-27P
[81]Hopfield, J. J.,1982. Neural networks and physical systems with emergent collective computational properties. Proceedings of the National Academy of Sciences, vol.79, pp. 2554-2558.
[82]Hopfield. J.J.,1984. Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences, vol. 81, pp.3088-3092.
[83]Rumelhart, D. E. and McClelland. J. L.. eds.,1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol.1. Cambridge.MA:MIT Press.
[84]Rumelhart, D. E., Hinton, G E. and Williams. R. J.,1986. Learning representations by back-propagaing errors. Nature, vol.323, pp.533-536.
[85]汤江龙 土地利用规划人工神经网络模型构建及应用研究[D]南京农业大学2006
[86]国土资源部中国地质调查局中华人民共和国多目标区域地球化学图集——江西省鄱阳湖及周边经济区[M]北京地质出版社,2010.9
[87]刘付程.史学正.于东升,等.基于统计学和GIS的太湖典型地区土壤属性制图研究以土壤全氮制图为例.土壤学报,2004,41(1)：20-27
[88]ZHU W, HOPE D, GRIES C. et al. Soil Characteristics and the Accumulation of Inorganic Nitrogen in an Arid Urban Ecosystem. Ecosystems.2006, (9):711-724.
[89]JORDAN C. ZHANG C, HIGGINS A. Using GIS and statistics to study influences of geology on probability features of surface soil geochemistry in Northern Ireland. Journal of Geochemical Exploration.2007. (93):135-152
[90]潘瑜春.刘巧芹.閻波杰，陆洲,周艳兵.采样尺度对土壤养分空间变异分析的影响.土壤通报.2010.(2)
[91]杨忠华,刘方,赵泽英,冯廷玺.基于GIS和地统计学的农田土壤养分空间变异性研究.贵州农业科学2009,37(9)：120～123
[92]Schroder J J. Neeteson J J, and Withagen J C M. et al. Effects of N Application on Agronomic and Environmental Parameters in Silage Maize Production on Sandy Soils. Field Crops Research,1998,58:55-67
[93]Bangar A R. Fertilization of Sorghum Based on Modified Mitscherlich-Bray Equation Under Semi-Arid Tropics. Journal of the Indian Society of Soil Science,1998,46 (3):383-391
[94]Ghosh P C and Misra U K. Modified Mitscherlich-Bray Equation for Calculation of Crop Response to Applied Phosphate. Journal of the Indian Society of Soil Science,1996,44 (4):786-788
[95]Sonawane S S and Sonar K R,Application of Mitscherlic-BrayEquation for-FertilizerUse Peari Millet on Vertisol. Journalof the Indian Society of Soil Science,1995,43 (2):276-277
[96]杨忠华,刘方,赵泽英,冯廷玺.基于GIS和地统计学的农田土壤养分空间变异性研究.贵州农业科学2009,37(9)：120-123.
[97]徐赛华.软件需求分析研究[J].吉林师范大学学报(自然科学版),2006(1)：104-105
[98]石伟,王中华.一种用E-R模型实现数据库设计的方法[J].计算机技术与应用,2000,20(2)：22-25
[99]蔡德利,翟瑞常,侯雪坤.养分平衡法配方施肥数据库建模[J].中国农学通报,2007,23(6)：401-405
[100]刘艳飞基于测土配方施肥试验的肥料效应与最佳施肥量研究[D]华中农业大学 2008
[101]陆海燕精确农业变量施肥决策系统研究[D]江苏大学2005
[102]吴才聪精确农业变量施肥决策研究与技术经济分析[D]吉林大学2003
[103]唐秀美 基于 GIS的县域耕地地力分析及测土配方施肥技术研究[D] 山东农业大学2008
[2]Russell. E.J. Soil conditions and plant growth. John Wiley&Sons. New York.,1988.
[3]赵其国,叶方.信息化与农业现代化.土壤学报，2004.41)3)：449-4550
[4]李仁岗.肥料效应函数.农业出版社1987.1-87.
[5]Angus JF. Bowden JW and Keating BA. Modelling nutrient responses in the field. In:BarrowNJ (ed.)Plant nutrition from genetic engineering to field practice. Kluwer Publisher,1993,59～68.
[6]Black CA. Soil fertility evaluation and control. LEWIS Publishers, Bockaton,1992,1～69.
[7]Baum EL, Heady EO and BlackmoreJ. Economic analysis of fertilizer use data. The Iowa State College Press-Ames. Iowa, U. S. A. Pretace.1956.
[8]王兴仁,陈新平.张福锁.毛达如施肥模型在我国推荐施肥中的应用植物营养与肥料学报19984(1)67-74
[9]王兴仁、张福锁等编著.现代肥料试验设计.中国农业出版社,1996,164～185.
[10]高亮之等水稻计算机模拟模型及其应用之一：水稻钟模型-水稻发育的计算机模型,中国农业气象,1989.10(2)：3-10
[11]张宁陶灿炎.冬小麦生长发育的模拟研究南京气象学院学报,1991,14(1)：113-121.
[12]黄耀高亮之.水稻群体茎雍动态的计算机模型,生态学杂志,1994.13(4)27-32.
[13]赵春江,诸德辉,李鸿祥等小麦栽培管理计算机专家系统的研究与应用,中国农业科学,1997,30(5)：42-49
[14].曹卫星,潘洁等.基于生长模型与Web应用的小麦管理决策支持系统.农业工程学报2007.23(1)：133-138.
[15]Reetz HFJr. Site-specific nutrient management systems for the 1990s. Better crops with plant food(78). No.4:14-19.
[16]Atherton. B.C..M.T.Morgan,S.A.Shearere.T.S.Stombawgh,and A.D.Ward.Site-specific farming:A perspective on information needs. benefits,and limitations. J. Soil Water Conserv. 1999,54(2):455～61.
[17]Blackmore B S. Wheeler P N, Morris R M, et al. The Role of Precision Farming in Sustainable Agriculture;A European Perspective. Presented at the 2nd International Conference on Site Specific Management for Agriculture Systems. Minneapolis USA. March,1994.
[18]Earl R, Wheeler PN. Blackmore BS and Godwin RJ, Precision farming:the management of variability. Landwards,1996.51(4):18～23.
[19]Yao Chi Lu,Craig Daughtry,Galen Hart et al,The current state of precision farm ing. Food Rev.Int,1997,(2):141～162.
[20]Johnson, Ron C., Target Farming, A Brief Introduction to Precision Agriculture, Proceed ings-1998 Direct Seeding Conference, Saskatchewan Soil Conservation Association, Regina 1998,15-18.
[21]van Schilfgaarde, J. Is precision agriculture sustainable? Am. J. Altern. Agric.1999,14(1): 43～46.
[22]Bullock, D.G, D.S. Bullock, E.D. Nafzinger, T.A. Doerge,S.R.Paskiewicz, P.R. Carter, and T.A.Peterson. Does variable rate seeding of corn pay? Agron.J.1998,90:830-836.
[23]Bullock, D.S., and D.G. Bullock. Economic optimality of input application rates in precision farming. Precis. Agric.2000,2:71～101.
[24]JESS L D. A Bird's Eye View of Precision Ag [A]. Precision AG[C],2004.8-10.
[25]BOUMA J, FINKE P A. Origin and Nature of Soil Resource Variability. Soil Specific Crop Management [M]. ASA, CSSA,SSSA, Madison, WI,1993.3-14.
[26]DOBERMANN A, WITT C, DAWE D. Increasing the Productivity of Intensive Rice Systems Through Site-specific Nutrient Management[M]. Science Publishers, IRRI, Enfield& LosBanos. 2004
[27]TASNEE A, YOST R S. A Site-specific Nutrient Management Approach for Maize [J]. Better Crops International,2003,17(1):3-7.
[28]YADAV K S, HIRA N. Site-specific Nutrient Management for Optimal Foodgrain Production in Haryana [J]. Better Crops,2004,88(2):21-23.
[29]ARVIND K, SHUKLA V K, SINGH B S, et al. Site-specific Nutrient Management for Maximum Economic Yield of the Rice-Wheat Cropping System [J]. Better Crops,2004,88 (4):18-21.
[30]JOSE E.FRANCISCO M.SERGIO C.et al.GIS-based Site-Specific Management of Cocoa[J]. BetterCrpps.2006,90(1):36-39.
[31]徐吉炎和R.Webster,土壤调查数据地域统计的最佳估值研究---彰武县表层土全氮量的半方筹图和块状Kriging估值.土壤学报,1983.20(4)：419-430.
[32]李菊梅和李生秀,几种营养元素在土壤中的空间变异.干旱地区农业研究,1998；16(2)：58-64
[33]周慧珍,龚子同和LampJ,土壤空间变异性研究.土壤学报,199633(3)：232---2410
[34]张有山,林启美,秦耀东和李保国,大比例尺区域土壤养分空间变异定量分析.华北农学报,1998.，13(1)：122-128
[35]王学锋和章衡,1995.土壤有机质的空间变异性.土壤,27(2)：85-89
[36]姜城.不同经营体制下土壤养分空间变异规律及管理技术研究,中国农业科学院研究生院博士学位论文，北京.2000。
[37]胡克林,李保国.林启美,李贵铜和陈德立,农田土壤养分的空间变异性特征，农业工程学报,1999.15(3)：33-38
[38]杨俐苹,评价区域性土壤肥力的取样技术的回顾与展望,土壤肥料.20001：3-8
[39]郭旭东，傅伯杰,陈利顶.河北省遵化平原土壤养分的时空变异特征-变异函数与K riging插值分析.地理学报，200055(5)：555-566
[40]雷咏雯,危常州.李俊华,侯振安，冶军，鲍柏杨，不同尺度下土壤养分年空间变异特征的研究,土壤，2004,36(4)：376-381
[41]白由路,金继运.杨俐苹,不同尺度的土壤养分变异特征与管理,见：金继运,白由路主编,精准农业与十壤管理,北京：中国大地出版社,2001：51-59
[42]朱安宁,张佳宝.张玉铭，信秀丽.潮褐土土壤养分的空间分布特征，土壤通报.2004.35(2)：.97-101。
[43]白由路,金继运，杨俐苹，梁鸣早,基于GIS的土壤养分分管理模型研究,中国农业科学,2001,34(1)：46-50
[44]盛建东，蒋平安,文启凯，武红旗,杨玉玲.李荣,基于GIS的区域土壤养分管理与作物推荐施肥信息系统研究,土壤,2002,第2期：77-81
[45]黄绍文,金继运,杨俐苹,程明芳.县级区域粮田土壤养分空间变异与区管理技术研究,土壤学报200340(1)：79
[46]危常州,侯振安,雷咏雯,朱和明,张福锁,鲍柏杨,郭深,王桂花,不同地理尺度下综合肥模型的建模与验证.植物营养与肥料学报,2005.11(1)：13-20
[47]Suan-Pheng, K., Applying GIS technology to natural resource management in developing countries:The realities behind the promises. In The practice of GIS applications:Problems and challenges,Part 1. Proc. Seminar on GIS and Developing Countries,2nd. June 1993. GISDECO, Utrecht,Netherlands.1993. p.1-20.
[48]Hartkamp A. D, Jeffrey W. White and Gerrit Hoogenboom, Interfacing Geographic Information Systems with Agronomic Modeling---A Review, Agronomy Journal 1999.91:761-772.
[49]Campbell WG, Church M.R., Bishop GD., Mortenson D.C., Pierson S.M. The role for a geographical information system in a large environmental project. Int. J. Geogr.Inf.Syst.1989;3(4):349-362.
[50]Hartkamp A. D, Jeffrey W. White and Gerrit Hoogenboom, Interfacing Geographic Information Systems with Agronomic Modeling---A Review, tlgronomy Journal 1999.91:761-772.
[51]Tim U.S.Coupling Vadose zone models with GIS:Emerging trends and potential bottlenecks J.Environ.Qual.1996;25:535-544.
[52]Lam, D.C.L., D.A. Swayne, C.I. Mayfield, and D.D. Cowan.Integration of GIS with other software systems:Integration versus interconnection. In Proc. Int.Conf.Workshop on Integrating GIS and Environmental Modeling,3rd, Santa Fe, NM.21-25 Jan.1996. Natl. Ctr. for Geographic Information and Analysis, Santa Barbara, CA.1996.
[53]Burrough, P.A. Environmental modeling with geographical information systems. In A. Stein, et al.(ed.) Models in action. Proc. Seminar Ser., Quantitative Approaches in Systems Analysis. DLO Res.Inst. for Agrobiology and Soil Fertility and C.T. de Wit Graduate School for Production Ecology,Wageningen, Netherlands.6. June 1996. p.59-69.
[54]李生秀.植物营养与肥料学科的现状与望[J].植物养与肥料学报,1999,5(3)：193-205
[55]农牧渔业部农业局.配方施肥技术工作要点[J].土壤肥料,1987,(1)：6-12
[56]金耀青.配方施肥的方法及其功能(-对我国配方施肥工作的评述)[J].土壤通报,1989,20(1)：46-49
[57]周鸣铮.中国的测土施肥[J].土壤,1987,(1)：7-13
[58]侯彦林施肥模型研究综述[J].土壤通报,200435(4)：493-501
[59]刘文通,刘声元,郝景发.长春地区诊断施肥量计算公式中几个参数的探讨[J].土壤通报,1984.15(3)：117-120
[60]张宽,赵景云，王秀芳，等.黑土供磷能力与磷肥经济合理用量问题的初步研究[J].土壤通报，1984,(3)：120-123
[61]姜文彬.杨铁成,单文波.玉米诊断施肥技术的研究与应用.吉林农业大学学报[J].1986,8(4)：62-68
[62]张大光.刘武仁,边秀芝，等.玉米测土施肥中几个主要参数及其应用的研究[J].吉林农业科学.1987(1)：58-63
[63]周鸣铮，王竺美.浙江省水稻土“因产定氮”基本公式及其有关参数的探讨[J].土壤学报,1987,24(2)：127-133
[64]李伟波,李运东,王辉.用15N研究吉林黑土春玉米对氮肥的吸收利用[J].土壤学报,2001,38(4)：476-482
[65]侯彦林.“生态平衡施肥”的理论基础和技术体系[J].生态学报.2000.20(4)：653-658
[66]张绍林,朱兆良,徐银华,等.关于太湖地区稻麦上氮肥的适宜用量[J].土壤，1988.20(1)：5-9
[67]陈德立.朱兆良.稻田十壤供氮能力的解析研究[J].土壤学报,1988,25(3)：262-267
[68]朱兆良.关于稻田土壤供氮量的预测和平均适宜施氮量的应用[J].土壤，1988,20(2)：57-61
[69]Greenland D J and Watanabe I. The Continuing Nitrogen Enigma.Trans.12th Inter-congr [J]Soil Sci.,1982,5:123-137.
[70]刘文通,刘声元,郝景发.长春地区诊断施肥量计算公式中几个参数的探讨[J].土壤通报，1984，15(3)：117-120
[71]张宽,赵景云,王秀芳，等.黑十供磷能力与磷肥经济合理用量问题的初步研究[J].土壤通报，1984,(3)：120-123
[72]张大光,刘武仁：.边秀芝,等.玉米测十施肥中儿个主要参数及其应用的研究[J].吉林农业科学.1987(1)：58-63
[73]姜文彬,杨铁成,单文波.玉米诊断施肥技术的研究与应用.吉林农业大学学报[J].1986,8(4)：62-68.
[74]Andreas Gransee and Wolfgang Merbach. Phosphorus Dynamics in A Long-Term P Fertilization Trial on Luvic Phaeozem at Halle[J]. J.Plant Nutr.Soil Sci.,2000,163:353-357
[75]唐近春.中国十壤肥料工作的成就与任务[J].土壤学报，1994,31(4)：341-347
[76]Schroder J J, Neeteson J J, and Withagen J C M, et al. Effects of N Application on Agrono
--mic and Environmental Parameters in Silage Maize Production on Sandy Soils. Field Crops Research,1998,58:55-67.
[77]Bangar A R. Fertilization of Sorghum Based on Modified Mitscherlich-Bray Equation Under Semi-Arid Tropics. Journal of the Indian Society of Soil Science,1998,46 (3):383-391.
[78]Ghosh P C and Misra U K. Modified Mitscherlich-Bray Equation for Calculation of Crop Response to Applied Phosphate. Journal of the Indian Society of Soil Science,1996,44 (4):786-788
[79]Sonawane S S and Sonar K R Application of Mitscherlic-BrayEquation for Fertilizer Use in Peari Millet on Vertisol. Journalof the Indian Society of Soil Science,1995,43 (2):276-277
[80]Narendra K S.Identification and control of dynamic systems using neural networlcs,IEEE Trans. on Neural Networks.1990,1 (1):4-27P
[81]Hopfield, J. J.,1982. Neural networks and physical systems with emergent collective computational properties. Proceedings of the National Academy of Sciences, vol.79, pp. 2554-2558.
[82]Hopfield. J.J.,1984. Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences, vol. 81, pp.3088-3092.
[83]Rumelhart, D. E. and McClelland. J. L.. eds.,1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol.1. Cambridge.MA:MIT Press.
[84]Rumelhart, D. E., Hinton, G E. and Williams. R. J.,1986. Learning representations by back-propagaing errors. Nature, vol.323, pp.533-536.
[85]汤江龙 土地利用规划人工神经网络模型构建及应用研究[D]南京农业大学2006
[86]国土资源部中国地质调查局中华人民共和国多目标区域地球化学图集——江西省鄱阳湖及周边经济区[M]北京地质出版社,2010.9
[87]刘付程.史学正.于东升,等.基于统计学和GIS的太湖典型地区土壤属性制图研究以土壤全氮制图为例.土壤学报,2004,41(1)：20-27
[88]ZHU W, HOPE D, GRIES C. et al. Soil Characteristics and the Accumulation of Inorganic Nitrogen in an Arid Urban Ecosystem. Ecosystems.2006, (9):711-724.
[89]JORDAN C. ZHANG C, HIGGINS A. Using GIS and statistics to study influences of geology on probability features of surface soil geochemistry in Northern Ireland. Journal of Geochemical Exploration.2007. (93):135-152
[90]潘瑜春.刘巧芹.閻波杰，陆洲,周艳兵.采样尺度对土壤养分空间变异分析的影响.土壤通报.2010.(2)
[91]杨忠华,刘方,赵泽英,冯廷玺.基于GIS和地统计学的农田土壤养分空间变异性研究.贵州农业科学2009,37(9)：120～123
[92]Schroder J J. Neeteson J J, and Withagen J C M. et al. Effects of N Application on Agronomic and Environmental Parameters in Silage Maize Production on Sandy Soils. Field Crops Research,1998,58:55-67
[93]Bangar A R. Fertilization of Sorghum Based on Modified Mitscherlich-Bray Equation Under Semi-Arid Tropics. Journal of the Indian Society of Soil Science,1998,46 (3):383-391
[94]Ghosh P C and Misra U K. Modified Mitscherlich-Bray Equation for Calculation of Crop Response to Applied Phosphate. Journal of the Indian Society of Soil Science,1996,44 (4):786-788
[95]Sonawane S S and Sonar K R,Application of Mitscherlic-BrayEquation for-FertilizerUse Peari Millet on Vertisol. Journalof the Indian Society of Soil Science,1995,43 (2):276-277
[96]杨忠华,刘方,赵泽英,冯廷玺.基于GIS和地统计学的农田土壤养分空间变异性研究.贵州农业科学2009,37(9)：120-123.
[97]徐赛华.软件需求分析研究[J].吉林师范大学学报(自然科学版),2006(1)：104-105
[98]石伟,王中华.一种用E-R模型实现数据库设计的方法[J].计算机技术与应用,2000,20(2)：22-25
[99]蔡德利,翟瑞常,侯雪坤.养分平衡法配方施肥数据库建模[J].中国农学通报,2007,23(6)：401-405
[100]刘艳飞基于测土配方施肥试验的肥料效应与最佳施肥量研究[D]华中农业大学 2008
[101]陆海燕精确农业变量施肥决策系统研究[D]江苏大学2005
[102]吴才聪精确农业变量施肥决策研究与技术经济分析[D]吉林大学2003
[103]唐秀美 基于 GIS的县域耕地地力分析及测土配方施肥技术研究[D] 山东农业大学2008
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |