摘要
土壤养分是土壤提供的植物生长发育所必需的营养元素。由于受到自然因素和人为因素的共同作用,土壤养分具有高度的空间变异性。土壤的这种特性不仅表现在区域尺度上,而且也表现在田块尺度上。在研究方法上经历了从传统统计学到地统计学,再到神经网络、地理信息技术以及高精度曲面建模等新方法的不断改进过程。文章从地统计学方法引入到土壤养分空间变异研究中为出发点,论述了国内外基于地统计学的土壤养分空间变异的研究现状,主要包括利用地统计学方法来确定合理的土壤采样数目,土壤养分空间变异的定量化研究,土壤养分空间变异的尺度效应;然后简述了神经网络、地理信息技术、高精度曲面建模等技术在土壤养分空间变异研究中的研究现状和应用。最后对比分析了各种研究方法在应用中存在的缺陷,同时指明了今后应加强作物生长的不同时期土壤养分的空间变异性、土壤在四维空间尺度上的演变机理以及环境信息获取的不确定性等方面的研究。
Soil nutrients are the necessary nutrients which the soil provides for plants growth.Some research showed that the soil nutriment has high spatial variability which affected by natural and human factors such as soil parent materials,regional climate,terrain,cropping system,fertilization and so on.The characteristic of soil spatial variability can be found not only in the regional scale but also in the field scale.In recent years,for aiming at minimizing fertilization and optimizing management of precision agricultural,many methods which improved continuously from fisher statistics to geostatistics,and then the new methods of neural network,geographical information technology and high accuracy surface model have been applied on measuring the spatial variability and distribution of the soil nutrients in different scales.Taking the geostatisics applied on the research on the spatial variability of soil nutrients as the starting point,the status of geostatistics-based research on the spatial variability of soil nutriments has been reviewed,which include using geostatistics to determine the reasonable soil sampling scheme,quantitative study and scale effect of the spatial variability of soil nutrients.On this basis,the recent research progress of methods and technologies of soil nutrients spatial variability were briefly described,which includes neural network technology,3S technology and high accuracy surface model.At the last,it comparatively analyzed shortages of all methods in researching the spatial variability of soil nutrients.Geostatistics method is concerned with detecting,estimating and mapping the spatial variability of soil nutrients,however it usually with intensive subjectivity and based on to much assumed conditions,moreover,the smoothing effect was existed in Kriging interpolation.The neural network technology and high accuracy surface model can improved the accuracy of interpolation and eliminate the smoothing effect,but the parameters of neural network can not be determined easily and the data operan
出处
《生态环境学报》
CSCD
北大核心
2011年第8期1379-1386,共8页
Ecology and Environmental Sciences
基金
广东省科技计划项目(2009B020315012)
广东省教育部产学研项目(2010B090400155)
关键词
土壤养分
空间变异
模型与方法
soil nutrients
spatial variability
models and methods
