摘要
在不同取样尺度下采集了农田土壤样品并测定土壤有机质、全N和有效P含量,研究不同尺度下地统计学方法对土壤养分空间变异分析的适用性和空间插值质量.研究结果表明,大比例尺下土壤有机质含量、有效P含量、全N含量的分布呈正态分布,在中小比例尺下呈现轻微偏态分布.各养分空间变异系数存在明显空间变异(C.V.介于17%～27%之间),大比例尺下,空间变异较小;中小比例尺下,空间变异程度大(C.V和峰度值较小).在各种尺度下,土壤养分均存在典型的半方差结构,表明在各种取样比例尺下采用地统计学方法进行土壤养分空间变异分析都是可行的.在大中比例尺下,各养分块金系数介于0.28～0.38之间,表明土壤养分具有明显空间自相关;而在小比例尺下,块金系数介于0～0.17之间,表明土壤养分具有强烈空间自相关,其中有机质含量具有恒定的空间自相关(块金系数为0),有效P含量的空间自相关程度相对较弱;空间插值和交互检验结果表明,球状模型空间插值结果与实测结果符合性很好,交互检验均达到极显著水平.在小比例尺下其估计比大比例尺下精度更高.不同养分的空间预测结果质量不尽相同,土壤有机质含量空间预测符合度最高.
Soil samples different in scale were taken, and organic matter (O.M.), total nitrogen (Total N) and available phosphorus (Avail. P) were measured to study the feasibility and adaptability of GeoStatistic in analyzing soil spatial variability. Results indicated that soil O.M., avail. P and total N appeared in normal symmetrical distribution in samples large in scale but in samples medium or small in scale, they showed a slight skew distribution. The C.V. of different soil nutrients ranged between 0.17~0.27, showing a significant spatial variability, namely, when large in scale, soil spatial variability is low and when medium or small in scale, the variability is high (with small C.V. and kurtosis). A typical semivarigram structure was observed in samples of all scales, showing that the Geostatistic method works in analyzing soil nutrient spatial variability in samples of all scales. In samples small in scale soil nutrients have strong spatial autocorrelation (nugget value is among 0~0.17), and soil O.M. content constant spatial autocorrelation, while in samples medium or large in scale, the soil nutrient spatial autocorrelation is medium, but weak with soil Avail. P. Spatial interpolation and cross-validation showed that spatial prediction data and measured data fitted very well with the spherical model, and that no matter whether large, medium or small in scale, spatial analysis and prediction with the Geostatistical technology is a useful tool in soil spatial variability analysis and precision fertilization. The precision of prediction is higher in small scale than in large or medium scale. Among soil O. M., soil total N and soil available P, the prediction of soil O. M. has the highest precision.
出处
《土壤》
CAS
CSCD
北大核心
2004年第4期376-381,391,共7页
Soils
基金
新疆生产建设兵团科技发展计划项目(GKB03SDXGJ27DX):试验在教育部
新疆生产建设兵团共建"绿洲生态农业重点实验室"完成。
