The spatial estimation for soil properties was improved and sampling intensities also decreased in terms of incorporated auxiliary data. In this study, kriging and two interpolation methods were proven well to estimate auxiliary variables： cokriging and regression-kriging, and using the salinity data from the first two stages as auxiliary variables, the methods both improved the interpolation of soil salinity in coastal saline land. The prediction accuracy of the three methods was observed under different sampling density of the target variable by comparison with another group of 80 validation sample points, from which the root-mean-square error （RMSE） and correlation coefficient （r） between the predicted and measured values were calculated. The results showed, with the help of auxiliary data, whatever the sample size of the target variable may be, cokriging and regression-kriging performed better than ordinary kriging. Moreover, regression-kriging produced on average more accurate predictions than cokriging. Compared with the kriging results, cokriging improved the estimations by reducing RMSE from 23.3 to 29% and increasing r from 16.6 to 25.5%, regression-kriging improved the estimations by reducing RMSE from 25 to 41.5% and increasing r from 16.8 to 27.2%. Therefore, regression-kriging shows promise for improved prediction for soil salinity and reduction of soil sampling intensity considerably while maintaining high prediction accuracy. Moreover, in regression-kriging, the regression model can have any form, such as generalized linear models, non-linear models or tree-based models, which provide a possibility to include more ancillary variables.
The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity （ECb） in a coastal saline field and design an optimized spatial sampling scheme of ECb based on a sampling design algorithm, the variance quad-tree （VQT） method. Soil ECb data were collected from the field at 20 m interval in a regular grid scheme. The smooth contour map of the whole field was obtained by ordinary kriging interpolation, VQT algorithm was then used to split the smooth contour map into strata of different number desired, the sampling locations can be selected within each stratum in subsequent sampling. The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy. The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large. Thus the sampling efficiency can be improved, hence facilitate an assessment methodology that can be applied in a rapid, practical and cost-effective manner.
One approach to apply precision agriculture to optimize crop production and environmental quality is identifying management zones. In this paper,the variables of soil electrical conductivity (EC) data,cotton yield data and normalized differ-ence vegetation index (NDVI) data in an about 15 ha field in a coastal saline land were selected as data resources,and their spatial variabilities were firstly analyzed and spatial distribution maps constructed with geostatistics technique. Then fuzzy c-means clustering algorithm was used to define management zones,fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Finally one-way variance analysis was performed on 224 georefer-enced soil and yield sampling points to assess how well the defined management zones reflected the soil properties and produc-tivity level. The results reveal that the optimal number of management zones for the present study area was 3 and the defined management zones provided a better description of soil properties and yield variation. Statistical analyses indicate significant differences between the chemical properties of soil samples and crop yield in each management zone,and management zone 3 presented the highest nutrient level and potential crop productivity,whereas management zone 1 the lowest. Based on these findings,we conclude that fuzzy c-means clustering approach can be used to delineate management zones by using the given three variables in the coastal saline soils,and the defined management zones form an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies.
