There are two important aspects to the analysis of soil. The first is nutrients, and the second is lyricism. The analysis of nutrients requires sampling. Using soil drills and sampling boxes to obtain suitable representative soil samples is an important prerequisite for analysis. After obtaining the samples, we used the soil nutrient tester to analyze and test the soil nutrients.
In sampling and analysis of soil nutrients, multiple soil nutrient attributes are often available, and many studies have found that soil nutrients have a good correlation. In view of this, in this study, the correlation between multiple soil nutrients was used to predict and compare common Kriging and co-Kriging spatial interpolation at medium regional scales, and efforts were made to promote spatial interpolation of soil nutrients in the middle region.
In the study of the spatial variation of soil nutrients, Ordinary Kriging (OK) method, inverse distance weighted interpolation method, and radial basis function interpolation method are mainly used. However, the application of the Cokriging (COK) method to study the spatial variability of soil nutrients is still relatively small. The existing studies mainly focus on a Cokriging interpolation study of soil nutrients at different sampling numbers or the relationship between soil nutrients and elevation. Coqriging interpolation research, and many limited to the regular field trial community.
1. The normal distribution test and descriptive statistical analysis showed that the available potassium at the soil sampling point accords with the normal distribution: the organic matter, alkali-hydrolyzable nitrogen and available phosphorus are normally distributed after logarithm or Box-Cox transformation: variation of soil nutrient elements The coefficients ranged from 13.05% to 40.63%, showing medium variation intensity: the correlation between soil nutrients generally reached 0.01 significant levels, indicating a significant correlation between soil nutrients.
The analysis of degree test parameters shows that the prediction accuracy of soil organic matter, alkaline solution, available phosphorus and available potassium can be obtained by using OK and COK. From RRMSE, the accuracy of alkaline hydrolysis of nitrogen by the COK method increased significantly (2.87%), followed by available potassium (2.19%), organic matter (1.31%), and available phosphorus (0): RR showed the highest organic matter (39.10%), followed by Alkaline nitrogen (13.36%), available potassium (1.83%), and available phosphorus (1.36%). Therefore, under the same sampling quantity at the middle scale, the COK method takes full account of the cross-correlation of soil nutrients, fuses more spatial information, and achieves the best unbiased estimate over a wider range.
3. Many studies have found that the higher the degree of correlation of cofactors is, the higher the degree of correlation of cofactors is in the use of COK for interpolation. However, the effect of COK interpolation is better: but most are based on regular field levels, and cofactors rarely involve other nutrients at the same sampling point. In this study, multiple nutrients from soil sampling sites were used as covariates to participate in COK interpolation, and it was further confirmed that using the significantly correlated soil nutrient factors to participate in COK interpolation can effectively reduce the prediction error range and improve interpolation at medium regional scales. The overall accuracy. In addition, due to the limitations of data acquisition, this paper does not consider the influence of terrain, climate, rivers, and other factors on the accuracy of the interpolation in the region, and it needs to be further studied in the future.
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