The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they (especially the categorical variables) are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging (CK) approaches. The root mean squared error (RMSE), the relative improvement value of RMSE and Aitchison’s distance (DA) were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.

The variation among Chinese genotypes of Brassica napus L. for seed tocopherols content and their analysis using gas chromatography has not been comprehensively reported till to date. In the present study, the tocopherol contents of four Chinese genotypes of Brassica napus L., namely, Gaoyou 605, Zhejiang 619, Zheshuang 758, and Zheshuang 72, were evaluated using three modified sample preparation protocols (P1, P2, and P3) for tocopherol extraction. These methods were distinguished as follows. Protocol one (P1) included the evaporation of solvent after extraction without silylation. Protocol two (P2) followed the direct supernatant collection after overnight extraction without drying and silylation. Protocol three (P3) included trimethylsilylation with N,O-bis(trimethylsilyl) trifluoroacetamide. Genotypic comparison of tocopherol and its isoforms revealed that Gaoyou 605 was dominant over the other genotypes with (140.5±10.5), (316.2± 9.2), and (559.1± 24.3) μg g-1 of seed meal α-, γ-, and total (T-) tocopherol, respectively, and a 0.44±0.04 α- to γ-tocopherol ratio. The comparison of the sample preparation protocols, on the other hand, suggests that P3 is the most suitable method for the tocopherol extraction from Brassica oilseeds and for the analysis of tocopherols using gas chromatography flame ionization detector (GC-FID). Trimethylsilylation is the key step differentiating P3 from P1 and P2. Variations detected in tocopherol contents among the Chinese rapeseed (B. napus) genotypes signify the need to quantify a wide range of rapeseed germplasm for seed tocopherol dynamics in short and crop improvement in long.