Soil scientists are accustomed to geostatistical methods and tools such as semivariograms and kriging for analysis of observational data. Such methods assume and exploit that observations are spatially correlated. Conversely, analysis of variance (ANOVA) of designed experiments assumes that observations from different experimental units are independent, an assumption that is justified based on randomization. It may be beneficial, however, to perform an ANOVA assuming a geostatistical covariance model. Also, it is increasingly common to have multiple observations per experimental unit. Simple ANOVA assuming independence of observations is not appropriate for such data. Instead, a linear mixed model accounting for correlation among observations made on the same plot is required for proper analysis. The purpose of this paper is to demonstrate the benefits of integrating geostatistical covariance structures and ANOVA procedures into a linear mixed modelling framework. Two examples from designed experiments are considered in detail, making a link between terminologies and jargon used in geostatistical analysis on the one hand and linear mixed modelling on the other hand. We provide code in R and SAS for both examples in two supporting companion documents.

中文翻译：

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用于土壤科学中的设计实验的线性混合模型和地统计学：两种完全不同的方法还是同一枚硬币的两个侧面？

土壤科学家习惯于使用地统计学方法和工具（例如半变异函数图和克里金法）来分析观测数据。这样的方法假设并利用了观测值在空间上的相关性。相反，设计实验的方差分析（ANOVA）假设来自不同实验单位的观察是独立的，这一假设基于随机化是合理的。但是，假设地统计协方差模型执行ANOVA可能是有益的。而且，每个实验单元具有多个观测值越来越普遍。假设观测独立性的简单方差分析不适用于此类数据。取而代之的是，为了进行正确的分析，需要使用线性混合模型来说明在同一图上进行的观测之间的相关性。本文的目的是证明将地统计协方差结构和ANOVA程序集成到线性混合建模框架中的好处。将详细考虑设计实验中的两个示例，一方面将地统计分析中使用的术语与术语联系起来，另一方面将线性混合建模联系起来。我们在两个支持的随附文档中为这两个示例提供了R和SAS代码。