Experimental Designs and Statistical Analyses for Rootstock Trials

Modern agricultural research, including fruit tree rootstock evaluations, began in England. In the mid-1800s, field plots were established at the Rothamsted Research Station to evaluate cultivars and fertilizer treatments for annual crops. By the early 1900s, farmers questioned the value of field experimentation because the results were not always valid due to inadequate randomization and replication and poor data summarization. During the first half of the 20th century, Rothamsted statisticians transformed field plot experimentation. Field trials were tremendously improved by incorporating new experimental concepts, such as randomization rather than systematically arranging treatments, the factorial arrangement of treatments to simultaneously test multiple hypotheses, and consideration of experimental error. Following the classification of clonal apple rootstocks at the East Malling Research Station in the 1920s, the first rootstock trials were established to compare rootstocks and evaluate rootstock performance on different soil types and with different scion cultivars. Although most of the statistical methods were developed for annual crops and perennial crops are more variable and difficult to work with, rootstock researchers were early adopters of these concepts because the East Malling staff included both pomologists and statisticians. Many of the new statistical concepts were incorporated into on-farm demonstration plots to promote early farmer adoption of new practices. Recent enhancements in computing power have led to the rapid expansion of statistical theory, the development of new statistical methods, and new statistical programming environments, such as R. Over the past century, in many regions of the world, the adoption of new statistical methods has lagged their development. This review is intended to summarize the adoption of error-controlling experimental designs by rootstock researchers, to describe statistical methods used to summarize the resulting data, and to provide suggestions for designing and analyzing future trials.