On the analysis of long-term experiments

Thomas M. Loughin, Mollie Poehlman Roediger, George A. Milliken, John P. Schmidt

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Long-term experiments are commonly used tools in agronomy, soil science and other disciplines for comparing the effects of different treatment regimes over an extended length of time. Periodic measurements, typically annual, are taken on experimental units and are often analysed by using customary tools and models for repeated measures. These models contain nothing that accounts for the random environmental variations that typically affect all experimental units simultaneously and can alter treatment effects. This added variability can dominate that from all other sources and can adversely influence the results of a statistical analysis and interfere with its interpretation. The effect that this has on the standard repeated measures analysis is quantified by using an alternative model that allows for random variations over time. This model, however, is not useful for analysis because the random effects are confounded with fixed effects that are already in the repeated measures model. Possible solutions are reviewed and recommendations are made for improving statistical analysis and interpretation in the presence of these extra random variations.

Original languageEnglish (US)
Pages (from-to)29-42
Number of pages14
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Issue number1
StatePublished - Jan 1 2007


  • Analysis of variance
  • Random effect
  • Repeated measures
  • Split plot
  • Sustainable agriculture
  • Variance component


Dive into the research topics of 'On the analysis of long-term experiments'. Together they form a unique fingerprint.

Cite this