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On the analysis of long‐term experiments

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

Abstract

Summary. 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.

Suggested Citation

  • Thomas M. Loughin & Mollie Poehlman Roediger & George A. Milliken & John P. Schmidt, 2007. "On the analysis of long‐term experiments," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 29-42, January.
    • Handle: RePEc:bla:jorssa:v:170:y:2007:i:1:p:29-42
    DOI: 10.1111/j.1467-985X.2006.00435.x
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    References listed on IDEAS

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    1. Murari Singh & Michael Jones, 1997. "Estimating time to detect time trends in continuous cropping," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(6), pages 659-670.
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