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Estimating time to detect time trends in continuous cropping

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  • Murari Singh
  • Michael Jones

Abstract

In long-term field trials comparing different sequences of crops and husbandry practices, the identification and understanding of trends in productivity over time is an important issue of sustainable crop production. This paper presents a statistical technique for the estimation of time trends in yield variables of a seasonal annual crop under continuous cropping. The estimation procedure incorporates the correlation structure, which is assumed to follow first-order autocorrelation in the errors that arise over time on the same plot. Because large differences in annual rainfall have a major effect on crop performance, rainfall has been allowed for in the estimation of the time trends. Expressions for the number of years (time) required to detect statistically significant time trends have been obtained. Illustrations are based on a 7-year data set of grain and straw yields from a trial in northern Syria. Although agronomic interpretation is not intended in this paper, the barley yield data indicated that a significant time trend can apparently be detected even in a suboptimal data set of 7 years' duration.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:659-670
    DOI: 10.1080/02664769723404
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    Cited by:

    1. 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.
    2. Murari Singh & Michael Jones, 2000. "Statistical estimation of time-trends in two-course crop rotations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 589-597.

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