Abstract

In the face of a changing climate, yield stability is becoming increasingly important for farmers and breeders. Long-term field experiments (LTEs) generate data sets that allow the quantification of stability for different agronomic treatments. However, there are no commonly accepted guidelines for assessing yield stability in LTEs. The large diversity of options impedes comparability of results and reduces confidence in conclusions. Here, we review and provide guidance for the most commonly encountered methodological issues when analysing yield stability in LTEs. The major points we recommend and discuss in individual sections are the following: researchers should (1) make data quality and methodological approaches in the analysis of yield stability from LTEs as transparent as possible; (2) test for and deal with outliers; (3) investigate and include, if present, potentially confounding factors in the statistical model; (4) explore the need for detrending of yield data; (5) account for temporal autocorrelation if necessary; (6) make explicit choice for the stability measures and consider the correlation between some of the measures; (7) consider and account for dependence of stability measures on the mean yield; (8) explore temporal trends of stability; and (9) report standard errors and statistical inference of stability measures where possible. For these issues, we discuss the pros and cons of the various methodological approaches and provide solutions and examples for illustration. We conclude to make ample use of linking up data sets, and to publish data, so that different approaches can be compared by other authors and, finally, consider the impacts of the choice of methods on the results when interpreting results of yield stability analyses. Consistent use of the suggested guidelines and recommendations may provide a basis for robust analyses of yield stability in LTEs and to subsequently design stable cropping systems that are better adapted to a changing climate.

Keywords

Coefficient of variation; Cropping systems; Mixed models; Statistics; Taylor's power law; Variability

Published in

Agronomy for Sustainable Development
2021, Volume: 41, number: 2, article number: 27
Publisher: SPRINGER FRANCE

SLU Authors

• Reckling, Moritz

  • Department of Crop Production Ecology, Swedish University of Agricultural Sciences
    
  • Leibniz Centre for Agricultural Landscape Research (ZALF)

Sustainable Development Goals

Take urgent action to combat climate change and its impacts


UKÄ Subject classification

Agricultural Science


Publication identifier

DOI: https://doi.org/10.1007/s13593-021-00681-4

Permanent link to this page (URI)

https://res.slu.se/id/publ/111677