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Multivariate analysis of water-related agroclimatic factors limiting spring wheat yields on the Canadian prairies

Authors
Journal
European Journal of Agronomy
1161-0301
Publisher
Elsevier
Publication Date
Volume
30
Issue
2
Identifiers
DOI: 10.1016/j.eja.2008.09.003
Keywords
  • Spring Wheat Yield
  • Agroclimatology
  • Versatile Soil Moisture Budget
  • Principal Component Analysis
  • Canonical Correlation Analysis
Disciplines
  • Earth Science

Abstract

Abstract Water use by spring wheat and soil water contents at meteorological stations on the Canadian prairies were simulated with the Versatile Soil Moisture Budget model for different crop growth stages. Six water-related agroclimatic indices at five growth stages (seeding–emergence, emergence–jointing, jointing–heading, heading–soft dough and soft dough–harvest) and previous non-growing season were correlated to spring wheat yields in the three prairies provinces and in the entire prairie region for the years 1976–2006. Principal component analysis was applied to explore major modes of joint variability in the regional water-related agroclimatic indices. Canonical correlation analysis was employed to further identify joint variability patterns of the water-related indices associated with regional spring wheat yields. Results showed some common features of the effects of the water-related factors at different growth stages: lower-than-normal moisture stress at the jointing–heading stage favoured spring wheat yields in all three provinces. Regional differences were also seen, for example, a slight moisture stress at the heading–soft dough stage could be beneficial to spring wheat yields in Manitoba because of its relatively wetter climate compared to the other two provinces. The results can be used for a better understanding of the effects of water-related agroclimatic conditions at different growth stages on final spring wheat yields on the Canadian prairies, leading to the improvement of crop management. The results can also be used in regional yield forecasting and in the projection of climate change impacts on crop production. This study provided an example of how to quantify crop–climate relationships by the use of statistical multivariate analysis tools.

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