Combining historical agricultural and climate datasets sheds new light on early 20th century barley performance

Joanna Raymond, Ian Mackay, Steven Penfield, Andrew Lovett, Haidee Philpott, Stephen Dorling

Research output: Contribution to journalArticlepeer-review

Abstract

Barley (Hordeum vulgare ssp. vulgare) is cultivated globally across a wide range of environments, both in highly productive agricultural systems and in subsistence agriculture and provides valuable feedstock for the animal feed and malting industries. However, as the climate changes there is an urgent need to identify adapted barley varieties that will consistently yield highly under increased environmental stresses. Our ability to predict future local climates is only as good as the skill of the climate model, however we can look back over 100 years with much greater certainty. Historical weather datasets are an excellent resource for identifying causes of historical yield variability. In this research we combined recently digitised historical weather data from the early 20th century with published Irish spring barley trials data for two heritage varieties: Archer and Goldthorpe, following an analysis first published by Student in 1923. Using linear mixed models, we show that interannual variation in observed spring barley yields can be partially explained by recorded weather variability, in particular July maximum temperature and rainfall, and August maximum temperature. We find that while Archer largely yields more highly, Goldthorpe is more stable under wetter growing conditions, highlighting the importance of considering growing climate in variety selection. Furthermore, this study demonstrates the benefits of access to historical trials and climatic data and the importance of incorporating climate data in modern day breeding programmes to improve climate resilience of future varieties.
Original languageEnglish
Pages (from-to)381-396
Number of pages16
JournalAnnals of Applied Biology
Volume182
Issue number3
Early online date17 Feb 2023
DOIs
Publication statusPublished - May 2023

Keywords

  • Spring barley
  • Student
  • breeding
  • climate variability
  • statistical modelling

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