Should you blame the weather? The influence of weather parameters, month, and day of the week on spring herbaceous plant sales in the U.S. midwest

Bridget K. Behe, Kristin L. Getter, Chengyan Yue

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Sales of many products, including umbrellas and skis, depend on weather conditions. Anecdotal evidence from plant producers and retailers indicate that their sales are also heavily reliant on weather conditions. Still, little published literature documents weather's influence on plant sales. Daily sales data of herbs, vegetables, and flowering annuals were acquired from 42 retail stores in Indiana, Ohio, and Michigan (which were divided into four regions based on zip code). The multisite Midwestern retailer sells food and household items year-round but seasonally sells plants in outdoor covered areas. The data were analyzed using time series regression and the model produced significant results, but the amount of variance captured by region, weather parameters, month, and day of the week was only'40% for herbs and vegetables (H+V) and flowering annual plants (FA). Precipitation amount had no effectonsales of H+V and FA, likely because the plants were merchandised under cover. Increasing units of sunshine lowered sales by 1%. H+V sales were greatest in the southeast Michigan region but for FA were greatest in the mid-Michigan region. Lower minimum air temperature reduced sales for sales of both H+V and FA, whereas higher maximum air temperature increased sales. Sales were substantially higher in May and lower in June and July compared with April. Sales were higher in 2009 than 2007. Compared with Wednesday, sales were higher everyday and highest on Saturday. Day of week and month had a greater impact on sales than did any weather parameter. Thus, region, weather, month, and weekday do influence daily plant sales but did not account for most of the variability in 42 U.S. midwestern retail outlets.

Original languageEnglish (US)
Pages (from-to)71-73
Number of pages3
JournalHortScience
Volume47
Issue number1
DOIs
StatePublished - Jan 2012

Keywords

  • Economics
  • Marketing
  • Time series regression

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