Pre‐Induction Age and Post‐Induction Temperature Effects on Meadowfoam Flowering

Abstract

The ability to predict meadowfoam (Limnanthes alba Hartweg ex. Benth.) phenology could help in commercial production management, and control of flowering date is also useful for managing crossing schedules in controlled environments. Effects of four pre‐induction ages (30, 45, 60, and 90 d from seeding) and four post‐induction temperature regimes (constant 12, 18, and 24 °C, and 24/12 °C day/ night) on days from induction to first flower (f) in ‘Mermaid’ meadowfoam (L. alba subsp. alba] were investigated. Post‐induction temperature had a greater effect than pre‐induction age on progress towards flowering (1/f). The relative responsiveness to post‐induction temperature (slope of the function) increased as pre‐induction age increased to 60 d. Post‐induction temperatures did not affect l/f in plants induced at 90 d, and these plants maintained a high 1/f even at the lowest temperature treatment. For pre‐induction ages up to 60 d, 1/f was estimated using a multiple linear regression model, which included only the additive effects of both pre‐induction age and post‐induction temperature. Post‐induction base temperature when 1/f equaled zero was about 5 °C regardless of pre‐induction age. For all pre‐induction ages, thermal sum from both seeding and photoinduction to first flower increased linearly as temperature increased to 20 °C and then decreased. As pre‐induction age increased, thermal sum from photoinduction to first flower decreased, whereas thermal sum from seeding to first flower increased. It was concluded that the effect of post‐induction temperature on progress towards flowering depends on plant age at induction and the differences become larger at higher temperatures.

Improving prophylaxis for pollen allergies: Predicting the time course of the pollen load of the atmosphere of major allergenic plants in France and Spain

“…To date, few phenology models for herbaceous species phenology exist (Bringfelt et al 1982, Frenguelli et al 1989, Iglesias et al 1998, Larsson 1993, Pipper et al 1996, Seddigh & Jolliff 1994, Sinclair et al 1991, Subiza et al 1992). Most of them are statistical models, predicting flowering dates or periods of pollen shedding using correlations with climatic variables, such as mean temperature of March and April (Bringfelt et al 1982, Frenguelli et al 1989, Larsson 1993).…”

“…Most of them are statistical models, predicting flowering dates or periods of pollen shedding using correlations with climatic variables, such as mean temperature of March and April (Bringfelt et al 1982, Frenguelli et al 1989, Larsson 1993). Few of them are process-based models like phenological models developed for tree taxa, (Pipper et al 1996, Seddigh & Jolliff 1994, Sinclair et al 1991. Two of these latter are based on an experimental background on soybean phenology (Pipper et al 1996, Sinclair et al 1991.…”