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Olive Flowering dependence on winter temperatures - linking empirical results to a dynamic model
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Olive (
Olea europaea
) yield depends on the number of
inflorescences forming in springtime. This number depends on the
sufficiency of cold periods and the lack of warm ones during the
preceding winter. Despite this basic understanding, a satisfactory
quantitative model forecasting the expected flowering under natural
temperature conditions is still lacking. Previous models simply sum the
number of ‘cold hours’ during winter, as a proxy for flowering, but
exhibit only mediocre agreement with empirical flowering values,
possibly because they overlook the order of occurrence of different
temperatures. Here, we tested the effect of heat spells of different
durations on olive flowering and gene expression. We constructed a
dynamical model, describing the response of a putative flowering factor
to the temperature signal. The crucial ingredient in the model is an
unstable intermediate, produced and degraded at temperature-dependent
rates. Our model accounts not only for the number of cold and warm hours
but also for their order. We used sets of flowering and temperature data
to fit the model parameters, applying numerical optimization techniques.
We validated the model outcomes and showed its robustness. This model is
the first step toward a practical predictive tool that could be used
under various temperature conditions.
Title: Olive Flowering dependence on winter temperatures - linking empirical results to a dynamic model
Description:
Olive (
Olea europaea
) yield depends on the number of
inflorescences forming in springtime.
This number depends on the
sufficiency of cold periods and the lack of warm ones during the
preceding winter.
Despite this basic understanding, a satisfactory
quantitative model forecasting the expected flowering under natural
temperature conditions is still lacking.
Previous models simply sum the
number of ‘cold hours’ during winter, as a proxy for flowering, but
exhibit only mediocre agreement with empirical flowering values,
possibly because they overlook the order of occurrence of different
temperatures.
Here, we tested the effect of heat spells of different
durations on olive flowering and gene expression.
We constructed a
dynamical model, describing the response of a putative flowering factor
to the temperature signal.
The crucial ingredient in the model is an
unstable intermediate, produced and degraded at temperature-dependent
rates.
Our model accounts not only for the number of cold and warm hours
but also for their order.
We used sets of flowering and temperature data
to fit the model parameters, applying numerical optimization techniques.
We validated the model outcomes and showed its robustness.
This model is
the first step toward a practical predictive tool that could be used
under various temperature conditions.
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