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Int J Biometeorol. 2000 Aug;44(2):88-96.

Forecasting of the flowering time for wild species observed at Guidonia, central Italy.

Author information

1
Department BEA, University of Udine, Italy.

Abstract

It is well known that forecasting the flowering time of wild vegetation is useful for various sectors of human activity, particularly for all agricultural practices. Therefore, continuing previous work by Cenci et al., we will present here three new phenoclimatic models of the flowering time for a set of wild species, based on an original data sample of flowering dates for more than 500 species, observed at Guidonia (42 degrees N in central Italy) by Montelucci in the period 1960-1982. However, on applying the bootstrap technique to each species sample to check its basic statistical parameters, we found only about 200 to have data samples with an approximately Gaussian distribution. Eventually only 57 species (subdivided into eight monthly subsets from February to September) were used to formulate the models satisfactorily. The flowering date (represented by the z variable), is expressed in terms of two variables x and y by a nonlinear equation of the form z=axbeta+gammay. The x variable represents either the degree-day sum (in model 1), or the daily-maximum-temperature sum (in model 2), or the daily-global-insolation sum (in model 3), while y for all three models corresponds to the rainy-day sum. Note that all summations involved in the computation of the variables x and y take place over a certain period of time (preceding the flowering phase), which is a parameter to be determined by the fitting procedure. This parameter, together with the threshold temperature (needed to compute the degree-days in model 1), represents the two implicit parameters of the process, thus the total number of parameters (including these last two) becomes respectively, five for model 1, and four for the other two models. The preliminary results of this work were reported at the XVI International Botanical Congress (1-7 August 1999, St. Louis, Missouri USA).

PMID:
10993563
[Indexed for MEDLINE]

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