2019 ASHS Annual Conference

The best niche market for strawberry production in the U.S. is the winter period from November to February, during which time the grower prices are above the annual average. To take the best advantage of premium prices, it is critical for winter strawberry producers in Florida to predict the impact of weather conditions on seasonal yield distribution. Accurate prediction of phenological events must take into account various climatic conditions, as well as the sensitivity of crop to climate variables. In this study, a model was generated and calibrated to predict flowering and fruiting peaks using information published by Mackenzie and Chandler in 2015 for the 2005-2006 and 2006-2007 production seasons in Florida. To evaluate the phenological events, physiological days (p-days) introduced for potatoes by Sands et al. in 1979 was used. Its concept is similar to degrees-days, but it models a non-linear relationship between temperature and development of the crop, with an optimal value and two limit values of temperature under which and over which there is no phenological development. The thermal limits used in this model were taken from the published literature on the effects of temperature on the flowering and fruiting of strawberries. In the model, both flowering and fruiting were assumed to have three waves. These waves were modeled using a Gaussian function, which estimates the peak of each event (where the mean value is located) and the most probable range of starting or ending of each wave, according to the value of the standard deviation of the mean. Using these intervals, p-days were derived for each event in each season. The estimation of p-days was further modified by considering the effect of cloudiness. For horticultural management purposes, we suggest dividing the phenology development into two phases. The divisive point of phases should be the end of the second wave of fruiting, which in turn was coincident with the peak of the third wave of flowering. The first two fruiting waves would be responsible for the early production of the crop and its variation with the climate would allow us to adjust the planting date and density to maximize early productivity of the crop. The model proposed was validated using independent experimental for strawberry yield data recorded in the same area during the 2016-2017 and 2017-2018 seasons.