Early-season yield fluctuations of strawberry (Fragaria ×ananassa Duch.) follow a bimodal Gaussian model

Abstract

Strawberries (Fragaria ×ananassa Duch.) are highly perishable and require intensive labor inputs for harvesting. Therefore, yield forecasting is critical for improving labor management and marketing decision-making in strawberry production. In Florida, United States, strawberry yields during winter months have a unique distribution pattern characterized by multiple waves. We hypothesized that individual yield waves can be described using Gaussian distribution, a model that represents a symmetrical bell-shaped curve. Two short-day cultivars (‘Florida Radiance’ and ‘Florida Brilliance’) and one day-neutral cultivar (‘Florida Beauty’) were grown in west-central Florida. Harvesting was performed 30 times, generally twice a week from November through February. Yield data were converted to weekly values prior to model fitting. The first two yield waves were described by a bimodal Gaussian distribution model, which was then converted into two unimodal Gaussian distribution models. The goodness of fit was very high (R2 = 0.934–0.959) for both yield waves in all tested cultivars. Different yield distribution patterns of the tested cultivars were characterized quantitatively by estimating not only the yield but also the timing and duration of each yield wave. Our modeling approach provides insights into understanding cultivar-dependent fruiting phenology, yielding capacity, and fruit earliness. Such information can help optimize yield distribution through breeding and reduce yield gaps by using different cultivars or staggered planting dates. The model developed here only applies to the first two waves of fruit production. Future research will aim to model the following yield waves to fully characterize the strawberry yield distribution.