Developing and Parameterizing the Cropgro Model to Simulate Strawberry Growth and Production

Strawberry (Fragaria × ananassa) production in Florida is the second largest in the United States amounting to 200 million pounds from 11,000 acres with a production value of 300 million dollars in 2014.  However, there has been little attention to modeling strawberries, despite the economic importance of the industry. The objectives of this study were to adapt the CROPGRO model for simulating strawberry growth and development by providing cardinal temperatures and process information from literature, along with optimizing additional parameters with a hybrid Metropolis-Hastings-Gibbs algorithm.  This information and parameter values in the CROPGRO-Strawberry model influence crop development, daily dry matter (DM) production, fruit set, and DM partitioning. The model was evaluated with field experimental data collected in Wimauma, Florida from October 2014 to March 2015.  Input information consists of weather, soil, irrigation and fertilizer management during the growing season.  Two cultivars, Florida Radiance and Sweet Sensation, were grown in six replicates. Plants for each cultivar were randomly sampled for destructive measurement of dry mass of leaves, petioles, roots, seeds, and fruits at biweekly intervals.  Leaf area, width and height of plants were measured to parameterize canopy photosynthesis in the model.  Fruits were harvested twice per week to measure the fresh weight, dry weight, numbers and sizes of harvested fruits.  CROPGRO already has code for predicting fresh weight, dry matter concentration, size, and maturation of tomato fruits so new parameterization will be introduced for strawberry fruits.  After adaptation, the developed model will predict yield response to changes in weather, soil water supply, N supply, and crop management practices.  The developed model can be a valuable tool for modeling strawberry production response to weather variability or climate change.