[Simulation model for the development stages of processing tomato based on physiological development time].

Area of Science:

• Agricultural Science
• Plant Physiology
• Crop Modeling

Context:

• Processing tomatoes are crucial crops affected by environmental factors like temperature and light.
• Accurate prediction of crop development stages is essential for optimizing agricultural management and yield.
• Existing models, such as the growth degree day (GDD) model, have limitations in predicting crop development under varying conditions.

Purpose:

• To introduce and validate a novel simulation model for predicting the development stages of processing tomatoes.
• To incorporate the intrinsic development factor (IDF) and physiological development time (PDTv) into a crop simulation model.
• To compare the predictive accuracy of the new model against the traditional GDD-based model.

Summary:

• A simulation model for processing tomato development stages was constructed using the concept of physiological development time (PDTv) and the intrinsic development factor (IDF).
• The model was validated using experimental data from different years, ecological zones, varieties, and planting modes.
• The developed model demonstrated high accuracy, with root mean squared errors (RMSE) for key stages (sowing to emergence, flowering, fruit-setting, maturing, ending) ranging from 1.09 to 2.77 days.

Impact:

• The new PDTv-based model significantly outperforms the GDD-based model in predicting processing tomato development stages, with substantially lower RMSE values.
• This enhanced predictive capability can lead to improved agricultural practices, resource management, and potentially increased yields for processing tomatoes.
• The study provides a valuable tool for researchers and farmers to better understand and manage tomato crop development under diverse environmental conditions.