Submitted to: Annals Of Botany
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/19/2001
Publication Date: N/A
Citation: N/A Interpretive Summary: Due to temperature differences among growing seasons, it is often difficult for commercial growers, of numerous horticulturally important crops, to select planting dates that result in a desired harvest date. Temperature is a major environmental variable influencing crop development. Various forms of temperature summations, commonly referred to as thermal units or growing degree days, have been utilized in numerous studies to time phenological events for both agronomic and horticultural crops. Utilizing information gathered in previous growth chamber and field experiments, we developed a simple temperature-driven crop development model of muskmelon for use by commercial growers. This model would time crop development and aid in harvest date predictions. The model was tested against independent data sets from Uvalde, Texas. It was able to predict main vine node numbers within 1 to 3 nodes and harvest dates generally within 3 days.
Technical Abstract: Utilizing information gathered in previous growth chamber and field experiments, we developed a simple temperature-driven crop phenology model of muskmelon (Cucumis melo L.) for use by commercial growers to time crop phenological events and aid in harvest date predictions. The model quantifies vegetative development in terms of main-stem node numbers which allows it to simulate either a direct seeded or a transplanted crop. The model operates on an hourly time-step. It only requires daily weather data and a few cultivar specific parameters, including plastochron interval and thermal time requirements, to reach six predefined developmental stages. The model was tested against an independent data set consisting of three cultivars of muskmelon grown in five transplanting dates. Tests of the model indicate an average ability to predict main-stem node numbers within one to two nodes of observed values. Estimated harvest date predictions were more variable than that for main-stem node number but an average accuracy of one to 3 days was obtained in model tests with the data set used to construct the model. Further testing of harvest date predictions with independent data sets is needed. Procedures for calibrating the model for different cultivars, cultural practices, and environments are outlined.