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Comparison of Different Models Coupled with Temperature Changes and Farmland Variation for Modeling Growth of Iceberg Lettuce
Fig. 1. Monthly average temperature, daylight hours, and accumulated precipitation during the growth periods for the experimental datasets of iceberg lettuce.
Fig. 1. Monthly average temperature, daylight hours, and accumulated precipitation during the growth periods for the experimental datasets of iceberg lettuce.

Author:Chu-Chung Chen, Dar-Yuan Lee, and Kai-Wei Juang*

Abstract:

Iceberg lettuce (Lactuca sativa L.) grown during the cool seasons in Taiwan is the flagship vegetable to export,. High temperatures in summer will retard or violate the heading physiology of iceberg lettuce, so Taiwan’s iceberg lettuce is routinely grown from early autumn to the late spring of next year. In the growth of iceberg lettuce, the outer leaves expending would be a major source of photosynthates; the leafy head which accumulates abundant photosynthates would be a sink. The growth of outer leaves will govern the yield of the leafy head. In smart agriculture, it is essential to optimize the growth model to increase the benefits of cropping management in smart agriculture, while coping with the growth variation in different crops. Linear (LIN), Gopertz (GOP), and Logistic (LOG) models are frequently used for modeling crop growth. In the present study, relative leafgrowth rate (RLR) and relative growth rate (RGR) are fundamental to developing the growth model of iceberg lettuce. The growth curves for shoot dry weight and outer leaf area were fitted to LIN, GOP, and LOG models. And model coupling procedures with the seasonal temperature changes and farmland variation were proposed to improve the prediction of the growth modeling. Two experimental sites, No. 427 and 382, are located at Erlun Township, Yunlin County and the other two sites, No. 474 and 550, are located at Mailio Township, Yunlin County. The growth survey of lettuce plants at sites No. 427 and 382 was carried out in the Winter 2017 and Spring 2018 cropping seasons, respectively. At sites No. 474 and 550, the growth survey of lettuce plants was only conducted in the cropping season Winter 2017. A total of six datasets were used in the study for model fitting assessment and validation. The results showed that LIN, GOP, and LOG models were well fitted to the growth curves of the outer leaf area and shoot dry weight. The goodness-of-fit for the models would be varied by the climate of growing seasons and environmental variation of sites. Parameters a and b obtained from the models fitting could be used to describe the dynamics of RGR and RLR in growth periods; also, the values of a and b were imported by the coupling expressions, a(T) and b(T), with temperature (T) to evaluate the influences of seasonal temperature changes on the growth rates. In addition, a(T) and b(T) were combined with data of plant growth recordings on the site (S) to obtain coupling parameters a', b', and c'. Then, the growth modeling functions, FS(t|a', b', c')LIN, FS(t|a', b', c')GOP, and  FS(t|a', b', c')LOG, corresponding to LIN, GOP, and LOG models were developed, respectively. Compared with FS(t|a', b', c')LIN and FS(t|a', b', c')GOP, FS(t|a', b', c')LOG was more accurate in the predictions of outer leaf area and shoot dry weight at the later growth periods; thus, LOG model used in the coupled modeling procedure would be more suitable for prediction of the time to harvest and the yield of leafy heads. If the range of application and convenience of use are the priorities for modeling the growth of iceberg lettuce, LIN, and GOP models will be the superior options in models coupling. 

Key words: Growth analysis, Models coupling, Linear model, Gopertz model, Logistic model.

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