Predicting Pineapple Harvest Date in Different Environments, Using a Computer Simulation Model
- Eric Malézieux,
- Jingbo Zhang,
- Eric R. Sinclair and
- Duane P. Bartholomew
- C tr. de Coopération Int. en Recherche Agronomique pour le Developpement (CIRAD), Dép. des Productions Fruitiéres et Horticoles (FHLOR),, v. du Val Montferrand, BP5035, 34032 Montpellier cedex 01, France
, Dep. of Agronomy and Soil Science, Univ. of Hawaii, Hawaii, 1910 East-West Rd., Honolulu, HI 96822
S inclair, Golden Circle, Ltd, P.O. Box 150, Nundah, QLD 4012, Australia
D ep. of Agronomy and Soil Science, Univ. of Hawaii, 1910 East-West Rd., Honolulu, HI 96822
Prediction of pineapple [Ananas comosus (L.) Merr.] fruit harvest date is essential to the scheduling of labor and fresh fruit marketing efforts. A previously developed heat-unit model for pineapple in the Smooth Cayenne group could not satisfactorily predict fruit harvest date in the range of environments in which it is grown in Hawaii. Our objective was to develop a model based on daily maximum and minimum air temperature for the prediction of pineapple fruit harvest date in the wide range of environments where the crop is grown. For modeling, two phases of fruit development are distinguished: the time from induction of fruit development with a growth regulator (forcing, DAYFOR) to opening of the first flower (DAYFF), and from DAYFF to harvest (DAYH50, the date when 50% of the fruits are one-third yellow). From DAYFOR to DAYFF, predictions are based on the accumulation of heatunits, based primarily on air temperature. After DAYFF, heat-units are accumulated from estimated fruit temperature. The decrease in development rate at above-optimum temperatures during the day is also simulated. The model was calibrated and tested using data sets from several important producing countries with different environments (southeastern Queensland, Australia, 26° Slat; Côte d'Ivoire, 5° N lat; Hawaii, 20 ° N lat; and Thailand, 13.5° N lat). The model predicted harvest date with a mean error of 11, 3, 12, and 5 d in Australia, Côte d'Ivoire, Hawaii, and Thailand, respectively. We believe this margin of error would be acceptable to most pineapple growers. Improvements in the model are likely to come when the relationship between fruit temperature and environmental factors is better understood.
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