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Predicting Harvest Maturity of the 'Fuji' Apple using a Beta Distribution Phenology Model based on Temperature

온도기반의 Beta Distribution Model 을 이용한 후지 사과의 성숙기 예측

  • Choi, In-Tae (Division of Climate Change & Agroecology, Department of Agricultural Environment, National Institute of Agricultural Sciences) ;
  • Shim, Kyo-Moon (Division of Climate Change & Agroecology, Department of Agricultural Environment, National Institute of Agricultural Sciences) ;
  • Kim, Yong-Seok (Division of Climate Change & Agroecology, Department of Agricultural Environment, National Institute of Agricultural Sciences) ;
  • Jung, Myung-Pyo (Division of Climate Change & Agroecology, Department of Agricultural Environment, National Institute of Agricultural Sciences)
  • 최인태 (농촌진흥청 국립농업과학원 기후변화생태과) ;
  • 심교문 (농촌진흥청 국립농업과학원 기후변화생태과) ;
  • 김용석 (농촌진흥청 국립농업과학원 기후변화생태과) ;
  • 정명표 (농촌진흥청 국립농업과학원 기후변화생태과)
  • Received : 2017.09.05
  • Accepted : 2017.11.14
  • Published : 2017.11.30

Abstract

The Fuji variety of apple, introduced in Japan, has excellent storage quality and good taste, such that it is the most commonly cultivated apple variety in Gunwi County, North Gyeongsang Province, Korean Peninsula. Accurate prediction of harvest maturity allows farmers to more efficiently manage their farm in important aspects such as working time, fruit storage, market shipment, and labor distribution. Temperature is one of the most important factors that determine plant growth, development, and yield. This paper reports on the beta distribution (function) model that can be used to simulate the the phenological response of plants to temperature. The beta function, commonly used as a skewed probability density in statistics, was introduced to estimate apple harvest maturity as a function of temperature in this study. The model parameters were daily maximum temperature, daily optimum temperature, and maximum growth rate. They were estimated from the input data of daily maximum and minimum temperature and apple harvest maturity. The difference in observed and predicted maturity day from 2009 to 2012, with optimal parameters, was from two days earlier to one day later.

Keywords

References

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