원예과학기술지 (Horticultural Science & Technology)

  • 제29권3호
  • /
  • Pages.181-186
  • /
  • 2011
  • /
  • 1226-8763(pISSN)
  • /
  • 2465-8588(eISSN)
한국원예학회 (Korean Society of Horticultural Science)
권호 표지

발육 속도 모델을 이용한 포도 '캠벨얼리'의 발아기 예측

Developmental Rate Equations for Predicting Bud Bursting Date of 'Campbell Early' (Vitis labrusca) Grapevines

  • Yun, Seok-Kyu (Fruit Research Division, National Institute of Horticultural and Herbal Science) ;
  • Shin, Yong-Uk (Fruit Research Division, National Institute of Horticultural and Herbal Science) ;
  • Yun, Ik-Koo (Fruit Research Division, National Institute of Horticultural and Herbal Science) ;
  • Nam, Eun-Young (Fruit Research Division, National Institute of Horticultural and Herbal Science) ;
  • Han, Jeom-Wha (Fruit Research Division, National Institute of Horticultural and Herbal Science) ;
  • Choi, In-Myung (Fruit Research Division, National Institute of Horticultural and Herbal Science) ;
  • Yu, Duk-Jun (Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Lee, Hee-Jae (Research Institute for Agriculture and Life Sciences, Seoul National University)
  • 투고 : 2010.11.15
  • 심사 : 2011.04.01
  • 발행 : 2011.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

포도 '캠벨얼리'의 발아기 예측을 위해 자발 휴면 타파 이후 발아일까지의 발육 속도(DVR) 계산식을 도출하였다. 항온 실험에서 계산한 DVR은 온도에 대하여 지수 함수식 또는 선형 함수식 모형으로 증가하는 경향이며 DVR 기울기는 0.0019 내외였다. 포도 DVR 계산식은 대기 온도에 대하여 $DVR=0.0249+0.0020e^{0.1654x}$ 또는 DVR = 0.0019x + 0.0187이었으며, DVR 계산식에 일 평균 기온을 대입하여 계산한 예측 발아일의 실측 발아일에 대한 적합도 검정 RMSE 값이 4일 이하로 작았다. 또한 DVR 계산식에 시간별 온도 자료를 대입한 경우에는 일 평균 기온 자료를 사용한 경우보다 오차 값이 작았으며 이때 RMSE 값은 3일 이하였다. 이는 본 연구에서 계산한 DVR 계산식이 포도 발아기 예측에 유용한 예측 값을 제공할 수 있다는 것을 의미한다.

To predict the bud bursting date of 'Campbell Early' grapevines, the bud developmental rate (DVR) models were constructed. The DVRs for bud bursting were calculated from the demanded times at controlled air temperatures. The DVRs were examined on the 'Campbell Early' grapevines incubated in three different temperatures at 4.6, 11.8, and $16.6^{\circ}C$. The DVR increased exponentially or linearly on the air temperature with a slope of about 0.0019. The DVR equations were computed as $DVR=0.0249+0.0020e^{0.1654x}$ or DVR = 0.0019x + 0.0187. These DVR equations offered developmental indices and predicted dates for bud bursting with air temperature data. The DVR equations were validated to the bud bursting data observed in the field. When bud bursting dates were calculated with daily temperature data, the root mean squared error (RMSE) between the observed and the predicted dates was less than 4 days. When those were calculated with hourly temperature data, on the other hand, the RMSE was less than 3 days. These results suggest that the DVR models are useful to predict bud bursting date of 'Campbell Early' grapevines.

키워드

참고문헌

  1. Aoki, A., T. Kaneko, and K. Yamazaki. 1985. Forecasting the flowering date of Japanese pear trees. Bull. Tochigi Agr. Exp. Stn. 31:77-86.
  2. Aono, Y. 1993. Climatological studies on blooming of cherry tree (Prunus yedoensis) by means of DTS method. Bull. Univ. of Osaka 45:155-192.
  3. Aono, Y. and C. Moriya. 2003. A generalized model to estimate flowering for cherry tree (Prunus yedoensis) considering both processes of endodormancy completion and development. J. Agr. Meteorol. 59:165-177. https://doi.org/10.2480/agrmet.59.165
  4. Aono, Y. and Y. Omoto. 1990. A simplified method for estimation of blooming date for the cherry by means of DTS. J. Agr. Meteorol. 46:147-151. https://doi.org/10.2480/agrmet.46.147
  5. Behdani, M.A., A. Koocheki, M. Nassiri, and P. Rezvani. 2008. Models to predict flowering time in the main saffron production regions of Khorasan province. J. Appl. Sci. 8:907-909. https://doi.org/10.3923/jas.2008.907.909
  6. De Wit, C.T., R. Brouwer, and F.W.T. Penning de Vries. 1970. The simulation of photosynthetic systems. Proc. IBP/PP Tech. Mtg., PUDOC, Wageningen. p. 47-60.
  7. Honjo, H. 2007. Effects of global warming on dormancy and flowering behavior of temperate fruit crops in Japan. Hort. Res. 6:1-5.
  8. Honjo, H., R. Fukui, T. Sugiura, and Y. Aono. 2006. The DTS accumulation model for predicting the flowering date of Japanese pear tree in Japan. Acta Hort. 707:151-158.
  9. Horie, T. and H. Nakagawa. 1990. Modeling and prediction of developmental process in rice. Jpn. J. Crop Sci. 59:687-695. https://doi.org/10.1626/jcs.59.687
  10. Kawakami, H., A. Yamao, K. Morioka, H. Ikeda, and Y. Hada. 2009. Artificial control of blooming on Prunus × yedoensis Matsum. ('Somei-yoshino', Rosaceae) using the number of days transformed to standard temperature. Naturalistae 13:1-7.
  11. Kume, N. and K. Takezawa. 1994. Prediction of time of bud opening in the first crop of tea plants by nonparametric DVR method. J. Agr. Meteorol. 50:221-224. https://doi.org/10.2480/agrmet.50.221
  12. Kwon, E.Y., G.C. Song, and J.I. Yun. 2005. Prediction of dormancy release and bud burst in Korean grapevine cultivars using daily temperature data. Kor. J. Agr. For. Meteorol. 7:185-191.
  13. Moncur, M.W., K. Rattigan, D.H. Mackenzie, and G.N. McIntyre. 1989. Base temperatures for bud break and leaf appearance of grapevines. Amer. J. Enol. Vitic. 40:21-26.
  14. Murakami S., C. Ishii, Z. Inaba, and S. Nakamura. 2009. Forecasting blooming date based on developmental rate of the ecodormancy stage in 'Kawazu-zakura' (Prunus lannesiana Wils. 'Kawazu-zakura') cherry trees. Shokubutsu Kankyo Kogaku 21:24-28. https://doi.org/10.2525/shita.21.24
  15. Murakami, S., C. Kato, Z. Inaba, and S. Nakamura. 2008. Modeling the developmental rate in the ecodormancy stage and the effect of temperature on cut flowers of 'Kawazu-zakura' cherry tree. Hort. Res. 7:579-584.
  16. Murakami, S., N. Suematsu, K. Mito, and S. Nakamura. 2006. Flowering time of 'Kawazu-zakura' (Prunus lannesiana Wils.) cherry trees in Minamiizu. Hort. Res. 5:331-336.
  17. National Academy of Agricultural Science (NAAS). 1990. The climatic characteristics of the main fruit cultivation regions in Korea. Rural Dev. Admin., Korea p. 125-191.
  18. Noro, S., N. Obara, N. Kudo, S. Saito, and H. Ichinohe. 1986. Estimation of apple bloom date by the developmental zero and the total effective temperature after bud break. J. Jpn. Soc. Hort. Sci. 54:405-415.
  19. Omoto, Y. and Y. Aono. 1989. Estimation of blooming date for Prunus yedoensis by means of kinetic method. J. Agr. Meteorol. 45:25-31. https://doi.org/10.2480/agrmet.45.25
  20. Ono, S., T. Konno, T. Okuno, and S. Asano. 1988. Effects of temperature on the number of days for budding and flowering of Japanese pear. J. Agr. Meteorol. 44:203-208. https://doi.org/10.2480/agrmet.44.203
  21. Rea, R. and E. Eccel. 2006. Phenological models for blooming of apple in a mountainous region. Intl. J. Biometeorol. 51:1-16. https://doi.org/10.1007/s00484-006-0043-x
  22. Richardson, E.A., S.D. Seeley, D.R. Walker, G.L. Anderson, and G.L. Ashcroft. 1975. Pheno-climatography of spring peach bud development. HortScience 10:236-237.
  23. Sameshima, R. and S. Iwakiri. 1987. Studies on crop-weather relationship of soybean. I. Relationship among developmental rate, daylength, and temperature during the period from seeding to flowering. J. Agr. Meteorol. 42:375-380. https://doi.org/10.2480/agrmet.42.375
  24. Sugiura, T. and H. Honjo. 1997. A dynamic model for predicting the flowering date developed using an endodormancy break model and a flower bud development model in Japanese pear. J. Agr. Meteorol. 52:897-900. https://doi.org/10.2480/agrmet.52.897
  25. Yoshitaka, H. 2010. Extent of misestimation of number of days transformed to standard temperature when calculated by daily mean temperature and a correction using the daily temperature range. Jpn. J. Crop Sci. 79:342-350. https://doi.org/10.1626/jcs.79.342