Characteristics and Forecasting Technology of Greenhouse Temperature Deficit in Southern Plastic Greenhouse

doi:10.3969/j.issn.1000-6362.2019.04.005

Abstract

Abstract:

The Greenhouse Temperature Deficit (GTD) refers to the phenomenon that the daily minimum temperature in the greenhouse is lower than that outside the greenhouse when insulation measures have been taken. By using the meteorological data and manual operation records (film-covered insulation, window ventilation and so on) of plastic greenhouses in Zhejiang province during the winter and early spring of 2010-2015, the characteristics and key influencing factors of the GTD in southern China were explored. Five common neural networks (BP, GA-BP, RBF, GRNN, PNN) were employed to construct the GTD prediction models. The ensemble forecasting model of the result was built based on the forecasting accuracy of the selected five models. The results showed that: (1) the frequency of GTD in early spring and winter (Mar. and Dec.) was three times higher than that in severe winter (Jan.-Feb). The occurrence probability of the GTD at 1.5m was three times higher than that at 0.5m. At the height of 0.5m, the probability of GTD at the edge was 8 to 13 times higher than that at the center. At the height of 1.5m, the difference in the probability of GTD between the center and edge was small. (2) When the GTD happened, the daily minimum temperature outside the greenhouse mainly concentrated on 2 to 11 degrees Celsius, and the insulation method was single film or double film coverage, the proportion of single film coverage was over 93%. The GTD mostly occurred when the opening height of the east and west side windows was high (35?40cm) in daytime and the small gap ventilation was put in the night. When the GTD occurred at 0.5m, the weather conditions were obviously deviated from that of 1.5m. The daily minimum relative humidity, total cloud and average daily wind speed were higher, and the sunshine hours were less. (3) The prediction accuracy of the five selected neural networks for the GTD was basically about 80%, and the GA-BP model was with the highest prediction accuracy. The ensemble model for GTD at 0.5m height was above 85%, and that at 1.5m height was above 80%. The stability of ensemble model was better than that of single model.

Key words: Greenhouse temperature deficit, Plastic greenhouse, Temperature, Neural network, Ensemble prediction

YANG Dong, DING Ye-yi, SUN Jun-bo, LI Qing-bin, WEI Sha-sha, HUANG He-lou. Characteristics and Forecasting Technology of Greenhouse Temperature Deficit in Southern Plastic Greenhouse[J]. Chinese Journal of Agrometeorology, 2019, 40(04): 240-249.

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