主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

Journal of Meteorology and Environment ›› 2024, Vol. 40 ›› Issue (4): 131-137.doi: 10.3969/j.issn.1673-503X.2024.04.016

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Assessment of temperature suitability and chilling injury risk for overwinter vegetable growing in insulated steel-framed greenhouses in Tianjin

Tao LIU1(),Chaoyang DONG1,Tengge WANG2,Fangying TAN3,Fang LIU1,Xueyan MA1,Zhenfa LI1,*()   

  1. 1. Tianjin Climate Centre, Tianjin 300074, China
    2. Tianjin Agricultural University, Tianjin 300384, China
    3. National Meteorological Centre, Beijing 100081, China
  • Received:2023-10-31 Online:2024-08-28 Published:2024-10-11
  • Contact: Zhenfa LI E-mail:liu258690365@sina.com;lzfaaa@126.com

Abstract:

Using the indoor and outdoor meteorological observation data from two novel insulated steel frame greenhouses in Tianjin over the period from December of 2021 to March of 2022, we applied the Random Forest (RF) method to simulate the distribution of temperature within the two greenhouses, analyzed the variation in heat resources under different climatic conditions and the temperature suitability of vegetable growing, and assessed the risk of low temperature. The results indicated that the indoor air temperature simulated by the RF method correlates highly with the measured values, with a coefficient of determination exceeding 0.85 and a consistency index D value above 0.988 in both novel insulated steel frame greenhouses. The minimum temperature within the heated insulated steel frame greenhouses is consistently maintained above 10 ℃, with minimal fluctuations, making it ideal for the cultivation of semi-tolerant and warm vegetables (with a suitability degree ranging from 0.86 to 0.97 and 0.73 to 0.90, respectively, and no risk of low temperature injury). Conventional insulated steel frame greenhouses can maintain a minimum temperature above -3 ℃, suitable for cold-hardy and half-cold-hardy vegetables (with a suitability degree ranging from 0.59 to 0.91 and 0.43 to 0.86, respectively, and a low risk of low temperature injury).

Key words: Random Forest (RF), Neural network, Overwintering crop, Heat resources

CLC Number: