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作 者:赵洋 韩佳 郜慧萍[2] 徐瑢[2] 周青华[2] 邢金香[2] 于吉祥[2] 李华[2] Zhao Yang;Han Jia;Gao Huiping;Xu Rong;Zhou Qinghua;Xing Jinxiang;Yu Jixiang;Li Hua(College of Geographical Science,Taiyuan Normal University,Jinzhong 030619,China;Shanxi Academy of Forest Sciences,Taiyuan 030012,China)
机构地区:[1]太原师范学院地理科学学院,山西晋中030619 [2]山西省林业科学研究院,山西太原030012
出 处:《山西林业科技》2018年第2期11-15,共5页Shanxi Forestry Science and Technology
摘 要:利用2014年10月至2015年9月在大同市日光温室内监测的温度数据和同期地面气象要素的观测资料,选取影响日光温室内温度变化的相关气象因子,通过Pearson简单相关系数对不同天气条件下日光温室内外最低气温相关性进行分析,并用逐步回归分析方法建立晴天、多云天和阴天3种不同天气条件下日光温室内最低温度预测模型。研究表明:夜间室内外气温相关性表现为晴天>多云>阴天,白天则表现为多云>晴天>阴天;晴天、多云天和阴天的模拟值与观测值的决定系数(R2)分别为0.937 5,0.905 1和0.957 4,3种天气条件下模拟值和观测值的相关性(r值)均大于0.9,预测方程一致性较好、拟合程度较为理想,为实现对温室生产的合理调控提供了可靠的理论依据。Meteorological factors related to the temperature change in solar greenhouse were chosen based on the temperature data monitored in solar greenhouse of Datong and observation data of ground meteorological elements from October 2014 to September 2015. Correlation of the lowest temperature in and out of solar greenhouse under different weather conditions was analyzed by Pearson simple correlated coefficient. The lowest temperature prediction models in solar greenhouse under three different weather conditions(sunny days,cloudy days and overcast days) were established using the method of stepwise regression analysis. The results showed that correlation of the temperature at night between in and out of solar greenhouse was sunny days cloudy days overcast days. And correlation of the temperature during the day between in and out of solar greenhouse was cloudy days sunny days overcast days. Determination coefficients(R2) of simulated values and observed values at sunny,cloudy and overcast days were 0. 937 5,0. 905 1 and 0. 957 4 respectively. Correlation(r) of simulated values and observed values under 3 kinds of weather conditions were greater than 0. 9. The predictive equations could provide a reliable theoretical basis for reasonable control of greenhouse production because their consistence and fitting degree were better.
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