摘要
以油茶(Camellia oleifera)高产无性系赣71为例,为掌握良种油茶油脂转化期的树干茎流速率变化规律,分析其与气象要素的相关关系。采用针式茎流计连续监测油茶树干茎流速率,分析在不同天气状况下的气象因子对油茶茎流速率的影响程度。利用多元线性回归方法对油茶树干茎流速率和气象因子进行输入回归,建立茎流速率与气象因子的多元线性回归模型。结果表明,油茶树干茎流速率有明显的昼夜变化规律,均呈倒“U”形单峰曲线,8—10月的峰值逐渐降低,油茶树干茎流速率影响因子从大到小排序依次为太阳辐射、VPD、平均气温、相对湿度、风速、降水量。经过回归系数和相关系数检验,多元线性回归方程均达到了极显著水平。油茶油脂转化期内,8月的茎流速率最大,典型天气的茎流速率排序为晴天>多云>雨天>阴天。
Taking Camellia oleifera Gan 71 clone as an example,in order to master the change law of trunk and stem flow rate of improved Camellia oleifera during the oil and fat conversion period,the correlation between stem flow rate and meteorological factors was analyzed. Needle stem flow meter was used to continuously monitor the stem flow rate of Camellia oleifera and analyze the influence of meteorological factors on the stem flow rate of Camellia oleifera under different weather conditions. Multiple linear regression was used to input regression of stem flow rate and meteorological factors in Camellia oleifera,and the multiple linear regression model of stem flow rate and meteorological factors was established. The results showed that the stem flow rate of Camellia oleifera had an obvious diurnal variation pattern,showing an inverted“U”shape single-peak curve,and the peak value gradually decreased from August to October. The influencing factors of stem flow rate of Camellia oleifera from large to small were in the order of solar radiation,VPD,average temperature,relative humidity,wind speed and precipitation. After regression coefficient and correlation coefficient test,the multiple linear regression equation reached the extremely significant level. During the oil transformation period,the stem flow rate in August was the highest,and the order of stem flow rate in typical weather was sunny day > cloudy day > rainy day > overcast day.
作者
蔡哲
郭瑞鸽
左继林
刘文英
CAI Zhe;GUO Rui-ge;ZUO Ji-lin;LIU Wen-ying(Jiangxi Agricultural Meteorological Center,Nanchang 330096,China;Jiangxi Academy of Forestry,Nanchang 330013,China)
出处
《湖北农业科学》
2022年第20期69-73,共5页
Hubei Agricultural Sciences
基金
国家公益性行业(气象)科研专项(GYHY201506017)
江西省科技计划重点项目(20151BBF60017)。
