基于气象条件的油茶产量模型研究

Research on yield model of camellia based on meteorological factors

为给每年油料加工规模的规划提供分析依据,基于1 222项气象指标数据,利用湖南省77县(区)1969—1979年油茶历史产量和24个测产点2006年以来的测产序列,选用逐步回归方法,建立了油茶不同物候期气象条件与油茶产量的关系模型,及以各物候期拟合产量数据为因子构建的油茶产量模型,比较分析了各产量模型的平均相对误差和对增、减产的拟合能力。结果表明:以开花期和果实成熟期气象条件建立的产量模型其拟合效果最好,但仍比不上以11个物候期拟合产量作为因子建立的油茶产量模型的效果好;拟合效果与地理条件密切相关,低海拔地区的湘北以及湘中偏南地区的平均相对误差均较小,而海拔较高的湘西北、湘西南和湘南南部山区的平均相对误差均较大。同时,分析建立的101个油茶产量模型的因子结构发现:开花期、果实成熟期、油脂转化和积累高峰期为模型入选频次最高的3个物候期,其中开花期入选频次最高的气象因子为气温类因子;果实成熟期入选频次高的因子有平均最低气温、连续有日照天数、15℃以上的积温;油脂转化和积累高峰期入选频次最多的为气温尤其是高温日数。文中因此认为,基于开花期气象条件建立的油茶产量模型可用于油茶产量预估,入选产量模型频次高的因子可为油茶产量气候潜力的挖掘提供参考依据。

Based on the data of 1 222 meteorological indexes, the historical yield of camellia from 1969 to 1979 in 77 counties （districts） and the production sequence of 24 stations from 2006 in Hunan province. The stepwise regression method was used to establish the relationship between meteorological conditions and the yield of camellia, and yield model of camellia oleifera were determined by the yield data of each phenophase. The average relative error and the fitting ability of increasing and decreasing yield of each yield model were compared, the yield model established in the flowering period and the fruit ripening stage were better. But the effect is not as good as the yield of the camellia yield model established by 11 phenophase fitting yields as factors.It is also found that the- fitting effect is closely related to the geographical conditions. The average relativeerror between the low-altitude areas in the northern part of Hunan and thesouth-central region of Hunan is relatively small, and the relative relative error is relatively high in the northwest of Hunan, southwest Hunan and southern Hunan. At the same time, the analysis of factors in the 101 camellia yield model, found that flowering period, the fruit ripening period, the oil conversion and accumulation peak are the highest frequency phenotypes. The temperature is the highest frequency factor in the fruit ripening period. The average minimum temperature, continuous sunshine days and more than 15 ℃ accumulated temperature are the higher frequency in fruit ripening. The temperature is the highest frequency factor in the grease conversion and accumulation of peak, especially the high temperature days is particularly prominent. It can be seen that the camellia yield model based on themeteorological conditions of flowering can be used to forecast the yield of camellia and provide the basis for the planning of the annual oil processing scale. The high frequency factor of the selected yield model can provide the basis for the climatic potential mining of the camellia yield.