摘要: The light reactions of photosystem Ⅱ (PSⅡ) is greatly associated with the photosynthetic capacity. In order to capture more detailed information describing the status of PSⅡ during leaf senescence and rapidly screen cotton (Gossypium L.) genotypes with different duration of photosynthetic capacity, the PSⅡ photochemistry of the first leaves counted from the stem top of three cotton genotypes ('Baimian1' 'Baimian5' and 'DP99B') presented different leaf senescence progresses in production were examined by chlorophyll a fluorescence (Chl F) analysis during leaf senescence. The results showed that 'Baimian1' 'Baimian5' and 'DP99B' were late, intermediate and early aging types, respectively, based on the performance index of light absorption (PIABS). The three genotypes complied with the similar patterns in electrical transferring inhibition accompanying leaf senescence. The depletion of oxygen-evolving complex (OEC) was obvious at the late growth stage. The inhibition of the acceptor side of PSⅡ was greater than that of the donor side. The electron flow that through the light reactions of photosystem Ⅱ and photosystem Ⅰwas significantly limited accompanying leaf senescence. With the duration of leaf senescence, the energy distributed to thermal dissipation and the primary quinone electron acceptors of PSⅡ (QA) restoration increased, and correspondingly the energy used to transport an electron into the electron transport chain beyond QA- (the reduction state of QA) declined. However, three cotton genotypes showed greater and greater electron transferring inhibition, except the number of QA reduction events between time=0 and time to reach maximal fluorescence, in the order of 'DP99B' > 'Baimian5' > 'Baimian1' with the duration of leaf senescence. It can be seen that the chlorophyll fluorescence characteristics can quickly and noninvasively reflect the senescence and the internal physiological mechanism of leaf senescence among different cotton genotypes.
摘要: 为了解烟叶化学成分与生态因子之间的定量化关系，提高烤烟品质评价的智能化程度，使用2009—2017年玉溪市9个烤烟‘K326’典型定位点烟叶主要化学成分（烟碱、总糖、还原糖、总氮、钾、氯）数据与对应不同生育期的生态因子（气象和土壤）数据，分析得到生态因子影响综合指数，在此基础上建立了烟叶各化学成分机理生态预测模型。根据2018年生态因子数据，预测了各定位点烟叶主要化学成分含量，并与实测值进行比较。同时，使用相同的90个烤烟定位点数据，利用最大信息系数（maximum information coefficient，MIC）筛选输入变量，使用经过灰狼算法优化的BP神经网络建立智能算法的烟叶化学成分生态预测模型。机理算法的生态预测模型R2平均值为0.29，RMSE平均值为0.13，只有还原糖RMSE略大于0.2；智能算法的生态预测模型R2均大于0.95，RMSE均小于0.1。结果表明智能算法的生态模型预测效果优于机理算法的生态模型，能够为烤烟品质提升与调优栽培管理提供一定理论支撑。