真空干燥模块法快速测定天然橡胶鲜胶乳干胶含量的应用

阐述真空干燥模块法快速测定天然橡胶鲜胶乳干胶含量的工作原理和测定方法,并将该方法的测定结果与烘箱干燥法(标准法)的测定结果进行了比较。比较结果表明：真空干燥模块法测定准确度与烘箱干燥法基本一致,其测定速度远远快于传统的烘箱烘干法,且其操作简易、快捷、测定过程稳定、不受测定环境条件的影响。     

汕优63制种中实效积温的应用及穗期预测数学模型的建立

提出了实效积温的概念，并确立了汕优６３制种生产中父母本实效积温的区间为１３～２６℃。根据扬州市红旗良种场连续３ａ的实践，以实效积温为主要依据安排播差是可靠的。还建立了实效积温为主要自变量预测父母本始穗期的数学模型。     

Development of Temporal Modeling for Forecasting and Prediction of the Incidence of Lychee,Tessaratoma papillosa (Hemiptera: Tessaratomidae), Using Time-Series (ARIMA) Analysis

The most destructive enemy of the lychee, Litchi chinensis Sonn. (Sapindales: Sapindaceae), in India is a stink bug, Tessaratoma papillosa (Drury) (Hemiptera: Tessaratomidae). The population of T. papillosa on lychee trees varied from 1.43 ± 0.501 to 9.85 ± 3.924 insects per branch in this study. An increase in the temperature and a decrease in the relative humidity during summer months (April to July) favor the population buildup of T. papillosa. A forecasting model to predict T. papillosa incidences in lychee orchards was developed using the autoregressive integrated moving average (ARIMA) model of time-series analysis. The best-fit model for the T. papillosa incidence was ARIMA (1,1), where the P-value was significant at 0.01. The highest T. papillosa incidences were predicted for April in 2010, January in 2011, May in 2012, and February in 2013. A model based on time series offers longer-term forecasting. The forecasting model, ARIMA (1,1), developed in this study will predict T. papillosa incidences in advance, thus providing functional guidelines for effective planning of timely prevention and control measures.