Updated prediction for the BSE epidemic in dairy cattle in Japan

Following the detection of the first case of Bovine spongiform encephalopathy (BSE) in Japan in September 2001, nine million cattle were tested for BSE up to the end of 2008. As a result, a further 28 cases were detected in dairy cattle. Using the mathematical model previously developed and surveillance data up to the end of 2008, we estimated the prevalence of BSE-infected animals within each birth cohort for the years 1995–2001. We predicted historic and future trends in the number of BSE-infected animals to be culled and anticipated BSE cases from each birth cohort. The results indicate that more infected animals (428 (95% CI: 59–727)) than previously estimated would have been culled from 1995 to 2001, and more cases (53 (95% CI: 25–101)) than previously predicted would have been detected during this period with a higher peak in 2001, if a BSE surveillance program as comprehensive as the present one was applied. In and after 2009, 0–2 cases of BSE would likely to be detected. As previously predicted, the BSE epidemic should be eradicated by 2012.     

灰色GM(1,1)及其残差修正模型在不同程度春旱发生年份预测中的应用

针对近年来临沂春旱发生频率和程度较前期有所增加的事实,利用临沂1971-2009年的降水资料,采用降水距平法进行春季干旱划分,在此基础上,利用灰色GM(1,1)及其残差修正模型分别建立当地轻度和中度以上春旱的预测模型。结果表明:轻度以上春旱模型以原始序列建模方程精度最高,而中度以上春旱模型则以3次残差序列模型的精度最高;分别用最佳轻度和中度以上GM(1,1)模型进行未来5个时刻的预测,得出下一次轻度春旱时间在2010年;而轻度和中度以上预测模型共同预测结果表明2018年临沂将发生中度以上程度的春旱,为将灾害损失降到最小,对此次春季旱情应引起高度重视,并提前做好防灾减灾应对措施。     

掌握草原害鼠数量变动规律开展综合防治

草原鼠害已成为影响畜牧业发展的主要灾害之一,控制鼠害的主要途径是研究主要害鼠生态及种群数量变动规律,从而进行综合防治。害鼠数量消长经过低谷—上升—高峰—下降—低谷四个期。黑线仓鼠、长爪沙鼠,布氏田鼠的数量变动一般低谷期10年以上,上升期1～2年,高峰期1～2年,下降期1～2年。掌握鼠的数量变动规律,建立预测公式,开展预测预报,在鼠数量上升期选用抗凝血杀鼠剂把鼠密度控制在危害阈值之下;对退化草场采取多种措施如禁牧、围栏、封育等使草场恢复生产力,对未退化的草场制定合理载畜量防止过牧,不给鼠类提供适宜的栖息环境;保护鼠类天敌等措施进行综合防治,把害鼠密度长期控制在不危害的程度,实现草地可持续发展。     

基于BP神经网络的武汉市分层水温预报模型探究

为探讨武汉市分层水温的变化特征及预报方法,本文以武汉市金银湖为例,利用2019-2020年地面观测和分层水温资料,分析气温、气压、蒸发、地温、日照及辐射等气象要素对垂向水温变化的影响,建立武汉地区基于拟牛顿法反向传播(back propagation,BP)神经网络的20cm、40cm、60cm及80cm等层次水温预报模型。仿真结果表明：拟牛顿法BP神经网络的水温预报模型能够表达水温和气象要素的非线性关系,平均预报准确率超过90%,具有较高的预报精度。     

以灰色系统理论 (GM) 模型为基础构建印度洋捕捞渔获量预测模型

为了建立捕捞渔获量预测模型，实验利用2000—2016年印度洋渔获量数据，采用灰色系统理论方法，分析了影响其总渔获量的主要渔获类别，建立多种GM模型(Grey model)并进行比较，同时利用2017年与2018年的数据进行验证，得到的最优GM模型用来预测2019—2025年印度洋总捕捞渔获量。结果显示，影响印度洋总渔获量的主要类别有底层鱼类、甲壳类、中上层鱼类、其他海洋鱼类和头足类，其灰色关联度均在0.70以上，经过筛选得到的最优预测模型为GM (1, 5)和GM (1, 6)，平均相对误差分别为1.83%和1.90%，灰色关联度均在0.9以上。2017年和2018年预测平均相对误差分别为3.78%和3.42%。2019—2020、2021—2025年印度洋总渔获量预测值分别为1 186万～1 290万t、1 227万～1 324万t，其主要渔获量增加可能来自中上层鱼类、头足类以及底层鱼类等。研究表明，2021—2025年印度洋总渔获量的增长幅度有限，总增长量在80万t以内，基本处于充分开发阶段，建议未来应严格控制渔业发展规模，确保印度洋海洋渔业的可持续发展和渔业资源可持续利用。     

树木抗风能力测试新方法

介绍一台由我国自行研发的树木抗风能力测试装备与方法，基于树木力学原理，通过将角传感器合理布置在树干上实时监测树干角度变化，结合实时风速，利用树木抗风能力预测分析系统，可测算该树能够承受的最大风速或最大风力等级。以2株樟Cinnamomum camphora为研究对象，采集了樟在风载荷作用下树干形变的角响应信息，测得樟能够承受的最大风速和临界断裂的破坏方式及断裂位置。本方法可为树木在大风或台风来临前是否需要防护提供科学依据，同时可为抗风性树种选择提供理论支撑。     

基于主成分分析降水量指标预测棉铃虫百株卵量模型研究

棉铃虫是中国棉花主要害虫之一,降水量对其种群数量具重要影响。采用连续10a棉铃虫第2代和第3代的百株卵量与同期降水量指标资料,通过相关系数法筛选出10个预测第2代、第3代棉铃虫的降水量指标,采用主成分分析法建立第2代、第3代棉铃虫百株卵量的特征预测模型。检验结果表明,第2代棉铃虫的预测模型信度较好,历史符合率为70%;而第3代棉铃虫的预测模型信度较差,历史符合率为40%。由于入选因子均为前兆因子,因此,第2代棉铃虫的预测模型可用于区域棉铃虫发生程度的监测和预警研究。     

The potential for commonly measured at-harvest fruit characteristics to predict chilling susceptibility of ‘Hort16A’ kiwifruit

The propensity for physiological disorders to arise during low temperature storage of kiwifruit is a significant commercial risk. The potential to use fruit characteristics (flesh colour, soluble solids content (SSC), dry matter and firmness) estimated non-destructively at harvest as markers for the susceptibility of ‘Hort16A’ kiwifruit to chilling injury (CI) has been investigated for individual fruit. While the fruit that developed CI during storage were some of the least advanced fruit on each orchard, the flesh colour, SSC, firmness and dry matter of the susceptible fruit differed considerably among orchards, such that there was not a clear minimum or maximum threshold for which fruit did or did not develop CI across all orchards. There was a large ‘orchard factor’ in the susceptibility of fruit to CI that was as important, if not more important, than the flesh colour, SSC, firmness and dry matter values. The ‘orchard factor’ may derive from a combination of environmental conditions and/or orchard management practices, in conjunction with fruit growth and development. Hence it is concluded that a generally applicable at-harvest prediction of ‘Hort16A’ fruit susceptibility to CI is not possible from an at-harvest non-destructive estimation of flesh colour, SSC, firmness and dry matter.