提高波尔山羊繁殖效率的意义与方法

波尔山羊为季节性多次发情品种 ,产羔率较低 ,但肉用性能好 ,与国内多个品种杂种对生长速度和肉用性能有良好的杂种优势。为了提高肉羊生产经济效益 ,提高波尔羊繁殖效率具有重要意义。季节、光照和温度以及饲养管理等因素对波尔羊繁殖力有影响。加强选育和饲养管理 ,诱导青年羊发情排卵 ,缩短成年羊产羔间隔 ,提高窝产羔数 ,合理应用胚胎移植技术 ,提高公羊性欲和精液品质及保存效果等 ,均可提高波尔羊的繁殖力。     

Geostatistical interpolation and aggregation of crop growth model outputs

Many crop growth models require daily meteorological data. Consequently, model simulations can be obtained only at a limited number of locations, i.e. at weather stations with long-term records of daily data. To estimate the potential crop production at country level, we present in this study a geostatistical approach for spatial interpolation and aggregation of crop growth model outputs. As case study, we interpolated, simulated and aggregated crop growth model outputs of sorghum and millet in West-Africa. We used crop growth model outputs to calibrate a linear regression model using environmental covariates as predictors. The spatial regression residuals were investigated for spatial correlation. The linear regression model and the spatial correlation of residuals together were used to predict theoretical crop yield at all locations using kriging with external drift. A spatial standard deviation comes along with this prediction, indicating the uncertainty of the prediction. In combination with land use data and country borders, we summed the crop yield predictions to determine an area total. With spatial stochastic simulation, we estimated the uncertainty of that total production potential as well as the spatial cumulative distribution function. We compared our results with the prevailing agro-ecological Climate Zones approach used for spatial aggregation. Linear regression could explain up to 70% of the spatial variation of the yield. In three out of four cases the regression residuals showed spatial correlation. The potential crop production per country according to the Climate Zones approach was in all countries and cases except one within the 95% prediction interval as obtained after yield aggregation. We concluded that the geostatistical approach can estimate a country’s crop production, including a quantification of uncertainty. In addition, we stress the importance of the use of geostatistics to create tools for crop modelling scientists to explore relationships between yields and spatial environmental variables and to assist policy makers with tangible results on yield gaps at multiple levels of spatial aggregation.     

木材和人造板中五氯苯酚含量测定不确定度的评价

为测定人造板中的五氯苯酚含量,通过对LY/T 1985-2011《防腐木材和人造板中五氯苯酚含量的测定方法》的研究,得出了不确定度来源及其数学模型,对各不确定度分量进行计算,结果发现样品处理及设备重复性对不确定度贡献最大,因此应尽量提高试验结果平行性,以提高结果的准确度。     

广义螺旋曲面工件轮廓度统一测量方法

对国标规定的标准螺旋曲面的测量方法进行分析,得出普通螺纹和丝杠形位误差测量方法中的共性问题。利用微分几何的曲面包络理论,结合坐标投影变换法和相对运动法对工件的曲面方程进行研究,得到了在母线曲面已知的情况下广义螺旋曲面的统一方程。通过广义螺旋曲面测量要素分析,得出异型螺旋面的测量方法。结合曲面统一方程,提出了广义螺旋面的统一测量方法。实例表明,该方法能够快速、准确地测量出对应工件的型面尺寸,避免了测量的复杂性和低效性。     

机床传动误差测量中的空域法分析

从空间域的角度讨论了信号处理的一种特殊方法，介绍了它的特点，建立了空域傅氏变换数学模型，并且给出了实用效果。实践证明空域分析法应用于机床传动链一类的特殊场合是有效的。     

车载式拖拉机座椅舒适性测试系统的研究

应用美国模拟器件公司的ADXL105微型力平衡式加速度传感器、美国Cygnal公司F320型单片机组成车载式拖拉机座椅舒适性测试系统，系统易标定、成本低、供电方式简单。试验结果表明，在其他条件相同时，拖拉机座椅固有频率只要避开人体敏感的频率范围，座椅的舒适性就能得到明显的改善。     

LabVIEW在V带疲劳寿命试验台上的应用

介绍了在V带疲劳试验台上数据采集及控制的自动测量系统。该系统基于LabVIEW平台编写了控制程序,人机界面统一采用了Windows风格的单元,醒目方便;使用精度高、便于自动控制的变频电机驱动、电涡流测功机加载;用计算机完成整个试验台的自动化控制,操作简单、准确度高。     

柴油机瞬时转速测量误差分析研究

通过分析柴油机飞轮瞬时转速的测量误差,指出角度误差是飞轮瞬时转速的主要误差来源,从而提出了减少误差的方法,提高了瞬时转速的测量精度。     

基于高斯过程建模的物联网数据不确定性度量与预测

物联网已经成为农业大数据最重要的数据源之一,自动观测数据的质量控制对农业生产分析以及基础科研数据应用非常重要。针对农业物联网观测的一类非平稳时间序列数据中的数据缺失、野值剔除、感知故障预警和长时间预测等问题,采用光滑弱假设高斯先验,构建了基于高斯过程的自回归模型表征的动态系统,并通过样本集学习,形成能考虑噪声干扰的传感变化规律建模,并可提供预测误差带用于预测数据的不确定性度量。针对原始数据的缺失和野值问题,采用基于高斯过程的短期预测,可补齐缺失数据,利用其不确定性度量可甄别数据野值,进行野值剔除与替换,并在此基础上判断感知故障;给出了基于输入数据不确定性传播的多步迭代预测方法,使长期预测仍可以跟踪农业数据的动态轨迹,并可为其预测值提供不确定性度量;将温室采集的真实传感数据用于分析试验,验证了高斯过程用于服务器端的农业时间序列数据采集质量控制的可行性。     

Analysis of the uncertainty associated with the estimation of nitrogen losses from farming systems

This work describes the analysis of the uncertainty linked to the annual direct and indirect losses of different nitrogenous compounds at the scale of a group of farms. The nitrogen (N) forms taken into account are: ammonia (NH₃), nitric oxide (NO), nitrous oxide (N₂O), dinitrogen (N₂) and nitrate (NO₃). The gaseous N emissions for the different components of the farms are estimated with a selection of adapted emission factors. The NO₃ losses at the farm scale are calculated as the difference between the surplus of the farm-gate N balance and the gaseous N emissions.