渔获量不确定性对印度洋大眼金枪鱼资源评估的影响

大眼金枪鱼(Thunnus obesus)是最具经济价值的热带金枪鱼类,其资源状况一直是区域性金枪鱼渔业管理组织关注的重点。由于多种渔业作业、捕捞船队构成复杂,印度洋大眼金枪鱼的历史渔获量统计存在一定的偏差(Bias),但国际上近些年开展资源评估时都忽略了这一偏差。本研究根据1979~2015年的年渔获量、年龄结构渔获量及相对丰度指数数据,运用年龄结构资源评估模型(ASAP)对印度洋大眼金枪鱼资源进行评估,重点考查渔获量的不确定性(观测误差和统计偏差)对资源评估结果的影响。结果显示,印度洋大眼金枪鱼当前资源总体没有过度捕捞,但2015年初显示轻微的过度捕捞,通过对比基础模型与8个灵敏度分析模型的评估结果发现,渔获量观测误差(CV)的预设对资源开发状态的判断有一定的影响。当渔获量统计偏差调整量为15%时(即历史渔获量被低估了),评估结果与基础模型基本一致;统计偏差调整量为20%时,评估结果有过度捕捞的趋势。本研究结果表明,资源评估模型中渔获量观测误差的设定和历史渔获量统计偏差均会对评估结果产生影响,后者更为明显,因此,二者均不能忽略。     

DPD分光光度法测定水中游离氯的不确定度评定

[目的]建立分光光度法测定水中游离氯的不确定度评定方法。[方法]根据HJ 586—2010规定的检测方法,对DPD分光光度法测定水中游离氯进行了不确定度评定。对测量不确定度分量的来源,包括标准物质、标准溶液配制和稀释过程、测量重复性、标准曲线线性拟合等进行分析。最后计算了合成不确定度和扩展不确定度。[结果]曲线线性拟合引入的不确定度对合成不确定度贡献最大,其次为测量重复性和标准系列溶液配制引入的不确定度,标准贮备液引入的不确定度分量贡献最小,可忽略不计。[结论]该研究可为准确测定水中游离氯含量提供理论支持。     

半微量凯氏法测定植株中全氮及测量不确定度评定

[目的]测定植株中全氮并对测量不确定度进行评定。[方法]以浓H_2SO_4和H_2O_2为消煮液,采用优化后的升温程序,用半微量凯氏法测定植株中全氮。[结果]通过对植株样品和标准植株样的测定及不确定度评定可知,测定数据可靠,结果准确合理。[结论]测定过程中的不确定度主要来源于盐酸标准溶液和试样测定重复性,为实验室准确快速测定植株中全氮提供参考。     

有机磷农药混合标准溶液配制不确定度评价（英文）

标准溶液的不确定度对农药残留检测起着非常重要的作用,影响着检测结果的准确性。笔者以实际应用的14种有机磷农药混合标准溶液为例,采用配制混合标准溶液过程单元操作的不确定度计算方法(top down),分析了在配制混合标准溶液过程中的所有影响因素,得到了最终的扩展不确定度。最终结果显示使用精准的移液器将储备液配置为混合标液,得到最终的扩展不确定度相对小。     

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。     

液相色谱法测定牛奶中三聚氰胺残留量的不确定度

通过数学建模,对GB/T22388-2008中HPLC法测定牛奶中三聚氰胺残留量的不确定度进行分析。将各个分量分化,运用最小二乘法对标准曲线拟合的不确定度,用极差法对测定次数较少时引起的不确定度,添加回收率的不确定度等进行分析。结果表明,合成标准不确定度U（X）=0.040,当取样量为2.00g,k=2（95%置信度）时,测得牛奶中三聚氰胺的含量为（2.00±0.160）mg/kg。     

Above- and belowground biomass measurements in an unthinned stand of Sitka spruce (<Emphasis Type="Italic">Picea sitchensis</Emphasis> (Bong) Carr.)

Reporting carbon (C) stocks in tree biomass (above- and belowground) to the United Nations Framework Convention on Climate Change (UNFCCC) should be transparent and verifiable. The development of nationally specific data is considered ‘good practice’ to assist in meeting these reporting requirements. From this study, biomass functions were developed for estimating above- and belowground C stock in a 19-year-old stand of Sitka spruce (Picea sitchensis (Bong) Carr.). Our estimates were then tested against current default values used for reporting in Ireland and literature equations. Ten trees were destructively sampled to develop aboveground and tree component biomass equations. The roots were excavated and a root:shoot (R) ratio developed to estimate belowground biomass. Application of the total aboveground biomass function yielded a C stock estimate for the stand of 74 tonnes C ha⁻¹, with an uncertainty of 7%. The R ratio was determined to be 0.23, with an uncertainty of 10%. The C stock estimate of the belowground biomass component was then calculated to be 17 tonnes C ha⁻¹, with an uncertainty of 12%. The equivalent C stock estimate from the biomass expansion factor (BEF) method, applying Ireland’s currently reported default values for BEF (inclusive of belowground biomass), wood density and C concentration and methods for estimating volume, was found to be 60 tonnes C ha⁻¹, with an uncertainty of 26%. We found that volume tables, currently used for determining merchantable timber volume in Irish forestry conditions, underestimated volume since they did not extend to the yield of the forest under investigation. Mean stock values for belowground biomass compared well with that generated using published models.     

不确定度在链条极限拉伸载荷测量中的应用

在链条极限拉伸载荷测量中引入了不确定度，通过大量的试验测试找出了影响不确定度的主要因煮。在此基础上，推导出计算链条极限拉伸载荷不确定度的公式，给出了不确定度在报告中的表述方法，讨论了不确定度对测量结果的影响。     

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.