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.     

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。     

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。     

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.     

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。     

乏信息材料布氏硬度测量不确定度蒙特卡罗评定

应用蒙转卡罗方法实现小样本材料布氏硬度测量不确定度的评定.依据测量误差源标定数据或布氏硬度测量结果两种数据处理途径,分析已知数据的概率分布特性,通过产生大样本量的随机数进行仿真,拓宽数据样本空间,进而分别依据不确定度合成和贝塞尔公式计算出乏信息材料布氏硬度测量不确定度.通过具体的测量实例验证了本方法的可行性.     

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。     

A semi-empirical model to assess uncertainty of spatial patterns of erosion

Distributed erosion models are potentially good tools for locating soil sediment sources and guiding efficient Soil and Water Conservation (SWC) planning, but the uncertainty of model predictions may be high. In this study, the distribution of erosion within a catchment was predicted with a semi-empirical erosion model that combined a semi-distributed hydrological model with the Morgan, Morgan and Finney (MMF) empirical erosion model. The model was tested in a small catchment of the West Usambara Mountains (Kwalei catchment, Tanzania). Soil detachability rates measured in splash cups (0.48–1.16 g J^− 1) were close to model simulations (0.30–0.35 g J^− 1). Net erosion rates measured in Gerlach troughs (0.01–1.05 kg m^− 2 per event) were used to calibrate the sediment transport capacity of overland flow. Uncertainties of model simulations due to parameterisation of overland flow sediment transport capacity were assessed with the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The quality of the spatial predictions was assessed by comparing the simulated erosion pattern with the field-observed erosion pattern, measuring the agreement with the weighted Kappa coefficient of the contingency table. Behavioural parameter sets (weighted Kappa > 0.50) were those with short reinfiltration length (< 1.5 m) and ratio of overland flow power α to local topography power γ close to 0.5. In the dynamic Hortonian hydrologic regime and the dissected terrain of Kwalei catchment, topography controlled the distribution of erosion more than overland flow. Simulated erosion rates varied from − 4 to + 2 kg m^− 2 per season. The model simulated correctly around 75% of erosion pattern. The uncertainty of model predictions due to sediment transport capacity was high; around 10% of the fields were attributed to either slight or severe erosion. The difficult characterisation of catchment-scale effective sediment transport capacity parameters poses a major limit to distributed erosion modelling predicting capabilities.     

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。