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。     

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.     

指纹技术识别泥沙来源:不确定性研究进展

泥沙指纹技术是近年来研究流域泥沙来源与动态变化的一项重要技术方法,其主要局限性在于诸多的前提假设与实践条件的差异带入了一系列不确定性。泥沙从源到汇的迁移过程受自然环境和人为干扰的影响,其源地特征、迁移路径以及泥沙性质的潜在变化等均是指纹技术应用的不确定性因素,而采样方案设计、指纹选择方法、混合模型应用的一些主观因素进一步增加了不确定性的来源。不确定性分析是泥沙指纹技术的重要组成部分,是评估泥沙来源指纹识别结果可靠性的主要依据。本文综述了泥沙指纹技术的诸多不确定性问题以及主要应对方法的研究进展,总结了当前不确定性定量评估的研究现状并提出了未来的研究展望。     

Comparing theoretical irrigation requirement and actual irrigation for citrus in Florida

Florida ranks first in citrus production, with nearly 68% of all U.S. citrus growing in the season 2005-2006. Most of the citrus groves are located from central to south Florida, and agricultural irrigation permitting is regulated by three of Florida's five water management districts. Most of the permitting for citrus production in Highlands, Polk and Hillsborough counties is conducted by the Southwest Florida Water Management District (SWFWMD), and quantities are based on the District's AGMOD computer program. In 2003, the SWFWMD implemented new permit criteria so that permitted amounts were more representative of actual water use. This paper compares grower reported citrus irrigation water use in Highlands, Polk and Hillsborough counties from 1994 through 2005 with permitted and theoretical irrigation requirements calculated by a daily water balance. Two different sets of crop coefficients (K_c's) developed for citrus in Florida were compared in the daily soil water balance calculation of theoretical irrigation requirements. The percentage of irrigated area considered in this study ranged from 40 to 60% to simulate a range of grower practices. Meteorological data from two weather stations and additional rainfall information from 50 locations within the three counties was used in the water balance. Missing and error values in the meteorological historical record data were filled with weather generators. The multiannual average water consumption (including cold protection water use) from growers ranged from 243 (Hillsborough) to 406 mm (Highlands) and the multiannual average permitted irrigation requirement (without cold protection) ranged from 295 to 557 mm. The simulated gross irrigation requirements under different scenarios of location-K_c-wetted area were variable but mostly lower than the limits established by the district, except for some scenarios in Polk County, whose maximum simulated irrigation value reached 578 mm year⁻¹. In general, permitted limits recommended by the SWFWMD seem to be reasonable for the actual water use by growers in these counties.     

FSWM: A hybrid fuzzy-stochastic water-management model for agricultural sustainability under uncertainty

A hybrid fuzzy-stochastic water-management (FSWM) model is developed for agricultural sustainability under uncertainty, based on advancement of a multistage fuzzy-stochastic quadratic programming (MFSQP) approach. In MFSQP, uncertainties presented in terms of fuzziness and randomness can be incorporated within a multilayer scenario tree, such that revised decisions are permitted in each time period based on the realized values of the uncertain events. Moreover, fuzzy quadratic terms are used in the objective function to minimize the variation of satisfaction degrees among the constraints; it allows an increased flexibility in controlling the system risk in the optimization process. Results of the case study indicate that useful solutions for the planning of agricultural water management have been obtained. In the FSWM model, a number of policies for agricultural water supply are conducted. The results obtained can help decision makers to identify desired water-allocation schemes for agricultural sustainability under uncertainty, particularly when limited water resources are available for multiple competing users.     

滚动轴承摩擦力矩的非线性特

以混沌理论为基础,通过试验研究滚动轴承摩擦力矩时间序列的非线性特征评估问题,为精确计算滚动轴承摩擦力矩的动态性能奠定基础.用关联维数标准差刻画摩擦力矩的不确定性,用关联维数均值描述摩擦力矩非线性特征的演变历程.研究表明,滚动轴承属于确定的非线性动力学系统,但其摩擦力矩呈现出明显的不确定性.随着转速的增加,滚动轴承摩擦力矩关联维数标准差与均值都呈现出非线性增大的趋势.     

烘干法测定甘蔗糖分分析中蔗渣水分的不确定度评定

[目的]建立烘干法测定蔗渣水分不确定度评定的方法。[方法]通过分析不确定度的主要来源,建立数学模型,确定合成不确定度和扩展不确定度,得到该检测方法的测量不确定度评定方法。[结果]研究表明,烘干法测定蔗渣水分不确定度评定的方法中由温度引入的不确定度大。通过统计分析得出,烘干法测定蔗渣水分的测量扩展不确定度为0.915 1%。[结论]研究可为评定蔗渣水分测量不确定度提供参考依据。     

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。