“试验设计与数据处理”教学改革探讨——基于本科生毕业论文常见问题的分析

“试验设计与数据处理”是食品类专业的一门重要课程。在分析毕业论文存在问题的基础上,找出这些问题存在的原因并寻求解决办法,为提高“试验设计与数据处理”课程的教学质量和教学效果提供参考和依据。     

大数据背景下的信息化育种

信息化是育种的主流趋势和必然选择。为促进作物育种信息化建设,从种质资源管理、数据采集、系谱分析、试验设计、参试进程管理、遗传解析和生理学解析、品种产业化推广等方面探讨了大数据背景下信息化在育种中的应用。指出了信息化育种实施步骤和要点,以河南省新乡市农业科学院为例,规划了软件应用、科研管理、人才培养、硬件更新、信息共享等实施信息化育种的重点步骤。认为信息化育种存在的主要问题有不被育种家广泛接受、技术有待完善和数据共享存在障碍等3个方面,并展望了其应用前景。     

一种新型卷积神经网络植物叶片识别方法

为提高植物叶片识别的准确率及减少计算代价,在Pytorch框架下提出一种融合了深度卷积生成式对抗网络(DCGAN)和迁移学习(TL)的新型卷积神经网络叶片识别方法。首先,对植物叶片图像进行预处理,通过DCGAN对样本数据库扩充;其次,利用迁移学习将Inception v3模型应用于图像数据处理上,以提高植物叶片识别的准确率;最后,通过对比实验对该方法的有效性进行验证。结果表明:该方法可以获得96.57%的植物叶片识别精度,同时参数训练的迭代次数由4000次缩短到560次。     

野生动物非法交易犯罪案件信息特征分析

对野生动物非法交易案件进行信息特征分析,可以把握案件的发生规律,提供决策者参考,以便有针对性预防和打击犯罪,更好地保护野生动物资源。本研究应用大数据分析的方法,对野生动物非法交易犯罪案件的文本数据进行结构化处理,建立了word2vec模型。从犯罪因素的角度统计分析了本类案件的信息特征,包括侵害人的特征、作案行为特征、侵害的动物或动物制品、侵害的时间和案件发生的地点等。在梳理基本特征的基础上应用关联算法构建了关联规则。根据特征分析和关联规则,提出了开展季节性防控、对特定群体和场所进行有针对性预防、对上游下游犯罪进行联动打击、与网管等多部门合作,运用现代科技成果进行打击和防范的对策建议。     

A stochastic surplus production model in continuous time

Surplus production modelling has a long history as a method for managing data‐limited fish stocks. Recent advancements have cast surplus production models as state‐space models that separate random variability of stock dynamics from error in observed indices of biomass. We present a stochastic surplus production model in continuous time (SPiCT), which in addition to stock dynamics also models the dynamics of the fisheries. This enables error in the catch process to be reflected in the uncertainty of estimated model parameters and management quantities. Benefits of the continuous‐time state‐space model formulation include the ability to provide estimates of exploitable biomass and fishing mortality at any point in time from data sampled at arbitrary and possibly irregular intervals. We show in a simulation that the ability to analyse subannual data can increase the effective sample size and improve estimation of reference points relative to discrete‐time analysis of aggregated annual data. Finally, subannual data from five North Sea stocks are analysed with particular focus on using residual analysis to diagnose model insufficiencies and identify necessary model extensions such as robust estimation and incorporation of seasonality. We argue that including all known sources of uncertainty, propagation of that uncertainty to reference points and checking of model assumptions using residuals are critical prerequisites to rigorous fish stock management based on surplus production models.     

农业大数据的发展现状与发展趋势

介绍了农业大数据的概念和发展现状,阐述了农业大数据理论在农业中的主要应用,分析了农业大数据发展过程中存在的问题,并预测了农业大数据的发展趋势。     

基于物联网的国家兽药追溯系统的建设与应用

介绍了基于物联网的国家兽药追溯系统的内容和建设历程,概述了国家兽药追溯系统的试点应用和全面应用的情况以及特点和成效,分析了国家兽药追溯系统的制约因素和对策,并对国家兽药追溯系统的数据应用前景予以展望,以期为"互联网+兽药"的实现和兽药行业大数据的应用奠定扎实的基础。     

Barriers to Open Government Data (OGD) initiative in Tanzania: Stakeholders’ perspectives

Open Government Data (OGD) initiatives are picking up across the globe with their emphasis upon the tenets of transparency, collaboration, and participation. While much of the research has remained confined at unraveling the theoretical and conceptual dimensions of OGD, empirical investigations are visibly lacking, especially in the African context. Using a qualitative research methodology, we analyze the responses received from relevant stakeholders—representatives from public and private sector—regarding their views about the OGD initiative in Tanzania. Our findings show that the OGD initiative in Tanzania is in an emerging stage and there are barriers (organizational, social, legal, and technical) toward instituting a robust OGD initiative in the country. Further, we find that there is inertia among the government departments in terms of publishing data sets. Our findings provide a sounding board for further research on the sustainability of OGD initiatives in Tanzania and the other African countries, by and large.     

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km²), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m⁻² d⁻¹ and 2.7–6.1 g C m⁻² d⁻¹ for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.