成渝地区葡萄园营养状况初步研究与分析

在成渝地区具有代表性的葡萄园内进行了叶样和土样采集分析,结果表明:与标准值对比,大部分葡萄园内叶片中营养元素P与Mg含量水平较低,微量元素Mn与Fe含量水平则偏高；在大量使用有机肥的葡萄园内,土壤中速效性的营养元素N、P、K含量水平相对较高.     

何首乌茎尖嫩叶主要营养成分分析及评价

测定了何首乌茎尖嫩叶的VB2、VC、粗纤维等主要营养成分含量和K、Fe、Zn等矿质元素含量,以期为何首乌茎尖嫰叶的合理开发利用提供理论依据。结果表明,何首乌茎尖嫩叶营养丰富,各种营养成分较为全面,其主要营养成分和矿质元素含量也远远高于其他4种常见蔬菜,这为何首乌茎尖嫩叶的食用价值提供了科学依据,也为改善人们的膳食营养和餐饮文化、开发蔬菜资源及提高其利用价值提供了参考。     

以发展创造教育为导向 改革部队教育评价体系

传统的教育评价体系一定程度上阻碍了创造教育的实施,因此探索与创造教育相适应的评价体系将成为时代之必须。本文基于部队院校评价现状,在学习借鉴当今世界最新的评价理念和总结已有研究成果的基础上,从评价机构和评价指标和评价法方面,提出与部队院校相适应的创造教育评价体系。     

山东农业科技期刊学术影响力分析与动态评估

利用动态评估模型对2008~2012年山东13种农业科技期刊的学术影响力进行分析。结果显示,2008~2012年中,13种农业类科技期刊的总被引频次和影响因子总体呈上升趋势,5年间,平均总被引频次增加了84.85%,平均影响因子增加了47.35%;平均学术影响力呈快速增长趋势,5年间增长了268%。各期刊发展不均衡,其中,《山东农业科学》和《中国烟草科学》学术影响力分别位居第一和第二,且发展态势良好。     

学科影响指标在期刊评价中的作用探讨

对学科影响指标反映期刊学术影响力的情况、与其他评价指标之间的联系、在期刊学科分类工作中的作用及其存在的局限性进行了初步分析和探讨。     

保健食品中原花青素测定的不确定度评定

对保健食品中原花青素的测定进行不确定度的评定,建立不确定度评定程序和方法。依据《保健食品检验与评价技术规范》建立数学模型,进行不确定度的计算并合成不确定度。找出了影响保健食品中原花青素测定的主要因素,分析归纳了不确定度分量的主要来源。评定程序和方法符合规范要求,适用于同类型试验不确定度评定。     

农业科研机构创新绩效评价浅议

文章简要探讨农业科研机构创新绩效评价的目的意义、内容方法、评价指标体系和结果运用等，为农业科研机构创新绩效评价体系建立提供参考。     

各向同性人造板材料弹性模量测量及其标准差评价

弹性模量是衡量人造板材料力学性能的重要指标,用标准差法对其进行综合评价尤为必要,对其实测及方差分析具有较好的应用价值。采用三点弯曲静力法和悬臂梁自由端振动法,测定各向同性HDF、三合板和OSB试材的静弹性模量和动弹性模量。在实测数据分析中,为反映总体弹性模量实测值数据集的离散程度及落在样本统计值某一区内的概率,还确定了其置信区间,评价了置信度。结果表明:各向同性人造板材料的静弹性模量和动弹性模量测试结果一致,其置信区间越大,置信水平越高。     

Environmental Change and the Sustainability of European Forests

Summary

It is some years now since forest decline was a major public concern in Europe and was one of the principal environmental issues around which international research programs were focused. A number of internationally coordinated activities were initiated in the 1980s and 1990s and have continued until now; these contributed significantly to our current understanding of forestry and to the way in which forestry policies have developed. In short, the concept of sustainable development has had an increasing influence and is now of immeasurable value in forest policy, with sustainable forest management well established as its guiding principle. This sequence of events is examined here. The extent to which understanding has advanced is remarkable; much has changed. Arguably this period gave the first indication of the extent to which forests and ecosystems globally are threatened by environmental change. On the basis of the last 20 years, it is tempting to conclude that we now have an effective institutional framework and have made excellent progress. However, some of the recorded ecosystem responses seem anomalous; there are surprises in system responses, e.g., the linkage between sulphur and nitrogen depositions and forest growth. Even more importantly some specific pollutant problems remain and will intensify, and climate change has become an environmental issue of overwhelming importance.     

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.