杂粮供应链区块链多链追溯监管模型设计

针对杂粮产品供应链链条长、主体多、区块链追溯过程数据无法差异化共享、链上数据难以实时监管等难题,通过分析杂粮供应链环节业务流程与监管特性,提出了基于区块链多链架构的杂粮追溯模型,并在此基础上建立多链数据存储架构,设计了基于监管授权组网建链的网络准入机制,并通过智能合约实现数据的链前监管与追溯节点的链上管控。为验证模型有效性,基于Hyperledger Fabric设计并实现区块链追溯系统,对山西忻州杂粮应用案例进行分析。在安全方面,企业组网授权扩散性测试密文平均改变率为82.53%,相关性测试密文平均改变率为82.39%,具备较高的安全性与混淆性。在效率方面,消费者查询公开追溯数据平均时间为0.415 s,监管部门调用跨链接口查询企业敏感追溯数据平均时间为0.871 s。结果表明,该研究设计并实现的面向监管的杂粮多链追溯系统在满足消费者追溯需求的基础上,能够实现追溯数据账本与链间交易记录的实时管控,为农产品区块链追溯监管系统研究提供借鉴与参考。     

加快农业科技创新赋能农业高质量发展

科技创新是高质量发展的重要引擎.文章分析了我国现代农业发展现状和存在的问题,梳理了现代农业发展对农业科技创新的新要求,即保障粮食安全、推动农业高质量发展、增强农业产业竞争力等,并从强化关键核心技术攻关、构建农业产业全链条技术支撑体系、加快农业科技成果转化应用、改善科技创新生态等方面,提出了科技创新引领和支撑农业高质量发...     

新时期农业科研院所学科领域调整和布局的思考——以广东省农业科学院为例

学科建设作为一项综合性、系统性、长期性的基础建设工程,是科研院所的立院之本.文章通过简要分析新时期学科调整的背景和意义,分析了广东省农业科学院在学科建设方面取得的成绩以及存在的不足和短板,在此基础上,提出了加强顶层设计、坚持协同创新、强化条件建设、培育一流学科团队等促进学科建设的对策建议,以期为农业科研院所加强学科建设...     

地市级农科所以科技创新推动地方产业发展的实践与思考 ——以江苏里下河地区农业科学研究所为例

文章以江苏里下河地区农业科学研究所为例,总结了其以科技创新推动地方产业发展的实践经验,分析了地市级农科所以科技创新推动地方产业发展存在的问题,如农业科技创新投入强度偏低、科研成果与市场脱节、农业科研与科技推广不能有效衔接等,并从加大对基层科研单位的科研投入力度、发挥市场和政府等多元化主体作用、优化农技推广服务体系、转变传统科技创新思路等方面提出了有针对性的建议,以期推动科技创新与产业发展深度融合。     

河北省土壤水资源划区探讨

综述了土壤水和土壤水资源的概念以及土壤水资源划区的意义,以土壤的地带性规律为基础,与农业综合区划相协调并保持乡级行政区界的完整性为划区原则,提出了河北省土壤水资源的划区方案.即河北省土壤水资源分为区、亚区和片3级,共划分为7个土壤水资源区、14个土壤水资源亚区和28个土壤水资源片.     

大数据背景下高职院校数据素养教育研究——基于广东省科技干部学院商科学生的抽样调查

[目的/意义]针对高职数据素养教育缺位的现状,以师生对数据素养的认知情况为培养现状的分析依据,为高职院校建立科学合理的数据素养培养体系、建设相应的课程体系、合理分配有限教学资源提供参考。[方法/过程]基于已有的数据素养指标体系,构建高职院校数据素养评价体系,采用问卷的方式调查高职学生的数据意识、数据收集、数据组织与管理、数据分析、数据利用与归档、数据伦理6个方面的认知与能力,结合文献提出“三阶递进”式的高职数据素养教育体系。[结果/结论]师生总体具备一定的数据意识,但对商业数据的有效获取能力比较缺乏。具备基础的数据分析能力及表达能力,数据价值的深度挖掘能力不足。数据收集、组织与管理、分析、利用与归档能力认知在群体间的差异明显,认为应从“底层-进阶-高级”3个层次进行教育体系的设计,提升数据素养综合能力。     

The concept of habitat specificity revisited

Habitat specificity indices reflect richness (α) and/or distinctiveness (β) components of diversity. The latter may be defined by α and γ (landscape) diversity in two alternative ways: multiplicatively () and additively (). We demonstrate that the original habitat specificity concept of Wagner and Edwards (Landscape Ecol 16:121–131, 2001) consists of three independent components: core habitat specificity (uniqueness of the species composition), patch area and patch species richness. We describe habitat specificity as a family of indices that may include either area or richness components, or none or both, and open for use of different types of mean in calculation of core habitat specificity. Core habitat specificity is a beta diversity measure: the effective number of completely distinct communities in the landscape. Habitat specificity weighted by species number is a gamma diversity measure: the effective number of species that a patch contributes to landscape richness. We compared 12 habitat specificity indices by theoretical reasoning and by use of field data (vascular plant species in SE Norwegian agricultural landscapes). Habitat specificity indices are strongly influenced by weights for patch area and patch species richness, and the relative contribution of rare vs. common species (type of mean). The relevance of properties emphasized by each habitat specificity index for evaluation of patches in a biodiversity context is discussed. Core habitat specificity is emphasized as an ecologically interpretable measure that specifically addresses patch uniqueness while habitat specificity weighted by species number combines species richness and species composition in ways relevant for conservation biological assessment. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Modeling the water budget in a deep caldera lake and its hydrologic assessment: Lake Ikeda, Japan

The hydrologic assessment of a lake water budget can be helpful in achieving proper water management and sustainable water use. A model to analyze a lake water budget was developed and verified for Lake Ikeda, Japan. Lake evaporation was estimated by numerical analyses of lake water temperature and the lake energy budget. Inflow from the lake catchment area and leakage from the lake bottom were estimated based on the tank model and Darcy's law, and the model parameters were optimized by the shuffled complex evolution method. The estimated monthly lake evaporation rate is consistent with the evaporation rate estimated by the energy budget Bowen ratio method based on in situ data from 2004 to 2005. Moreover, the calculated time series of daily lake levels agrees well with those of measured lake levels during 1983 to 1999. Thus, the model is useful for evaluating the lake water budget. Numerical analysis reveals seasonal and annual variation characteristics in the water budget components. Precipitation, inflow from the catchment area, and river water supply are generally high during the rainy season from June to July with substantial annual variation. Lake evaporation is greatest in October and least in April, but the annual variation is relatively small. Agricultural water use is relatively high from April to September. There are no marked seasonal changes in leakage and drinking water use. The lake level is generally highest in September and lowest in March, which is characterized by seasonal changes in water budget components. The model was also applied to 17-year simulations under hypothetical hydrologic conditions to examine the effect of water use and agricultural water management on the lake level. Results indicate that river water supply, provided under the agricultural water management system, effectively compensates for the decrease in lake water resulting from agricultural water use.     

Combined use of optical and radar satellite data for the detection of tillage and irrigation operations: Case study in Central Morocco

The objective of this study is to present a new application of optical and radar remote sensing with high spatial (∼10 m) and temporal (a few days) resolutions for the detection of tillage and irrigation operations. The analysis was performed for irrigated wheat crops in the semi-arid Tensift/Marrakech plain (Central Morocco) using three FORMOSAT-2 images and two ASAR images acquired within one week at the beginning of the 2005/2006 agricultural season.The approach we developed uses simple mapping algorithms (band thresholding and decision tree) for the characterisation of soil surface states. The first images acquired by FORMOSAT and ASAR were processed to classify fields into three main categories: ploughed (in depth), prepared to be sown (harrowed), and not ploughed-not harrowed. This information was combined with a change detection analysis based on multitemporal images to identify harrowing and irrigation operations which occurred between two satellite observations.The performance of the algorithm was evaluated using data related to land use and agricultural practices collected on 124 fields. The analysis shows that drastic changes of surface states caused by ploughing or irrigation are detected without ambiguity (consistency index of 96%). This study provided evidence that optical and radar data contain complementary information for the detection of agricultural operations at the beginning of agricultural season. This information could be useful in regional decision support systems to refine crop calendars and to improve prediction of crop water needs over large areas.     

农业车辆视觉实际导航环境识别与分

分析了对路径识别影响较大的变光照环境、杂草环境和阴影环境对农业车辆导航路径的影响,提出一种实际环境中的农业车辆视觉导航研究方法,即先采用神经网络算法对农田环境进行自动分类,然后再相应的选择不同的路径识别方法进行处理.环境识别与分类试验结果证明,该方法能够提高农业车辆视觉导航系统的实用性和可靠性,导航环境的分类准确率为95%,单幅图像平均耗时23 ms.