A social learning video method: Identifying and sharing successful transformation knowledge for sustainable soil management in Switzerland

To enhance sustainable land use, a From Farmer to Farmer project was conducted in Switzerland (2001–2010). A multi‐stakeholder discussion group co‐produced nine videos with experienced farmers and wine producers showing sustainable soil management practices. We analysed the video audio‐visual content and drew on reflections of the co‐production process, referring to concepts of system, target and transformation knowledge, as well as on social learning. The analysis showed a broad range of means (or actions) for sustainable soil management in arable land management, fodder production and wine growing that are aligned to transformation knowledge. The research showed that farmers refer to three phases of social learning, light‐bulb moments, coping with challenges and gaining successful expertise. These are not just linear processes of individuals. Four types of social learning were found in the video analysis: (a) learning from observing actions of others, (b) sharing experiences with storytelling, (c) informal social interactions and (d) being a role model with a large social network. Videos enable transformation knowledge to be shared with peers using storytelling; this powerful narrative communication style provides credibility and respects the ‘thought style’ of the target audience group. We conclude that for successful implementation of sustainable actions, it is important to address a specific target group and share their transformation knowledge built upon system and target knowledge. The social learning video method is a viable way to enable social learning between science, administration and practice and has potential for fostering change in sustainable soil management.     

Using webinars to extend the reach of soil learning in New South Wales: A first look

Increasingly knowledge is shared using new web‐based channels. The use of these channels is relatively new in the realm of soil science. However, they may prove to be very useful for connecting time‐poor, geographically dispersed audiences in an efficient and cost‐effective way. There has been limited study of this form of knowledge sharing for natural science (let alone soil science). In New South Wales (NSW), Australia, the NSW Department of Primary Industries (NSW DPI) has coordinated the Soil Network of Knowledge (SNoK) monthly webinar over 3 years, with over 40 webinars targeting an audience of next users of soil information. Aggregated data collected from these webinars indicated that the majority of information is used for professional development by participants. Whether this will eventually lead to on‐ground practice change because of the improved professional and technical knowledge of participants is still a moot point and will require further investigation over time. The information collected on attendance and participation showed that webinars are a good way to connect with some target audiences such as other government bodies that have a historical relationship with NSW DPI. However, other target audiences that NSW DPI has always found difficulty in engaging, such as private advisers, require more active overtures to drive participation. The growing subscriber base is encouraging.     

A市低收入边缘群体特征及针对性减贫路径探讨

【目的/意义】随着脱贫攻坚进程不断深入,低收入边缘群体问题日益突出,通过分析低收入边缘群体特征及其存在的问题,探索更高效的帮扶手段,为其他经济发达地区提供借鉴。【方法/过程】基于A市249户低收入边缘群体的调查数据,运用文献法、问卷调查和重点访谈法等研究方式,研究调查低收入边缘群体脱贫攻坚落实情况。【结果/结论】基于对问题的分析,提出准确甄别低收入边缘群体,完善收入核准机制;充分挖掘社会资源,聚焦扶志立业工作;加强基础工作指导,提升基层干部信心;涵养乡风文明,重塑家庭责任意识等对策建议。     

Performance and uncertainty analysis of a short-term climate reconstruction based on multi-source data in the Tianshan Mountains region,China

Short-term climate reconstruction, i.e., the reproduction of short-term(several decades) historical climatic time series based on the relationship between observed data and available longer-term reference data in a certain area, can extend the length of climatic time series and offset the shortage of observations. This can be used to assess regional climate change over a much longer time scale. Based on monthly grid climate data from a Coupled Model Inter-comparison Project phase 5(CMIP5) dataset for the period of 1850–2000, the Climatic Research Unit(CRU) dataset for the period of 1901–2000 and the observed data from 53 meteorological stations located in the Tianshan Mountains region(TMR) of China during the period of 1961–2011, we calibrated and validated monthly average temperature(MAT) and monthly accumulated precipitation(MAP) in the TMR using the delta, physical scaling(SP) and artificial neural network(ANN) methods. Performance and uncertainty during the calibration(1971–1999) and verification(1961–1970) periods were assessed and compared using traditional performance indices and a revised set pair analysis(RSPA) method. The calibration and verification processes were subjected to various sources of uncertainty due to the influence of different reconstructed variables, different data sources, and/or different methods used. According to traditional performance indices, both the CRU and CMIP5 datasets resulted in satisfactory calibrated and verified MAT time series at 53 meteorological stations and MAP time series at 20 meteorological stations using the delta and SP methods for the period of 1961–1999. However, the results differed from those obtained by the RSPA method. This showed that the CRU dataset produced a low degree of uncertainty(positive connection degree) during the calibration and verification of MAT using the delta and SP methods compared to the CMIP5 dataset. Overall, the calibrated and verified MAP had a high degree of uncertainty(negative connection degree) regardless of the dataset or reconstruction method used. Therefore, the reconstructed time series of MAT for the period of 1850(or 1901)–1960 based on the CRU and CMIP5 datasets using the delta and SP methods could be used for further study. The results of this study will be useful for short-term(several decades) regional climate reconstruction and longer-term(100 a or more) assessments of regional climate change.     

基于供应网络能力研究框架分析我国乡村旅游供应链发展现状

游客需求的多样化、旅游目的地功能强化、信息技术影响加剧都在表明传统的旅游模式已经 无法再满足的客户的需求与市场的变化，旅游供应链的构建与优化是发展旅游业的大势所趋。本文 将借助供应网络能力的研究框架，从网络定位、网络构造、网络管理三个方面分析我国旅游供应链的 发展现状。     

基于SegNet模型的高原鼠兔的图像分割

针对高原鼠兔图像目标尺寸小、背景复杂、特征不显著、基于活动轮廓的图像分割模型无法有效分割的问题，采用基于卷积神经网络的SegNet语义模型对高原鼠兔图像进行分割：首先将采集的高原鼠兔图像进行预处理，尺度归一化后制作成与Pascal VOC数据集格式一致的数据集；然后将数据集分为训练集与测试集，采用训练集对SegNet模型训练，测试集对模型进行分割测试。对高原鼠兔图像分割的试验结果表明：与基于活动轮廓的Chan_Vese模型相比，基于卷积神经网络的SegNet模型对高原鼠兔图像分割时的交并比、平均像素精度、Dice相似性指数和Jaccard指数分别提高了68.33%、9.35%、30.61%和47.98%，过分割率和欠分割率分别降低了87.20%、16.52%。     

推进种养结合的制约因素及政策建议

随着粮食增产的迫切需求和化肥工业的快速发展,我国种植业与养殖业养分物质循环断裂,推进种养结合成为实现农业绿色发展重要途径。但目前,我国从农户层面以及区域层面都存在着种植与养殖之间的纽带断裂,造成了农业资源利用的错位,也产生了资源环境双重压力。推进种养结合的堵点主要表现在养殖场责任尚未压实、有机肥施用不便利、堆肥质量标准缺失、社会化服务体系不完善等多方面。本文结合对国内和国际上推进种养结合的典型做法的分析,在强化提升国家粮食安全保障能力的大背景下,从优化种养布局规划、压实养殖主体责任、强化科技支撑、培育社会化服务组织等方面提出了当前构建全新的种养循环体系的建议。     

基于PLSR-BP复合模型的红壤有机质含量反演研究

对红壤地区土壤有机质进行快速预测,以满足智慧农业与精准施肥的需要。以江西省奉新县北部为研究区域,采用1 km×1 km标准格网划分研究区进行采样,共得到红壤样本248个。对土壤光谱进行了包含分数阶导数在内的3种数学变换方法,将经过P=0.01显著性检验的波段用于模型的构建,选用偏最小二乘回归(PLSR)和BP神经网络建立土壤有机质含量预测模型。结果表明:当对红壤光谱数据进行1.5阶导数变换后再使用PLSR-BP复合模型对土壤有机质含量进行预测时的结果为最优,训练集R~2=0.89,RMSE=4.68g·kg~(-1),验证集R~2=0.87,RMSE=5.55g·kg~(-1),RPD=2.75。1.5阶导数对红壤光谱数据的变换能够更好地突出与有机质相关的特征信息,有助于其含量预测。PLSR-BP复合模型预测精度优于单一模型,能够较好地预测红壤有机质含量,为精准农业快速监测红壤有机质含量提供了新的途径。     

PPP模式下公共图书馆提供公共文化服务新探析 ——"图书馆+纪录片院线"

[目的/意义]文章以中国PPP模式下公共图书馆提供公共文化服务为研究对象,提出“图书馆+纪录片院线”的构想,该项目不仅可以丰富公共图书馆提供公共文化服务的形式,还能促进纪录片产业的良性发展。[方法/过程]简要概述了公共图书馆与社会资本合作的现状,指出了发展过程中所存在的一些问题,并对“图书馆+纪录片院线”项目的意义和可行性进行了分析。[结果/结论]提出努力开拓市场、提高宣传效力,树立正确观念、合理合法收费,选择合适伙伴,发挥公共图书馆优势、提供特色服务,联合多方力量、克服潜在阻力等对策。