您的位置: 首页 > 院士专题 > 专题列表

共检索到264条,权限内显示50条;

[前沿资讯] 北京农机精准作业管理现场会在大兴召开 进入全文

农民日报

为全面提升北京市农机精准作业水平,日前,由北京市农业机械试验鉴定推广站与大兴区农业机械技术推广站联合主办的“2019年北京市农机精准作业管理现场会”在大兴区榆垡镇召开。本次活动通过机具静态展示、田间作业演示、室内系统演示等多种方式,集中展现了京郊农机合作社应用信息化技术、智能装备促进农机作业质量、农机管理水平双提升所取得的经验和成效。近几年,北京市农机化发展呈现“农机装备种类齐全、农机科技发展迅猛、农机服务提质增效”的态势,在农机化与信息化融合理念指导下,市农机鉴定推广站以农机合作社为突破点,开展技术引进试验、集成示范,积极探索、大力推进,以农机作业质量监测信息化技术和智能装备武装农机合作社,不断促进农机合作社农机作业质量水平和农机管理水平提升。 

[学术文献] The potential for using smartphones as portable soil nutrient analyzers on suburban farms in central East China 进入全文

Nature

Soil testing is frequently conducted to specify nutrient supply recommendations. By adjusting fertilizer type and application rates, farmers can achieve desired crop yields with lower production costs and are thereby less likely to contribute to eutrophication of nearby waterbodies. However, traditional methods of soil testing can be costly, time-consuming and are often impractical in rural and resource-poor regions in China, where rapid population growth and consequent food demand must be balanced against potential environment risks. Smartphones are nearly ubiquitous and offer a ready capability for providing additional support for existing extension advice. In this study, we used an Android-based smartphone application, in conjunction with commercially-available Quantofix test strips, to analyze soil samples with a goal of providing specific fertilizer recommendations. The app transforms the smartphone into a portable reflectometer, relating the reaction color of the test strips to the concentration of soil nutrients available. A 6-month long field study involving two growing seasons of vegetables was conducted in a suburban area of Nanjing, Jiangsu Province of China to evaluate the accuracy and precision of smartphone-mediated soil analysis. Results obtained via the smartphone correlated well with the yield response of the common green vegetable Ipomoea aquatica (water spinach) and could be applied in calculations of necessary off-farm inputs throughout the open-field vegetable growing season. Together, the smartphone and test strip in combination were shown to offer an acceptable screening tool for soil nutrient concentration assessment with the potential to result in substantial monetary savings and reduction of nutrient loss to the environment.

[前沿资讯] 用活“大数据” 接力“大扶贫” 进入全文

农民日报

“手机一点就能看到,养鸡、养牛生产用药的注意事项、各种病虫害防治实用技术,广西农业大数据平台网站让我们学到了很多知识,平时我也将学到的种养知识传授给本村贫困户,让他们早日脱贫。”广西灵山县烟墩镇邓三哥乐哈哈地说。广西农业信息中心用大数据来助力精准扶贫,打造扶贫攻坚样板间,强化扶贫领域相关部门数据的融合交换、共享使用,创新完成农业大数据移动终端(APP)开发应用,聚集农业资源、农业生产、农业经济、农业综合等4大类37项数据,采集2亿多条农业专项数据,形成广西特色优势产业扶贫大数据分析,探索出了一条“大数据+大扶贫”的融合发展道路,全区产业扶贫工作取得了良好成效,产业扶贫覆盖建档立卡贫困户113万多户,覆盖率超过80%。2018年10月,广西被评为“全国产业扶贫十大机制创新典型之一”。

[会议论文] Estimation of Leaf Nitrogen Concentration of Winter Wheat Using UAV-Based RGB Imagery 进入全文

IFIP International Federation for Information Processing 2019

Leaf nitrogen concentration (LNC) of winter wheat can reflect its nitrogen (N) status. Rapid, non-destructive and accurate monitoring of LNC of winter wheat has important practical applications in monitoring N nutrition and fertilizing management. The experimental site of winter wheat was located at Xiaotangshan National Demonstration Base of Precision Agricultural Research located in Changping District, Beijing, China. High spatial resolution digital images of the winter wheat were acquired using a low-cost unmanned aerial vehicle (UAV) with digital camera system at three key growth stages of booting, flowering and filling during April to June in 2015. Firstly, the acquired UAV digital images were mosaicked to generate a Digital Orthophoto Map (DOM) of the entire experimental site and 15 digital image variables were constructed. Then, based on the ground measured data onto LNC and digital image variables derived from the DOM for 48 sampling plots of winter wheat, linear and stepwise regression models were constructed for estimating LNC. Finally, the optimum model for estimating LNC was screened out by comprehensively considering the coefficient of determination (R2 ), the root mean square error (RMSE), the normalized root mean square error (nRMSE) and the simplicity of model calibrating and validating. The experimental results showed that the linear regression model of r/b that was one of the digital image variables for estimating LNC had the best accuracy with the model’s calibration and validation of R2 , RMSE and nRMSE were 0.76, 0.40, 11.97% and 0.69, 0.43, 13.02%, respectively. The results suggest that it is feasible to estimate LNC of winter wheat based on the DOM acquired by UAV remote sensing platform carrying a low-cost, high-resolution digital camera, which can rapidly and non-destructively obtains the LNC of winter wheat experiment site and provide a quick and low-cost method for monitoring N nutrition and fertilizing management.

[专业会议] AgTech Expo 进入全文

Agtechexpo

The market for agricultural technology products is robust and growing quickly--which means it can be a challenge to keep up with the latest products and developments. Farm Journal AgTech Expo provides a 360-degree, customizable learning experience for farmers and retailers. The Farm Journal AgTech Expo is focused on all practical aspects of technology, not just data. It also provides unparalleled access to technology products and company experts for farmers and retailers.  In addition to hearing from industry-leading presenters, Farm Journal AgTech Expo attendees can choose from a two-track schedule of learning sessions covering topics such as: The latest techniques in precision ag; Innovative software to help maximize farm management and accounting; Using benchmarking data to compare prices and performance in the field; The latest in driverless and other automated equipment.

[学术文献] Examining the social and biophysical determinants of U.S. Midwestern corn farmers’ adoption of precision agriculture 进入全文

Precision Agriculture

Precision agricultural technologies (PA) such as global positioning system tools have been commercially available since the early 1990s and they are widely thought to have environmental and economic beneft; however, adoption studies show uneven adoption among farmers in the U.S. and Europe. This study aims to tackle a lingering puzzle regarding why some farmers adopt precision agriculture as an approach to food production and why others do not. The specifc objective of this study is to examine the social and biophysical determinants of farmers’ adoption of PA. This paper flls a research gap by including measurements of farmer identity—specifcally their own conceptions of their role in the food system—as well as their perceptions of biophysical risks as these relate to the adoption of PA among a large sample of Midwestern U.S. farmers. The study has identifed that farmer identity and perceptions of environmental risk do indeed infuence PA adoption and that these considerations ought to be incorporated into further studies of PA adoption in other jurisdictions. The fndings also appear to highlight the social force of policy and industry eforts to frame PA as not only good for productivity and efciency but also as an ecologically benefcial technology. 

热门相关

意 见 箱

匿名:登录

个人用户登录

找回密码

第三方账号登录

忘记密码

个人用户注册

必须为有效邮箱
6~16位数字与字母组合
6~16位数字与字母组合
请输入正确的手机号码

信息补充