PAFiC: Precision Agriculture for Family-farms in China

PAFiC：中国家庭农场的精准农业

• 批准号: ST/N006801/1
• 负责人: Zhenhong Li
• 金额: $ 164.22万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FN006801%2F1
• 关键词: PAFiC; Precision; Agriculture; Family; farms

Rapid advances in fertiliser use and other inputs to crops have dramatically improved Chinese crop production over recent decades, but this has not been done in a sustainable manner and it is estimated that >10M t of synthetic nitrogen fertiliser is wasted annually in China. The number of small to medium-sized commercial family farms is increasing from a merging of smaller, non-commercial family plots. It is desirable to support these farms to maintain rural populations and economies. These family-farmers also need technological assistance to manage larger areas that they have no historical connection to. Precision agriculture, allowing for fine-scale within-field management of crops based on detailed spatial data collection, has an essential role to play in increasing fertiliser and resource use efficiency on farms. This will increase production efficiency (profitability) as well as reduce the environmental footprint of agricultural practices linked to fertilizer use. However, in China there are fundamental barriers to uptake of precision agriculture methods and technology, including high costs relative to income and unquantified financial benefits, a lack of data and services and a lack of awareness and acceptance by growers, communities and administrative agencies. This joint UK-China collaboration aims to improve the use efficiency of nutrients and agri-chemicals in crop production in China, by addressing key technological, agricultural and social or economic barriers to the use of precision agriculture methods in commercial family farms. The project will develop new technology and data sources for agricultural decision making, including the application of advanced hyperspectral cameras, able to measure many wavelengths of light and provide detailed information on crop health, and improved technology for precise spatial positioning within fields. Improved methods to utilise satellite imagery, especially from radar sensors systems, to provide accessible data on crop nutrient levels and growth will also be developed and the advantages of combining data from multiple sources (satellites, airborne sensors and ground monitoring) will be assessed. These improved data layers, providing frequent and detailed spatial information on crop growth, crop health and soils, will then be combined with models of crop growth to provide a system for agricultural decision making that is applicable to family farms in China. This will promote the optimal use of agricultural resources, such as fertiliser. Developed methods will be tested on exemplar farms in China, covering a range of geographic regions and crop systems that have been established in previous research projects. To facilitate both the maximum engagement from a diversity of community and industry members, and the maximum usage of the agri-technologies and precision agriculture methods by farmers, it is critical to incorporate both scientific and local (community and practitioner) expertise into the project. This is critical to understanding and addressing issues specific to these farming system. An integral aspect of the project is to therefore undertake focussed research on the societal and economic barriers to uptake and to use of these technologies. This research will identify and address these barriers via the mode of development and the delivery of the project outputs onto family-farms. This work will also form the basis for wide-reaching and effective public engagement, knowledge exchange and policy translation to ensure the latest methods are adopted in China. Activities will include the development of a data information portal for crop management, stakeholder workshops and technical training for local growers and agricultural specialists.

近几十年来，化肥使用和其他作物投入的快速进步极大地提高了中国的作物产量，但这并不是以可持续的方式实现的，据估计，中国每年浪费超过 1000 万吨合成氮肥。中小型商业家庭农场的数量通过合并较小的非商业家庭农场而不断增加。支持这些农场以维持农村人口和经济是可取的。这些家庭农民还需要技术援助来管理与他们没有历史联系的更大地区。精准农业允许基于详细的空间数据收集对农作物进行精细化的田间管理，在提高农场肥料和资源利用效率方面发挥着至关重要的作用。这将提高生产效率（盈利能力）并减少与化肥使用相关的农业实践的环境足迹。然而，在中国，采用精准农业方法和技术存在根本性障碍，包括相对于收入而言成本高昂、经济收益无法量化、缺乏数据和服务以及种植者、社区和行政机构缺乏认识和接受。这项中英联合合作旨在通过解决商业家庭农场使用精准农业方法的关键技术、农业和社会或经济障碍，提高中国作物生产中养分和农用化学品的使用效率。该项目将为农业决策开发新技术和数据源，包括应用先进的高光谱相机，能够测量多种波长的光并提供有关作物健康的详细信息，以及改进田间精确空间定位技术。还将开发利用卫星图像（特别是雷达传感器系统）的改进方法，以提供有关作物养分水平和生长的可获取数据，并将评估结合多个来源（卫星、机载传感器和地面监测）数据的优势。这些改进的数据层提供了有关作物生长、作物健康和土壤的频繁而详细的空间信息，然后将与作物生长模型相结合，提供适用于中国家庭农场的农业决策系统。这将促进肥料等农业资源的优化利用。开发的方法将在中国的示范农场进行测试，涵盖先前研究项目中已建立的一系列地理区域和作物系统。为了促进各种社区和行业成员的最大程度参与，以及农民最大限度地利用农业技术和精准农业方法，将科学和当地（社区和从业者）专业知识纳入项目至关重要。这对于理解和解决这些农业系统特有的问题至关重要。因此，该项目的一个组成部分是对采用和使用这些技术的社会和经济障碍进行重点研究。本研究将通过发展模式和向家庭农场交付项目成果来识别和解决这些障碍。这项工作还将为广泛有效的公众参与、知识交流和政策转化奠定基础，以确保最新的方法在中国得到采用。活动将包括开发作物管理数据信息门户、利益相关者研讨会以及为当地种植者和农业专家提供技术培训。

项目成果

Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

作物生产系统中推导产量区的多时相产量模式分析方法

• DOI: http://dx.10.1007/s11119-020-09719-1
• 发表时间: 2020
• 期刊: Precision Agriculture
• 影响因子: 6.2
• 作者: Blasch G

Estimation of soil moisture in farmland using improved water cloud model and Radarsat-2 data

利用改进的水云模型和Radarsat-2数据估算农田土壤湿度

• 发表时间: 2016
• 期刊: Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE)
• 作者: Guijun Yang

Commercial Off-the-Shelf Digital Cameras on Unmanned Aerial Vehicles for Multitemporal Monitoring of Vegetation Reflectance and NDVI

用于植被反射率和 NDVI 多时相监测的无人机商用现成数码相机

• DOI: http://dx.10.1109/tgrs.2017.2655365
• 发表时间: 2017
• 期刊: IEEE Transactions on Geoscience and Remote Sensing
• 影响因子: 8.2
• 作者: Berra E

Use of Google Earth Engine to Generate a 20-Year 1 Km × 1 Km Monthly Air Temperature Product Over Yellow River Basin

利用Google Earth引擎生成黄河流域20年1公里×1公里月气温产品

• DOI: http://dx.10.1109/jstars.2021.3116258
• 发表时间: 2021
• 期刊: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• 影响因子: 5.5
• 作者: Gao M

Application of an Improved Method in Retrieving Leaf Area Index Combined Spectral Index with PLSR in Hyperspectral Data Generated by Unmanned Aerial Vehicle Snapshot Camera

无人机快拍相机生成的高光谱数据中结合光谱指数和PLSR的改进叶面积指数反演方法的应用

• DOI: http://dx.10.3724/sp.j.1006.2017.00549
• 发表时间: 2017
• 期刊: Acta Agronomica Sinica
• 作者: GAO L