Geospatial Resource for Agricultural Species and Pests with integrated workflow modelling to support Global Food Security (GRASP-GFS): a prototype

农业物种和害虫地理空间资源与集成工作流程建模以支持全球粮食安全 (GRASP-GFS)：原型

• 批准号: BB/K004034/1
• 负责人: Michael Jackson
• 金额: $ 15.35万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FK004034%2F1
• 关键词: Geospatial; Resource; Agricultural; Species; Pests

Access to a wide range of information, from rigorous scientific results to 'hear-say' farmer's knowledge is becoming critical to be able to target efforts in achieving food security planning at community or country levels. Also, designing scientific and intervention strategies within changing climates and markets is a fundamental challenge. Developments of technologies for data collection in mobile communications, sensor platforms, spatial search and pervasive computing are fundamentally changing research in agriculture. However, inter-disciplinary research needed to transform raw data into useful intelligence and knowledge to improve the planet's environmental, economic and societal well being is still constrained by disciplinary and organisational silos and legacy concepts and an non-existent or non-rigorous approach to quantifying the uncertainty intrinsic in any collected dataset. GRASP-GFS will use a geospatially-anchored 'genotype' database integration principle to query such multidimensional data information, including papers, reports, indigenous, socio-economic and farmer's knowledge. This framework will enable uncertainty assessments through the use of quality weighting descriptors of the different components within a chosen geo-workflow model for food security.Cross-disciplinary expertise driven from geospatial sciences methodologies will be used to develop this integrating framework across all subjects relevant to Food Security. The driving focus will be the agricultural species germplasm for genotype characteristics with the data ordered by geospatial origin with the higher level descriptor being the 'agricultural trait'. A particular novel aspect is the combined use of climate records or scenarios and land ground condition data with known (and new) sources of traits in crop, animal and microbial species of agricultural importance. This will permit new perspectives on genetic diversity, identifying new sources of germplasm and sources of trait variation, geolocating suitable germplasm by a combination of agro-ecological modelling and matching principles, planning breeding objectives with the greatest likely impact by taking into acccount the added information of local market and farmer knowledge. These modelling capabilities will come from framing each above model within a generic approach allowing workflow composing based on semantic description of data and processes and workflow quality assessment for uncertainty/error propagation. Two use cases modelling with wheat crop in the UK and bambara groundnut in Malaysia will demonstrate the approach with crop specific data and processing models to forecast geospatial trait variation for these two crops.Supporting the Crops for the Future Research Centre (CFFRC) in Malaysia, the GRASP integrated geospatial platform for agricultural species, including major pests and diseases, will allow future investigators to shape the data handling and integration according to their subject requirements, before contributing to the population of the prototype database. Data capture from sensor network to remote sensing, including crowd-sourcing from farmers will be further integrated within the GRASP platform allowing other refinements of the workflow modelling and multiple scale scenario risk assessments.Using open standards and interoperability principles developed by the Open Geospatial Consortium (OGC), the platform deliverable software will be released under open source license to enable wide use and further developments also ensuring sustainability of the project. Both desktop interfaces and web interfaces with compiled current databases (with updating facilities) will be released, enabling wide audience usage even from remote places with weak internet connections. The GRASP-GFS aims to link with other global initiatives such as GEOSS (Global Earth Observation System of Systems) and GeoNode to develop productive interaction between bench, economic and social scientists.

获取广泛的信息，从严格的科学结果到“道听途说”的农民知识，对于能够有针对性地努力实现社区或国家层面的粮食安全规划变得至关重要。此外，在不断变化的气候和市场中设计科学和干预策略也是一项根本挑战。移动通信、传感器平台、空间搜索和普适计算等数据收集技术的发展正在从根本上改变农业研究。然而，将原始数据转化为有用的情报和知识以改善地球的环境、经济和社会福祉所需的跨学科研究仍然受到学科和组织孤岛、遗留概念以及不存在或不严格的量化方法的限制。任何收集的数据集中固有的不确定性。 GRASP-GFS将使用地理空间锚定的“基因型”数据库集成原理来查询此类多维数据信息，包括论文、报告、土著、社会经济和农民知识。该框架将通过使用选定的粮食安全地理工作流程模型中不同组成部分的质量加权描述符来实现不确定性评估。由地理空间科学方法驱动的跨学科专业知识将用于开发这一跨所有与粮食安全相关的学科的综合框架。粮食安全。驱动焦点将是农业物种种质的基因型特征，数据按地理空间起源排序，更高级别的描述符是“农业性状”。一个特别新颖的方面是将气候记录或情景以及土地条件数据与具有农业重要性的作物、动物和微生物物种的已知（和新）性状来源相结合。这将为遗传多样性提供新的视角，识别新的种质来源和性状变异来源，通过结合农业生态模型和匹配原则对合适的种质进行地理定位，通过考虑附加信息来规划具有最大可能影响的育种目标当地市场和农民知识。这些建模功能将来自于在通用方法中构建上述每个模型，允许基于数据和流程的语义描述进行工作流组合以及不确定性/错误传播的工作流质量评估。英国小麦作物和马来西亚班巴拉花生的两个用例模型将展示利用作物特定数据和处理模型来预测这两种作物地理空间性状变异的方法。支持马来西亚未来作物研究中心 (CFFRC)， GRASP 针对农业物种（包括主要病虫害）的综合地理空间平台将允许未来的研究人员根据其主题要求制定数据处理和集成，然后再为原型数据库的数量做出贡献。从传感器网络到遥感的数据捕获，包括来自农民的众包，将进一步集成到 GRASP 平台中，从而允许对工作流程建模和多尺度场景风险评估进行其他改进。使用开放地理空间联盟开发的开放标准和互操作性原则（ OGC），平台可交付软件将在开源许可下发布，以实现广泛使用和进一步开发，同时确保项目的可持续性。桌面界面和带有已编译的当前数据库（带有更新设施）的网络界面都将被发布，即使是互联网连接较弱的偏远地区，也可以让广泛的受众使用。 GRASP-GFS 旨在与 GEOSS（全球地球观测系统系统）和 GeoNode 等其他全球倡议联系起来，以发展基础科学家、经济和社会科学家之间的富有成效的互动。

项目成果

Simulating Eyespot Disease Development and Yield Loss Using APSIM for UK Wheat

使用 APSIM 模拟英国小麦的眼斑病发展和产量损失

• DOI: http://dx.10.1016/j.proenv.2015.07.192
• 发表时间: 2015
• 期刊: Procedia Environmental Sciences
• 作者: Al

Geospatial binding for transdisciplinary research in crop science: the GRASPgfs initiative

作物科学跨学科研究的地理空间结合：GRASPgfs 倡议

• DOI: http://dx.10.1186/s40965-017-0034-3
• 发表时间: 2017
• 期刊: Open Geospatial Data, Software and Standards
• 作者: Leibovici D

A geoprocessing modelling interoperable framework for AgriGIS using open data and open standards

使用开放数据和开放标准的 AgriGIS 地理处理建模互操作框架

• DOI: http://dx.10.7287/peerj.preprints.2136
• 发表时间: 2016
• 作者: Santos R