Open geospatial infrastructure for data management and analytics in interdisciplinary research

Jeppesen, Jesper; Ebeid, Emad; Jacobsen, Rune Hylsberg; Toftegaard, Thomas Skjødeberg

doi:10.1016/j.compag.2017.12.026

Cited by 35 publications

(22 citation statements)

References 26 publications

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“…Creating a database and storage system (for easy accessibility) that houses crop, animal, genomics, soil, environmental, social, and economic data with well‐defined metadata information (data quality) is an important step prior to using Big Data for agricultural decision support tools (Cambra Baseca et al., 2019; Gill et al., 2017; Jeppesen et al., 2018; Lokers et al., 2016; Zhou et al., 2016). Knowledge on combined effects of agricultural production factors (Genetics × Environment × Management × Postharvest [G×E×M×P]) and interaction of each of these factors under future climate scenarios will be helpful to meet global food demands.…”

Section: Resultsmentioning

confidence: 99%

Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions

et al. 2020

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Big Data in agriculture is growing rapidly through advancements in metagenomics, precision agriculture, and on-farm sensor technologies, as well as through increased capacity to collect, process, and store these data. Concurrent with 60% increases in food production demands by 2050 and the need for sustainable intensification, is the increased need for data synthesis across temporal and spatial scales. Therefore, in our data-rich world, what is lacking is a data management system across spatial and temporal resolutions including workflows, interpretation methodology, and a delivery structure for identifying optimal systems for sustainable intensification or diversification. Consequently, the objective of this paper is to explore the current state of handling spatially and temporally disparate data and offer solutions for developing a platform for bridging component parts (encompassing multiple scales and disciplines) to analyze system functionality for greater resiliency, which may help manage risk. Two datasets were generated using bibliometrics (research articles from systematic literature reviews) evaluated trends and historical Big Data applications in agronomy. Results indicate research and industry progress is advancing towards web-based real-time output delivery systems using several well-established Big Data handling platforms (e.g., Amazon Web Service, Google Cloud, Microsoft Azure), which are not yet widely used by or designed for agronomic researchers. Cloud-based computing may provide opportunities to extrapolate agricultural research results across larger scales. Authors suggest training and educating agricultural practitioners on Big Data principles, database management, improved data visualization, as well as incentives for data sharing for optimizing Big Data in systems agriculture as these research innovations emerge.

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Section: Resultsmentioning

confidence: 99%

Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions

et al. 2020

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“…The study [31] developed smart platform which helps farmers to efficiently manage their greenhouses and to interact with other farmers. Authors in [32] implemented open geospatial infrastructure for data management and analytics in interdisciplinary research. Authors in [33] proposed a Bayesian network method to optimize infrastructure projects by assessing their social contribution.…”

Section: Fig1 the Missing Links In National Collaboration In Agriculmentioning

confidence: 99%

“…Tse-Chuan proposed IoT agriculture platform for cloud fog computing that integrated cloud-tophysical networking to improve the computing speed of IoT [35]. Jeppesen et al in [36] developed an open geospatial infrastructure for data management and analytics in interdisciplinary research within the agricultural domain. Bu and Wang [37] developed a smart agriculture IoT system based on deep reinforcement learning.…”

Section: Fig1 the Missing Links In National Collaboration In Agriculmentioning

confidence: 99%

Systems Science: Digital Transformation andDeveloping Business Model toward Smart Farms’platform

Ibrahim

Elramly

Hassan

2021

International Journal of Circuits, Systems and Signal Processing

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This paper describes efforts by National Authority for Remote Sensing and Space Sciences (NARSS) to help the Egyptian government to manage and monitor the national projects. We successfully developed a geospatial data sharing portal (NARSSGeoPortal) as part of the government need to build national Decision Support System (DSS). We were able to solve the software development issues as well as the satellite imagery sourcing issues, but the main challenge remains around how to collect complete and correct data from the public about their private businesses nationwide. The most challenging is how to engage the public and encourage the business owners who are the main sources of data to provide the government Geoportal with data about their businesses. It is also challenging to engage the scientists and experts from government research centers into the data sharing Geoportal. Furthermore, it is a challenge to integrate the government research centers with the public businesses’ daily operation. The data sharing Geoportal is built for all national projects and government authorities, however, in this paper we focus on the Agriculture authorities and farming businesses where the challenge is how to collect correct and complete data per acre about the seeds, fertilizers, water, pest control and all other farm related data that the satellite imagery does not provide. The goal is to integrate the farms into unified national monitoring, and control system while developing advanced smart farms with the use of Internet of Things (IoT). The proposed collaboration agriculture platform fills the gap between two groups. The first group includes the government authorities, financial institutions, and research centers. The second group includes farmers, supply chain, and agriculture engineers. The platform show how employment can be generated by transforming the national ecosystem. The paper also fills a major gap in industry as well as in academia by providing the first Bluetooth Low Energy computer aided design tool that will facilitate testing, designing, deploying, managing and debugging of real Bluetooth Low Energy networks.

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“…Nevertheless, if such complexities could be encompassed within an application, then planning ecosystem service enhancements within a sophisticated GIS tool may be the more valuable approach. However, to date, the uptake of sophisticated decision support and GIS tools by land managers and farmers has been limited and with respect to GIS this is largely confined to precision farming applications rather than bespoke environmental planning and management (Aubert et al, 2012;Fountas et al, 2015;Jeppesen et al, 2018;Kaloxylos et al, 2012;Rose et al, 2016;Sørensen et al, 2010).…”

Section: Planning Ecosystem Service Enhancementsmentioning

confidence: 99%

Developing practical techniques for quantitative assessment of ecosystem services on farmland

Tzilivakis

Warner

Holland

2019

Ecological Indicators

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The application of the concept of ecosystem services in the context of environmental management of agricultural landscapes is a relatively new and developing topic. There is increasing demand for the delivery of ecosystem services, especially with respect to ensuring that outcomes from policy interventions are realised. Consequently, there is a need for knowledge, tools and techniques to aid the identification of appropriate options for the given circumstances. This paper presents the outputs from a study that aimed to derive practical approaches that could be used to quantify ecosystem services from features on farmland in Europe. More specifically it aimed to integrate the outputs from the Quantification of Ecological Services for Sustainable Agriculture project into an existing prototype software package (the Ecological Focus Areas Calculator). The ecosystem services explored are soil erosion; pollination; pest control; aesthetics; and carbon sequestration. Following an explanation of the methodology, case study landscape features are used to illustrate the outputs generated. The quantitative outputs are also compared to the outputs from the existing qualitative techniques in the Ecological Focus Areas Calculator to highlight the advantages and disadvantages of the two approaches. The study concludes that the development of more quantitative approaches is an improvement over more qualitative techniques. However, quantitative techniques are not available for all ecosystem services, whereas the qualitative approach covers more ecosystem services and thus provides a more holistic perspective. It will be important to further develop the techniques as new science emerges; to ground truth the techniques to confirm and improve their reliability; and to improve delivery tools to meet the requirements of different end users which may evolve in the future. As the intellectual, economic and technical capacity of the land management sector increases, the level of sophistication that is be deemed to be practical will also evolve, thus the tools and techniques available need to keep pace with this.

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Open geospatial infrastructure for data management and analytics in interdisciplinary research

Cited by 35 publications

References 26 publications

Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions

Spatially and temporally disparate data in systems agriculture: Issues and prospective solutions

Systems Science: Digital Transformation andDeveloping Business Model toward Smart Farms’platform

Developing practical techniques for quantitative assessment of ecosystem services on farmland

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