The Foodie platform hub aims at enabling in an easy manner the (re)use of open data in the agricultural domain in order to create new applications that provide added value to different stakeholder groups.
Land Use Change and European Biofuel PoliciesDavid Laborde
This presentation gives a detailed overview of the 2011 report done by IFPRI (Laborde, 2011) for the European Commission on the land use consequences of EU biofuel mandates (available at http://www.ifpri.org/sites/default/files/publications/biofuelsreportec2011.pdf )
The report aims to compute iLUC (indirect land use change) factor for different feedstocks using the MIRAGE-Biof CGE model.
Land Use Change and European Biofuel PoliciesDavid Laborde
This presentation gives a detailed overview of the 2011 report done by IFPRI (Laborde, 2011) for the European Commission on the land use consequences of EU biofuel mandates (available at http://www.ifpri.org/sites/default/files/publications/biofuelsreportec2011.pdf )
The report aims to compute iLUC (indirect land use change) factor for different feedstocks using the MIRAGE-Biof CGE model.
How to take dynamic data in life cycle inventory from brazilian sugarcane eth...eSAT Journals
Abstract
Ethanol derived from sugarcane is the energy source with the highest growth in Brazil over the past 40 years. The explanation
can be summarized by its ability to replace fossil fuels with lower emissions and be produced by a renewable raw material with
energetic potential eight times higher than gasoline. Studies indicate increased ethanol demand by 2050. The increase in
production implies higher environmental interference according to the cultivation time in the field, with the resources used and
the local production of sugarcane. The method of Life Cycle Assessment (LCA) is a tool for measuring the performance of a
product during its life cycle. However, agricultural or regional factors are dynamic over time and affect its performance in a
future evaluation. Thus, this article aims to suggest an inventory methodology life cycle that can represent the dynamic behavior
of the determinants of environmental effects in the assessment of ethanol life cycle. The methodology involves principles of Input
Output Analysys (IOA) adjusted for regression statistical technique to structure the flows from product system of LCA
Keywords: Sugarcane, Dynamic Variables, Inventory Lifecycle, Life Cycle Assessments
BC3 Policy Briefing Videos Series: Reports that synthesise the research work carried out by the team from the Basque Centre for Climate Change (BC3). This content is intended to be of use for the agents involved in decision-making on climate change.
This Policy Briefing was authored by Agustin del Prado, Patricia Gallejones and Guillermo Pardo.
Presentation at workshop: Reducing the costs of GHG estimates in agriculture to inform low emissions development
November 10-12, 2014
Sponsored by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Food and Agriculture Organization of the United Nations (FAO)
Seasonal Dynamic of Mineral Macronutrients in Three Varieties of Clementine (...IJERA Editor
The nutrient composition in terms of macronutrients of three different varieties (Early season “Orogrande”, season “Nules” and late season “Nour”) of clementine leaves (Citrus reticulata) was monitored at monthly intervals during three years in order to provide information for the development of nutrition management guidelines more suited to local conditions of southern Morocco. Nutrient concentrations at the sampled leaves for all the three elements, nitrogen (N), phosphorus (P) and potassium (K), changed seasonally for all tested varieties. Seasonal dynamic of those three elements doesn‟t differ significantly between years, particularly during the spring-summer period. Results of statistical analysis show a significant variability between the tested varieties in term of leaf nitrogen and potassium concentrations. However, no significant differences between tested varieties were recorded in term of the leaf phosphorus concentration.
"Partnering for Impact: IFPRI-European Research Collaboration for Improved Food and Nutrition Security" presentation by Luisa Marelli, European Commission – DG Joint Research Centre (JRC) Institute for Energy and Transport, Sustainable Transport Unit, on 25 November 2013 in Brussels, Belgium.
Poster: Spatiotemporal Visualization Method for Interrelation-based Analysis...Muz Ahmad
This poster was presented during Our Common Future Under Climate Change International Scientific Conference (CFCC 15), 7-10 July 2015, UNESCO Paris, France.
http://www.reporterre.net/IMG/pdf/programme_cfcc15_7-10_juillet-2.pdf
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Presentation at the 3rd European Sustainable Phosphorus Conference (ESPC3), Helsinki, 11 - 13 June 2018, co-organised by the Baltic Sea Action Group (BSAG) and the European Sustainable Phosphorus Platform (ESPP), brought together nearly 300 participants from 30 countries talking about nutrient recycling and stewardship.
See for all information and outcomes www.phosphorusplatform.eu/ESPC3
A study Report on Implementation of GIS in Solid Waste ManagementAM Publications
Increasing human population and economic development and urbanization has resulted in generation of huge quantities of municipal solid waste (MSW). It involves many activities like collection, transportation and disposal of wastes. There is tremendous amount of loss in terms of environment degradation, health hazards and economic descend, due to direct disposal of waste. It is better to segregate waste at initial stages where it is generated. Planners are thus forced to consider alternate and available means of disposal, especially by minimizing damage to the ecosystem and human population. GIS has proved to be boon to such planners by visualizing the real solid waste situations and facilitating route analysis through mapping. A Geographic Information System (GIS) is a computer system for capturing, storing, checking and displaying data related to positions on Earth’s surface.
How to take dynamic data in life cycle inventory from brazilian sugarcane eth...eSAT Journals
Abstract
Ethanol derived from sugarcane is the energy source with the highest growth in Brazil over the past 40 years. The explanation
can be summarized by its ability to replace fossil fuels with lower emissions and be produced by a renewable raw material with
energetic potential eight times higher than gasoline. Studies indicate increased ethanol demand by 2050. The increase in
production implies higher environmental interference according to the cultivation time in the field, with the resources used and
the local production of sugarcane. The method of Life Cycle Assessment (LCA) is a tool for measuring the performance of a
product during its life cycle. However, agricultural or regional factors are dynamic over time and affect its performance in a
future evaluation. Thus, this article aims to suggest an inventory methodology life cycle that can represent the dynamic behavior
of the determinants of environmental effects in the assessment of ethanol life cycle. The methodology involves principles of Input
Output Analysys (IOA) adjusted for regression statistical technique to structure the flows from product system of LCA
Keywords: Sugarcane, Dynamic Variables, Inventory Lifecycle, Life Cycle Assessments
BC3 Policy Briefing Videos Series: Reports that synthesise the research work carried out by the team from the Basque Centre for Climate Change (BC3). This content is intended to be of use for the agents involved in decision-making on climate change.
This Policy Briefing was authored by Agustin del Prado, Patricia Gallejones and Guillermo Pardo.
Presentation at workshop: Reducing the costs of GHG estimates in agriculture to inform low emissions development
November 10-12, 2014
Sponsored by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Food and Agriculture Organization of the United Nations (FAO)
Seasonal Dynamic of Mineral Macronutrients in Three Varieties of Clementine (...IJERA Editor
The nutrient composition in terms of macronutrients of three different varieties (Early season “Orogrande”, season “Nules” and late season “Nour”) of clementine leaves (Citrus reticulata) was monitored at monthly intervals during three years in order to provide information for the development of nutrition management guidelines more suited to local conditions of southern Morocco. Nutrient concentrations at the sampled leaves for all the three elements, nitrogen (N), phosphorus (P) and potassium (K), changed seasonally for all tested varieties. Seasonal dynamic of those three elements doesn‟t differ significantly between years, particularly during the spring-summer period. Results of statistical analysis show a significant variability between the tested varieties in term of leaf nitrogen and potassium concentrations. However, no significant differences between tested varieties were recorded in term of the leaf phosphorus concentration.
"Partnering for Impact: IFPRI-European Research Collaboration for Improved Food and Nutrition Security" presentation by Luisa Marelli, European Commission – DG Joint Research Centre (JRC) Institute for Energy and Transport, Sustainable Transport Unit, on 25 November 2013 in Brussels, Belgium.
Poster: Spatiotemporal Visualization Method for Interrelation-based Analysis...Muz Ahmad
This poster was presented during Our Common Future Under Climate Change International Scientific Conference (CFCC 15), 7-10 July 2015, UNESCO Paris, France.
http://www.reporterre.net/IMG/pdf/programme_cfcc15_7-10_juillet-2.pdf
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Presentation at the 3rd European Sustainable Phosphorus Conference (ESPC3), Helsinki, 11 - 13 June 2018, co-organised by the Baltic Sea Action Group (BSAG) and the European Sustainable Phosphorus Platform (ESPP), brought together nearly 300 participants from 30 countries talking about nutrient recycling and stewardship.
See for all information and outcomes www.phosphorusplatform.eu/ESPC3
A study Report on Implementation of GIS in Solid Waste ManagementAM Publications
Increasing human population and economic development and urbanization has resulted in generation of huge quantities of municipal solid waste (MSW). It involves many activities like collection, transportation and disposal of wastes. There is tremendous amount of loss in terms of environment degradation, health hazards and economic descend, due to direct disposal of waste. It is better to segregate waste at initial stages where it is generated. Planners are thus forced to consider alternate and available means of disposal, especially by minimizing damage to the ecosystem and human population. GIS has proved to be boon to such planners by visualizing the real solid waste situations and facilitating route analysis through mapping. A Geographic Information System (GIS) is a computer system for capturing, storing, checking and displaying data related to positions on Earth’s surface.
Yield Forecasting to Sustain the Agricultural Transportation UnderStochastic ...IJRESJOURNAL
ABSTRACT: Agricultural transportation is a major part of the United States’ transportation systems. This system follows a complex multimodal network consisting of highway, railway, and waterways which are mostly based on the yield of the agricultural commodities and their market values. The yield of agricultural commodities is dependent on stochastic environment such as weather conditions, rainfall, soil type and natural disasters. Different techniques such as leaf growth index, Normalized Difference Vegetation Index (NDVI), and regression analysis are used to forecast the yield for the end of harvest season. The yield forecasting techniques are used to predict the agricultural transportation needs and improve the cost minimization. This study provides a model for yield forecasting using NDVI data, Geographical Information System (GIS), and statistical analysis. A case study is presented to demonstrate this model with a novel tool for collecting NDVI data.
Farming Tools for external nutrient Inputs and water MAnagement (FATIMA)ExternalEvents
The FATIMA project aims to develop innovative and new farm capacities, which help the intensive farm sector to optimize their external input (nutrients, water) management and use, with the vision of bridging sustainable crop production with fair economic competitiveness.
Investigation of the Socioeconomic Factors Influencing Municipal Solid Waste ...mustansar khan
Investigation of the Socioeconomic Factors Influencing Municipal Solid Waste Generation and Development of Waste Generation forecast Model Using Machine Learning for Dubai
Applications of Aqua crop Model for Improved Field Management Strategies and ...CrimsonpublishersMCDA
To quantify, integrate and assess the impacts from weather and climate change/variability on crop growth and productivity, crop models have been used for several years as decision support tools in the world. This paper is reviewed to assess applications of Aqua crop model as a decision support tool for simulating and validating crop management practices and climate change adaptation strategies. This model is devised by the FAO irrigation and drainage team. This model is very important especially, to guide as a decision support tool for dry land areas where soil moisture is very critical to affect crop productivity. It maintains the balance between simplicity, accuracy and robustness. The model has been calibrated and validated to simulate growth and productivity of crops, soil moisture balance, water use efficiency, evapo-transpiration and climate change impact assessment in different climate, management (water, fertilizer, sowing date, spacing etc.) practices around the world, especially in areas where soil moisture stress prevails. Maize, wheat, barley, tee, sorghum, pulse crops such as groundnut, soybean, vegetables (tomato, cabbage) have been tested using this model. The model comprehensively uses stress coefficients (water stress, fertilizer and temperature coefficients) to compute the effect of the factors on crop canopy, dry matter, stomatal closure, flowering, pollination and harvest index build up.
https://crimsonpublishers.com/mcda/fulltext/MCDA.000558.php
For more open access journals in Crimson Publishers please click on link: https://crimsonpublishers.com
For more articles on International Journal of Agronomy please click on below link: https://crimsonpublishers.com/mcda/
Sequential Methodology for the Selection of Municipal Waste Treatment Alterna...AproximacionAlFuturo
Most municipalities in developing countries lack technical and economic resources to improve their municipal solid waste management (MSWM) system. Therefore, tools are needed that enable the most appropriate solutions to be identified to put waste to better use.
Climate change impact assessment in agriculture – MOSAICC development and pil...FAO
Information on potential impacts of climate change is key in the elaboration of national adaptation plans and policies. Given the large uncertainties about the future of the climate and the response of human systems, model simulations offer interesting possibilities to test scenarios, explore potential impacts and understand how different processes interact with each other. In agriculture in particular, assessing climate change impacts requires to take into consideration its multiple aspects (crop response, irrigation and field management, pests and diseases, socio-economic context etc.) to obtain the overall picture of the issue. In the framework of the joint EU-FAO programme on improving global governance for hunger reduction, FAO is developing an integrated system to carry out climate change impact assessments at national level called MOSAICC. Delivered through capacity strengthening activities, MOSAICC is designed to provide information to support decision making in adaptation, Land and Water Days in Near East & North Africa, 15-18 December 2013, Amman, Jordan
What is Green Finance? How to structure a market to attrach green investments? Which are the instruments and mechanism to make it succesfull operative and monitorable?
Presented by Veronique (Niki) De Sy at a workshop on 'Sharing insights across REDD+ countries: Opportunities and obstacles for effective, efficient, and equitable carbon and non-carbon results' from 21-23 February 2017 in Naypyidaw, Myanmar.
Operational Drainage Water Reuse Guidelines, by Shaden Abdel-Gawad, Professor and Former President National Water Research Center, Cairo, Egypt , Land and Water Days in Near East & North Africa, 15-18 December 2013, Amman, Jordan
D5.1.2 pilots description and requirements elicitation reportFOODIE_Project
The document describes the pilots that are going to be executed throughout the project and presents all the information gathered in this phase.
The analysis has been done in a formal way in order to be able to help as an input for the platform description,
so the use of a well-established methodology has been an important point to agree on. This analysis will follow
the RM-ODP methodology which has been successfully applied in many previous EU research projects related to
the geospatial, environmental and agricultural domains. Methodological approach is described in chapter two.
FOODIE concepts and objectives will be demonstrated in three different pilot scenarios across Europe: Spain, Czech Republic and Germany.
This deliverable, D4.3.2 Event based notification services, is the result of task T4.4 Reporting and alerts,
which aims at defining a set of components that enable FOODIE platform to generate - in an automatic
manner - diverse notifications and reports for its users (principally for the farmers) based on the data
collected and stored in its various repositories of information.
This document introduces the second prototypes related to the Advanced Rich Interfaces. It includes both a description of the interfaces specifically developed for the dashboard, the widgets, and a section including guidelines
and recommendations on how to develop further widgets. A final section deals with the mobile application implemented for the project, a native android application.
This deliverable defines so called business/application logic of the FOODIE platform which could be defined as a set of processes allowing data import, creation, storage, processing and display to a user. Processes implemented in the FOODIE project according to this deliverable will be then distributed through open and proprietary interfaces, as already defined by the WP3. Definition of the business/application logic of the
FOODIE platform has been developed according to the user requirements, as identified in WP5.
This document describes the Marketplace application as a component of a bigger ecosystem called “FOODIE Cloud Platform”. The document explains architectural as well as implementation issues.
Data fusion tools combine data from different heterogeneous sources together to provide more efficient representation
of data.
Data fusion tools aim to associate textual and/or spatial data in different structures from different sources in terms of
geometry. Additionally process of data from multiple image sources is achieved by Data fusion services.
D3.3.2 sematic tagging and open data publication toolsFOODIE_Project
Semantic tagging and open data publication tools is a system of services which can be used to extract additional
knowledge from unstructured data such as plain text or text files in different formats, transform (semi-) structured
data into semantic format, and publish the generated data according the Linked Data principles.
FOODIE project has just entered in the last 6 months of its lifetime. This issue intends to keep you updated about the
progress in pilot sites and technical activities. Scroll down and find out important dates to be saved in your agenda! Do
not miss the last occasions to meet project partners!
FOODIE project has entered in the last year of its schedule. This seventh issue covers the period from March 2016 to
June 2016 and gives evidence of the more mature technical results and progress in pilot sites. In addition, you are
provided with an overview of the upcoming events and a brief summary of the past events where the FOODIE Team
presented the project. We hope you find the information interesting!
Issue 6 (M24, delivered in February 2016): This issue announce the signature of the Memorandum of Understanding (MoU) between FOODIE and three other projects. The MoU lays down the basis for a joint action for speeding up the process of data harmonisation. In addition this newsletter gives evi-dence of FOODIE support to OGC (Open Geospatial Consortium) - committed to the quality open standards for the global geospatial community - and GEOSS (Global Earth Observation System of Systems). Then, the newsletter concisely describes the last progress of the project pilots. To close this 6th issue, a list of the next external appointments with FOODIE partners in occasion of relevant international events is provided.
Issue 5 (M20, issued in October 2015): This newsletter presents the pilot execution progress: i) Installation of weather station and the first sensor mote in the vineyards of Terras Gauda (Spanish Pilot); ii) Experimentation with various settings of monitoring units to find the most suitable values of parameters affecting data collecting frequency (Czech Pilot); iii) Transfer of anonymized data of all participants to show orthoimages, LPIS data and tracks on FOODIE (German Pilot). Finally, reports of the FOODIE relevant events held during 4-month period (July-October) are embedded in the document.
Issue 3 (M12), delivered in February 2015. It was dedicated to the activities performed during the first year of the project, along with an overview of the preliminary results achieved, the main information on pilot execution, and details of the events attended. Finally, an outlook to the upcoming conferences was provided as well as direct links to FOODIE news and direct link to the press releases.
Issue 2 (M8), was released in October 2014. It was mainly dedicated to the progresses related to pilot scenarios and FOODIE Service Platform Specification, based on the initial elicitation of FOODIE pilots’ descriptions and end-user requirements for the scope of tailoring the services that will fulfil user concrete and daily needs.
Issue 1 (M4), was released in June 2014. It provided information on the project objectives, the three Pilots scenario and FOODIE Consortium. The FOODIE group photograph was embedded in order to make it well-acquainted. Moreover, the details of the events attended and an outlook to the upcoming conferences were provided as well as direct links to FOODIE news and direct link to the press releases.
This document describes the Training materials, which is currently available on FOODIE Moodle platform.
On the base of Training plan, initial scenarios focused on methods used in sustainable agriculture were
prepared. Special lectures were established in order to stimulate demands of stakeholders for new tools
and methods of using on-line open data for agriculture production, context and sharing data in on line
repositories. There were prepared first set of scenarios, focused on good agriculture practices and also
first material focused on data fusion. The Training Material for FOODIE has following parts:
Introduction – a general four-step process been modified for each pilot. These four steps include:
building the initial scenarios, vision building workshops, testing of initial scenarios, and user requirements collection workshops.
Chapters for training - the first material for methods used in sustainable agriculture was already
prepared by project partners. There are two categories: Methods of Farm Management and Software Components and Tools of Farm Management.
Conclusion defining next steps - initial Training materials have been created. Training related to tools will be organized in later stage. Vision building workshops will be organized during Months 13 – 14.
D5.1.1 Pilots description and requirements elicitation reportFOODIE_Project
In the FOODIE project, it is of crucial importance to have well defined, and detailed, functional and technical specifications of the FOODIE service platform hub. In view of this objective, the task 5.1 Pilots
Specification and Stakeholders Requirements Elicitation, within the Work Package 5, has an important role in serving as primary input for technical work packages. By taking feedback from users as soon as possible and delivering software incrementally, FOODIE will greatly improve results and will be focused on user needs and not on technical needs.
Pilots in three different countries have been established: Czech Republic, Germany and Spain. These pilots
and their stakeholders will be the basis for collecting the requirements to design the FOODIE platform and
provide the services that will fulfil their needs. In addition to the stakeholders involved in the pilots, other end-users partners will provide requirements and participate in the testing of the platform
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
2. pollution. Such evidence stems from the legal frameworks as
well as from farmer’s and citizen’s needs. Some examples
thereof may be found in weather (soil and air temperature, soil
and air moisture, wind speed), crop growth monitoring for
precise application of fertilizers and pesticides, ground water
level and quality observation, monitoring of nitrate leaching
into water, etc. It is beneficial to use the sensors networks
according to the OGC implementation specification adopted
also as ISO 19156:2011 [15]. The O&M approach is the key
for near real time obtaining of relevant data, its filtering
according to the user’s needs as well as publishing in the form
standard OGC Web services and/or technologies like Google
Earth API (Application Programming Interface).
The European projects called “Farm-Oriented Open Data in
Europe” (FOODIE), funded between years 2014 and 2017, and
FArming Tools for external nutrient Inputs and water
Management” (FATIMa), funded between years 2015 and
2018 address the above mentioned agronomical and
environmental issues [25]. After presenting the methodology in
this paper, the open data model for (precision) agriculture
applications and agricultural pollution monitoring is presented
in detail in textual as well as formalized way using the class
diagram in the Unified Modelling Language. In the conclusion,
benefits, opportunities and future development are mentioned.
II. METHODOLOGY
The primary inspiration for the methodology development
lies in the existing international and European initiatives that
aim at facilitating the exchange and access to a wealth of
heterogeneous data sets related to the environmental and
agricultural domains.
References to the main European policies were also
included to define the context of the agriculture sector, such as
Common Agricultural Policy [7] or Water Framework
Directive [8]; and these have to be taken into account in the
decision making process of the stakeholders. In this sense, call
for global data collection for agricultural monitoring is
analyzed by [26]. Principles of common agricultural policy are
provided by [4]. Influence of Water Framework Directive on
agriculture is discussed by [1]. Processing of biomass data
from remote sensing is described in [22]. See [23] for
information on Infrastructure for spatial data in Europe
(INSPIRE) including the application schemas for agriculture
and aquaculture. European nitrate directive and its influence on
the farm performance were described by [21]. Discussion on
accessing structural functionality and landscape service is
provided in [27].
The results from relevant projects provide an overview of
the different architectural approaches followed by various
projects in the environmental and agricultural domain which
represent the basis for designing FOODIE architecture and
specifying its building blocks. For instance, an architecture for
environmental and agronomical data that may be re-used for
FOODIE purposes is advertised by [5], [2], [13] and [20].
Cataloging of the collected data follows the principles defined
for environmental spatial data by [24]. Spatial data
harmonization of openly available databases is further analyzed
by [3], while the mechanisms for its visualization is provided
by [28].
The analysis of user requirements and the derivation of
requirements on various software/hardware components cannot
take place without having in mind a common FOODIE system
architecture. Here, it rely upon agreed international standards
such as ISO Reference Model for Open Distributed Processing
(RM-ODP [18]). The RM-ODP defines, among other, the
Information Viewpoint as the semantics of information and
information processing and contains the information resources
identified as a use case extension. The modelling basis has
been extended by the standardization documents originating
from the European INSPIRE (2007/2/EC) Directive. The
underlying principles are described in [12].
To sum up, the FOODIE and FATIMa projects have a lot
of similarities with the above mentioned initiatives. Data model
and searching including metadata originates from INSPIRE.
Parts of global initiatives called GEOSS (Global Earth
Observation System of Systems) and COPERNICUS could be
used and integrated as a part of FOODIE hub. It is important
for the FOODIE and FATIMa implementation to establish a
link with GODAN (Global Open Data for Agriculture and
Nutrition) initiative, which is trying to define world Wide
standards for Agriculture Open Data and CGIAR (Consultative
Group on International Agricultural Research), which is active
on a global scale in similar area as FOODIE and FATIMa.
The methodological point of view addresses the issues of
user requirements, development of the proposal for the open
data model, its verification through processes definition
including business as well as environmental perspectives.
The FOODIE user requirements have been collected for
three pilots, where each pilot contains three use cases
(scenarios). The pilots foreseen in the funded project phase are:
Pilot 1: Precision Viticulture (Spain) focuses on
appropriate management of the inherent variability
of crops, an increase in economic benefits and a
reduction of environmental impact.
Pilot 2: Open Data for Strategic and Tactical
Planning (Czech Republic) aims at improving
future management of agricultural companies
(farms) by introducing new tools and management
methods, which follows the cost optimization path
and reduction of environmental burden, improving
the energy balance while maintaining the
production level.
Pilot 3: Technology allows integration of
logistics via service providers and farm
management including traceability (Germany)
focuses on integrating the German machinery
cooperatives systems with existing farm
management and logistic systems as well as
developing and enlarging existing cooperation and
business models with the different chain partners
to create win-win situations for all of them with
the help of IT solutions.
98
3. The user requirements matrix consisting of two hundreds of
functional, information and non-functional requirements has
been established for nine use cases. After having described the
use cases in a semi-formal way using the template developed
internally within the FOODIE project, formalization was
conducted through the UML (Unified Modelling Language)
using activity diagrams for processes and class diagrams for
data modelling (e.g. see Fig. 1).
The presented methodology aims at repeatable
development of the open data model. Such development is
based on the iterative approach for incrementally growing
concept and corresponding implementation based on user
requirements. It also has to support the evidence on
environmental burden. The open data model should support the
evidence of all treatments that were used in a certain place as
well as (where appropriate) to store relevant information on the
application of those treatments. The stored data should together
answer the questions like “What amount of which treatment
was used in a certain place?”, “When it will be safe to apply
another treatment?” or “Is the treatment registered and allowed
in the European Union/Member State?”
The methodology also includes a definition of the scope,
planning, actors and collaboration when focusing on how to
perform the monitoring and management change, how risks
will be managed and how the pilot execution will be evaluated
attending to Key Performance Indicators described at FOODIE
proposal stage. In addition, indicators to estimate the costs and
benefits for the end users are also included.
The proposed methodology is applied to the three stages,
proof-of-concept, test and production, which address the
FOODIE pilots realization in an incremental way. This paper
describes the results obtained from the proof-of-concept phase.
III. OPEN DATA MODEL
The INSPIRE data model for Agricultural and Aquaculture
Facilities ([11], hereinafter AF) is composed by core
information in relation to the geographical description of
entities defined as "farming equipment and production facilities
(including irrigation systems, greenhouses and stables)". The
AF data model is based on the Activity Complex model [10].
Within INSPIRE, “Activity Complex” denotes a generic name
agreed across thematic domains trying to avoid specific
thematic connotations such as “Plant”, “Installation”,
“Facility”, “Establishment” or “Holding”. Such scope may be
identified for this paper as the Nitrate Directive [6] or Water
Framework Directive [8].
The basic feature types in the AF data model were
preserved also for the open data model for (precision)
agriculture applications and agricultural pollution monitoring.
These are:
Activity Complex: a whole area and all
infrastructures it includes, under the control of an
operator. In the AF theme, Activity Complex has a
specialized representation named Holding.
Holding: a whole area and all infrastructures it
includes, under the control of an operator to
perform agricultural or aquaculture activities. It
may be composed of one or more ― Sites.
Site: belonging to a holding, it is the geographical
representation of land that constitutes a
management unit. It includes all infrastructure,
equipment and materials.
To sum up, the AF data model lacks more detailed levels of
information. For instance, missing levels of information are an
area for a certain crop or management zones for applications of
fertilizers, pesticides, water etc. As a result, the AF data model
cannot be re-used for the environmental monitoring since it
does not contain the relevant information.
In order to ensure the maximum degree of data
interoperability, the open data model for (precision) agriculture
applications and agricultural pollution monitoring (hereinafter
open data model) follows the INSPIRE generic data models, in
particular the aforementioned AF data model, by extending and
specializing them (see Fig. 1).
Thus, when taking the Figure 1 into consideration, the AF
data model may be further specialized. For these purposes,
there should be a feature on a more detailed level than “Site”.
The main motivation is to represent a continuous area of
agricultural land with one type of crop species, cultivated by
one user in one farming mode (conventional vs. organic
farming) or having the same environmental features (e.g.
organic matter in soil, the amount of fertilizers and/or
pesticides inserted in a certain time). Such feature is called
“Plot” in the open data model, being its elementary reference
item.
Note that the term “Plot” originates from the concept
presented in the AF data specification. The precision
agriculture domain uses the term “Management zone” for the
concept that is in INSPIRE and open data model designated as
“Plot”. Please bear in mind that both terms are meant as
synonymous when reading the paper and the model.
Each “Plot” has a unique identifier to distinguish a “Plot”
from any other “Plot”. Please, note that a “Plot” does not imply
any explicit relation to the cadaster. For instance, a “Plot” may
be only a part of a cadastral parcel. In other words, a cadastral
parcel may contain from zero to many (0...N) “Plots”.
The parent entities named “Site” and “Holding” remain the
same as defined in the underlying INSPIRE data model.
In order to illustrate the application of open data model,
consider the following example. A company named
“FoodieAgroProfi, Inc.” cultivates 4,000 hectares in the
southern part of the country. This whole area is, according to
the open data model specification, called a “Holding”. Such
“Holding” is composed of three types of “Sites”: arable land,
grassland and vineyards. Vineyards are composed of 164
explicitly geometrically-defined “Sites” where the wine grapes
grow. Arable lands are composed of 120 geometrically-defined
“Sites” where wheat, spring-barley and oil-seed rape are
produced. These 120 geometrically-defined “Sites” are
composed of 150 “Plots” according to the crop species. It
means that a “Site” may contain several crop species.
99
5. A “Plot” is therefore the elementary spatial unit within the
open data model. Note that the geometrical definition of a
“Plot” may vary in time, typically from year to year. A “Plot”
does not automatically mean relationship to the crop species. A
“Plot” may be established from the intervention, soil type etc.
points of view. In other words, a “Zone” would be more
appropriate designation. See the explanation above for keeping
the term “Plot”.
Regarding the initial import of data, it is possible to re-use
the data already contained, at least, in an LPIS (Land Parcel
Identification System) of any Member State of the European
Union. In that sense the INSPIRE data model and the LPIS
concepts are complementary approaches. Thus, for instance,
LPIS data may be imported to the open data model on the level
of “Site” feature which is equal to LPIS farmer’s block level. A
farmer may then add the crop species information to the
imported LPIS data to obtain data ready for the “Plot” level.
Open data model enables extensions through associations
and/or attributes that may further specialize the “Plot” feature.
The aim of extensions is to provide modularity and enable any
farmer/external service provider to extend the data model ac-
cording to his/her needs.
Detailed description of the open data model is presented
towards the end of this section. It should be emphasized that
the “ActivityComplex”, “Holding” and “Site” have been re-
used from the AF data model, while the rest of the model is a
new addition developed within the FOODIE and FATIMa
projects. It is recommended to compare the following text
with the Fig. 1.
The “Plot” is the key feature type in the open data model
since it:
(1) is the level to which majority of agronomical and
environmental data are related;
(2) acts as a mediator between diverse developed
application schemas.
So far, three application schemas have been developed in
order to support the complex modelling of the (precision)
agriculture and environmental applications:
FOODIE core application schema (presented in this
paper);
Sensor application schema (beyond the scope of this
paper) covering the observations and measurements
for sensors as well as human volunteers having e.g. a
smartphone;
Transport application schema (beyond the scope of
this paper) covering the issues of fleet management of
tractors and application machines.
The UML class diagram presented in Fig. 1 depicts the
version 3.2 of the FOODIE core application schema. As such,
it may also be re-used for an LPIS in any Member State of the
European Union. The following paragraphs describe the main
aspects of the open data model.
The developed open data model is as tightened to the
standardization frameworks as possible. For that reason, it re-
uses the data types defined in ISO standards (especially [16],
[17] and [19]) as well as standardization efforts published
under the INSPIRE Directive [9] (like structure of unique
identifiers). Where feasible, the allowed units of the Measure
data type should be limited to SI units or non-SI units accepted
for use with the International System of Units.
The “Plot” feature type contains two kinds of data. The first
kind could be considered as metadata about a “Plot” since it
describes:
code: unique identification of a “Plot” within the
system; it is defined as an Identifier data type, i.e. a
data type composed of a code (e.g. 178646A64BF),
code space (MyFarm) and version in the form of date
and time according to the ISO 8601 standard [19] (e.g.
version 2015-02-18T18:09:07);
validFrom: date (and time) when the “Plot” started to
exist in the real world;
validTo: date (and time) when the “Plot” no longer
exists in the real world;
beginLifespanVersion: date and time at which this
version of the “Plot” was inserted or changed in the
database;
endLifespanVersion: date and time at which this
version of the “Plot” was superseded or retired in the
database;
geometry: the geometry defining the spatial extent of
the “Plot”;
description: free text description of a “Plot” from a
user point of view;
originType: origin of the “Plot” when taking into
consideration only two options: manual (created by
human) and system (e.g. produced by the FOODIE
platform).
The second kinds of attributes related to the “Plot” are
intended to support the agronomical and environmental data,
specifically:
production: containing relevant production-related
data, that is defined as a ProductionType data type
comprising:
o productionDate: date and time at which the
information on production was inserted or
changed in the database;
o variety: an assemblage of cultivated
individuals which are distinguished by any
characters (morphological, physiological,
cytological, chemical or others) significant
for the purposes of agriculture;
o productionAmount: the value of a physical
quantity of the produced variety, together
101
6. with its unit according to the Measure data
type as defined in ISO/TS 19103 [17];
o productionAnalysis: a general mechanism for
analysis of all kinds of propertied that may
be related to the production; it comprises of:
productionAnalysisDate: date (and
time) at which the analysis of
production was conducted;
property: property that is being
estimated by the analysis, together
with its unit according to the
Measure data type as defined in
ISO/TS 19103 [17];
soil: that is defined as a SoilIdentification feature type
comprising:
o soilType referring predominantly to the
various sizes of mineral particles in the
“Plot” expressed according to the national-
and/or data provider- classification;
o soilAnalysis that is defined as a
SoilAnalysisResultsType data type
comprising:
dateOfAnalysis: date (and time) at
which the analysis (interpretation)
of the soil properties was
conducted;
pH: a measure of the acidity or
basicity of a soil;
organicMatter: percentage of the
organic matter component of soil,
consisting of plant and animal
residues at various stages of
decomposition, cells and tissues of
soil organisms, and substances
synthesized by soil organisms;
soilNutrients: that is defined as a
SoilNutrientsType data type,
enabling to describe any soil
nutrient, comprising:
nutrientName: full name of
a (essential) nutrient like
nitrogen or phosphorus;
nutrientMeasure:
description of a method
that was used to obtain the
amount of the nutrient;
nutrientAmount: the
amount of a nutrient,
together with its unit
according to the Measure
data type as defined in
ISO/TS 19103 [17];
electricConductivity: electrical
conductivity as the ability of a
material to conduct (transmit) an
electrical current in soil, together
with its unit (typically milliSiemens
per meter abbreviated as mS/m)
according to the Measure data type
as defined in ISO/TS 19103 [17];
notes: any kind of further
information on soil properties
inserted by a user.
Furthermore, we may identify three associations between
“Plot” on one hand and feature types CropSpecies, Alert and
Intervention on the other hand.
CropSpecies: identification of the planted crop species
as a feature type comprising:
o beginDate: date (and time) when the crop
species started to be planted on the “Plot”;
o endDate: date (and time) when the crop
species ended to be planted on the “Plot”;
typically date (and time) of harvest;
o cropArea: the geometry defining the spatial
extent of the crop species on the “Plot”;
o cropSpecies: designation under which the
crop species is commonly known;
Alert: alerts automatically generated by the models
integrated in the system as a feature type comprising:
o code: unique identification of the Alert in the
system;
o type: type of Alert according to the user-
defined classification, e.g. phytosanitary;
o description: a brief narrative summary of the
Alert content and rationale;
o checkedByUser: indication whether the user
is aware of the Alert or not (as Boolean
values: true/false);
o alertDate: date (and time) in which the Alert
was created by the system;
o alertGeometry: the geometry defining the
spatial extent for which the Alert is
applicable;
Intervention: the basic feature type for any application
with explicitly defined geometry comprising:
o type: type of the intervention, e.g. tillage or
pruning as a free text since it was not
feasible to provide a common code list of all
types of intervention, the types of
intervention vary from country to country as
well as from farmer to farmer;
o description: human readable description of
the type of intervention which may be a sub-
102
7. type of intervention or a broader description
intended for the common understanding of
the intervention;
o notes: any kind of further information on
intervention inserted by a user;
o status: status of the intervention, e.g.
approved, pending, completed as a free text
since it was not feasible to provide a
common code list of all statuses of
intervention, the statuses and naming of an
intervention vary according to the type of an
intervention as well as from farmer to
farmer;
o creationDateTime: date and time at which
the intervention was inserted in the database;
o interventionStart: date (and time) when the
intervention started in the real world;
o interventionEnd: date (and time) when the
intervention ended in the real world;
o interventionGeometry: the geometry defining
the spatial extent of the intervention;
o supervisor: a person or a body who has the
power and authority to give instructions and
guarantee the conducted intervention;
o operator: identification (typically at least
name and surname) of a person (or several
persons) who has conducted an intervention;
o evidenceParty: a person or a body who
inserted the intervention into the database;
Intervention feature type has direct and indirect
associations to the following types:
Treatment: that is defined as a feature type
comprising:
o quantity: the value of a physical quantity of
applied treatment, together with its unit
according to the Measure data type as
defined in ISO/TS 19103 [17];
o tractorId: unique identification of a tractive
vehicle for the machine applying the
treatment; such unique identification is
primarily used as a connection between the
FOODIE core application schema and the
Transport application schema; it is defined as
an Identifier data type, i.e. a data type
composed of a code of a tractor (e.g.
TRCT00721), code space (e.g. Zetorka) and
version in the form of date and time
according to the ISO 8601 standard (e.g.
version 2015-02-25T13:55:00);
o machineId: unique identification of a
machine applying the treatment; such unique
identification is primarily used as a
connection between the FOODIE core
application schema and the Transport
application schema; it is defined as an
Identifier data type, i.e. a data type composed
of a code of a machine (e.g. MCHN005),
code space (e.g.
ManufacturerOfMyMachine) and version in
the form of date and time according to the
ISO 8601 standard (e.g. version 2015-02-
25T13:56:04);
o motionSpeed: recommended speed for the
application of the treatment that should be
expressed together with its unit according to
the Measure data type as defined in ISO/TS
19103 [17]; it is recommended to use km•h-1
as a motionSpeed unit;
o pressure; recommended pressure for the
application of the treatment that should be
expressed together with its unit according to
the Measure data type as defined in ISO/TS
19103 [17];
o flowAdjustment: indication whether a flow
adjustment was needed for the application of
the treatment; should be expressed together
with its unit according to the Measure data
type as defined in ISO/TS 19103 [17];
o applicationWidth: a width in which a
machine is capable of applying the treatment,
should be expressed together with its unit
according to the Measure data type as
defined in ISO/TS 19103 [17]; it is
recommended to use meters (m) as an
applicationWidth unit;
o areaDose: the maximum application rate of
the treatment, should be expressed together
with its unit according to the Measure data
type as defined in ISO/TS 19103 [17]; it is
recommended to use kilograms (kg) or liters
(l) per hectare as an areaDose unit;
o formOfTreatment: identification of the
treatment application in the form of a code
list comprising the following values: manual
(i.e. conducted manually by a person),
applicationMachine (i.e. automatic or semi-
automatic application by a machine), aerial
(i.e. aerial application from an airplane);
o treatmentPurpose: rationale why the
treatment was used in the form of a code list
comprising the following values: weed (i.e.
to reduce plants considered undesirable in a
particular situation), pest (i.e. a plant or
animal detrimental to a human), disease (i.e.
a pathological condition that affects part or
all of the production);
o treatmentDescription: any further
information related to the treatment that may
facilitate understanding;
103
8. TreatmentPlan that is defined as a feature type
comprising:
o treatmentPlanCode: unique identification of
the treatment plan from the user point of
view;
o description: a brief narrative summary of the
treatment plan content and rationale;
o type: classification of a treatment plan from
the user point of view; any classification
system may be used;
o campaign: a period to which the treatment
plan was designed; typically a campaign may
be an agronomical year or a season; a
campaign is expressed as the TM_Period
data type, i.e. an extent in time limited by
two instances (beginning and ending ), e.g.
between 2015-03-01 and 2015-09-30;
o treatmentPlanCreation: date and time at
which the treatment was inserted in the
database;
o notes: any further information related to the
treatment plan that may help for a better
understanding;
ProductPreparation that is defined as a feature type
comprising:
o productQuantity: the value of a physical
quantity of the applied product, together with
its unit according to the Measure data type as
defined in ISO/TS 19103 [17];
o solventQuantity: the value of a physical
quantity for the solvent that was applied for
the product application, together with its unit
according to the Measure data type as
defined in ISO/TS 19103 [17];
o safetyPeriod: a period in which a dissolved
product may be used; a safetyPeriod is
expressed as the TM_Period data type, i.e. an
extent in time limited by two instances
(beginning and ending), e.g. between 2015-
03-01 and 2015-03-15;
Product that is defined as a feature type comprising:
o productCode: unique identification of the
product from the user point of view;
o productName: designation under which the
product is commonly known;
o productType: type of the product, e.g.
pesticide, as a free text since it was not
feasible to provide a common code list of all
types of products, the types of a product vary
from country to country as well as from
farmer to farmer;
o productSubType: more detailed classification
of a type of the product, e.g. biochemical
insecticide, as a free text since it was not
feasible to provide a common code list of all
sub-types of product, the sub-types of
product vary from country to country as well
as from farmer to farmer;
o productKind: origin of a product in the form
of a code list comprising the following
values: organic and mineral;
o description: a brief narrative summary of the
product;
o manufacturer: identification of a producer of
the product;
o nutrients: identification of nutrients, i.e.
chemical elements and compounds that are
necessary for plant growth, defined as the
NutrientsType data type comprising:
N: the amount of nitrogen, together
with its unit according to the
Measure data type as defined in
ISO/TS 19103 [17];
P2O5: the amount of phosphorus
pentoxide, together with its unit
according to the Measure data type
as defined in ISO/TS 19103 [17];
K2O: the amount of potassium
oxide, together with its unit
according to the Measure data type
as defined in ISO/TS 19103 [17];
MgO: the amount of magnesium
oxide, together with its unit
according to the Measure data type
as defined in ISO/TS 19103 [17];
CaO: the amount of calcium oxide,
together with its unit according to
the Measure data type as defined in
ISO/TS 19103 [17];
S: the amount of sulphur, together
with its unit according to the
Measure data type as defined in
ISO/TS 19103 [17];
Zn: the amount of zinc, together
with its unit according to the
Measure data type as defined in
ISO/TS 19103 [17];
Cu: the amount of copper, together
with its unit according to the
Measure data type as defined in
ISO/TS 19103 [17];
Fe: the amount of iron, together
with its unit according to the
104
9. Measure data type as defined in
ISO/TS 19103 [17];
B: the amount of bismuth, together
with its unit according to the
Measure data type as defined in
ISO/TS 19103 [17];
Mn: the amount of manganese,
together with its unit according to
the Measure data type as defined in
ISO/TS 19103 [17];
Mo: the amount of molybdenum,
together with its unit according to
the Measure data type as defined in
ISO/TS 19103 [17];
o safetyInstructions: information on the safe
manipulation with the product;
o storageHandling: information on the safe
storage of the product;
o registrationCode: unique identification of a
product according to the national or any
other relevant registration scheme;
o registerUrl: link to the national or any other
relevant register where the product was
registered;
ActiveIngredients that is defined as a data type
comprising:
o code: unique identification of an active
ingredient from the national or user point of
view;
o ingredientName: designation under which
the active ingredient is commonly known;
o ingredientAmount: the value of a physical
quantity of applicable active ingredient,
together with its unit according to the
Measure data type as defined in ISO/TS
19103 [17].
The developed model was also transformed into the
database schema for PostgreSQL (open source) database in
version 9.3.6 together with its spatial extension PostGIS in
version 2.1.0. Furthermore, the developed database schema
was replicated in the Cloud (Infrastructure as a Service)
provided by the Poznań Supercomputing and Networking
Center (in Poland) using OpenStack as an Open Source Cloud
Computing Software.
Verification of the data model has been conducted in two
ways. The first one was population of the developed database
schema by data from external systems, such as Land Parcel
Identification System and cadaster. Information of a farmer and
his/her parcels were successfully imported. The second
verification has been conducted through historical data for last
10 years for two farms in the Czech Republic, called Vajglov
(1’089 ha) and Tršice (1’291 ha). Currently, the developed data
model is being verified on the present data for the season 2015.
IV. CONCLUSIONS
The open data model for (precision) agriculture
applications and agricultural pollution monitoring has been
successfully verified during demo tests in the proof-of-concept
phase in the cloud environment. Such approach enables to
disseminate the developed solution to all the interested
stakeholders within the agricultural and environmental domains
in the future. It may, therefore, replace proprietary data models
used within the Farm management information systems. The
biggest advantages are openness and interoperability with other
systems such as (inter)national environmental and agronomical
registers, customizability and scalability. The openness seems
even more important than in the past since the commercial
vendors propose that data measured by a commercial machine
shall remain an ownership of such commercial vendor.
The presented open data model for (precision) agriculture
applications and agricultural pollution monitoring has been
together with the FOODIE platform registered under the
GEOSS (Global Earth Observation System of Systems)
Architecture Implementation Pilot – Phase 8 in order to support
the wide variety of demands that are primary aimed at
agriculture and water pollution monitoring. The data stored
according to the developed data model may also be re-used for
environmental monitoring of nitrogen, potassium, heavy metals
leaching into water etc. The number of applications has not
been limited since the data model enables to store any kind of
data related to the environmental burden monitoring.
The developed data model was discussed with the Joint
Research Centre (JRC) of the European Commission. The JRC
is, among others, responsible for the development of the new
data model for Land Parcel Identification Systems (LPIS).
Implementation of the new LPIS data model is legally binding
for the European Member States till 2018. We may state that
the developed data model and the new LPIS model are
complementary. In other words, it enables to import data from
LPIS model into the developed data model as well as to export
the corrected data back to the LPIS model.
The future work aims at explicit definition of processes in
order to define the open and lightweight application
programming interfaces (APIs) on the top of the open data
model. Two types of APIs are foreseen for the development
between 2015 and 2017.
The first ones are open APIs standardized by the
International Organization for Standardization, World Wide
Web Consortium and/or Open Geospatial Consortium. Their
advantages lie in the area of interoperability, i.e. a possibility
that the open data model may be integrated into a system
accepting the above mentioned standards and best practices.
On the other hand, we have to bear in mind the biggest
disadvantage of open API – their performance criteria. It is
common that the transmission of spatial data through the open
APIs may require a long time to be finished for some requests.
For that reason, where appropriate, the open APIs are
accompanied with the second type of provided APIs, i.e. the
lightweight APIs. Their purpose is exactly the opposite.
Lightweight APIs provide responses in a very short time;
however, it comes at a price of interoperability. JavaScript
105
10. Object Notation (JSON) and similar formats are used at least to
use the open standards for the data transmission between a
server and web application.
One of the main open issues lies in the area that affects Big
Data in all its forms. Especially farmers usually distrust the
companies aggregating data. Farmers are afraid, that their
sensitive detailed data may be misused. Future development
would, therefore, be on the technological level as well as on the
personal level to ensure the usefulness of the open data model
as well as FOODIE platform in daily life.
V. ACKNOWLEDGEMENT
This project has received funding from the European
Union’s Seventh Framework Programme for research,
technological development and demonstration under grant
agreement No. 621074 called “Farm-Oriented Open Data in
Europe” (FOODIE), from Horizon 2020 research and
innovation programme under grant agreement No. 633945
called “FArming Tools for external nutrient Inputs and water
Management” (FATIMa) and from the project No.
MUNI/A/0952/2013 called “Analysis, evaluation and
visualization of global environmental changes in the
Component Earth’s Spheres”.
REFERENCES
[1] I.J. Bateman, R. Brouwer, H. Davies, B.H. Day, A. Deflandre, S.D.
Falco and T.R. Kerry, “Analysing the Agricultural Costs and
Non‐market Benefits of Implementing the Water Framework Directive”
in Journal of agricultural economics, vol. 57, no. 2, 2006, pp. 221-237.
[2] A. Bröring, J. Echterhoff, S. Jirka, I. Simonis, T. Everding, C. Stasch
and R. Lemmens, “New generation sensor web enablement” in Sensors,
vol. 11, no. 3, 2011, pp. 2652-2699.
[3] O. Čerba, K. Charvát, J. Janečka, K. Jedlička, J. Ježek, T. Mildorf, “The
Overview of Spatial Data Harmonisation Approaches and Tools” in
Proceedings of the 4th International Conference on Cartography and
GIS, 2012, vol. 1, pp. 113-124.
[4] P.F. Donald, G. Pisano, M.D. Rayment and D.J. Pain, “The Common
Agricultural Policy, EU enlargement and the conservation of Europe's
farmland birds” in Agriculture, Ecosystems & Environment, vol. 89, no.
3, 2002, pp. 167-182.
[5] J. Douglas, T. Usländer, G. Schimak, J.F. Esteban and R. Denzer, “An
open distributed architecture for sensor networks for risk management”
in Sensors, vol. 8, no. 3, 2008, pp. 1755-1773.
[6] European Commission, “Council Directive 91/676/EEC of 12 December
1991 concerning the protection of waters against pollution caused by
nitrates from agricultural sources“ [online], 1991, available at URL
<http://eur-lex.europa.eu/legal-
content/EN/TXT/HTML/?uri=CELEX:31991L0676&from=EN>.
[7] European Commission, "The Common Agricultural Policy: A
partnership between Europe and Farmers" [online], 2012, available at
URL <http://ec.europa.eu/agriculture/cap-overview/2012_en.pdf>.
[8] European Commission, “Directive 2000/60/EC of the European
Parliament and of the Council of 23 October 2000 establishing a
framework for Community action in the field of water policy” [online],
2000, available at URL <http://eur-lex.europa.eu/legal-
content/EN/TXT/HTML/?uri=CELEX:32000L0060&from=EN>.
[9] European Commission, "Directive 2007/2/EC of the European
Parliament and of the Council of 14 March 2007 establishing an
Infrastructure for Spatial Information in the European Community
(INSPIRE)" [online]. Official Journal L 108. Published 25th April 2007,
p. 0001 – 0014. Cited 30th June 2011. Available at: <http://eur-
lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2007:108: 0001:01:
EN:HTML>.
[10] European Commission, "INSPIRE Data Specifications – Base Models –
Activity Complex" [online], 2013, available at URL
<http://inspire.ec.europa.eu/documents/Data_Specifications/D2.10.3_Ac
tivity_Complex_v1.0rc3.pdf>.
[11] European Commission, "Data Specification on Agricultural and
Aquaculture Facilities – Technical Guidelines" [online], 2013, available
at URL
<http://inspire.ec.europa.eu/documents/Data_Specifications/INSPIRE_D
ataSpecification_AF_v3.0.pdf>.
[12] European Commission, "INSPIRE Generic Conceptual Model, version
3.4" [online], 2014, available at URL:
<http://inspire.jrc.ec.europa.eu/documents/Data_Specifications/D2.5_v3.
4rc2.pdf>.
[13] K. Feiden, F. Kruse, T. Řezník, P. Kubíček, H. Schentz, E. Eberhardt
and R. Baritz, “Best Practice Network GS SOIL Promoting Access to
European, Interoperable and INSPIRE Compliant Soil Information” in
“ISESS 2011”, vol. 359, J. Hřebíček, G. Schimak, and R. Denzer, R.,
Eds. Heidelberg:Springer, 2011, pp. 226–234
[14] Food and Agriculture Organisation of the United Nations, “Chapter 1:
Introduction to agricultural water pollution” [online], available at URL
<http://www.fao.org/docrep/W2598E/w2598e04.htm>.
[15] International Organization for Standardization, "ISO 19156:2011
Geographic information -- Observations and measurements", Geneva.
[16] International Organization for Standardization, “ISO 19115-1:2014
Geographic information -- Metadata -- Part 1: Fundamentals”, Geneva.
[17] International Organization for Standardization, “ISO/TS 19103:2005
Geographic information -- Conceptual schema language”, Geneva.
[18] International Organization for Standardization, “ISO/IEC 10746-1:1998
Information technology -- Open Distributed Processing -- Reference
model: Overview”, Geneva.
[19] International Organization for Standardization, “ISO 8601:2004 Data
elements and interchange formats -- Information interchange --
Representation of dates and times”, Geneva.
[20] P. Kubíček, J. Kozel, R. Štampach, V. Lukas, “Prototyping the
visualization of geographic and sensor data for agriculture” in
Computers and Electronics in Agriculture, Elsevier, 2013, vol. 97, no. 9,
pp. 83-91.
[21] C.J.M. Ondersteijn, A.C.G. Beldman, C.H.G. Daatselaar, G.W.J. Giesen
and R.B.M Huirne, “The Dutch Mineral Accounting System and the
European Nitrate Directive: implications for N and P management and
farm performance” in Agriculture, ecosystems & environment, 2002,
vol. 92, no. 2, pp. 283-296.
[22] V. Pechanec, A. Vavra, M. Hovorkova, J. Brus and H. Kilianova,
"Analyses of moisture parameters and biomass of vegetation cover in
southeast Moravia", in International Journal of Remote Sensing, vol. 35,
no. 3, pp. 967-987.
[23] T. Řezník, “Geographic information in the age of the INSPIRE
Directive: discovery, download and use for geographical information
research” in Geografie, 2013, vol. 118, no. 1, pp. 77-93.
[24] T. Řezník, R. Chudý, E. Mičietová, “Normalized evaluation of the
performance, capacity and availability of catalogue services: a pilot
study based on INfrastruture for SPatial InfoRmation in Europe
(INSPIRE)”, in International Journal of Digital Earth, Taylor & Francis,
in press.
[25] T. Řezník, V. Lukas, K.Charvát, Š. Horáková and K. Charvát jr.,
“Towards Farm-Oriented Open Data in Europe: the scope and pilots of
the European project FOODIE” in Agris on-line Papers in Economics
and Informatics, Faculty of Economics and Management CULS
Prague2015, vol. 2015, no. 1, in press.
[26] J. Sachs, R. Remans, S. Smukler, L. Winowiecki, S.J. Andelman, K.G.
Cassman, D. Castle, R. DeFries, G. Denning, J. Fanzo, L.E. Jackson, R.
Leemans, J. Lehmann, J.C. Milder, S. Naeem, G. Nziguheba, C.A. Palm,
P.L. Pingali, J.P. Reganold, D.D. Richter, S.J. Scherr, J. Sircely, C.
Sullivan, T.P. Tomich, and P.A. Sanchez, “Monitoring the world's
agriculture” in Nature, 2010, vol. 466, no. 7306, pp. 558 – 560.
[27] H. Skokanova, “Can we combine structural functionality and landscape
services assessments in order to estimate the impact of landscape
structure on landscape services?” in Moravian geographical reports,
2013, vol. 21, no. 4, pp. 2-14.
106
11. [28] Z. Štěrba, Č. Šašinka, Z. Stachoň, "Usability testing of cartographic
visualizations: principles and research methods" in Proceedings of the
5th International Conference on Cartography and GIS Proceedings,
2014, vol. 1 and vol. 2, pp. 147-256.
107