The document describes the development of an open data model for precision agriculture applications and agricultural pollution monitoring. It aims to identify user requirements from agricultural and environmental domains. The presented open data model was registered under the GEOSS program to support demands in agriculture and water pollution monitoring. The model extends existing INSPIRE and agricultural facility models by adding a "Plot" feature to represent a continuous area with the same crop, user, farming method, or environmental features in order to enable environmental monitoring at a more detailed level.
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.
ISPIRE, GMES and GEOSS Activities, Methods and Tools towards a Single Inform...Karel Charvat
Book describes how INSPIRE, GMES and GEOSS could be integrated into Single European Information Space. The paper deals with the main task of INSPIRE, GMES and GEOSS and also with tools which could integrate all the three initiatives. The document gives an overview of single contributions in the book and how Theky explain the roles of single initiatives and their integration into the vision. The paper also explains the role of Earthlook technologies in the concept of integration of INSPIRE, GEOSS and GMESS into SISE.
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.
ISPIRE, GMES and GEOSS Activities, Methods and Tools towards a Single Inform...Karel Charvat
Book describes how INSPIRE, GMES and GEOSS could be integrated into Single European Information Space. The paper deals with the main task of INSPIRE, GMES and GEOSS and also with tools which could integrate all the three initiatives. The document gives an overview of single contributions in the book and how Theky explain the roles of single initiatives and their integration into the vision. The paper also explains the role of Earthlook technologies in the concept of integration of INSPIRE, GEOSS and GMESS into SISE.
Precision Farming (PF) is introduced and history in short is reviewed. Essential activities of GPS locating, soil mapping, GIS dataprocessing and presentation and VRT application are described. Basic principles of PF are shown to be:
• Precision Farming is the management process of within-field variability.
• This management must bring profit or at least reduce the risk of loss
• This management must reduce the impact of farming on environment.
Techniques used in Precision Farming are described. Economics of Precision Farming is discussed. A general cost/benefit analysis and profitability of PF are reviewed. The price of PF adoption facing a farmer is discussed. Methods of process analysis and activity based costing are shown as useful instruments for PF process analysis and model building. PF process is analysed and process graph is developed.
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
The “Club of Ossiach”, a group of agriculturists, agribusiness managers, agriculture technologists and agricultural ICT specialists from around the world, met at Ossiach between 17-19 June 2013 at the “AgriFuture Days” Conference. They reviewed current trends and
possible discontinuities resulting from political, social, environmental and technological changes, potentially impacting on the future of agriculture, farming, rural viability, food and nutrition worldwide.
Characterization and the Kinetics of drying at the drying oven and with micro...Open Access Research Paper
The objective of this work is to contribute to valorization de Nephelium lappaceum by the characterization of kinetics of drying of seeds of Nephelium lappaceum. The seeds were dehydrated until a constant mass respectively in a drying oven and a microwawe oven. The temperatures and the powers of drying are respectively: 50, 60 and 70°C and 140, 280 and 420 W. The results show that the curves of drying of seeds of Nephelium lappaceum do not present a phase of constant kinetics. The coefficients of diffusion vary between 2.09.10-8 to 2.98. 10-8m-2/s in the interval of 50°C at 70°C and between 4.83×10-07 at 9.04×10-07 m-8/s for the powers going of 140 W with 420 W the relation between Arrhenius and a value of energy of activation of 16.49 kJ. mol-1 expressed the effect of the temperature on effective diffusivity.
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
Artificial Reefs by Kuddle Life Foundation - May 2024punit537210
Situated in Pondicherry, India, Kuddle Life Foundation is a charitable, non-profit and non-governmental organization (NGO) dedicated to improving the living standards of coastal communities and simultaneously placing a strong emphasis on the protection of marine ecosystems.
One of the key areas we work in is Artificial Reefs. This presentation captures our journey so far and our learnings. We hope you get as excited about marine conservation and artificial reefs as we are.
Please visit our website: https://kuddlelife.org
Our Instagram channel:
@kuddlelifefoundation
Our Linkedin Page:
https://www.linkedin.com/company/kuddlelifefoundation/
and write to us if you have any questions:
info@kuddlelife.org
"Understanding the Carbon Cycle: Processes, Human Impacts, and Strategies for...MMariSelvam4
The carbon cycle is a critical component of Earth's environmental system, governing the movement and transformation of carbon through various reservoirs, including the atmosphere, oceans, soil, and living organisms. This complex cycle involves several key processes such as photosynthesis, respiration, decomposition, and carbon sequestration, each contributing to the regulation of carbon levels on the planet.
Human activities, particularly fossil fuel combustion and deforestation, have significantly altered the natural carbon cycle, leading to increased atmospheric carbon dioxide concentrations and driving climate change. Understanding the intricacies of the carbon cycle is essential for assessing the impacts of these changes and developing effective mitigation strategies.
By studying the carbon cycle, scientists can identify carbon sources and sinks, measure carbon fluxes, and predict future trends. This knowledge is crucial for crafting policies aimed at reducing carbon emissions, enhancing carbon storage, and promoting sustainable practices. The carbon cycle's interplay with climate systems, ecosystems, and human activities underscores its importance in maintaining a stable and healthy planet.
In-depth exploration of the carbon cycle reveals the delicate balance required to sustain life and the urgent need to address anthropogenic influences. Through research, education, and policy, we can work towards restoring equilibrium in the carbon cycle and ensuring a sustainable future for generations to come.
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”.
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