Final Report Summary - FUTUREFARM (Integration of farm management information systems to support real-time management decisions and compliance of management standards)
Although most people can see the benefits of using a more precise approach to manage crops with additional information, the tools provided by precision farming and other information technologies have not yet moved into mainstream agricultural management. The increased complexity of the systems inhibits easy adoption and makes calculations as to the financial benefits uncertain. These issues can be resolved by improving the decision making process though better management information systems, improved data interchange standards and clear management methods.
The FUTUREFARM project's starting point has been the identification of the current and future data, information and knowledge management needs on the farms, as well as on the way that these needs will evolve in the future and that will influence farm data, farm information and farm knowledge management systems. Existing systems were categorised and evaluated through interviews with the project's pilot farms.
Farm management information system (FMIS) specification was produced, by using a user-centric approach. The system boundaries were identified as well as the farmer's personal management strategies. The integration in the FMIS of information coming from online soil sensors was used as an integration case study. The architecture of the proposed system is based on the service oriented architecture. The main characteristic of such architecture is that it allows to different publishers to develop components of the FMIS which can then be integrated to it with the use of a common vocabulary. The concept of the assisting services for the future FMIS was defined. Actors and information flows, usage processes and data elements for the FMIS have been modelled and analysed, and functional requirements of FMIS have been determined. The outlined system elements and requirements are very complex and diverse depending on the farm production type, level of automation and inherent business processes. When looking to the future, external services as decision making assisting features will become an important part of FMIS concept. At the moment, the utilisation of scientific models together with the large amounts of data in different formats produced by modern farm machinery, sensors located within the farm, remote sensing, etc. is still an open area of research and new methods are developed continuously. The seamless incorporation of new functionality and assisting features into an existing FMIS is of paramount importance.
An analysis of selected agricultural standards resulted in a methodology on how (and under which conditions) these standards could be stored in a machine readable format. Then, the software architecture as well as a prototype system for automated agricultural standards retrieval (and evaluation) was produced. Although specific problems still need to be solved, whether this system will be utilised or not is mainly a political question.
Further investigation is required in order to find out how automated retrieval of agricultural rules and standards can be adopted by the agricultural sector in Europe. Also, developing autonomous and visual crop detection and crop modelling in order to model nitrogen response and weed development in combination with the water response functions is now required in order to prove the advantages of the use of precision farming technologies. The use of semantics is inevitable for an open service oriented FMIS, but therefore the development of a common ontology language for the agricultural sector in Europe is required.
Precision farming was seen within the project as a technology that demands the development of information systems in agriculture. Therefore, the strategies in which farmers communicate and cooperate in the adoption of precision agriculture were identified as well as the precision farming potential of the European Union (EU) areas. The most prominent precision farming technology to be used in the near future was found to be control traffic farming on the basis of its economic returns. The highest precision farming adoption potential has areas on the central parts of western Europe.
The specifications of a farm's portal from the external stakeholders point of view, revealed that the history of the farm, information about the producers in the form of curriculum vitae, farm location, climatic and soil conditions and, last but not least, farming practices, is the information that the consumers would like to see in it. Farmers would also like to be able to market other farm services through the portal, in the case of a multifunctional farm.
The consortium believes that further information and communication (ICT) developments in agriculture will include the development of agricultural robotics in collaboration with advanced FMIS systems.
Project context and objectives:
FUTUREFARM project basic idea:
Developing codes of good farming practice, diversifying markets and production systems as well as European standards of sustainable agricultural production systems require implementation of more elaborate management strategies. These have to respect specific ecological conditions, demands from the rural regions and those from the value-added chains. On top of that, these strategies have to be simple, but flexible enough to be adapted easily to changing economic or environmental conditions and they need proof of their compliance. Beyond that, the demand for information about the production processes is growing, both from the perspective of the value-added chains (traceability) as well as from regional stakeholders in order to fulfil multifunctional objectives by farming. An important prerequisite for farmers to comply with all these different demands is to easily have sufficient and timely information available for decision making or providing documentary evidence. The rapid development of technologies for information and communication, new sensors as well as the vast potentials for providing geo-referenced data (remote-sensing, on-line sensors, public databases etc.) also allows farmers to access new and high quality data and use them as specific information in decision making or process documentation. With automated data acquisition and handling in an on-farm management information system the farmers can be seen to comply with a rapidly growing demand of standards in the management of the production processes.
Precision farming (PF) in Europe uses new technologies in information handling and management as well as in managing the spatial and temporal variability found on all farms. Such explicit information use improves economic returns and reduces environmental impact. Precision farming is very data intensive and historically linked with site specific activities and management on the field. It has become very clear in recent years that PF is not limited to site-specific farming. The use of techniques and methods that form precision farming can provide a wealth of information and tools to handle and apply information properly for any type of farm in any region. This information-driven approach can be used to help improve crop management strategies and proof of compliance through documentation.
The introduction of advanced ICT technologies into agriculture will also be a significant progress in all efforts for measurements oriented payments within agro-environmental programs and related efforts to enforce environmentally sound systems in land use within the EU. This also includes the best management practice according to the cross compliance scheme.
Crop products going into the food chain must show their certified provenance through a recognised management strategy and subsidy payments to farmers are now linked to respect of the environment through compliance to standards. To this end, an integration of information systems is needed to advise managers of formal advice, recommended guidelines and implications resulting from different scenarios at the point of decision making during the crop cycle. This can be achieved by integrating real-time modelling (a crop growth and development model linked to sensors within the growing canopy), with expert systems that have been configured with the guidelines from a recommended management strategy, e.g. organic, integrated crop management (ICM), integrated pest management (IPM), factored risk etc., as well as legal guidance (such as health and safety and environmental protection). This will directly help the farm or crop manager to make better decisions. Expert knowledge in the form of models and expert systems can be published and made available in a machine readable form on the internet or made available as web-services to be dynamically bound into the end-user software. As the relevant farm data is already in the proposed information system, or may be automatically integrated using standardised services, documentation in the form of instructions to operators, certification of crop province and cross compliance of adopted standards can be generated more easily than with current systems.
Crop products can also stay on the farm - besides traditionally fodder this will be in the future the internal use of biofuels or bio energy. That would boost the possibility of moving towards a highly energy-efficient or even energy-neutral farm. This is supported by the significant reduction of energy required by small smart machines that can work by themselves while intelligently targeting inputs.
The FUTUREFARM project attempted to address the balance of technological opportunities combined with environmental and socioeconomic needs with the key role of information management. Intensive use of information and knowledge will be a substantial activity of all commercial EU farms in future.
General aims:
Although most people can see the benefits of using a more precise approach to manage crops with additional information, the tools provided by precision farming and other information technologies have not yet moved into mainstream agricultural management. The increased complexity of the systems inhibits easy adoption and makes calculations as to the financial benefits uncertain. These issues can be resolved by improving the decision making process though better management information systems, improved data interchange standards and clear management methods.
Therefore the collaborative project FUTUREFARM defined the following objectives as relevant and worked on delivering them:
Objective one:
Develop a vision of the farm of tomorrow from the perspective of the project team and invited stakeholders to show a better understanding of how farming will develop. This will include identifying relevant drivers and their potential impact on crucial processes in knowledge management in arable crop production.
Call objectives met: Vision of new knowledge based biological, technical, social and economic innovations.
Objective two:
Identify and analyse a range of formal and informal management strategies in crop production and identify required indices in terms of management and practices that would constitute compliance to standards within the strategy.
Call objectives met: Cost efficient compliance with standards as an integral part of farm operations.
Objective three:
Analyse and specify the required knowledge, information and methods needed to adopt specific management strategies. Produce a set of specifications that can be used to define a flexible and dynamic FMIS.
Call objectives met: Special requirements for high value markets, sharing good farming practices, recognition and communication of ecological and cultural diversity as well as regional demands on multifunctional production of non-commodities.
Objective four: Apply and test the general principles of a FMIS by developing a prototype of an integrated a FMIS. This will include elements from different methods and sources, e.g. geographic information systems (GIS), decision support systems (DSS), expert systems, trusted third party knowledge, formal and informal knowledge transfer etc. Ease of use and largely automated data handling procedures will be an important aspect. Working prototype will be made available for evaluation.
Call objectives met: Integrated technologies and ICT tools to make cost efficient compliance with standards an integral part of farm operations.
Objective five:
Provide a socioeconomic, environmental and technology assessment to understand the drivers and issues from objectives one to four. Recommendations will be expected to show how development can be made to take advantage of opportunities and avoid obvious problems.
Call objectives met: Understanding of overall trends of European societies, new models of relationships with consumers and citizens, rural economy, the multifunctional European farming model delivering public goods.
Objective six:
Assess the influences of robotics and biofuels on economic and energetic efficiencies of farm production. Existing robotic and closed-loop on-farm biofuel systems will be demonstrated and evaluated as examples of internal flow management.
Call objectives met: New models of material flow management, based on information and knowledge management supporting on-farm or local integration of environmentally friendly closed loop processing facilities, energy efficient cultivation with light machinery, precision farming and robotics.
Objective seven:
Develop a typology for information technologies in European farming (like precision farming) and a typology on its suitability for different farm groups within the member states of the EU. Application, integration, demonstration, generalisation and dissemination of project results on commercial farms within EU-countries.
Call objectives met: Generalisation of project results and demonstration of all of the above points and showing its feasibility for many future farms in the EU.
The FUTUREFARM project's starting point has been the identification of the current and future data, information and knowledge management needs on the farms, as well as on the way that these needs will evolve in the future and that will influence farm data, farm information and farm knowledge management systems. Existing systems were categorised and evaluated through interviews with the project's pilot farms.
Farm management information system (FMIS) specification was produced, by using a user-centric approach. The system boundaries were identified as well as the farmer's personal management strategies. The integration in the FMIS of information coming from online soil sensors was used as an integration case study. The architecture of the proposed system is based on the service oriented architecture. The main characteristic of such architecture is that it allows to different publishers to develop components of the FMIS which can then be integrated to it with the use of a common vocabulary. The concept of the assisting services for the future FMIS was defined. Actors and information flows, usage processes and data elements for the FMIS have been modelled and analysed, and functional requirements of FMIS have been determined. The outlined system elements and requirements are very complex and diverse depending on the farm production type, level of automation and inherent business processes. When looking to the future, external services as decision making assisting features will become an important part of FMIS concept. At the moment, the utilisation of scientific models together with the large amounts of data in different formats produced by modern farm machinery, sensors located within the farm, remote sensing, etc. is still an open area of research and new methods are developed continuously. The seamless incorporation of new functionality and assisting features into an existing FMIS is of paramount importance.
An analysis of selected agricultural standards resulted in a methodology on how (and under which conditions) these standards could be stored in a machine readable format. Then, the software architecture as well as a prototype system for automated agricultural standards retrieval (and evaluation) was produced. Although specific problems still need to be solved, whether this system will be utilised or not is mainly a political question.
Further investigation is required in order to find out how automated retrieval of agricultural rules and standards can be adopted by the agricultural sector in Europe. Also, developing autonomous and visual crop detection and crop modelling in order to model nitrogen response and weed development in combination with the water response functions is now required in order to prove the advantages of the use of precision farming technologies. The use of semantics is inevitable for an open service oriented FMIS, but therefore the development of a common ontology language for the agricultural sector in Europe is required.
Precision farming was seen within the project as a technology that demands the development of information systems in agriculture. Therefore, the strategies in which farmers communicate and cooperate in the adoption of precision agriculture were identified as well as the precision farming potential of the European Union (EU) areas. The most prominent precision farming technology to be used in the near future was found to be control traffic farming on the basis of its economic returns. The highest precision farming adoption potential has areas on the central parts of western Europe.
The specifications of a farm's portal from the external stakeholders point of view, revealed that the history of the farm, information about the producers in the form of curriculum vitae, farm location, climatic and soil conditions and, last but not least, farming practices, is the information that the consumers would like to see in it. Farmers would also like to be able to market other farm services through the portal, in the case of a multifunctional farm.
The consortium believes that further information and communication (ICT) developments in agriculture will include the development of agricultural robotics in collaboration with advanced FMIS systems.
Project context and objectives:
FUTUREFARM project basic idea:
Developing codes of good farming practice, diversifying markets and production systems as well as European standards of sustainable agricultural production systems require implementation of more elaborate management strategies. These have to respect specific ecological conditions, demands from the rural regions and those from the value-added chains. On top of that, these strategies have to be simple, but flexible enough to be adapted easily to changing economic or environmental conditions and they need proof of their compliance. Beyond that, the demand for information about the production processes is growing, both from the perspective of the value-added chains (traceability) as well as from regional stakeholders in order to fulfil multifunctional objectives by farming. An important prerequisite for farmers to comply with all these different demands is to easily have sufficient and timely information available for decision making or providing documentary evidence. The rapid development of technologies for information and communication, new sensors as well as the vast potentials for providing geo-referenced data (remote-sensing, on-line sensors, public databases etc.) also allows farmers to access new and high quality data and use them as specific information in decision making or process documentation. With automated data acquisition and handling in an on-farm management information system the farmers can be seen to comply with a rapidly growing demand of standards in the management of the production processes.
Precision farming (PF) in Europe uses new technologies in information handling and management as well as in managing the spatial and temporal variability found on all farms. Such explicit information use improves economic returns and reduces environmental impact. Precision farming is very data intensive and historically linked with site specific activities and management on the field. It has become very clear in recent years that PF is not limited to site-specific farming. The use of techniques and methods that form precision farming can provide a wealth of information and tools to handle and apply information properly for any type of farm in any region. This information-driven approach can be used to help improve crop management strategies and proof of compliance through documentation.
The introduction of advanced ICT technologies into agriculture will also be a significant progress in all efforts for measurements oriented payments within agro-environmental programs and related efforts to enforce environmentally sound systems in land use within the EU. This also includes the best management practice according to the cross compliance scheme.
Crop products going into the food chain must show their certified provenance through a recognised management strategy and subsidy payments to farmers are now linked to respect of the environment through compliance to standards. To this end, an integration of information systems is needed to advise managers of formal advice, recommended guidelines and implications resulting from different scenarios at the point of decision making during the crop cycle. This can be achieved by integrating real-time modelling (a crop growth and development model linked to sensors within the growing canopy), with expert systems that have been configured with the guidelines from a recommended management strategy, e.g. organic, integrated crop management (ICM), integrated pest management (IPM), factored risk etc., as well as legal guidance (such as health and safety and environmental protection). This will directly help the farm or crop manager to make better decisions. Expert knowledge in the form of models and expert systems can be published and made available in a machine readable form on the internet or made available as web-services to be dynamically bound into the end-user software. As the relevant farm data is already in the proposed information system, or may be automatically integrated using standardised services, documentation in the form of instructions to operators, certification of crop province and cross compliance of adopted standards can be generated more easily than with current systems.
Crop products can also stay on the farm - besides traditionally fodder this will be in the future the internal use of biofuels or bio energy. That would boost the possibility of moving towards a highly energy-efficient or even energy-neutral farm. This is supported by the significant reduction of energy required by small smart machines that can work by themselves while intelligently targeting inputs.
The FUTUREFARM project attempted to address the balance of technological opportunities combined with environmental and socioeconomic needs with the key role of information management. Intensive use of information and knowledge will be a substantial activity of all commercial EU farms in future.
General aims:
Although most people can see the benefits of using a more precise approach to manage crops with additional information, the tools provided by precision farming and other information technologies have not yet moved into mainstream agricultural management. The increased complexity of the systems inhibits easy adoption and makes calculations as to the financial benefits uncertain. These issues can be resolved by improving the decision making process though better management information systems, improved data interchange standards and clear management methods.
Therefore the collaborative project FUTUREFARM defined the following objectives as relevant and worked on delivering them:
Objective one:
Develop a vision of the farm of tomorrow from the perspective of the project team and invited stakeholders to show a better understanding of how farming will develop. This will include identifying relevant drivers and their potential impact on crucial processes in knowledge management in arable crop production.
Call objectives met: Vision of new knowledge based biological, technical, social and economic innovations.
Objective two:
Identify and analyse a range of formal and informal management strategies in crop production and identify required indices in terms of management and practices that would constitute compliance to standards within the strategy.
Call objectives met: Cost efficient compliance with standards as an integral part of farm operations.
Objective three:
Analyse and specify the required knowledge, information and methods needed to adopt specific management strategies. Produce a set of specifications that can be used to define a flexible and dynamic FMIS.
Call objectives met: Special requirements for high value markets, sharing good farming practices, recognition and communication of ecological and cultural diversity as well as regional demands on multifunctional production of non-commodities.
Objective four: Apply and test the general principles of a FMIS by developing a prototype of an integrated a FMIS. This will include elements from different methods and sources, e.g. geographic information systems (GIS), decision support systems (DSS), expert systems, trusted third party knowledge, formal and informal knowledge transfer etc. Ease of use and largely automated data handling procedures will be an important aspect. Working prototype will be made available for evaluation.
Call objectives met: Integrated technologies and ICT tools to make cost efficient compliance with standards an integral part of farm operations.
Objective five:
Provide a socioeconomic, environmental and technology assessment to understand the drivers and issues from objectives one to four. Recommendations will be expected to show how development can be made to take advantage of opportunities and avoid obvious problems.
Call objectives met: Understanding of overall trends of European societies, new models of relationships with consumers and citizens, rural economy, the multifunctional European farming model delivering public goods.
Objective six:
Assess the influences of robotics and biofuels on economic and energetic efficiencies of farm production. Existing robotic and closed-loop on-farm biofuel systems will be demonstrated and evaluated as examples of internal flow management.
Call objectives met: New models of material flow management, based on information and knowledge management supporting on-farm or local integration of environmentally friendly closed loop processing facilities, energy efficient cultivation with light machinery, precision farming and robotics.
Objective seven:
Develop a typology for information technologies in European farming (like precision farming) and a typology on its suitability for different farm groups within the member states of the EU. Application, integration, demonstration, generalisation and dissemination of project results on commercial farms within EU-countries.
Call objectives met: Generalisation of project results and demonstration of all of the above points and showing its feasibility for many future farms in the EU.
