New research projects in FISA http://www.fisaonline.de/ Here you will find the latest 20 projects that have been included in the Information System for Agriculture and Food Research (FISA). en-en Federal Office for Agriculture and Food TYPO3 Collaborative project: Systemic optimization of the value chain meat using the example of pig farming through the development and embedding of digital tools - subproject H On the basis of a software solution to be developed, SPECK will (1) guarantee the traceability of animal related data within the value chain (VC), including the use of drugs in pig farming; (2) further develop concepts of Smart Farming and Smart Food Factory and implement them in context for the first time; (3) significantly increase resource and energy efficiency as well as (4) animal welfare and product quality. This will significantly reduce (5) rejects and waste production along the VC and (6) emissions in both animal husbandry and downstream sectors. To this end, the work is carried out in an integrative inter- (animal sciences, breeding, agriculture, process, automation and electrical engineering, energy technology, software development, veterinary medicine, ecotrophology) and transdisciplinary (animal breeding and husbandry, agricultural consulting, technology providers in the fields of software and automation technology, slaughter and processing, veterinary stock management, business management, science) approach, which is why the consortium includes both innovative representatives from the relevant industries and an interdisciplinary research team.

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Collaborative project: Systemic optimization of the value chain meat using the example of pig farming through the development and embedding of digital tools - subproject G On the basis of a software solution to be developed, SPECK will (1) guarantee the traceability of animal related data within the value chain (VC), including the use of drugs in pig farming; (2) further develop concepts of Smart Farming and Smart Food Factory and implement them in context for the first time; (3) significantly increase resource and energy efficiency as well as (4) animal welfare and product quality. This will significantly reduce (5) rejects and waste production along the VC and (6) emissions in both animal husbandry and downstream sectors. To this end, the work is carried out in an integrative inter- (animal sciences, breeding, agriculture, process, automation and electrical engineering, energy technology, software development, veterinary medicine, ecotrophology) and transdisciplinary (animal breeding and husbandry, agricultural consulting, technology providers in the fields of software and automation technology, slaughter and processing, veterinary stock management, business management, science) approach, which is why the consortium includes both innovative representatives from the relevant industries and an interdisciplinary research team.

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Collaborative project: Systemic optimization of the value chain meat using the example of pig farming through the development and embedding of digital tools - subproject F On the basis of a software solution to be developed, SPECK will (1) guarantee the traceability of animal related data within the value chain (VC), including the use of drugs in pig farming; (2) further develop concepts of Smart Farming and Smart Food Factory and implement them in context for the first time; (3) significantly increase resource and energy efficiency as well as (4) animal welfare and product quality. This will significantly reduce (5) rejects and waste production along the VC and (6) emissions in both animal husbandry and downstream sectors. To this end, the work is carried out in an integrative inter- (animal sciences, breeding, agriculture, process, automation and electrical engineering, energy technology, software development, veterinary medicine, ecotrophology) and transdisciplinary (animal breeding and husbandry, agricultural consulting, technology providers in the fields of software and automation technology, slaughter and processing, veterinary stock management, business management, science) approach, which is why the consortium includes both innovative representatives from the relevant industries and an interdisciplinary research team.

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Collaborative project: Systemic optimization of the value chain meat using the example of pig farming through the development and embedding of digital tools - subproject E On the basis of a software solution to be developed, SPECK will (1) guarantee the traceability of animal related data within the value chain (VC), including the use of drugs in pig farming; (2) further develop concepts of Smart Farming and Smart Food Factory and implement them in context for the first time; (3) significantly increase resource and energy efficiency as well as (4) animal welfare and product quality. This will significantly reduce (5) rejects and waste production along the VC and (6) emissions in both animal husbandry and downstream sectors. To this end, the work is carried out in an integrative inter- (animal sciences, breeding, agriculture, process, automation and electrical engineering, energy technology, software development, veterinary medicine, ecotrophology) and transdisciplinary (animal breeding and husbandry, agricultural consulting, technology providers in the fields of software and automation technology, slaughter and processing, veterinary stock management, business management, science) approach, which is why the consortium includes both innovative representatives from the relevant industries and an interdisciplinary research team.

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Collaborative project: Systemic optimization of the value chain meat using the example of pig farming through the development and embedding of digital tools - subproject D On the basis of a software solution to be developed, SPECK will (1) guarantee the traceability of animal related data within the value chain (VC), including the use of drugs in pig farming; (2) further develop concepts of Smart Farming and Smart Food Factory and implement them in context for the first time; (3) significantly increase resource and energy efficiency as well as (4) animal welfare and product quality. This will significantly reduce (5) rejects and waste production along the VC and (6) emissions in both animal husbandry and downstream sectors. To this end, the work is carried out in an integrative inter- (animal sciences, breeding, agriculture, process, automation and electrical engineering, energy technology, software development, veterinary medicine, ecotrophology) and transdisciplinary (animal breeding and husbandry, agricultural consulting, technology providers in the fields of software and automation technology, slaughter and processing, veterinary stock management, business management, science) approach, which is why the consortium includes both innovative representatives from the relevant industries and an interdisciplinary research team.

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Collaborative project: Systemic optimization of the value chain meat using the example of pig farming through the development and embedding of digital tools - subproject C On the basis of a software solution to be developed, SPECK will (1) guarantee the traceability of animal related data within the value chain (VC), including the use of drugs in pig farming; (2) further develop concepts of Smart Farming and Smart Food Factory and implement them in context for the first time; (3) significantly increase resource and energy efficiency as well as (4) animal welfare and product quality. This will significantly reduce (5) rejects and waste production along the VC and (6) emissions in both animal husbandry and downstream sectors. To this end, the work is carried out in an integrative inter- (animal sciences, breeding, agriculture, process, automation and electrical engineering, energy technology, software development, veterinary medicine, ecotrophology) and transdisciplinary (animal breeding and husbandry, agricultural consulting, technology providers in the fields of software and automation technology, slaughter and processing, veterinary stock management, business management, science) approach, which is why the consortium includes both innovative representatives from the relevant industries and an interdisciplinary research team.

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Collaborative project: Systemic optimization of the value chain meat using the example of pig farming through the development and embedding of digital tools - subproject B On the basis of a software solution to be developed, SPECK will (1) guarantee the traceability of animal related data within the value chain (VC), including the use of drugs in pig farming; (2) further develop concepts of Smart Farming and Smart Food Factory and implement them in context for the first time; (3) significantly increase resource and energy efficiency as well as (4) animal welfare and product quality. This will significantly reduce (5) rejects and waste production along the VC and (6) emissions in both animal husbandry and downstream sectors. To this end, the work is carried out in an integrative inter- (animal sciences, breeding, agriculture, process, automation and electrical engineering, energy technology, software development, veterinary medicine, ecotrophology) and transdisciplinary (animal breeding and husbandry, agricultural consulting, technology providers in the fields of software and automation technology, slaughter and processing, veterinary stock management, business management, science) approach, which is why the consortium includes both innovative representatives from the relevant industries and an interdisciplinary research team.

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Collaborative project: Systemic optimization of the value chain meat using the example of pig farming through the development and embedding of digital tools - subproject A On the basis of a software solution to be developed, SPECK will (1) guarantee the traceability of animal related data within the value chain (VC), including the use of drugs in pig farming; (2) further develop concepts of Smart Farming and Smart Food Factory and implement them in context for the first time; (3) significantly increase resource and energy efficiency as well as (4) animal welfare and product quality. This will significantly reduce (5) rejects and waste production along the VC and (6) emissions in both animal husbandry and downstream sectors. To this end, the work is carried out in an integrative inter- (animal sciences, breeding, agriculture, process, automation and electrical engineering, energy technology, software development, veterinary medicine, ecotrophology) and transdisciplinary (animal breeding and husbandry, agricultural consulting, technology providers in the fields of software and automation technology, slaughter and processing, veterinary stock management, business management, science) approach, which is why the consortium includes both innovative representatives from the relevant industries and an interdisciplinary research team.

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Collaborative project: Calf and Heifer Net - Integrated data and information management system for calves and heifers - subproject D Development of an integrated farm management system for data acquisition and structured data analysis of calves for rearing and heifers. The aim is to close the data chain from the birth of the calf to the dairy cow in order to optimize health management in the period that is most decisive for the subsequent performance of a dairy cow. The observance of natural behavior and the decision for management processes such as the first insemination due to animal-specific biorhythmics play a decisive role. Through the continuous recording and evaluation of data, a group-specific health standard is recorded, the increased accuracy of the statements makes the health status objectifiable and therefore simplifies the basic decision-making processes for young cattle rearing.

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Collaborative project: Calf and Heifer Net - Integrated data and information management system for calves and heifers - subproject C Development of an integrated farm management system for data acquisition and structured data analysis of calves for rearing and heifers. The aim is to close the data chain from the birth of the calf to the dairy cow in order to optimize health management in the period that is most decisive for the subsequent performance of a dairy cow. The observance of natural behavior and the decision for management processes such as the first insemination due to animal-specific biorhythmics play a decisive role. Through the continuous recording and evaluation of data, a group-specific health standard is recorded, the increased accuracy of the statements makes the health status objectifiable and therefore simplifies the basic decision-making processes for young cattle rearing.

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Collaborative project: Calf and Heifer Net - Integrated data and information management system for calves and heifers - subproject B Development of an integrated farm management system for data acquisition and structured data analysis of calves for rearing and heifers. The aim is to close the data chain from the birth of the calf to the dairy cow in order to optimize health management in the period that is most decisive for the subsequent performance of a dairy cow. The observance of natural behavior and the decision for management processes such as the first insemination due to animal-specific biorhythmics play a decisive role. Through the continuous recording and evaluation of data, a group-specific health standard is recorded, the increased accuracy of the statements makes the health status objectifiable and therefore simplifies the basic decision-making processes for young cattle rearing.

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Collaborative project: Calf and Heifer Net - Integrated data and information management system for calves and heifers - subproject A Development of an integrated farm management system for data acquisition and structured data analysis of calves for rearing and heifers. The aim is to close the data chain from the birth of the calf to the dairy cow in order to optimize health management in the period that is most decisive for the subsequent performance of a dairy cow. The observance of natural behavior and the decision for management processes such as the first insemination due to animal-specific biorhythmics play a decisive role. Through the continuous recording and evaluation of data, a group-specific health standard is recorded, the increased accuracy of the statements makes the health status objectifiable and therefore simplifies the basic decision-making processes for young cattle rearing.

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Collaborative project: Bee-based biomonitoring to unlock the synergetic mechanisms of agriculture and pollinator insects - subproject E OCELI's goal is the prototypical development, field testing and demonstration of a novel technology of the same name using honey bees and bumble bees as bioindicators. For this purpose, a networked camera system will be installed at the entrance of beehives or bumblebee colonies, which continuously films all incoming and outgoing animals. Neural networks are used to record and process the recorded activities qualitatively and quantitatively. The use of environmental data, e.g. from remote sensing, and the interpretation of the data using the simulation models BEEHAVE and Bumble- BEEHAVE allow the identification and assessment of hazards for pollinator insects and their interactions. The analysis of geo-, weather-, land-use and flight monitoring data (number and duration of collecting flights, mortality of pollinators, amount and type of collected flower pollen) shall establish causal relationships between changes in the environment of the populations (such as food shortages, in particular gaps in dress or lack of pollen diversity) and their development. The application of OCELI will be used to develop and verify pollinator-friendly agricultural practices and targeted measures against insect decline. Field studies with different environmental conditions (monocultures, small-scale producers, nature reserves, etc.) can provide insights for best practices. The project consortium combines complementary scientific and technical expertise and extensive preliminary work in the fields of artificial intelligence, networked sensor technology, ecotoxicology, entomology, geo-data processing and ecological modeling. The economic exploitation of the overall solution and individual parts is based on the original interest of the start-up apic.ai on the one hand and on the other hand on the market leadership of Eurofins as ecotoxicological contract laboratory and of Disy as eGovernment solution provider in the environmental sector.

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Collaborative project: Bee-based biomonitoring to unlock the synergetic mechanisms of agriculture and pollinator insects - subproject D OCELI's goal is the prototypical development, field testing and demonstration of a novel technology of the same name using honey bees and bumble bees as bioindicators. For this purpose, a networked camera system will be installed at the entrance of beehives or bumblebee colonies, which continuously films all incoming and outgoing animals. Neural networks are used to record and process the recorded activities qualitatively and quantitatively. The use of environmental data, e.g. from remote sensing, and the interpretation of the data using the simulation models BEEHAVE and Bumble- BEEHAVE allow the identification and assessment of hazards for pollinator insects and their interactions. The analysis of geo-, weather-, land-use and flight monitoring data (number and duration of collecting flights, mortality of pollinators, amount and type of collected flower pollen) shall establish causal relationships between changes in the environment of the populations (such as food shortages, in particular gaps in dress or lack of pollen diversity) and their development. The application of OCELI will be used to develop and verify pollinator-friendly agricultural practices and targeted measures against insect decline. Field studies with different environmental conditions (monocultures, small-scale producers, nature reserves, etc.) can provide insights for best practices. The project consortium combines complementary scientific and technical expertise and extensive preliminary work in the fields of artificial intelligence, networked sensor technology, ecotoxicology, entomology, geo-data processing and ecological modeling. The economic exploitation of the overall solution and individual parts is based on the original interest of the start-up apic.ai on the one hand and on the other hand on the market leadership of Eurofins as ecotoxicological contract laboratory and of Disy as eGovernment solution provider in the environmental sector.

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Collaborative project: Bee-based biomonitoring to unlock the synergetic mechanisms of agriculture and pollinator insects - subproject C OCELI's goal is the prototypical development, field testing and demonstration of a novel technology of the same name using honey bees and bumble bees as bioindicators. For this purpose, a networked camera system will be installed at the entrance of beehives or bumblebee colonies, which continuously films all incoming and outgoing animals. Neural networks are used to record and process the recorded activities qualitatively and quantitatively. The use of environmental data, e.g. from remote sensing, and the interpretation of the data using the simulation models BEEHAVE and Bumble- BEEHAVE allow the identification and assessment of hazards for pollinator insects and their interactions. The analysis of geo-, weather-, land-use and flight monitoring data (number and duration of collecting flights, mortality of pollinators, amount and type of collected flower pollen) shall establish causal relationships between changes in the environment of the populations (such as food shortages, in particular gaps in dress or lack of pollen diversity) and their development. The application of OCELI will be used to develop and verify pollinator-friendly agricultural practices and targeted measures against insect decline. Field studies with different environmental conditions (monocultures, small-scale producers, nature reserves, etc.) can provide insights for best practices. The project consortium combines complementary scientific and technical expertise and extensive preliminary work in the fields of artificial intelligence, networked sensor technology, ecotoxicology, entomology, geo-data processing and ecological modeling. The economic exploitation of the overall solution and individual parts is based on the original interest of the start-up apic.ai on the one hand and on the other hand on the market leadership of Eurofins as ecotoxicological contract laboratory and of Disy as eGovernment solution provider in the environmental sector.

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Collaborative project: Bee-based biomonitoring to unlock the synergetic mechanisms of agriculture and pollinator insects - subproject B OCELI's goal is the prototypical development, field testing and demonstration of a novel technology of the same name using honey bees and bumble bees as bioindicators. For this purpose, a networked camera system will be installed at the entrance of beehives or bumblebee colonies, which continuously films all incoming and outgoing animals. Neural networks are used to record and process the recorded activities qualitatively and quantitatively. The use of environmental data, e.g. from remote sensing, and the interpretation of the data using the simulation models BEEHAVE and Bumble- BEEHAVE allow the identification and assessment of hazards for pollinator insects and their interactions. The analysis of geo-, weather-, land-use and flight monitoring data (number and duration of collecting flights, mortality of pollinators, amount and type of collected flower pollen) shall establish causal relationships between changes in the environment of the populations (such as food shortages, in particular gaps in dress or lack of pollen diversity) and their development. The application of OCELI will be used to develop and verify pollinator-friendly agricultural practices and targeted measures against insect decline. Field studies with different environmental conditions (monocultures, small-scale producers, nature reserves, etc.) can provide insights for best practices. The project consortium combines complementary scientific and technical expertise and extensive preliminary work in the fields of artificial intelligence, networked sensor technology, ecotoxicology, entomology, geo-data processing and ecological modeling. The economic exploitation of the overall solution and individual parts is based on the original interest of the start-up apic.ai on the one hand and on the other hand on the market leadership of Eurofins as ecotoxicological contract laboratory and of Disy as eGovernment solution provider in the environmental sector.

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Collaborative project: Bee-based biomonitoring to unlock the synergetic mechanisms of agriculture and pollinator insects - subproject A OCELI's goal is the prototypical development, field testing and demonstration of a novel technology of the same name using honey bees and bumble bees as bioindicators. For this purpose, a networked camera system will be installed at the entrance of beehives or bumblebee colonies, which continuously films all incoming and outgoing animals. Neural networks are used to record and process the recorded activities qualitatively and quantitatively. The use of environmental data, e.g. from remote sensing, and the interpretation of the data using the simulation models BEEHAVE and Bumble- BEEHAVE allow the identification and assessment of hazards for pollinator insects and their interactions. The analysis of geo-, weather-, land-use and flight monitoring data (number and duration of collecting flights, mortality of pollinators, amount and type of collected flower pollen) shall establish causal relationships between changes in the environment of the populations (such as food shortages, in particular gaps in dress or lack of pollen diversity) and their development. The application of OCELI will be used to develop and verify pollinator-friendly agricultural practices and targeted measures against insect decline. Field studies with different environmental conditions (monocultures, small-scale producers, nature reserves, etc.) can provide insights for best practices. The project consortium combines complementary scientific and technical expertise and extensive preliminary work in the fields of artificial intelligence, networked sensor technology, ecotoxicology, entomology, geo-data processing and ecological modeling. The economic exploitation of the overall solution and individual parts is based on the original interest of the start-up apic.ai on the one hand and on the other hand on the market leadership of Eurofins as ecotoxicological contract laboratory and of Disy as eGovernment solution provider in the environmental sector.

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Networking and transfer measures for the 'Announcement on the promotion of innovations for digitalization in livestock farming' currently unavailable

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Risk prediction of animal welfare indicators based on the data network of the official milk recording in Bavaria The improvement of animal health, welfare, and fitness is fundamental for resource-efficient and sustainable livestock farming. The great density of animal individual data, especially originating from the official milk recording, offers the chance for early warning systems with regard to animal health, fitness, and welfare. The main objective of this project is the development of risk prediction models and the action recommendations derived from these based on statistic models integrating as many animal individual but also farm-related (sensor-)data sources.

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Collaborative project: Improvement in herd management through cost-effective, hybrid localisation and intelligent data integration - subproject H The sensor-based observation of farm animals is already widely used today, especially on large farms with stables, e.g. pedometers for oestrus detection. However, location information of the individual animal and movement information of the herd are hardly used, although they offer a considerable added value for many decisions of the farmer, from easier identification of animals not coming for milking on the pasture and in the barn to long-term monitoring of movement patterns and health management activities. One reason for the current situation is the available but very expensive technology for localisation in the barn (e.g. Ubisense system, SMARTBOW) or the only recently available localisation techniques for pasture (e.g. Blaupunkt Telematics, Telespor). Both techniques are currently not suitable for continuous operation at high transmission rates and are therefore not used, especially in the field of dairy farming. The University Bamberg aims to publish scientific papers.

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