Improving pig system performance through a whole system approach
Current EU pig production has suboptimal resource utilization, resulting in unnecessarily high emissions and wastes. Concurrently, animal welfare is of increasing concern, farmers are struggling to maintain economic competitiveness and the public image of the sector is poor. None of the many approaches for the improvement of individual aspects the system provide effective whole system consideration. PigSys addresses these issues by adopting a multi-disciplinary, system level approach.
PigSys will provide the sector with (a) a whole system model of energy and mass flows and decision support system (DSS); (b) measurement and control devices for improved barn climate control and animal welfare; (c) ‘big data’ to support barn and control system design; (d) LCA and LCCA; (e) increased animal welfare and performance; (f) increased sustainability; (g) reduced emissions, waste and carbon footprint; (h) improved public perception of the sector; (i) increased competitiveness of the sector
During the remaining 18 months of the project, information of field tests will be integrated into recommendations for practitioners and stakeholder seminars and trainings held, the controller will be development and implemented, the DSS will be extended and refined, including further data upload options, and the the LCA and LCCA analyses will be finalized.
The prototype of the DSS has been successfully launched and data from several field trials are included. INRA and IFIP have successfully merged the animal and building models. Therefore, it is possible to assess the interactions between the thermodynamic behaviour of a building with the performance of the pigs in terms of productivity in dependence of building design, breed of pigs and diet.
The field trials in Denmark, France, Germany and Sweden have produced valuable information on climatic conditions and its distribution for numerous building designs, weather conditions and animal age.
The developed automated observation system (camera system) in combination with information on air characteristics (temperature, humidity, velocity) and compositions (NH3, CO2) have given valuable information on their spatial distribution as well as the resulting animal behaviours. A big advantage of the developed system is that the animals can be observed when they are undisturbed. Initial results on the impact of showering on the NH3 concentration in the barn were created.
A Life Cycle Assessment (LCA) framework able to evaluate pig production systems through a holistic approach and capture the effect that variations in pig housing have on the system environmental impacts has been developed.
The first platform of data warehouse allows to manage large variable data sets from different farms in the involved countries, accelerates data transferring and real time analysis of data sets in a relatively cheap and non-invasive ways. The designed model to assess both direct and indirect consumption of energy in pig farms based on the obtained data from different farms could help to represent and predict the energy balance at the room level, based on characteristics of the building, management of the climate control tool and average performance of pigs. Furthermore, the developed LCA model is capable of quantifying the implications for environmental impact of modifications in pig housing and manure management. This framework is able to assess the economic performance of European pig production systems, by following the well-established cost – benefit analysis methodologies.
The developed machine vision technique which benefits of state-of-the-art machine learning approaches has been used to localize and detect individual pig lying and standing postures in different farming and rearing conditions. Results of the combined image processing and machine learning monitoring methods showed the performance of a high level of accuracy and lower time of scoring the animal behaviours compare to human observation.
Messages to:
A) Actors from private sector (entrepreneurs, traders, investors etc.)
Video surveillance and climate monitoring are conditions within one room can differ greatly and directly impact productivity of the animals.
B) Civil society and practitioners organisations
The results of the sensor-supported recording of the stall-climatic environmental conditions, which permanently affect the animals in the course of the keeping periods, reveal a direct potential for change in the current production process. In particular, the observed variations in the compartments require the examination of the effect of the existing barn air conditioning technology on the flow conditions. Afterwards, adjustments can be made to existing barn systems within the scope of the given possibilities.
C) Policy makers
Outcomes of the barn climate condition modelling, monitoring and data analysis will be of particular relevance for owners/producers, barn supplier and also for policy makers responsible for animal welfare and environmental protection.
Video surveillance has a high potential for production control if behavioural deviations to be defined (changes in activity, manipulation of the animals in the group, …) can be assessed by machine vision technique in the sense of benchmarking.
Digitalization of animal farm management support government initiatives and societies in EU level toward strengthening the home-based meat production sectors to underpin food security.
Nasirahmadi et al. (2019). Automatic scoring of lateral and sternal lying posture in grouped pigs using image processing and Support Vector Machine, Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2018.12.009
Komasilovs et al. (2018). Development of the data warehouse architecture for processing and analysis of the raw pig production data, http://agrofor.ues.rs.ba/paper.php?id=211
Knowledge networks:
The project was successfully presented at the EuroTier 2018 and thus, network was extended
Joint special issue of Sustainability with SusPigSys and SusPig
Networks of the individual partners are utilised for stakeholder engagement and dissemination of the project results
Coordinated by: Dr. Barbara Sturm - University of Kassel (GERMANY) - Contact
Funded by: BMEL, DAFA, ANR, VIAA, Formas and DEFRA as part of the ERA-NET Cofund SusAn through a virtual common pot model including EU Top-Up funding from the European Union´s Horizon 2020 research and innovation programme (grant agreement no 696231).
7 research partners:
DENMARK: SEGES Pig Research Centre
FRANCE: French Institute for Agricultural Research and Institut du Porc
GERMANY: Thuringian State Institute for Agriculture
LATVIA: Latvia University of Agriculture
SWEDEN: Swedish University of Agricultural Sciences
UNITED KINGDOM: Newcastle University
More information:
The PigSys project started on 1 September 2017 and runs until 31 August 2020.
Improving pig system performance through a whole system approach
PigSys will provide the sector with (a) a whole system model of energy and mass flows and decision support system (DSS); (b) measurement and control devices for improved barn climate control and animal welfare; (c) ‘big data’ to support barn and control system design; (d) LCA and LCCA; (e) increased animal welfare and performance; (f) increased sustainability; (g) reduced emissions, waste and carbon footprint; (h) improved public perception of the sector; (i) increased competitiveness of the sector
During the remaining 18 months of the project, information of field tests will be integrated into recommendations for practitioners and stakeholder seminars and trainings held, the controller will be development and implemented, the DSS will be extended and refined, including further data upload options, and the the LCA and LCCA analyses will be finalized.
The prototype of the DSS has been successfully launched and data from several field trials are included. INRA and IFIP have successfully merged the animal and building models. Therefore, it is possible to assess the interactions between the thermodynamic behaviour of a building with the performance of the pigs in terms of productivity in dependence of building design, breed of pigs and diet.
The field trials in Denmark, France, Germany and Sweden have produced valuable information on climatic conditions and its distribution for numerous building designs, weather conditions and animal age.
The developed automated observation system (camera system) in combination with information on air characteristics (temperature, humidity, velocity) and compositions (NH3, CO2) have given valuable information on their spatial distribution as well as the resulting animal behaviours. A big advantage of the developed system is that the animals can be observed when they are undisturbed. Initial results on the impact of showering on the NH3 concentration in the barn were created.
A Life Cycle Assessment (LCA) framework able to evaluate pig production systems through a holistic approach and capture the effect that variations in pig housing have on the system environmental impacts has been developed.
The first platform of data warehouse allows to manage large variable data sets from different farms in the involved countries, accelerates data transferring and real time analysis of data sets in a relatively cheap and non-invasive ways. The designed model to assess both direct and indirect consumption of energy in pig farms based on the obtained data from different farms could help to represent and predict the energy balance at the room level, based on characteristics of the building, management of the climate control tool and average performance of pigs. Furthermore, the developed LCA model is capable of quantifying the implications for environmental impact of modifications in pig housing and manure management. This framework is able to assess the economic performance of European pig production systems, by following the well-established cost – benefit analysis methodologies.
The developed machine vision technique which benefits of state-of-the-art machine learning approaches has been used to localize and detect individual pig lying and standing postures in different farming and rearing conditions. Results of the combined image processing and machine learning monitoring methods showed the performance of a high level of accuracy and lower time of scoring the animal behaviours compare to human observation.
Messages to:
A) Actors from private sector (entrepreneurs, traders, investors etc.)
Video surveillance and climate monitoring are conditions within one room can differ greatly and directly impact productivity of the animals.
B) Civil society and practitioners organisations
The results of the sensor-supported recording of the stall-climatic environmental conditions, which permanently affect the animals in the course of the keeping periods, reveal a direct potential for change in the current production process. In particular, the observed variations in the compartments require the examination of the effect of the existing barn air conditioning technology on the flow conditions. Afterwards, adjustments can be made to existing barn systems within the scope of the given possibilities.
C) Policy makers
Outcomes of the barn climate condition modelling, monitoring and data analysis will be of particular relevance for owners/producers, barn supplier and also for policy makers responsible for animal welfare and environmental protection.
Video surveillance has a high potential for production control if behavioural deviations to be defined (changes in activity, manipulation of the animals in the group, …) can be assessed by machine vision technique in the sense of benchmarking.
Digitalization of animal farm management support government initiatives and societies in EU level toward strengthening the home-based meat production sectors to underpin food security.
Knowledge products:
Knowledge networks:
Coordinated by: Dr. Barbara Sturm - University of Kassel (GERMANY) - Contact
Funded by: BMEL, DAFA, ANR, VIAA, Formas and DEFRA as part of the ERA-NET Cofund SusAn through a virtual common pot model including EU Top-Up funding from the European Union´s Horizon 2020 research and innovation programme (grant agreement no 696231).
7 research partners:
More information: