Keywords
Crop-livestock systems, multi-agent modelling, sustainability indicators, resilience, uncertainty
Start Date
15-9-2020 7:40 PM
End Date
15-9-2020 8:00 PM
Abstract
Fostering local exchanges between specialized arable and livestock farms can simultaneously help to develop crop diversification and re-localization of livestock feed production, two major sustainability challenges of industrial agriculture. However, the aggregated benefits and drawbacks of developing a “Territorial Crop-Livestock System” (TCLS) based on exchanges is yet to be stablished. A major lock-in is the lack of decision support systems to analyse the underpinning dynamics and associated uncertainties of necessary socio-technical changes and to design necessary organizational aspects within and among farms. This paper presents an innovative high-resolution multi-agent and dynamic spatial modelling framework of agricultural landscape, MAELIA, developed to overcome these issues. MAELIA supports interactive integrated assessment and design of TCLS scenarios to analyse the trade-offs and synergies between individual and collective objectives and performances. We applied MAELIA to support a group of five arable and two livestock farmers in the West France in the design and assessment of credible, salient and legitimate exchanges. With MAELIA, we explore three selected scenarios, co-designed with farmers, advisors and scientists that converges the production capacities from arable farmers to the needs of livestock farmers. With a set of 13 sustainability and resilience indicators, we identify sources of variabilities of exchange flows (uncertainties) and possible solutions to manage them. Simulations outcomes provide further evidences that a local self-sufficiency in animal-feed and resilience against economic and climate shocks can be achieved. Both our results and methodology can be used to support policy makers, as future policies and strategies need to be defined at the relevant levels at which impacts (e.g. biodiversity and economic return) and decisions occur, i.e. at the local level.
An innovative integrated modelling tool to assess and design territorial crop-livestock systems
Fostering local exchanges between specialized arable and livestock farms can simultaneously help to develop crop diversification and re-localization of livestock feed production, two major sustainability challenges of industrial agriculture. However, the aggregated benefits and drawbacks of developing a “Territorial Crop-Livestock System” (TCLS) based on exchanges is yet to be stablished. A major lock-in is the lack of decision support systems to analyse the underpinning dynamics and associated uncertainties of necessary socio-technical changes and to design necessary organizational aspects within and among farms. This paper presents an innovative high-resolution multi-agent and dynamic spatial modelling framework of agricultural landscape, MAELIA, developed to overcome these issues. MAELIA supports interactive integrated assessment and design of TCLS scenarios to analyse the trade-offs and synergies between individual and collective objectives and performances. We applied MAELIA to support a group of five arable and two livestock farmers in the West France in the design and assessment of credible, salient and legitimate exchanges. With MAELIA, we explore three selected scenarios, co-designed with farmers, advisors and scientists that converges the production capacities from arable farmers to the needs of livestock farmers. With a set of 13 sustainability and resilience indicators, we identify sources of variabilities of exchange flows (uncertainties) and possible solutions to manage them. Simulations outcomes provide further evidences that a local self-sufficiency in animal-feed and resilience against economic and climate shocks can be achieved. Both our results and methodology can be used to support policy makers, as future policies and strategies need to be defined at the relevant levels at which impacts (e.g. biodiversity and economic return) and decisions occur, i.e. at the local level.
Stream and Session
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