Representation of decision-making in European agricultural agent-based models

Huber, Robert; Bakker, Martha; Balmann, Alfons; Berger, T.; Bithell, Mike; Brown, Calum; Grêt‐Regamey, Adrienne; Xiong, Hang; Le, Quang Bao; Mack, Gabriele; Meyfroidt, Patrick; Millington, James D.A.; Müller, Birgit; Polhill, Gary; Sun, Zhanli; Seidl, Roman; Troost, Christian; Finger, Robert

doi:10.1016/j.agsy.2018.09.007

Cited by 118 publications

(76 citation statements)

References 119 publications

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“…The low influence of farm size, pig/poultry density, grassland density, and biotope index on the probability of crop cultivation in comparison with the soil variables can be explained by their low tendency to form spatial patterns or clusters in Lower Saxony, which is reflected in the high standard deviation values. In our analysis, we focused on environmental variables instead of economic variables because most of the studies concerning the cropping-plan decision making process of farmers consider economical and sociological drivers [3,61]. However, we could show the still high potential of soil variables as drivers for decision making, which is also confirmed by a study of Peltonen-Sainio et al [62].…”

Section: Reflections On the Methods Usedsupporting

confidence: 56%

Linking Arable Crop Occurrence with Site Conditions by the Use of Highly Resolved Spatial Data

Stein

Steinmann

Isselstein

2019

Land

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Agricultural land use is influenced in different ways by local factors such as soil conditions, water supply, and socioeconomic structure. We investigated at regional and field scale how strong the relationship of arable crop patterns and specific local site conditions is. At field scale, a logistic regression analysis for the main crops and selected site variables detected, for each of the analyzed crops, its own specific character of crop–site relationship. Some crops have diverging site relations such as maize and wheat, while other crops show similar probabilities under comparable site conditions, e.g., oilseed rape and winter barley. At the regional scale, the spatial comparison of clustered variables and clustered crop pattern showed a slightly stronger relationship of crop combination and specific combinations of site variables compared to the view of the single crop–site relationship.

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Section: Reflections On the Methods Usedsupporting

confidence: 56%

Linking Arable Crop Occurrence with Site Conditions by the Use of Highly Resolved Spatial Data

Stein

Steinmann

Isselstein

2019

Land

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“…Modelling in teractions by simulating material exchanges among economic agents in agro food networks is an approach that helps to design, quantify and assess the potential socioeconomic and ecological benefits of social change towards circular economy patterns (Elsawah et al, 2015;Filatova et al, 2013;Le Page et al, 2013). Agent based modelling is a particularly important as a tool that enables simulation of complex networks of agents in farming systems (Huber et al, 2018).…”

Section: Purposementioning

confidence: 99%

“…Concerning social simulation, imitating real world farmer choices can involve more complex decision making than in our simulated agents. More precisely, individual adaptation (Berger and Troost, 2014) and social learning (Huber et al, 2018), could potentially be included in FAN, for instance by allowing farms to alter land use or livestock populations in response to material availability in the network or after the alteration of global supply chains. d Lack of actual data to parametrize some key processes.…”

Section: Fan Evaluationmentioning

confidence: 99%

Flows in Agro-food Networks (FAN): An agent-based model to simulate local agricultural material flows

Fernández-Mena

Gaudou

Pellerin³

et al. 2020

Agricultural Systems

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Agro-food networks are characterized by complex material exchanges among farms, processors, consumers, and waste managers involved in fertilization, food, feed and bioenergy production. Better coordination of material exchanges at the local scale can facilitate more efficient resource use. Here, we present a new agent-based model, "Flows in Agro-food Networks" (FAN), which simulates the processing and exchange of fertilizers, feed, food and wastes among farms and multiple upstream or downstream partners (feed and fertilizer suppliers, food industries, waste processors, and anaerobic digesters) in small farming regions. FAN includes a series of environmental indicators that can be used to assess alternative scenarios in terms of ecosystem services, nutrient cycling, and resource autonomy. We use a French case study to demonstrate FAN's dynamics and to explore the sensitivity of key parameters. We show a strong influence of spatial distance between agents, their disposition to exchange, and their preference for specific materials on local agro-food network simulations. FAN is powerful theoretical tool to explore and assess opportunities for a circular economy in small farming regions and to unravel interactions between recycling, environmental performance and food production.

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“…The question of how to represent decision-making is almost a perennial topic of discussion among those interested in ABM, and indeed boundedly and non-rational decision-making more generally (e.g. [83,84]), but Groeneveld et al [85] found most researchers in ABM of LUCC are still using utility maximization, whilst Huber et al [86] note a lack of consideration of values, norms and social interactions in ABMs of European agriculture. There are a plethora of theories and algorithms for implementing decision-making in specific and general contexts.…”

Section: Decision-makingmentioning

confidence: 99%

Crossing the chasm: a ‘tube-map’ for agent-based social simulation of policy scenarios in spatially-distributed systems

Polhill

Hare

et al. 2019

Geoinformatica

Self Cite

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Agent based models (ABMs) simulate actions and interactions of autonomous agents/groups and their effect on systems as a whole, accounting for learning without assuming perfect rationality or complete knowledge. ABMs are an increasingly popular approach to studying complex, spatially distributed socio-environmental systems, but have still to become an established approach in the sense of being one that is expected by those wanting to explore scenarios in such systems. Partly, this is an issue of awareness -ABM is still new enough that many people have not heard of it; partly, it is an issue of confidence -ABM has more to do to prove itself if it is to become a preferred method. This paper will identify advances in the craft and deployment of ABM needed if ABM is to become an accepted part of mainstream science for policy or stakeholders. The conduct of ABM has, over the last decade, seen a transition from using abstracted representations of systems (supporting theory-led thought experiments) to more accessible representations derived empirically (to deliver more applied analysis). This has enhanced the perception of potential users of ABM outputs that the latter are salient and credible. Empirical ABM is not, however, a panacea, as it demands more computing and data resources, limiting applications to domains where data exist along with suitable environmental models where these are required. Further, empirical ABM is still facing serious questions of validation and the ontology used to describe the system in the first place. Using Geoffrey A. Moore's Crossing the Chasm as a lens, we argue that the way ahead for ABM lies in identifying the niches in which it can best demonstrate its advantages, working with collaborators to demonstrate that it can deliver on its promises. This leads us to identify several areas where work is needed.

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Representation of decision-making in European agricultural agent-based models

Cited by 118 publications

References 119 publications

Linking Arable Crop Occurrence with Site Conditions by the Use of Highly Resolved Spatial Data

Linking Arable Crop Occurrence with Site Conditions by the Use of Highly Resolved Spatial Data

Flows in Agro-food Networks (FAN): An agent-based model to simulate local agricultural material flows

Crossing the chasm: a ‘tube-map’ for agent-based social simulation of policy scenarios in spatially-distributed systems

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