Applying a biocomplexity approach to modelling farmer decision‐making and land use impacts on wildlife

Abstract

The biocomplexity approach refers to a fully integrated social‐ecological systems (SES) simulation that represents bidirectional feedbacks between social and ecological components. This method is essential to accurately assess impacts of economy and policy on SES such as agroecosystems, where feedbacks between the drivers and impacts of cropping changes need to be simulated. Here we exemplify the biocomplexity approach using energy maize, which is becoming an important source of bioenergy in Europe, and thus, might cause a significant change in land use with knock on‐effects for wildlife. The integrated simulation tool consisted of a farmer decision‐making agent‐based model fully coupled to the Animal Landscape and Man Simulatin System (ALMaSS), an agent‐based simulation system for predicting impacts of land use on a range of Danish wildlife species: the brown hare (Lepus europaeus), the grey partridge (Perdix perdix), the skylark (Alauda arvensis), a carabid beetle (Bembidion lampros), a linyphiid spider (Erigone atra) and the field vole (Microtus agrestis). This was used to assess the impacts of increasing demand on energy maize on the six animal species. Two types of experimental scenarios were evaluated, with and without feedback between the social and ecological system. The assessment of species responses was based on changes in population size, abundance and occupancy. The response to the cultivation of energy maize was negative for three vertebrate species (skylark, hare and field vole) and positive for partridge and the two invertebrate species. The feedback scenarios showed that the incorporation of information from ecological system to the farmer decision‐making affected both a trend in area cultivated with energy maize as well as the animal responses. Synthesis and applications. Fully coupling agent‐based decision‐making and environmental simulation allows a detailed representation and integration of both social and ecological components of agricultural systems at proper spatial and temporal scales as well as of dynamic feedbacks between the two systems. By employing easy to interpret measures of changes in abundance and spatial occupancy of animal species, the simulation results could inform and simplify decision‐making on expected impacts of economy and policy regulations on wildlife.

Integrated modeling of extended agro-food supply chains: A systems approach

“…Theory focused studies aim to explore the application of theories in understanding agents decision-making process (e.g., farmer, government, dealer, etc.) or develop new theories to explain the interactions among individual agents (e.g., Malawska and Topping (2018) ). Theories have already helped to describe the formation of cooperation networks, restructuring the partnerships, and rearrangement of the market power (See Utomo, Onggo and Eldridge (2018) ).…”

Ascertaining land allocation decisions of farmers about the adoption of carinata as a potential crop for sustainable aviation fuel production in the Southern United States

“…revealed interactions of supply chain between biomass power plant investors and farmers through a mathematical programing-based estimation procedure. The use of mathematical programming is also found in several other studies (Ding et al, 2015;Guillem et al, 2015;Jin et al, 2019;Malawska & Topping, 2018;Shu et al, 2020). Among other techniques, Schulze et al (2017) and Shastri et al (2011) applied the general economic equilibrium model, and Brown et al (2016) used multivariate statistical models.…”

“…Additionally, existing studies have only partially accounted for the interactions of farmers with their local environment. First, the models are not truly spatially explicit (except Malawska & Topping, 2018). For instance, the spatial environment of the model developed by Schulze et al (2017) is based on hypothetical grid‐based cells, where the size of land parcels is uniform for all the farmers.…”

An ecological-economic model of land-use decisions, agricultural production and biocontrol

“…As emphasized by [16], an important challenge is to develop feedback models that incorporate spatio-temporal interactions between landscape, ecological processes and stakeholder decisions. Despite their importance, such models, which have proved useful for studying conservation policies [17], the evolution of resistance to pesticides [18] and ecosystem services provision [19], are still rare.…”