A Conceptual Model Framework for Mapping, Analyzing and Managing Supply–Demand Mismatches of Ecosystem Services in Agricultural Landscapes

Abstract

Appreciation for agricultural sustainability and ecosystem services (ESS) has received considerable attention from the scientific community. However, research has not yet systematically and sufficiently considered the spatial dimension of ESS trade-offs as a source of conflicts. Moreover, approaches for ESS management that address a wide range of beneficiaries and their interactions at landscape scale are lacking. Our main research question is how to motivate different beneficiaries of agricultural landscapes to cooperate in reducing supply–demand mismatches and accompanied conflicts, as well as to assess how different scenarios would impact relevant Sustainable Development Goals (SDGs). We present a novel and conceptual integrated model in which we employ a combination of three methodological tools: participatory geographic information system (PGIS), agent-based modelling (ABM) and a Bayesian belief network (BBN). The objective of our model simulation is to identify and manage site-specific spatial trade-off patterns and to provide decision support for shifting competitive behavior of individual stakeholders in satisfying their demand for ESS to a collective and cooperative scheme, while jointly striving to attain relevant targets outlined in the SDGs. Attached to this work is a short video depicting our conceptual model. We strongly suggest that tackling a complex social-ecological system necessitates a highly integrated modelling approach that fosters the transition from farm- to landscape-scale management, from individualistic to collective action, and from competitive to cooperative behavior.

The Digital Agricultural Knowledge and Information System (DAKIS): Employing digitalisation to encourage diversified and multifunctional agricultural systems

“…For the modelling of social-ecological systems, we employ the ViSA (Viability of Socio-ecological Agroecosystem) agent-based model [ 48 , 49 ] to consider effects on social viability (see SM Section 2.3.4. for further information on the ViSA model). The ViSA model expands the functionalities of the DAKIS spatially and in terms of represented actors.…”

Exploring the Response of Ecosystem Services to Socioecological Factors in the Yangtze River Economic Belt, China

“…Both natural conditions and anthropogenic activities can influence ES supply and demand, and further affect the ES supply-demand balance [25,50,51]. According to the relevant literature [52][53][54] and the availability of county-level data, 16 potential socioe-cological factors were selected to estimate their impact on the ES supply, demand and supply-demand balance.…”

Participatory landscape sustainability assessment: where do we stand? A systematic literature review

“…These were closely followed by collective assessments relying on focus groups (Chakraborty et al, 2018) or collective workshops (Sapkota et al, 2021), and quantitative methods based, for instance, on surveys and household level socio-economic assessments (Sharma et al, 2016). Scenario-based approaches such as scenario analysis (Bohnet et al, 2011) or scenario planning (Karjala and Dewhurst, 2003), and modeling approaches such as agent-based modeling and Bayesian network (Shaaban et al, 2021) were the least frequent. Less than a third of the studied mobilized spatially explicit approaches through the use of remote sensing and GIS analysis (Kivinen, 2015) or participatory mapping (Bélisle et al, 2021).…”