TY - JOUR

T1 - A participatory impact assessment of digital agriculture

T2 - A Bayesian network-based case study in Germany

AU - MacPherson, Joseph

AU - Rosman, Anna

AU - Helming, Katharina

AU - Burkhard, Benjamin

N1 - Publisher Copyright: © 2024 The Authors

PY - 2024/12/13

Y1 - 2024/12/13

N2 - CONTEXT: The transition to digital agriculture is likely to lead to systemic changes that will affect production, consumption, governance, and the wider environment of agricultural systems. Nevertheless, the absence of sufficient evidence and ambiguities in perspectives create an ongoing lack of clarity regarding the potential impacts of digital agriculture. Therefore, to discern potential impacts while addressing system complexities, uncertainties, as well as normative aspects associated with this transition, future-oriented and participatory assessments are needed that actively involve diverse knowledge and values of affected stakeholders. OBJECTIVE: This research aims to explore the impacts and processes of agricultural digitalization according to stakeholders. The objectives are to identify key areas of impact that digital agriculture is likely to influence, identify and explore the causal pathways linking digital agriculture to impacts, and quantitatively examine the uncertainties of stakeholder perceptions associated with these impacts and causal pathways. METHODS: Through a participatory modelling procedure, diverse stakeholders from the German region of Brandenburg constructed a Bayesian Belief Network (BBN). The BBN facilitated the identification of the main impacts of digital agriculture and allowed for the modelling of uncertainties associated with these impacts. RESULTS AND CONCLUSIONS: Stakeholders perceived several socioeconomic advantages of digitalization, particularly in terms of bolstering economic stability through improved risk management and enhanced resource use efficiency, validating existing claims in the literature. The perception seems to be influenced by highly variable yields and market uncertainties, as well as shortages in labour in the region. On the other hand, there was significant uncertainty among stakeholders concerning landscape diversification and its impact on biodiversity. This uncertainty arises from the potential profitability of cultivating marginal land under heightened digitalization-induced efficiency, posing a risk of diminishing natural habitat and landscape heterogeneity. Local historical trends toward landscape simplification as result of technology-driven efficiency improvements may be a cause for this perception. SIGNIFICANCE: This study contributes to a growing body of future-oriented research assessing the impacts of digital agriculture through engaging stakeholder knowledge and values. While there is theoretical potential for digitalization to enhance biodiversity, realizing such positive impacts is improbable without improved communication and policy incentives, given the historical trend of efficiency-driven pathways. This study introduces a novel approach to assessing the impacts of agricultural digitalization through the application of a participatory Bayesian belief network.

AB - CONTEXT: The transition to digital agriculture is likely to lead to systemic changes that will affect production, consumption, governance, and the wider environment of agricultural systems. Nevertheless, the absence of sufficient evidence and ambiguities in perspectives create an ongoing lack of clarity regarding the potential impacts of digital agriculture. Therefore, to discern potential impacts while addressing system complexities, uncertainties, as well as normative aspects associated with this transition, future-oriented and participatory assessments are needed that actively involve diverse knowledge and values of affected stakeholders. OBJECTIVE: This research aims to explore the impacts and processes of agricultural digitalization according to stakeholders. The objectives are to identify key areas of impact that digital agriculture is likely to influence, identify and explore the causal pathways linking digital agriculture to impacts, and quantitatively examine the uncertainties of stakeholder perceptions associated with these impacts and causal pathways. METHODS: Through a participatory modelling procedure, diverse stakeholders from the German region of Brandenburg constructed a Bayesian Belief Network (BBN). The BBN facilitated the identification of the main impacts of digital agriculture and allowed for the modelling of uncertainties associated with these impacts. RESULTS AND CONCLUSIONS: Stakeholders perceived several socioeconomic advantages of digitalization, particularly in terms of bolstering economic stability through improved risk management and enhanced resource use efficiency, validating existing claims in the literature. The perception seems to be influenced by highly variable yields and market uncertainties, as well as shortages in labour in the region. On the other hand, there was significant uncertainty among stakeholders concerning landscape diversification and its impact on biodiversity. This uncertainty arises from the potential profitability of cultivating marginal land under heightened digitalization-induced efficiency, posing a risk of diminishing natural habitat and landscape heterogeneity. Local historical trends toward landscape simplification as result of technology-driven efficiency improvements may be a cause for this perception. SIGNIFICANCE: This study contributes to a growing body of future-oriented research assessing the impacts of digital agriculture through engaging stakeholder knowledge and values. While there is theoretical potential for digitalization to enhance biodiversity, realizing such positive impacts is improbable without improved communication and policy incentives, given the historical trend of efficiency-driven pathways. This study introduces a novel approach to assessing the impacts of agricultural digitalization through the application of a participatory Bayesian belief network.

KW - Biodiversity

KW - Digitalization

KW - Mental models

KW - Participatory modelling

KW - Responsible research and innovation

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85211718331&partnerID=8YFLogxK

U2 - 10.1016/j.agsy.2024.104222

DO - 10.1016/j.agsy.2024.104222

M3 - Article

AN - SCOPUS:85211718331

VL - 224

JO - Agricultural systems

JF - Agricultural systems

SN - 0308-521X

M1 - 104222

ER -