By leveraging a wide range of novel, data-driven technologies for agricultural production and agri-food value chains, digital agriculture presents potential enhancements to sustainability across food systems. Accordingly, digital agriculture has received considerable attention in policy in recent years, with emphasis mostly placed on the potential of digital agriculture to improve efficiency, productivity and food security, and less attention given to how digitalization may impact other principles of sustainable development, such as biodiversity conservation, soil protection, and human health, for example. Here, we review high-level policy and law in the German and European context to highlight a number of important institutional, societal, and legal preconditions for leveraging digital agriculture to achieve diverse sustainability targets. Additionally, we combine foresight analysis with our review to reflect on how future frame conditions influencing agricultural digitalization and sustainability could conceivably arise. The major points are the following: (1) some polices consider the benefits of digital agriculture, although only to a limited extent and mostly in terms of resource use efficiency; (2) law as it applies to digital agriculture is emerging but is highly fragmented; and (3) the adoption of digital agriculture and if it is used to enhance sustainability will be dependent on future data ownership regimes.
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.
A number of tools and models have been developed to assess farm-level sustainability. However, it is unclear how well they potentially incorporate ecosystem services (ES), or how they may contribute to attaining the United Nations Sustainable Development Goals (SDGs). Understanding how farm-level assessment tools and models converge on these new paradigms of sustainability is important for drawing comparison on sustainability performances of farming systems, conducting meta-analyses and upscaling local responses to global driving forces. In this study, a coverage analysis was performed for several farm-level sustainability assessment (SA) tools (SAFA, RISE, KSNL, DLG) and models (MODAM, MONICA, APSIM), in regard to their potential for incorporating ES and contribution to attaining the SDGs. Lists of agricultural-relevant CICES classes and SDG targets were compiled and matched against the indicators of the tools and models. The results showed that SAFA possessed the most comprehensive coverage of ES and SDGs, followed by RISE and KSNL. In comparison to models, SA tools were observed to have a higher degree of potential for covering ES and SDGs, which was attributed to larger and broader indicators sets. However, this study also suggested that, overall, current tools and models do not sufficiently articulate the concept of ecosystem services.
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