Speaker
Description
Planning for sustainable agricultural landscapes requires a comprehensive understanding of the needs of stakeholders. To achieve a sustainable agricultural system, the implementation of different strategies for diversification has been a promising approach. These strategies can target the overall landscape complexity to benefit biodiversity or diversify cropping systems to enhance and stabilise yields. Therefore, diversity at both agricultural field and landscape levels and their interconnection need to be considered (Reckling et al., 2023). However, the impacts of different diversification strategies at different levels are rarely investigated jointly. This study aims to detect the relationship between the diversification strategies at field and landscape levels and to identify potential drivers to understand what synergy and trade-off dynamics of sustainable landscape planning emerge.
As a case study, we studied agricultural landscapes in the Brandenburg region of Germany (Schiller et al., 2023). Crop rotational richness, Shannon's diversity, and evenness indices were measured per-field per-decade as proxies for crop rotational diversity. Landscape diversity was measured using land use land cover types and habitat types with the same metrics. As potential drivers of diversity, we included soil and climate characteristics and the proportion of agricultural and urban areas, along with geographical positions. All spatial information was aggregated within the landscape of 10x10 km in Brandenburg, Germany. We tested the links between all variables using interpretable machine learning methods to identify important modelled associations.
We found that more simplified landscapes with a higher proportion of agricultural area exhibited higher crop rotational diversity (r-squared = 0.2 – 0.6). Also, crop rotational diversity was higher with higher soil quality (r-squared = 0.1 – 0.13). Therefore, different diversity aspects can be found in contrasting regions in Brandenburg, where crop rotational diversity is particularly determined by local soil quality and the intensity of agricultural land use.
The more diverse the crop rotation, the simpler the landscape – This trade-off relationship implies the fundamental trade-off of diversification across scales due possibly to the limited high quality resources like soil for land development. We argue that a comprehensive understanding of the spatial distribution, synergies, and trade-offs of diversification (goals) within a landscape facilitates collaboration and planning among all parties involved in sustainable landscape planning (Duarte et al., 2018). As we move forward, integrating diverse stakeholder perspectives, socio-economic farming conditions, and advanced AI techniques will play a pivotal role in shaping more sustainable landscapes.
Duarte, G. T., Santos, P. M., Cornelissen, T. G., Ribeiro, M. C., & Paglia, A. P. (2018). The effects of landscape patterns on ecosystem services: Meta-analyses of landscape services. Landscape Ecology, 33(8), 1247–1257. https://doi.org/10.1007/s10980-018-0673-5
Reckling, M., Watson, C. A., Whitbread, A., & Helming, K. (2023). Diversification for sustainable and resilient agricultural landscape systems. Agronomy for Sustainable Development, 43(4), 44. https://doi.org/10.1007/s13593-023-00898-5
Schiller, J., Jänicke, C., Reckling, M., & Ryo, M. (2023). Higher crop diversity in less diverse landscapes [Preprint]. In Review. https://doi.org/10.21203/rs.3.rs-3410387/v1
| Keywords | landscape heterogeneity; crop rotation; diversification; explainable artificial intelligence; land use |
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