Remote sensing-based long-term assessment of bioenergy policy impact on agricultural land cover change: A case study of biogas in the Weser-Ems region in Germany

Climate change, population growth, and other global challenges are putting pressure on the limited land resources, in particular on agricultural land, to satisfy the demands for food, energy carriers, raw materials for the chemical industry, and ecosystem services. This study was conducted to understand the pressure applied on agricultural land due to the development of biogas sector. The Weser-Ems region in Germany, which is a traditional agricultural area, was used as a study area. Remote sensing (RS) and machine learning methods, in combination with data of the integrated administration and control system (IACS), were used to predict and map the agricultural land cover, and to observe its change over a 20 years period, starting at the time when the German renewable energy act (EEG) was established, which provided a guaranteed feed-in tariff and bonus payments for energy crop-based biogas plants. The observed land cover changes were related to the development of biogas production capacities in the studied area. The results show an overall increasing dominance of maize cultivation between the years 2004 and 2014, replacing other summer crops and grassland respectively in the southern and the northern regions of Weser-Ems. Further, major land cover changes have occurred in areas with an increased density of biogas plant installed electric capacity, indicating that there is a direct relation between biogas production development and agricultural land cover change in the region. Since the maize silage is used for biogas and feeding cattle, a simplified reference factor was applied to estimate the amount of maize used as cattle feed. This analysis further strengthened the initial findings and emphasized the link between biogas development and the expansion of maize cultivation areas. The developed approach can serve as a tool for ex-post policy impact analysis and provide useful insights to support long-term bioeconomy monitoring.