Background
The bioeconomy relies strongly on the availability of biomass, including biogenic waste, residues and by-products. The cost of supply often represents a significant proportion of the total value of the resource. However, there is limited insight into the current supply costs of wastes, residues and by-products. This includes straw, which is the most important agricultural by-product in Germany. Despite its importance, standardised information on supply costs or market prices, as well as their temporal and spatial variation, is missing.
Aim
Therefore, there is an urgent need for the temporal and spatial monitoring of individual cost components within total supply costs. This is essential to identify the most cost-effective options for the utilisation of agricultural by-products. Therefore, this study focuses on the case of straw to develop a model capable of visualising and mapping regional supply costs over time.
Method
We use an activity-based costing approach to calculate and monitor regional supply costs, defined as the monetary expenditure required to make straw available at the farm level. Our methodology combines typical technical and operational aspects of straw collection and transport with regional wage statistics, yield data, farm sizes, fuel prices and labour costs. We also consider storage costs and opportunity costs associated with nutrient replacement and conduct sensitivity analyses to measure their impact. To validate our calculations, we compare them with actual straw prices. To establish a reliable cost monitoring system, we propose an approach to assess the quality of input data.
Result
In 2011, the regional supply costs for straw varied from 45.72 EUR/Mg[FM] to 92.92 EUR/Mg[FM], showing a wide range. Over the years, the German average supply cost for straw increased from 56.78 EUR/Mg[FM] in 2010 to 58.79 EUR/Mg[FM] in 2020, with a peak of 61.24 EUR/Mg[FM] in 2018. This suggests that the temporal impact on mass-specific costs is relatively moderate compared to the spatial distribution of supply costs. The sensitivity analysis highlights storage time and costs, straw yield and wage levels as the main drivers of supply costs. Doubling the storage period from 3 to 6 months increases total costs by 20%. On average, the costs explain 75% of the straw price across all federal states, depending on annual price and cost levels. The quality assessment of input data shows that currently 68% of the data cannot be automatically extracted for continuous monitoring. Detailed results are available in a corresponding data publication: https://doi.org/10.5281/zenodo.8145082.
Conclusion
In the absence of standardised market prices, the model presented provides an approach to estimating the supply costs of straw, expressed in terms of the monetary cost to farmers of mobilising straw. This cost information could be a valid database for further techno-economic assessments or models to evaluate the economic feasibility of straw valorisation. Due to the modular structure of the model, the future development of supply costs can be considered if the input data are adapted to future scenarios.
Graphical Abstract
