Spatially constrained optimisation of sustainable maize straw availability for bio-based processing systems

Abstract

The deployment of bioenergy and carbon removal systems depends critically on the availability of sustainable and reliable biomass feedstocks. However, most biomass assessments rest on theoretical, technical or even economic potential estimates that do not reflect real-world supply constraints. This study develops a spatially explicit framework to quantify sustainability-constrained, reliability-adjusted and mobilisation-aware biomass supply, translating resource availability into a bankable feedstock metric for infrastructure planning. The framework integrates Earth observation-based crop mapping, straw production estimation, environmental sustainability constraints, probabilistic reliability assessment (P80) and socio-economic mobilisation modelling within a unified workflow. It is applied to maize straw in Lodzkie Voivodeship in Poland, over the period 2020-2025. Results show that technical straw potential ranged from 0.40 to 0.77 Mt yr 1, sustainability constraints reduced this by 36-61%. Incorporating interannual variability further reduced supply by converting mean availability into a conservative P80 estimate and mobilisation constraints limited practically accessible biomass to approximately 15-25% of technical straw. These results demonstrate that only a fraction of theoretical biomass can be considered sustainable, dependable and contractable. Comparison with the feedstock requirement of a 10- thr -1 biochar facility (75 kt yr -1) shows that supply is sufficient under sustainability and reliability constraints but becomes highly constrained once mobilisation is considered. This highlights the importance of integrating environmental, temporal and behavioural constraints in biomass assessment. The proposed framework provides a transferable and compatible approach with Monitoring, Reporting and Verification (MRV) systems, linking biomass resource assessment to deployment-relevant decision-making and establishes a foundation for subsequent optimisation of distributed bioenergy supply systems.