Trade‐Off Analysis Between Environmental Effects and Profitability in Agriculture: A Finnish Case‐Study

Finding equilibrium between profitability and environmental impacts poses a fundamental challenge in cropping systems management. Identifying trade-offs requires robust tools to reconcile diverse and frequently conflicting objectives, especially in low data availability scenarios. This study presents a novel methodological framework combining the outputs of a process-based crop model with a fuzzy-expert trade-off analysis system in order to perform a structured comparison of alternative cropping systems. Model outputs on soil organic carbon, nitrous oxide emissions, nitrate leaching, and economic return expressed as Net Present Value were aggregated using fuzzy logic to inform a composite index (Σi) ranking trade-off performance on a scale from 0 (worst) to 1 (best). The framework was applied in a case study in Finland, evaluating nine cropping systems under current and future climate conditions. The systems included both crop-based (annual cereals and oilseeds) and livestock-based (with temporary grass) rotations, managed conventionally or organically, and varying in fertilization strategy, residue management, and tillage depth. The analysis was applied using a balanced weighting scheme and three alternative schemes reflecting the priorities of distinct stakeholder categories (young farmers, an agrochemical company, and a Common Agricultural Policy paying agency), allowing assessment of how changes in analytical context affect the resulting index. Under current climate conditions, the conventional livestock-based system with mixed fertilization and residue retention performed best (Σi = 0.69) while the conventional crop-based system relying exclusively on mineral fertilization and residue removal underperformed (Σi = 0.30). This latter system experienced the sharpest Σi decline under future climate conditions (Σi = 0.10), while organic livestock-based systems exhibited stable outcomes (Σi ~ 0.50). When evaluation criteria were adjusted to reflect stakeholder priorities, the framework consistently captured shifts in system rankings. This application illustrates the capacity of the framework to differentiate contrasting systems under harmonized assumptions, supporting its potential transferability particularly where long-term site-specific empirical datasets are limited.