Soil quality is pivotal for crop productivity and the environmental quality of agricultural ecosystems. Achieving sufficient yearly income and long-term farm continuity are key goals for farmers, making sustainable soil management an economic challenge. Existing bio-economic models often inadequately address soil quality. In this study, we apply the novel FARManalytics model, which integrates chemical, physical, and biological indicators of soil quality indicator, quantitative rules on how these indicators respond to farmers’ production management over time, and an economic calculation framework that accurately calculates the contribution of production management decisions towards farm income. This is the first study applying this model on existing arable farms. FARManalytics optimizes crop rotation design, cover crops, manure and fertilizer application and crop residue management. Nine Dutch arable farms were analyzed with a high variation in farm size, soil type, and cultivated crops. First, we assessed farm differences in soil quality and farm economics. Second, we optimized production management to maximize farm income while meeting soil quality targets using farm-specific scenarios. Third, we explored the impact of recent policy measures to preserve water quality and to increase the contribution of local protein production. The results show that the case farms already perform well regarding soil quality, with 75% of the soil quality indicators above critical levels. The main soil quality bottlenecks are subsoil compaction and soil organic matter input. We show that even in front-runner farms, bio-economic modeling with FARManalytics substantially improves economic performance while increasing soil quality. We found that farm income could be increased by up to €704 ha⁻¹ year⁻¹ while meeting soil quality targets. Additionally, we show that to anticipate on stricter water quality regulation and market shift for protein crops, FARManalytics is able to provide alternative production management strategies that ensure the highest farm income while preserving soil quality for a set of heterogenous farms.

土壤质量对作物产量和农业生态系统的环境质量至关重要。实现足够的年收入和长期的农场连续性是农民的主要目标，这使得可持续的土壤管理成为一项经济挑战。现有的生物经济模型往往不能充分解决土壤质量问题。在这项研究中，我们应用了新颖的 FARManalytics 模型，该模型集成了土壤质量指标的化学、物理和生物指标、这些指标如何随时间响应农民生产管理的定量规则，以及准确计算生产管理决策对农场收入的贡献的经济计算框架。这是首次在现有耕地农场应用此模型的研究。FARManalytics 优化了作物轮作设计、覆盖作物、粪肥和肥料施用以及作物残留物管理。分析了 9 个荷兰耕地农场，这些农场在农场规模、土壤类型和栽培作物方面差异很大。首先，我们评估了农场在土壤质量和农场经济学方面的差异。其次，我们优化了生产管理，以最大限度地提高农场收入，同时使用农场特定的场景满足土壤质量目标。第三，我们探讨了近期政策措施对保持水质和增加当地蛋白质生产贡献的影响。结果表明，案例农场在土壤质量方面已经表现良好，75% 的土壤质量指标高于临界水平。主要的土壤质量瓶颈是底土压实和土壤有机质输入。我们表明，即使在领跑的农场中，使用 FARManalytics 进行生物经济建模也能显著提高经济绩效，同时提高土壤质量。 我们发现，在达到土壤质量目标的同时，农场收入最多可以增加 704 欧元 ^{ha-1 year-1}。此外，我们还表明，为了预测更严格的水质监管和蛋白质作物的市场变化，FARManalytics 能够提供替代生产管理策略，确保最高的农场收入，同时保持一组异质农场的土壤质量。