1- Leibniz Centre for Agricultural Landscape Research (ZALF)
2- University of Santiago de Compostela (USC)
2- University of Santiago de Compostela (USC)
Abstract: (3 Views)
Introduction: Subsoil degradation is one of the major sustainability challenges in agricultural systems, potentially affecting soil productivity and the resilience of agroecosystems. Management strategies such as fertilization, tillage, and agroecological subsoil management (ASM) strongly influence soil quality, productivity, and resilience. Although subsoil processes play a key role in biodiversity and ecosystem functions, they are often neglected in existing bio-economic models such as MODAM, which mainly focus on topsoil and limited farm constraints. ASM practices are constrained by economic uncertainty, high costs, and management complexity. This study extends the MODAM model to include ASM and evaluates its effects on farm profitability and key sustainability indicators, while identifying trade-offs and the need for potential policy support.
Methods: Within the SUS-SOIL project framework, three interventions, including cover crops, manure application, and agroforestry, were incorporated into the model. To analyze farm-level decision-making, Mixed Integer Programming (MIP), Positive Mathematical Programming (PMP), and Multi-Criteria Decision Analysis (MCDA) were applied. In addition, to represent the long-term effects of subsoil management, proxy indicators related to soil organic carbon (SOC) and reduced soil compaction were defined in the model. Five scenarios were defined to assess ASM impacts, including a conventional baseline and four intervention cases. Each scenario was simulated by activating specific constraints in a bio-economic farm model and analyzed separately within an MCDA framework.
Results: The results show that integrating subsoil processes into bio-economic models improves the assessment of trade-offs across economic, environmental, biodiversity, and soil indicators. ASM components exhibit heterogeneous effects, and combined interventions tend to deliver more balanced performance across sustainability dimensions.
Conclusion: This study highlights the importance of including subsoil agroecological practices in bio-economic assessments to better support sustainable agricultural decision-making. Since some benefits are external and not fully reflected in farm-level decisions, policy support is necessary to facilitate adoption.
Methods: Within the SUS-SOIL project framework, three interventions, including cover crops, manure application, and agroforestry, were incorporated into the model. To analyze farm-level decision-making, Mixed Integer Programming (MIP), Positive Mathematical Programming (PMP), and Multi-Criteria Decision Analysis (MCDA) were applied. In addition, to represent the long-term effects of subsoil management, proxy indicators related to soil organic carbon (SOC) and reduced soil compaction were defined in the model. Five scenarios were defined to assess ASM impacts, including a conventional baseline and four intervention cases. Each scenario was simulated by activating specific constraints in a bio-economic farm model and analyzed separately within an MCDA framework.
Results: The results show that integrating subsoil processes into bio-economic models improves the assessment of trade-offs across economic, environmental, biodiversity, and soil indicators. ASM components exhibit heterogeneous effects, and combined interventions tend to deliver more balanced performance across sustainability dimensions.
Conclusion: This study highlights the importance of including subsoil agroecological practices in bio-economic assessments to better support sustainable agricultural decision-making. Since some benefits are external and not fully reflected in farm-level decisions, policy support is necessary to facilitate adoption.
Keywords: Bio-economic modeling, Agroecological Subsoil Management, Decision-support, Multi-criteria optimization, Sustainability indicators
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