A mixed integer linear programming framework for mitigating enteric methane emissions on dairy farms through optimized crop and diet planning

In pursuit of agricultural resilience and sustainability, the European Union (EU) Common Agricultural Policy (CAP) advocates for effective and diverse crop rotations. Yet, dairy farmers face intricate planning challenges due to diet and market complexities. This study introduces a novel mixed intege...

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Published in:Journal of cleaner production Vol. 511; p. 145636
Main Authors: Gong, Yijing, Bellingeri, Andrea, Fumagalli, Francesca, Sechi, Gian Simone, Atzori, Alberto Stanislao, Masoero, Francesco, Gallo, Antonio, Cabrera, Victor E.
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.06.2025
Subjects:
Animal diet optimization
cattle feeds
Common Agricultural Policy
crop rotation
crude protein
dairy cows
Dairy farm management
diet
Enteric methane
Environmental policy
European Union (EU) common agricultural policy (CAP)
Grana cheese
Greenhouse gas (GHG) emissions
greenhouse gases
issues and policy
markets
methane
milk
neutral detergent fiber
pollution control
protected designation of origin
Dairy farm management
Animal diet optimization
Enteric methane
Environmental policy
Greenhouse gas (GHG) emissions
European Union (EU) common agricultural policy (CAP)
ISSN:0959-6526
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Summary:In pursuit of agricultural resilience and sustainability, the European Union (EU) Common Agricultural Policy (CAP) advocates for effective and diverse crop rotations. Yet, dairy farmers face intricate planning challenges due to diet and market complexities. This study introduces a novel mixed integer linear programming (MILP) model that optimizes both crop planning and dairy cow diet formulation within mixed crop-livestock farming systems, considering CAP crop rotation regulations and Greenhouse Gas (GHG) environmental policies. Our approach employs a two-step framework to address enteric methane emissions. First, a multi-objective optimization generates a set of Pareto-efficient solutions, offering policymakers insights into trade-offs between economic returns and methane emissions. Second, the TAX policy (introducing a methane emissions tax) and RED policy (imposing explicit methane reduction targets) can be used to determine the required tax rates or reduction levels to achieve the selected policy goals. These policies also reveal the effects of varying tax rates and reduction levels on crop planning and diet formulation. The proposed approach is illustrated, without loss of generality, on three representative Italian dairy farms – producing milk without Protected Designation of Origin (PDO) rules (STD), or following Grana Padano (GP), or Parmigiano Reggiano (PR) PDO rules. We consider 33 types of crops, allowing double cropping, and 11 animal groups that have distinct nutritional requirements. The multi-objective optimization highlights non-linear trade-offs between economic returns and methane emissions, underscoring the critical role of policymakers in balancing these objectives. RED policy scenarios prove more effective at GHG mitigation than TAX policies. Methane mitigation initiatives prompt dietary adjustments, resulting in higher crude protein percentages and reduced neutral detergent fiber content in cow diets, consequently influencing crop plans. Moreover, under stringent GHG mitigation requirements, dairy farmers lean towards purchasing feed from the market rather than cultivating it on-farm. [Display omitted] •A novel optimization model for crop rotation and animal diet planning is proposed.•Mixed Integer Linear Programming is used to enable crop allocation and rotation.•Alternative crop plans and diets suggested by model reduce enteric CH4 emissions.•Non-linear trade-offs are revealed between CH4 reduction and economic benefits.•Effective policies and tools are crucial to implement crop rotation and reduce GHG.
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ISSN:0959-6526
DOI:10.1016/j.jclepro.2025.145636