Cleaner Logistics and Supply Chain | 2026
Authors: Vázquez-Serrano J.I.; Cárdenas-Barrón L.E.; Peimbert-García R.E.; Vicencio-Ortiz J.C.; Smith N.R.; Céspedes-Mota A.; Koch-Schneider A.
DOI: 10.1016/j.clscn.2026.100328
Journal: Cleaner Logistics and Supply Chain
Year: 2026
Publisher: Elsevier Ltd
Document Type: Article
Open Access: All Open Access; Gold Open Access; Green Open Access
Cited by: 0
Crop production is fundamental to modern life, serving as the primary source for both food and livestock industries. Beyond the challenge of balancing supply and demand to mitigate market volatility and uncertainty, crop production must also address the need for increased sustainability across economic, environmental, and social dimensions. As environmental and social issues like waste and unmet demand arise, effective coordination among farmers is crucial, particularly in a supply chain where farmers often operate independently. This paper introduces a novel approach combining hybrid linear programming with discrete-event simulation to manage crop production across three collaboration scenarios: (1) distributed, (2) distributed with information sharing, and (3) centralized. The models account for multi-crop, multi-farm, and multi-season planning, as well as crop calendars, crop rotations, and carbon emissions, incorporating both deterministic and uncertain parameters. The framework is applied to a case study representative of irrigated cooperative agriculture in northwestern Mexico, with all parameters calibrated against official, and validated against documented crop benchmarks for the region. We evaluate each scenario based on sustainability indicators, including margin, unmet demand, waste, and fairness. Our findings reveal that the distributed scenario is the most equitable among farmers but results in economic losses. In contrast, the centralized scenario, while the least fair to individual farmers, is the most profitable and effectively reduces waste, unmet demand, and emissions. Additionally, the impact of a carbon tax is analyzed for each collaboration scenario. While the tax reduces profits, it does not affect unmet demand, waste, or unfairness. © 2026 The Author(s).
Carbon tax assessment; Collaboration scenarios; Discrete-event simulation; Linear model; Multi-crop production