SOC-PHNEJun 14, 2020

Transfer of Manure as Fertilizer from Livestock Farms to Crop Fields: The Case of Catalonia

arXiv:2006.09122v119 citations
Originality Incremental advance
AI Analysis

This work addresses environmental pollution from manure management in agriculture, offering incremental improvements in logistics efficiency for farmers and policymakers in Catalonia.

The paper tackles the logistics problem of transferring manure from livestock farms to crop fields as fertilizer in Catalonia, proposing three optimization approaches and finding that the centralized optimal algorithm is 8.5% more efficient than a decentralized ant-inspired method, which is 1.07 times more efficient than the existing naive practice.

Intensive livestock production might have a negative environmental impact, by producing large amounts of animal manure, which, if not properly managed, can contaminate nearby water bodies with nutrient excess. However, if animal manure is exported to nearby crop fields, to be used as organic fertilizer, pollution can be mitigated. It is a single-objective optimization problem, in regards to finding the best solution for the logistics process of satisfying nutrient needs of crops by means of livestock manure. This paper proposes three different approaches to solve the problem: a centralized optimal algorithm (COA), a decentralized nature-inspired cooperative technique, based on the foraging behaviour of ants (AIA), as well as a naive neighbour-based method (NBS), which constitutes the existing practice used today in an ad hoc, uncoordinated manner in Catalonia. Results show that the COA approach is 8.5% more efficient than the AIA. However, the AIA approach is fairer to the farmers and more balanced in terms of average transportation distances that need to be covered by each livestock farmer, while it is 1.07 times more eefficient than the NBS. Our work constitutes the first application of a decentralized AIA to this interesting real-world problem, in a domain where swarm intelligence methods are still under-exploited.

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