ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 139–146, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-139-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 139–146, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-139-2020

  03 Aug 2020

03 Aug 2020

TRANSFER OF MANURE FROM LIVESTOCK FARMS TO CROP FIELDS AS FERTILIZER USING AN ANT INSPIRED APPROACH

A. Kamilaris1,2, A. Engelbrecht3, A. Pitsillides4, and Francesc X. Prenafeta-Bold5 A. Kamilaris et al.
  • 1Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE), Nicosia, Cyprus
  • 2Department of Computer Science, University of Twente, The Netherlands
  • 3Department of Industrial Engineering, University Of Stellenbosch, South Africa
  • 4Department of Computer Science, University of Cyprus, Nicosia, Cyprus
  • 5Institute of Agriculture and Food Research and Technology (IRTA), Barcelona, Spain

Keywords: Animal Manure, Livestock farming, Environmental Impact, Logistic Problem, Optimization, Nature-Inspired Approach, Ant Behavior, Nitrogen Management

Abstract. Intensive livestock production might have a negative environmental impact, by producing large amounts of animal excrements, which, if not properly managed, can contaminate nearby water bodies with nutrient excess. However, if animal manure is exported to distant 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 crops needs by means of livestock manure. This paper proposes a dynamic approach to solve the problem, based on a decentralized nature-inspired cooperative technique, inspired by the foraging behavior of ants (AIA). Results provide important insights for policy-makers over the potential of using animal manure as fertilizer for crop fields, while AIA solves the problem effectively, in a fair way to the farmers and well balanced in terms of average transportation distances that need to be covered by each livestock farmer. 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.