Grieco R, Cervelli E, Bovo M, Pindozzi S, Scotto di Perta E, Tassinari P, Torreggiani D. The role of geospatial technologies for sustainable livestock manure management: A systematic review.
THE SCIENCE OF THE TOTAL ENVIRONMENT 2024;
954:176687. [PMID:
39366586 DOI:
10.1016/j.scitotenv.2024.176687]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 09/25/2024] [Accepted: 10/01/2024] [Indexed: 10/06/2024]
Abstract
Optimal livestock production is a key contributor to the achievement of sustainable development goals. The management and disposal of livestock manure is one of the main issues facing the sector in terms of soil, water and air pollution. Proper and sustainable management of livestock manure also requires a systemic approach to the problem, considering it at different territorial levels. In order to identify existing strategies to support this issue, this review investigated the use of Geographic Information System (GIS) analysis as a support for livestock manure management, highlighting the several GIS methodologies used to provide insight into the complexity, power, and potential offered by these approaches in study areas with different economic, social, and environmental variables, and to provide insights for future research. The study was performed on 139 papers chosen from a literature screening. Three study themes were identified by co-word analysis: Bioenergy, Environmental pollution and Landscape management/development, with a percentage division of research articles of 38 %, 47 % and 15 %, respectively. This study provides a theoretical and prospective framework for the long-term expansion of the livestock sector, which is critical to promoting a balance between sector development and environmental impact. The use of spatial analysis, along with additional tools and methods such as modelling, multivariate and spatial statistics, life cycle assessment, machine learning and multi-criteria analysis, has proven to be widely applied.
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