Algorithm for Evaluating the Difficulty of Land Consolidation Using Cadastral Data

Michał Maciąg, Klaudia Maciąg () and Przemysław Leń
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Michał Maciąg: Faculty of Environmental Engineering and Geodesy, University of Life Sciences in Lublin, 20-950 Lublin, Poland
Klaudia Maciąg: Faculty of Environmental Engineering and Geodesy, University of Life Sciences in Lublin, 20-950 Lublin, Poland
Przemysław Leń: Faculty of Geodesy and Geotechnics, Rzeszow University of Technology, 35-959 Rzeszów, Poland

Sustainability, 2024, vol. 16, issue 13, 1-24

Abstract: Optimum planning and effective land consolidation, widely discussed by contemporary authors, is a response to the perceivable need to modernise global agriculture to ensure the community’s food security and create steady, sustainable development in rural areas. Adequate leveraging of agricultural policy instruments requires setting a correct strategic direction, including allocating available funds and considering the technical feasibility of the adopted assumptions. The selection of relevant methods to ensure the efficient and complete accomplishment of the anticipated results should follow a rational analysis of the actual work complexity. This paper presents an innovative, proprietary method for evaluating the difficulty of potential land consolidation using a standardised cadastral data set. The designed tool, which relies on automated algorithms applied in a GIS environment, provides accurate data describing the expected land consolidation complexity at individual stages of the procedure. Detailed and current information on land ownership, use, and farm geometry processed using efficient spatial and statistical analysis methods provides transparent and unambiguous results. The proposed solution was used in developing the difficulty assessment of land consolidation in 58 villages of the Strzyżów district in southeastern Poland.

Keywords: land consolidation; rural land use; algorithm; GIS; cadastre (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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