Sustainable land management, with pressures from climate change, is a highly multidisciplinary research field. There are challenges to exploit an abundance of data and apply data-processing technologies to integrate environmental, economic, and social considerations and manage uncertainties originating from imperfect data quality. Motivated by these challenges, the present work proposes a multi-layered mapping methodological framework to bridge or reduce the problems identified by developing a transparent and explainable decision support system as a precision agriculture tool. This should be designed both for farmers and agriculture decision makers, and integrate soft (e.g., legislation, policy, regulation and experience) and hard data (measured data), along with geographical information that presents key information in the form of spatial mapping, information mapping, and causal structure mapping. Presented is a preliminary exploratory statistical case study analysis on grass growth data in order to examine patterns and determine which factors have the greatest influence on grass growth in Northern Ireland.
- Climate change
- Decision support system
- Exploratory statistical analysis
- Sustainable land management