An Integrated Framework for Predicting Long-Term Productivity of Pastures in the Kingdom of Saudi Arabia

Abstract

The population of Saudi Arabia is increasing so is the demand for food; however, the arable land that can support this demand is decreasing rapidly. To meet the increasing dietary (cereal, meat, milk, etc.), needs of people and the fodder needs of livestock require identification of additional cultivation regions and correspondingly suitable crop/grass varieties. The traditional methods to achieve these objectives are expensive, complex and time-consuming. Therefore, the exploration of novel and proven IT techniques and methodologies are needed to address this complex problem. In this paper, we propose a data-driven framework and present simulated results mapped to real data that show how predictive data mining, geographical information system and expert system can be integrated. This integration results in identifying promising cultivable regions for the long-term productivity of perennial pasture grasses in the Kingdom of Saudi Arabia. The proposed framework can ultimately assist in identification of promising rangeland areas, the identified areas subsequently explored as per necessary follow-up actions/procedures.

Sustainable development scores using spatial information systems-analytic hierarchy process

“…The combination of GIS, decision support system and a method of multi-criteria decision making was made to find the most appropriate industrial site in the selection process. Abdullah et al (2018) used data mining tool plus GIS to facilitate decision support of identifying cultivable uncultivated regions in the Middle East. Kim et al (2016) combined Delphi panel discussion, AHP and GIS in map algebra to decide a suitable land.…”