Panel data clustering and suitable area analysis for rice plantation

The objective of this study was to group areas by climatic factors affecting for rice plantation using the climate factors from CCAM model in 2021-2025 and to study suitable areas for rice cultivation in the future using 2018 soil series data with climate factors. K-means for joint Longitudinal data (KmL3D) method with the Calinski & Harabasz, Kryszczuk variant criteria for determining the optimal number of clusters and analysis of variance for cluster validation was employed using the R-project. Simple Additive Weighting (SAW) method with the analytic hierarchy process (AHP) was used to find the suitable area for rice planting. Inverse distance weight (IDW) estimation was employed to display spatial results using the Quantum Geographic Information System (QGIS).

It was found that there were 6 regions classified. Most areas of group A, B, C, D, E and F were in the central region (57.14%), the South (96.30%), the Northeast (100%), the central region (42.86%), in the North (66.67%), and the Eastern (58.33%) respectively. Their average rainfalls were 1.87, 6.04, 1.94, 2.52, 2.02 and 3.75 mm./day, respectively.

In addition, it was found that areas in Thailand were suitable for rice plantation, which were 8.87%, 70.97 and 20.16% for high, moderate, and low suitable level respectively. To consider with cluster analysis, it showed that the area with high suitable of group A, B, C, D, E and F were 36.36%, 0%, 9.09%, 45.45%, 9.09% และ 0% respectively