12-16, 14:50–15:10 (Asia/Bangkok), Room34-1104
Mapping land suitability is a critical approach for identifying appropriate land use for site selection and land-use planning. However, climate change exacerbates water shortages and droughts, significantly affecting land suitability and resulting in decreased crop yields, especially for sugarcane. While land suitability is typically evaluated based on multiple criteria such as soil properties, topography, climate, and socioeconomic factors, it is essential to incorporate drought conditions into land suitability mapping to mitigate climate change influences on crop yields. Therefore, this study aimed to map sugarcane land suitability using fuzzy AHP and multi-criteria evaluation approaches in the Northeast region of Thailand.
The study selected six significant criteria for sugarcane land suitability mapping: the ETDI as an agricultural drought index, slope, soil texture, distance from the river, distance from the road, and distance from the sugar mill. The ETDI was assessed by calculating the difference between spatial Potential Evapotranspiration (PET) and actual Evapotranspiration (AET). Spatial PET was analyzed using a PET estimation model based on integrated GNSS-derived Precipitable Water Vapor, processed with goGPS open-source software, along with the MODIS land surface temperature product. Concurrently, the spatial AET was derived from the SEBAL model, utilizing GRASS GIS software.
Subsequently, land suitability for sugarcane cultivation was evaluated by integrating fuzzy AHP and multi-criteria evaluation approaches. The results indicated that two primary factors affected sugarcane cultivation: the ETDI and distance from the river. The ETDI was the most significant factor, with an average weight of 0.66, while the distance from the river had an average weight of 0.34. Other factors, including slope, soil texture, distance from the road, and distance from the sugar mill, did not influence land suitability. The spatial distribution of these factors was consistent throughout the study area.
Suitable areas for sugarcane were predominantly found in the moderately suitable class (S2; 49.6%), followed by marginally suitable (S3; 36.0%) and highly suitable (S1; 11.2%). Actual sugarcane cultivation areas were mainly in the S3 class (49.0%), followed by S2 (43.2%) and S1 (6.7%). S3 class areas were concentrated in Wang Sam Mo district, Udon Thani province (129 km²), with a sugarcane yield of approximately 60.6 tons/ha. S2 class areas, primarily in Phu Khiao district, Chaiyaphum province (178 km²), yielded about 62.5 tons/ha, while S1 class areas in Phimai district, Nakhon Ratchasima province (30 km²), achieved a higher yield of 63.6 tons/ha.
S2 class areas could potentially be enhanced through irrigation systems and small ponds to mitigate drought risks. Limiting the distance from the river to within 2 km could increase sugarcane yields and promote areas to the S1 class, expanding S1 areas by 2.7 times and raising yields by approximately 1.1 tons/ha (1.8% of S2 yield). Areas classified as S1 exhibit significant potential for sugarcane cultivation expansion due to their underutilization. A total area of 6,519 km² within the S1 class was analyzed for suitability. Nakhon Ratchasima province has the greatest potential (2,272 km², 35%), followed by Khon Kaen (725 km², 11%), Chaiyaphum (592 km², 9%), Udon Thani (519 km², 8%), and Surin (441 km², 7%).
Encouraging a shift from currently cultivated crops (rice, corn, and cassava) to sugarcane in these potential areas is essential for optimal resource utilization. However, farmers often continue rice cultivation due to its traditional significance and shorter growth period, providing quicker income. Government policies should support participatory knowledge transfer on sugarcane cultivation, ensure price guarantees, and facilitate access to credit. Additionally, the high price of sugarcane could incentivize farmers to expand sugarcane cultivation to meet increasing domestic and export demand. Further research on a larger scale, covering the entire country, is necessary to enhance the accuracy of land suitability maps in addressing challenges posed by global climate change.
The integration of fuzzy AHP and multi-criteria evaluation was employed to re-analyze the suitability of land for sugarcane cultivation, based on its capabilities. The ETDI was applied as a crucial factor in the sugarcane land suitability analysis with an average factor weighting of 0.66, determined by considering both soil and vegetation conditions. The distance from river was also considered with an average factor weighting of 0.34. The paper presented that the suitable areas for sugarcane (61,566 km2) were mostly found in the moderately suitable class (S2; 30,521 km2, 49.6%), followed by the marginally suitable class (S3; 22,208 km2, 36.0%) and the highly suitable class (S1; 6,872 km2, 11.2%). In addition, the actual sugarcane plantation areas in 2020, sourced from the Office of the Cane and Sugar Board (OCSB), on the land suitability map was studied to determine the current situation of sugarcane land suitability. These findings indicated that actual sugarcane cultivation areas on the land suitability map (5,287 km2) were mostly cultivated in the S3 class (2,593 km2, 49.0%), followed by 43.2% (2,282 km2) of the S2 class and 6.7% (353 km2) of the S1 class. In addition, potential areas for sugarcane cultivation were analyzed to expand sugarcane cultivation areas and increase yield. It was observed that potential areas in the S1 class for sugarcane cultivation were 6,519 km2. Nakhon Ratchasima province showed the most potential areas for sugarcane cultivation (35%, 2,272 km2), followed by Khon Kaen (11%, 725 km2), Chaiyaphum (9%, 592 km2), Udon Thani (8%, 519 km2) and Surin (7%, 441 km2). Such provinces may be encouraged to convert from currently cultivating other crops (rice, corn, or cassava) to sugarcane to optimally utilize their resources. Government policies should support the participatory knowledge transfer programs on sugarcane cultivation, sugarcane price guarantee and credit access. Furthermore, the S2 class areas could potentially enhance by implementing irrigation systems, groundwater management, and establishing small ponds to reduce the drought risk. This approach could increase the sugarcane yield and encourage these areas into the S1 class, which expand the S1 class areas by 2.7 folds over the existing S1 class areas. The land suitability map in this study analyzed only prototype sugarcane areas in Northeastern Thailand. The different suitable sugarcane crop growth in this work can be used for crop management practices such as putting fertilizer, water management and other human controls. Therefore, future research should be studied on a larger scale covering the whole country to improve the accuracy of the map for decision-making and dealing with global climate change situations. Other factors should be incorporated to consider for mapping sugarcane land suitability, such as distance from groundwater, LU/LC, soil moisture, and climate data. Artificial Intelligence (AI) and machine learning approaches should be applied in the criteria classification to identify the misclassification and improve the suitability of land in further studies.
