# GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
## 1 Introduction  
Myanmar's agriculture-based economy has resulted in an abundance of biomass resources. Every year, Myanmar produces more than 20 million tons of paddy [1]. In addition, future population growth may increase the need for food production, quickly increasing the possibility of crop residue creation. Fuelwood, wood by-products, rice husks, straw, coconut, sugarcane, palm oil, cassava, maize, manure from livestock, and poultry sector by-products are the primary biomass sources in Myanmar [2]. The traditional practices of utilizing biomass in Myanmar are cooking, heating, animal feeding, dumping, and open burning [1]. However, current management practices frequently negatively affect the environment, society, and economy. According to the studies, Asian countries accounted for one-third of global biomass greenhouse gas emissions due to the open burning of agricultural wastes [3]. Correspondingly, the Food and Agriculture Organization of the United Nations explained that Myanmar has a carbon dioxide equivalent [CO\({}_{2}\) eq] emission value of 2124.5 for crop residue [4]. According to the waste management strategy in Myanmar, one of the principles is to promote the most efficient use of resources, including resource recovery and waste avoidance. Myanmar's climate change master plan also aims to develop practices for mainstreaming GHG emission reduction into agriculture [5]. Energy utilization of residualbiomass becomes an optimal solution for sustainable waste management to develop the country's energy security as renewable energy [6]. Agricultural residues, such as rice husk and rice straw, have been used worldwide to produce renewable energy [7]. Therefore, bioenergy conversion of crop residues has become one of the effective strategies for the sustainable management of crop residues in Myanmar. Scientific studies have demonstrated that it is possible to generate a wide variety of bioenergy and other applications from biomass residues and waste from raw goods slanted, processed, and consumed [8]. The technologies for energy conversion of biomass residue are mainly classified under thermochemical or biological methods [9]. There are some factors that influence the choice of a conversion technology to be applied to biomass in terms of quantity of the biomass feedstock, availability, choice of end-products, process economics, and environmental issues [10]. The combustion method is widely used because it is easy to implement into thermal or electrical mechanical energy. The energy content of crop residues depends on the available feedstock of crop residue and its heating value [11]. One popular method for estimating biomass feedstock from crop residues uses the residue-to-product ratio l [12; 13]. [PERSON] et al. calculated the crop residues using crop yield and total production of crops [14]. The entire crop of straws was estimated using the economic crop yield and the straw production coefficient [15]. [PERSON], [PERSON] et al. calculated the energy potential of crop residue using the official statistic of production data, residue-to-crop ratio, the lower heating value, and conversion factors [16]. [PERSON] et al. estimated the electricity generation of biomass using data from feedstock of crop residues, latent heat value, moisture content, and energy efficiency [17]. The spatial distribution of energy potential and feedstock crop residue can be identified using Geographic Information System (GIS). The goal of cluster analysis is to determine if a dataset comprises a single group or contains subgroups to select how many clusters are in the data [18; 19]. [PERSON] et al. considered that reducing communication and transportation expenses is the primary justification for clustering because geographic proximity enables businesses to access a big pool of suppliers [12]. For GIS cluster mapping, [PERSON] et al. used the ArcGIS tools of Spatial Autocorrelation Moran's I method, and Getis-Ord GI' function and density estimation using Kernel density tool to find the road accident hotspots [20]. [PERSON] et al.