Value Chain Analysis of Mango in Bangladesh
Keywords:Agriculture, Value Chain, Mango, Bangladesh, Supply Chain.
Bangladesh has immense potential in agricultural sector given the abundance of mango production due to its healthy soil and weather. Therefore, the present study was conducted in value chain analysis of mango that consisting value adding activities in supply chain from input suppliers to end users. The necessity of value chain analysis is to figure out exactly how much value and cost added in each stage of supply chain. Data were collected regarding supply chain from secondary sources and also from a few conducted interviews which were generated from Bangladesh Agricultural Research Institute (BARI) and were analyzed statistically. Mango value chain mapping of Rajshahi and Khagrachari districts gave a clear understanding of how the cost were added in different stages of the supply chain. There are several types of supply chain of mango in hill district including supplied rank of importance showed to understand the costing and importance that could be different in various supply chain systems. Data indicated that the average cost of mango cultivation in a year was Tk. 133,889 per hectare of which 57% was variable cost and the 43% was fixed cost. The farmers received an average Tk. 175,244 per hectare as net return and Tk. 233,039 as gross margins from mango cultivation. The study also found that only the intermediaries in supply chain were not responsible for cost increasing but the farmers were responsible for the cost increase for not following the agricultural specialist’s suggestions. The necessity of value chain analysis of mango may help the business persons, government and non-governmental experts, policy makers and academicians who were directly or indirectly involved in agribusiness to understand how the cost can be minimized. Furthermore, this baseline study will help in designing and implementing appropriate strategies to promote mango value chains in Bangladesh. However, researchers are in need of extensive or in depth data, which are not available now but will be in future for better conclusive results.