A training workshop for data enumerators on Estimate Aquaculture Production and Value Chain Performance in Bangladesh held.

প্রকাশিতঃ ৮:০৭ পূর্বাহ্ণ | ডিসেম্বর ০৮, ২০২০

Din Mohammed Dinu from BAU.

A training workshop through webinar 
has been inaugurated for data enumerators to implement the field research of the project ‘Harnessing Machine Learning to Estimate Aquaculture Production and Value Chain Performance in Bangladesh’  on Monday.
Bangladesh Agricultural University research system director Md Abu Hadi Noor Ali Khan was present as chief guest in the inaugural session of the workshop where the Asia Regional Coordinator of Feed the Future Innovation Lab for Fish and former Director General of BFRI, Dr. M. Gulam Hussain was present as the Guest of Honor. The US Principal Investigator (PI) of the project, Dr. Ben Belton, Associate Professor, Michigan State University, USA, elaborated on the purpose, activities, and expected outcomes of the project. The co-principal investigator of the project partner from WorldFish, Dr. Khondker Murshed-Jahan, Senior Specialist-MEL was present in the session. Hazrat Ali, PhD Fellow under the project spoke about the digital data collection method of the field team. Bangladesh Agricultural University,  aquaculture department professor Dr. Mohammad Mahfujul Haque,  also Principal Investigator (PI), Bangladesh of the project, presided over the opening ceremony of the training workshop.

Twenty data enumerators participated in the seven-day workshop in Nirala, Khulna. One tablet is provided to each enumerator to collect data from fish farmers and other value-chain stakeholders through digital survey. The project is a collaborative project among Michigan State University, Bangladesh Agricultural University, WorldFish and funded by the USAID under Feed the Future Innovation Lab for Fish.

The project is comprised of three components as surveys; remote sensing; and capacity building. Firstly, the objective of the project is to identify emerging technologies and innovative practices in aquaculture value chains and pilot digital extension approaches that accelerate their adoption, while reducing transaction costs and time associated with traditional forms of technical research and extension. Second, use machine learning to automate extraction of data on ponds from satellite images and integrate with georeferenced survey data to accurately estimate fish production, economic value, and employment to improve the accuracy of official statistics and enhance capacity to effectively target investments and regulation. Third, build organizational and individual capacity in Bangladesh for conducting rigorous research on socio-economic and spatial dimensions of aquaculture, and contribute to a more enabling environment for fostering sustainable aquaculture growth.