A Value Chain can be described as the full range of activities which are required to bring a product or service from conception, through the different phases of production (involving a combination of physical transformation and the input of various producer services), delivery to final consumers, and final disposal after use (Kaplinsky and Morris, 2000). Some Value Chains even favour stakeholders, who are bigger in scale, as compared to other vulnerable stakeholders. One such vulnerable tribal fishing community resides in a hamlet called Jamjhadpada, near Gorai village, in Borivali (W), Mumbai. The primary occupation of this community is to catch Mud Crabs (scylla serrata) from mangroves in the nearby creek, and sell them in local market. Apart from lacking basic necessities, e.g., electricity connection/ water supply, this community also has vulnerable livelihoods due to seasonality of mud crab catch size as well as price, because of which they are not able to capitalize upon the demand-supply gap for marketable size mud crabs. Over the years, urban water pollution and unregulated harvesting has also significantly diminished mud crab stocks here.
An attempt is being made to design an inclusive value chain using sustainable mud crab aquaculture as a tool, which in addition to economic growth, should also assist in promoting sustainable livelihoods practices, gender justice, & creation of community based enterprises.
Aquatic Livelihoods is building a prototype for 6-month grow-out culture for scylla serrata, using a recirculating aquaculture system. Sensors for real-time water quality parameters monitoring are part of this set-up, including those for Dissolved Oxygen, Ammonia, pH, Salinity, Temperature, and these sensors are integrated with our innovative controller cum communicator device, which monitors sensors parameters, logs data, controls locally as well as communicates over internet, with the server. Institute of Electrical and Electronics Engineers describes 'Internet of Things (IoT)' as "A network of items- each embedded with sensors- which are connected to the Internet" (IEEE, 2014). In the same vein, 'Machine Learning (ML) algorithms' build models of real world data in order to make predictions or decisions. In this scenario, the Quality Parameter Monitoring sensors would serve as the eyes and ears for an ML algorithm in the IoT framework. The data from these sensors is uploaded to an offsite machine learning platform where data analytics and machine learning algorithms would tease out meaningful recommendations from the stream of sensor data. The algorithms are designed to generate actionable intelligence and recommendations for individual crab farmers.
The objective behind this prototyping is to design frugal technological innovations/ solutions, with minimum onsite skilled supervision, which can be put in the backyard so that these can be replicated across thousands of marginalized fishing households.