Paper: II
For Prelims: Big Data.
For Mains: Government Policies and Interventions for Development in various sectors and Issues arising out of their Design and Implementation.
 
 
Why in the News?

• In the wake of Corona Virus Pandemic all sectors of Economy has got impacted, agrarian sector is not the sector that has been left untouched from this pandemic.  To help India Agriculture, big is considered as the next step in tackling stagnation in agriculture growth.
• In agriculture, big data is often viewed as a combination of technology and analytics that can collect and compile novel data and process it in a more useful and timely way to assist decision making.

What is Big Data in Agriculture?

• Big Data: It is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity.
• In Agriculture these big data is being used to improve the farming practices right from farmland to the food on the dinner table.

Importance of Big Data use in Agriculture

• Smart Farming: Big Data is expected to have a large impact on Smart Farming which involves the whole supply chain. Smart sensors and devices produce big amounts of data that provide unprecedented decision-making capabilities.
• Analyzing soil types and fertility levels, to predict which seeds and fertilizers to use, with the help of sensors on fields and devices installed in machines.
• Predictive Analysis: Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. 
• Predicting climate conditions and enabling apt weather forecasting by satellites and devices on aerial and ground levels.
• Analyzing the crop and preventing spoilage and potential diseases by providing accurate information via unmanned aerial vehicles, like drones.
• Shifting the Roles: Big Data is expected to cause major shifts in roles and power relations among traditional and non-traditional players. Governance and business models are key issues that will be changed by adopting big data benefitting each strata of Agrarian Community.
• Monitoring and evaluating supply chains via RFID tracking systems.
• Increasing crop yield and optimizing resource use by integrating information like weather conditions, soil types, and market opportunities.

Apprehensions regarding use of Big Data in Agriculture:
Moral and Ethical Aspect:

• Moral and ethical questions about access, cost, scale and support, which will determine whether it will ever be possible, or indeed desirable, for all farms to be ‘big data enabled’, or whether it is an inevitable progression of modernization in agriculture.

 Big data for large farmers:

• Another valid reason is being pointed out that, big data is for big farmers with large land holdings. In India marginal and small land holding constitutes more than 80% of the farming community.

 Technical and organizational ones:

• Data in Smart Farming can be created but there needs to be a clear return on investment. On the revenue side, there is a challenge to make solutions affordable for farmers, especially for those in developing countries.
• On the costs side, the challenge is to automate data acquisition in such a way that there are virtually no costs for the farmers using big data for their own need.

Conclusion:

• Big data can help every section of farmers provided, industry, research providers and government work consciously on transparency and inclusivity and choose a path for the future of big data in agriculture and subsequently to make appropriate changes in policy and finance.
• These big data provide access to explicit information and decision-making capabilities at a level that was not possible before. Considering the climate change, big data can help in shaping production of crops and choosing the right crop.
• The promise of Big Data in agriculture is alluring, but the challenges above have to be addressed. Although there are certainly technical issues to be resolved so firstly focus should be on the governance issues that were identified and design suitable models because these are currently the most inhibiting factors.