Big Data in Indian Agriculture D. Rama Rao Director, NAARM

Agricultural Data Sources  Farmers portals (~100TB)  Research data (~200TB)  Bioinformatics (~300TB)  Remote sensing (~100TB)  Weather, Remote sensing (~500TB)  Government departments (~10TB)  Communication & Media (~200TB)  Agribusiness (~10TB)  Many new sources  Features of above (~1500TB)  Variety (numerical, text, images, etc)  Velocity (daily, seasonal, annual etc.)  Veracity (regional differences, highly volatile)  Datasets are large and many cases not structured

Big Data Analytics Research Models and Decision Tools Public Data Sources Short term weather Longer term climate National soil database Digital elevation models Markets info. Regional and national inventories etc., Integrated farm models Crop growth models Soil-water balance models Machinery selection algorithms Best management practice evaluators Precision Agriculture Farm optimization tools etc., Private Data and Inputs Machinery and labor availability Crop rotations Marketing strategies Private inventories Remote sensing images Input prices Yield maps Crop input maps etc.,  Improving Farmer Decisions  Advancing Research Methods  Enabling Improved Policy Agricultural Big Data

Bioinformatics : Complex Genetic Interactions Genotype Environment Phenotype Data processing tools getting more and more sophisticated

Repositories in KRISHI (krishi.icar.gov.in) Technology Repository Experimental Data Repository Survey Data Repository Observational Data Repository Publication Repository Geo portal

Challenge-1: Bioinformatics Next-Generation Sequencing (NGS) platforms have exponentially increased the rate of biological data generation in the last two years For a large genome, DNA data can occupy many terabytes, and completing the genome sequence require months of computation on supercomputer A typical crop genetic dataset might include several million genetic markers controlling poly genes of various germplasm Data accumulating in computers and servers around the world concerns over privacy and security Improvements in computational infrastructure would permit more rapid analysis and enhance the impact

The diverse microbial communities (microbiome) in plant and the intestinal tracts of animals and humans Big Data analyses of the microbial populations provide a view into their millions of genes and gene products, and their mostly unknown intercommunications with host tissues. This would lead to better diet formulations, enhanced early life immunity, and reduced food safety concerns, disease resistance, etc The explosion of metagenomic knowledge has only begun to be explored for our soil and water resources Challenge-2: Microbes & Metagenomics

Drought Monitoring A system could integrate soil, weather, or satellite data, and models for parameters such as drought with locally collected environmental, genetic, and phenotypic data into a common framework

Studies that measure the cause of interactions among the crop, soil, water, weather, climate, and management differences are complex. Impact of environmental conditions on agricultural systems often yield conflicting results at different locations Vast need and scope for forewarning pest and disease occurrences well in advance and in a dynamic mode Using Big Data approaches, “Modeling and the ability to combine data from different sources, promises to revolutionize understanding of processes affecting management of natural resources” and thereby making Indian Agriculture CLIMATE SMART With availability of Big Data, drought monitoring can help in evolving suitable policy formulations Challenge-3: Environmental Modelling

Challenge-4: Market Intelligence For a number of commodities, online data is available from more than 1500 markets and huge data with high frequency is generated from Forward and Spot Markets Key challenges are integration of various data from markets and environmental conditions on agricultural systems would pave way for location specific assessment of risk, insurance, forecasting of demand and supply changes, etc for timely decision making by farmers, policy makers and other stake holders Forewarning food security threats

Challenge-5: Farm ERPs & Agri Portals With rising automation and access to IT, a variety of decision models are providing automation, knowledge and information services to farmers Big Data capability help Integration of farmers to agri-value chains through new markets for agricultural commodities. This help in evolution of farmers’ based DSS and also access to online and e-markets in food commodities

End Note Big Data offers tremendous hope in Indian agriculture. However, the implementation is likely to be “bumpy” and sporadic and may take quite long to realise substantial benefits Big Data has the potential to create the next major technological “sea change” in agriculture The technology may change the “balance of power” in the agri-food value chain There will be “winners” and “losers” with the new technology Ownership and control of data will be of concern