Big Data and Climate Smart Agriculture - Status and Implications for Agricultural Research and Innovation in India

  • N.H. Rao University of Hyderabad


Climate change will increase the vulnerability of agricultural production systems, unless scientists and farmers reorient their present approaches towards making them climate smart or climate resilient. The integration of recent developments in big data analytics and climate change science with agriculture can greatly accelerate agricultural research and innovation for climate smart agriculture (CSA).  CSA refers to an integrated set of technologies and practices that simultaneously improve farm productivity and incomes, increase adaptive capacity to climate change effects, and reduce green house gas emissions from farming. It is  a multistage, multiobjective, data-driven,  and knowledge  based approach to agriculture, with the farm as the most fundamental unit for both strategic and tactical  decisions. This paper explores how big data analytics can accelerate research and innovation for CSA. Three levels at which big data can enhance farmer field level insights and actionable  knowledge for the practice of CSA are identified: (i) developing a predictive capability to factor climate change effects to scales relevant to farming practice, (ii) speeding up plant breeding for higher productivity and climate resilience, and  (iii) delivery of customized and prescriptive real-time farm knowledge for higher productivity, climate change adaptation and  mitigation. The state-of-art on big data based approaches at each of  the three levels is assessed. The paper also identifies the research and institutional challenges, and the way forward for leveraging big data in research and innovation aimed at climate smart agriculture in India.

Author Biography

N.H. Rao, University of Hyderabad
KL Rao Chair Professor in Geospatial Sciences


AgFunder (2016) AgTech Funding Report 2015, 59pp
Alder JR and Hostetler SW (2015) Web based visualization of large climate data sets, Environmental Modelling & Software Vol 68 175-180

Altpeter F, Springer NM, Bartley LE, Blechl AE, Brutnell TP, Citovsky V, Conrad LJ, Gelvin SB, Jackson DP, Kausch AP, Lemaux PG, Medford JI, Orozco-Cárdenas ML, Tricoli DM, Van Eck J,Voytas DF, Walbot V, Wang K, Zhang ZJ, Stewart Jr CN (2016) Advancing Crop Transformation in the Era of Genome Editing, Plant Cell, originally published online June 22, 2016, doi:10.1105/tpc.16.00196

Andrade-Sanchez,P, Gore MA, Heun JT, Thorp KR, Carmo-Silva AE, Andrew BD, French AN, Salvucci ME, and White JW (2014) Development and evaluation of a field-based high throughput phenotyping platform, Functional Plant Biology, Vol 41, 68–79

Arthur WB (2011) The second economy, Mckinsey Quarterly, October 2011, pp 1-9 ( the-second-economy ).

Assunção MD, Calheiros RN, Bianchi S, Netto MAS and Buyya R (2015) Big Data computing and clouds: Trends and future directions, Journal of Parallel and Distributed Computing, Vol 79-80, 3-15.

Antle JM, Jones JW, and Rosenzweig CE (2017) Next generation agricultural system data, models and knowledge products: Introduction, Agricultural Systems, 155: 179–185.

Azmak O, Bayer H, Caplin A, Chun M, Glimcher P, Koonin S and Patrinos A (2015) Using Big Data to Understand the Human Condition: The Kavli Human Project, Big data, Vol 3 No. 3, 173-188.

Bell DE, Reinhardt F, Shelman M (2016) The Climate Corporation, Harvard Business School Press, 44pp.

Benestad R (2016) Downscaling climate information, Oxford Research Encyclopedia, Climate Science (, Oxford University Press USA, 2016, 37 pp, DOI: 10.1093 / acrefore/9780190228620.013.27
Blake VC, Birkett C, Matthews DE, Hane DL, Bradbury P and Jannic J (2016) The triticeae tool box: Combining phenotype and genotype data to advance small-grains breeding, Plant Genome, Vol9, No.2, 10pp
( plantgenome2014.12.0099)
Bomgardner MM (2016) Transforming agriculture, again, Chemical and Engineering News, Volume 94, Issue 34 pp. 32-38.
Butler D (2013) When Google got flu wrong, Nature, 494, 155-56.
Campbell BM, Vermeulen SJ, Aggarwal PK, Corner-Dolloff C, Girvetz E, Loboguerrero AM, Ramirez-Villegas J, Rosenstock T, Sebastian L, Thornton P and Wollenberg E (2016) Reducing risks to food security from climate change, Global Food Security, 11, 34-43.

Capalbo SM, Antle JM, Seavert C (2017), Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making, Agricultural Systems, 155, 191-99.

Carbonell IM (2016) The ethics of big data in big agriculture, Internet Policy Review, Vol 5, Issue 1, pp 1-13.

Chaturvedi R.K., Joshi J., Jayaraman M., Bala G., and Ravindranath N.H (2012) Multi-model climate change projections for India under representative concentration pathways, Current Science, Vol 103m No. 7, 791-802

Chen S, Wu C and Yu Y (2016) Analysis of Plant Breeding on Hadoop and Spark, Advances in Agriculture, Vol 2016, Article ID 7081491, 6 pages

Davenport TH (2014) Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, Harvard Business School Press.

Dhar V (2015) The scope of machine learning and deep learning, Big Data Volume 3 Number 3, 127-129.

Dhar V (2013) Data science and prediction. Commun ACM. 2013;56:64–73

Edwards JD, Baldo AM and Mueller LA (2016) RICEBASE: a breeding and genetics platform for rice, integrating individual molecular markers, pedigrees and whole-genome-based data, Database Vol. 2016, 1-6.

Ekström M., Grose, M.R., and Whetton P.H (2015) An appraisal of downscaling methods used in climate change research, WIREs Clim Change doi: 10.1002/wcc.339

Faghmous JH and Kumar V (2015) Climate change: the case for theory guided data science, Big Data, Vol 2 No. 3, 155-163.

Fahlgren N, Malia AG and Baxter I (2015) Lights, camera, action: high-throughput plant phenotyping is ready for a close-up, Current Opinion in Plant Biology, Vol 24, 93-99.

Fan J, Han F and Liu H (2014) Challenges of big data analysis, National Science Review (China) 1: 293-314
FAO (2010) Climate-Smart Agriculture: Policies, Practices and Financing for Food Security, Adaptation and Mitigation, FAO, 49 pp
Fischer EM and Knutti R (2015) Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes, Nature Climate Change, 5, 560–564.

Ford JD, Tilleard SE, Berrang-Ford L, Araosa M, Biesbroekb R, Lesnikowskia AC, MacDonald GK, Hsu A, Chen C, and Bizikov L (2016) Big data has big potential for applications to climate change adaptation, Proceedings, National Academy of Sciences, vol. 113, No. 39, 10729–10732.

Gandomi A, Haider M (2015) Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management Vol 35, pp 137–144

Gilpin, L. (2014). How big data is going to help feed 9 billion people by 2050.

Ginsberg J, Mohebbi MH, Rajan, Pate S, Brammer L, Smolinski MS, and Brilliant L (2009) Detecting influenza epidemics using search engine query data, Nature 457, 1012-1014

Girvetz, E.H., E.P. Maurer, P. Duffy, A. Ruesch, B. Thrasher, C. Zganjar, 2013, Making Climate Data Relevant to Decision Making: The important details of Spatial and Temporal Downscaling, The World Bank, March 27, 2013

Glendenning CJ and Ficarelli PP (2012) The relevance of content in ICT initiatives in Indian agriculture, International Food Policy Research Institute (IFPRI), Discussion paper 01180, IFPRI, Washington DC, USA, 40 pp.
Global Harvest Initiative (2014) The 2014 Global Agricultural Productivity Report, 65 pp,
Halevy A, Norwig P and Pereira F (2009) The unreasonable effectiveness of data, IEEE Intelligent Systems, March-April 2009, pp 8-12.
Hardwick S., and Graven H.(2016) Satellite observations to support monitoring of greenhouse gas emissions, Grantham Institute Briefing paper No 16, March 2016, Imperial College, London, UK, 16 pp
Hendler J (2015) Data integration for heterogeneous datasets, Big Data, Vol 2, No. 4, 205-15.
ICF International (2016) Charting a Path to Carbon Neutral Agriculture: Mitigation Potential for Crop Based Strategies, ICF International, 1725 I Street, NW ,Washington, DC 20006, USA, 145 pp.

IP Pragmatics Ltd (2016) Gene Editing technology: market assessment and Intellectual property Landscape, 74 pp.

Jackson E (2016) The value of big data in agriculture: inputs, farming and processing, Editor Introduction , International Food and Agribusiness Management Review, Special Issue -Volume 19 Special Issue A, 5-6

Jagdish, HV (2015) Big Data and Science: Myths and Reality, Big Data Research, Vol 2, 49-52
Janssen SJC, Porter CH, Moore AD, Athanasiadis JN, Foster I, Jones JW, Antle JM (2017) Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology, Agricultural Systems, 155, 269-288

Jin X , Benjamin WW, Cheng X, Wang Y (2015) Significance and challenges of big data research, Big Data Research, Vol2, pp 59-64.

Jones HD (2015) Future of breeding by genome editing is in the hands of regulators, GM Crops & Food, 6:4, 223-232.

Jones JW, Antle JM, Basso B, Boote KJ, Conant RT, Foster I, Godfray CJ, Herrero M, Howitt RE, Janssen S, Keating BA, Munoz-Carpena R, Porter CH, Rosenzweig C, and Wheeler TR (2017) Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science, Agricultural Systems, 155, 255-268

Knowledge@Wharton (2014) Sustainability in the Age of Big data, Special Report, 16 pp.

Kole C, Muthamilarasan M, Henry R, Edwards D, Sharma R, Abberton M, Batley J, Bentley A, Blakeney M, Bryant J, Cai H, Cakir M, Cseke LJ, Cockram J, de Oliveira AC, De Pace C, Dempewolf H, Ellison S, Gepts P, Greenland A, Hall A, Hori K, Hughes S, Humphreys MW, Iorizzo M, Ismail AM, Marshall A, Mayes S, Nguyen HT, Ogbonnaya FC, Ortiz R, Paterson AH, Simon PW, Tohme J, Tuberosa R, Valliyodan B, Varshney RK, Wullschleger SD, Yano M and Prasad M (2015) Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects. Frontiers in Plant Science Vol 6: Article 563.

Kune R, Konugurthi P, Agarwal, Rao CR and , Buyya R (2016) The anatomy of big data computing, software practice and experience, Vol 46 (1), 79-105

Magnin C (2016) How big data will revolutionize the global food chain, Digital McKinsey August 2016, 6 pp.
Maciejewski R and Douglas MC (2016) Visualization for data science: adding credibility, legitimacy, and saliency, Big Data, Vol 4, No.2, 73-74

McKinsey Global institute (2013) Game Changers: Five opportunities for US Growth and Renewal, 172 pp

National Research Council. 2016. Attribution of Extreme Weather Events in the Context of Climate Change. Washington, DC: The National Academies Press.

National Science Foundation (2012) Core Techniques and Technologies for Advancing Big Data Science & Engineering (BIGDATA), Programme solicitation, NSF-12-499, National science Foundation USA, 17 pp

National Science and Technology Council (2016); The Federal big data research and development strategic plan, Executive Office of the President, National Science and Technology Council, USA, 45 pp.

Navarro P.J., Pérez F, Weiss J., and Egea-Cortines M.(2016) Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants, Sensors 16, 641; doi:10.3390/s16050641

Oertel C, Matschullat J, Zurba K, Zimmermann F, Erasmi S (2016) Greenhouse gas emissions from soils—A review, Chemie der Erde Vol 76, 327–352

Paustian K., Lehmann J., Ogle S., Reay D., Robertson G.P., and Smith P.(2017) Climate -smart soils, Nature, Vol 532, pp 49-57

Ransbotham S (2015) Coca-Cola's unique challenge: turning 250 datasets into one; MIT Sloan Management Review, 6pp,

Rehman MN and Esmailpour A (2016) A Hybrid Data Center Architecture for Big Data, Big Data Research 3, 29–4

Rose DC, Sutherland WJ, Parker C, Lobley M, Winter M, Morris C, Twining S, Ffoulkes C, Amano T, Dicks LV (2016) Decision support tools for agriculture: Towards effective design and delivery, Agricultural Systems 149, 165–174.

Rosenstock TS, Lamanna C, Chesterman S, Bell P, Arslan A, Richards M, Rioux J, Akinleye AO, Champalle C, Cheng Z, Corner-Dolloff C, Dohn J, English W, Eyrich AS, Girvetz EH, Kerr A, Lizarazo M, Madalinska A, McFatridge S, Morris KS, Namoi N, Poultouchidou N, Ravina da Silva M,Rayess S, Ström H, Tully KL, Zhou W. (2016). The scientific basis of climate-smart agriculture: A systematic review protocol. CCAFS Working Paper no. 138. Copenhagen, Denmark: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Available online at:

Rosenzweig C, Jones JW, Hatfield JL, Ruan AC, Boote KJ, Thorburn P, Antle JM, Nelson GC, Porter C, Janssen S, Asseng S, Basso B, Ewert F, Wallach D, Baigorria G, Winter JM (2013) The Agricultural Model Intercomparison and Improvement Project (AgMIP):Protocols and pilot studies, Agricultural and Forest Meteorology Vol 170, 166–182

Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C,Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N, Neumann K, Piontek F, Pugh T, Schmid E, Stehfest E, Yang H, and Jones JW (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison, Proc Natl Acad Sci, U S A., 111(9), 3268-73
Runck BC, Kantar MB, Jordan NR, Anderson JA, Wyse DL, Eckberg JO, Barnes RJ, Lehman CL, DeHaan LR, Stupar RM, Sheaffer CC and Porter PM (2014) the reflective plant breeding paradigm : A robust system of germplasm development to support strategic diversification of agroecosystems, Crop Science, Vol 54, 1939-1948.

Scheben A and Edwards D (2017) Genome editors take on crops: Genome editing technologies may help to enhance global food security, Science, Vol 355, Issue 6330, 1122-23.

Schönberger VM and Cukier K (2013) “Big Data: A Revolution That Will Transform How We Live, Work, and Think”, Houghton Mifflin , Harcourt Publishing, New York

Shcherbak I, Millar N and Robertson GP (2014). Global meta-analysis of the nonlinear response of soil nitrous oxide (N2O) emissions to fertilizer nitrogen. Proc. Natl Acad. Sci. USA Vol 111, 9199–9204 (2014).

Shriffin RM (2016) Drawing causal inference from big data, Proc. National Academy of Sciences, vol. 113 no. 27, 7308–7309

Shirsath P.B., Aggarwal P.K., Thornton P., Dunnett A (2017), Prioritizing climate-smart agricultural land use options at a regional scale, Agricultural Systems, 51, 174–183

Singh A, Ganapathysubramanian B, Singh AK, and Sarkar S (2016) Machine learning for high-throughput stress phenotyping in plants, Trends in Plant Science, Vol. 21, No. 2, 110-23.

Snijders, C., Matzat, U., & Reips, U. D. (2012). Big data: Big gaps of knowledge in the field of Internet science. International Journal of Internet Science, 7, 1–5.

Sonka S (2016) Big data Characteristics, Big Data Volume 19, Special Issue A, International Food and Agribusiness Management Review, 7-13 .

Sprink T., Eriksson D., Schiemann J., Hartung F.(2016) Regulatory hurdles for genome editing: process- vs. product-based approaches in different regulatory contexts, Plant Cell Reports (2016) 35:1493–1506

Steenwerth KR, Hodson AK, Bloom AJ, Carter MR, Cattaneo A, Chartres CJ, Hatfield JL, Henry K, Hopmans JW, Horwath WR, Jenkins BM, Kebreab E, Leemans R, Lipper L, Lubell MN, Msangi S, Prabhu R, Reynolds MP, Solis SS, Sischo WM, Springborn M, Tittonell P, Wheeler SM, Vermeulen SJ, Wollenberg EK, Jarvis LS and Jackson LE (2014) Climate-smart agriculture global research agenda: scientific basis for action. Agriculture & Food Security, 3:11, 39 pp;

Stephens ZD, Lee SY, Faghri F, CampbellRH, Zhai C, Efron MJ, et al. (2015) Big Data:Astronomical or Genomical?. PLoS Biol 13(7): e1002195. doi:10.1371/journal.pbio.1002195

Thrasher B, Xiong J, Wang W, Melton F, Michaelis A, and Nemani R (2016) Downscaled climate projections suitable for resource management, EOS, Transactions American Geophysical Union 94 (37), 321-323.

UNFCC (2015) Report on the structured expert dialogue on the 2013–2015 review, FCCC/SB/2015/ INF.1 1 (

USDA (2015) USDA Roadmap for plant breeding, USDA, March 2015, 36 pp.
Varshney RK (2016) Exciting journey of 10 years from genomes to fields and markets: Some success stories of genomics-assisted breeding in chickpea, pigeonpea and groundnut, Plant Science, Vol 242 pp 98–107.
Voytas DF, Gao C (2014) Precision Genome Engineering and Agriculture: Opportunities and Regulatory Challenges. PLoS Biol 12(6): e1001877. doi:10.1371/journal.pbio.1001877

Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6):e0156720. doi:10.1371/journal.pone.0156720

World Meteorological Organization (2013) The Global Climate 2001-2010: A Decade of Climate Extremes, WMO-No.-1103, 118 pp

World Bank (2013). Turn Down the Heat: Climate Extremes, Regional Impacts, and the Case for Resilience. A report for the World Bank by the Potsdam Institute for Climate Impact Research and Climate Analytics. Washington, DC: 254pp.
World Bank (2016) World Bank Big Data Innovation Challenge Join us in rethinking climate resilience through big data solutions; Challenge Handbook; 18 pp.
World Economic Forum (2016) The Global Risks Report, 2016; 11th Edition, World economic Forum, Geneva, Switzerland, 103 pp.
Wolfert, S. Ge L., Verdouw C., Bogaardt M-J.(2017) Big Data in Smart Farming – A review, Agricultural Systems, vol 153, pp 69-80.

Xiong J, DingJ and Li Y (2015) Genome-editing technologies and their potential application in horticultural crop breeding, Horticulture Research 2, 15019; doi:10.1038/hortres.2015.19.

Zhang, J. Naik HS, Assefa T, Sarkar S, Chowda Reddy RV, Singh A, Ganapathysubramanian B, and Singh AK (2017) Computer vision and machine learning for robust phenotyping in genome-wide studies. Scientific Reports, 7, 44048; doi: 10.1038/srep44048.
How to Cite
RAO, N.H.. Big Data and Climate Smart Agriculture - Status and Implications for Agricultural Research and Innovation in India. Proceedings of the Indian National Science Academy, [S.l.], feb. 2018. ISSN 2454-9983. Available at: <>. Date accessed: 18 mar. 2018. doi:
Review Articles


Climate smart agriculture, big data, food security, innovation