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Big Data and Digital Augmentation for Accelerating Agroecological Intensification
The document outlines ICARDA's strategies for enhancing agricultural productivity through big data and digital technologies aimed at promoting agroecological intensification. It emphasizes the need for integrated systems and targeted interventions to improve resource use efficiency, resilience, and sustainability in farming practices. Key developments include real-time mapping of rice-fallow dynamics and the implementation of machine learning and geotagging for improved agricultural decision-making.
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Big Data and Digital Augmentation for Accelerating Agroecological Intensification
- 1. International Center forAgricultural Research in the Dry Areas icarda.org cgiar.org A CGIAR Research Center Big Data and Digital Augmentation for Accelerating Agroecological Intensification Biradar C, Ghosh S, Singh, R., Sarker A, Low, F., Kumar S, AlShama K, Attasi L, Amri A, Nangia V, Saharawat Y, Chandra P, Gumma M, Behera M, Sahoo R, Rathod A, Winston, W, Gaur A, Koo J, Ravan R, Aly, A and Werry J Innovations for economically and ecologically viable solutions 13th ICDD, Jodhpur
- 2. - more cropper drop - in a inch of land and a bunch of crop Increased land, water and system productivity while safe guarding the environmental flows and ecosystem services Data driven decision (space & time) for better strategy for investment, intervention, implementation and impact -water focus -multi dimensions -integrated systems Ecological intensification Target specific interventions Bridging the gaps Inputs use efficiency Agricultural policy Halt degradation Technology scaling - food and nutritional security - resilience and risk reduction - agro-ecosystem sustainability - adaption and mitigation - citizen science and collective actions - equity in trade, social security and stability Resilient Agroecosystems <<<more health per acre>>>
- 3. icarda.org Strategic Research Priorities GeneticResources: Mining crop diversity to develop germplasm resistant to heat, drought, cold, disease, higher nutrients; International public goods (open access) Adaption to Climate Change: Conventional and molecular breeding to develop climate-smart crops and livestock Building resilience: Integrated crop-livestock farming systems to address economic, social, and environmental conditions Promoting value chains, policies: Agriculture as an income- generating business for many poor smallholder households Enhancing water, land productivity: Rainfed, irrigated, and agro-pastoral farming; Reversal of environmental degradation; Enhance intensification Big Data ICTs Adoption of the SRPs in operational projects New 9: 5 SRPs + 4 CCTs
- 4. Earth Observation andBIG DATA… River Flow Hydrology Energy Livestock Delta Communication Agriculture Groundwater Water use Floods Drought Horticulture Weather Agro-biodiversity Forestry Pastures Agro- ecosystems Inclusively integrated Agroforestry
- 6. Cross-cutting disciplines andtrade offs
- 7. Digitization of farmingsystems Geo-Tagging Satellite data Crop data Climate data Soil data Water data Topography Demography Ecological data … Mapping Monitoring Targeting Estimating Forecasting Warning Lending Insurance Value chains Carbon-Credits Data Layers Computation Applications AI ML RF IOT Scalability Algorithms Biggest drivers research & outreach data Geo-taging and Agro-tagging Big data and Citizen science driven
- 8. AI ML Meta Analytics Big Data @ genetics,chemistry, weather, agronomy, trade… Inclusive Agroecosystems Demand driven Sustainable options Big Data is in everything and everywhere
- 9. icarda.org 2000 to 2018 Tracingthe changes to target interventions Nile Delta
- 10. Quantification of FarmingSystems @ multiple-scalesDigitalAgPlatform
- 11. Build resilient agroecosystems Ecological intensification Financial inclusion Resilientcropping systems better integration of crops, livestock, fish, trees & people Optimizing resource use by integrated approach compound intensification in cereal based systems Right crop at right place and time Better livelihoods 1. Functional productivity 2. Yield & Rotation 3. Crop growth 4. Incentives Pixel/Farm/Parcel A single entity for each & every developmental entry point 30m 10m 1.0m 0.3m Open source 3 days revisit <Biggest drivers Agreements Evolving areas in Big Data Science in inclusive Agriculture Paradigm shift to functional systems for ecologically and economically viable options
- 12. Better integration Bettermeasurements Better modelling Resilient Systems Rebuilding functional productivity is the key to exponential efficiency and growth in the world largest and oldest industry which has capacity fix most issues 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2018 Monitoring fieldsImaging the past, (re)construct field´s history, retrospective assessments 2019 Moving from narrow sense economic benefit to a new ecologically sound functional model for well being…
- 13. One use case#1: Sustainableintensification of rice-based systems in India
- 14. Fallows in Doublecropped area Fallows in Single cropped area Dynamics of rice fallow systems in target districts, West Bengal, India
- 15. 15 #/km2 Dynamics of CroppingSystems ▪ Integrated Agro-Ecosystems ▪ Sustainable Intensification and Diversification ▪ Input Use Efficiency-Conservation Agriculture ▪ Thematic Land-Water-Climate Resilience Agricultural Intensification Cropping Intensity Increase in Arable Land 72% 21% 7% Biradar and Xiao, 2009 Length of the crop fallows, start-date, end-date (Biradar et al., 2015)
- 17. 17 Mapping Rice-fallow dynamicsfor sustainable intensification From 2000 to current (real-time mapping) Mapping Realtime Rice- Fallows Soil Moisture and Water Harvesting Variety Suitability Agro-Tagging Food Legumes
- 18. 0.1 0.3 0.5 0.7 0.9 0.1 0.2 0.3 0.4 0.5 fitted EVI fittedNDVI EVI NDVI Linear (fitted EVI) Linear (fitted NDVI) Quantification of Rice Fallow Dynamics in Real time Powered by Moving from static mapping to real-time analytics
- 19. Oct 2018 DryMoist Wet Water Real-time rice fallows Real-time Soil moisture Static map Static Rice fallows Quantification of Rice Fallow Dynamics in Real time
- 20. Oct 2018 Nov2018 Dry Moist Wet Water Real-time rice fallows Real-time Soil moisture Static map Static Rice fallows Quantification of Rice Fallow Dynamics in Real time
- 21. Oct 2018 Nov2018 Dec 2018 Dry Moist Wet Water Real-time rice fallows Real-time Soil moisture Length of rice fallows in 2018/2019 Quantification of Rice Fallow Dynamics in Real time <30 days 31-60 61-90 91-120
- 22. Oct 2018 Nov2018 Dec 2018 Jan 2019 Dry Moist Wet Water Real-time rice fallows Real-time Soil moisture High Medium Low NS Suitable areas for Lentil in 2018/2019 Quantification of Rice Fallow Dynamics in Real time
- 23. 23 Rice fallows Rice fallows Ricefallows Rice fallows Rice fallows Rice fallows Rice fallows Gray to Green
- 24. 24 Gray to Green •Rice fallow under pulses (no irrigation, zero tillage) • Increased income (2-3 times) • Increased recourse efficiency • Rebuilding soil and biota • Better nutrition and health
- 25. 25 Rice fallows underpulse cultivation in this season (Today Feb 12, 2019) Monitor the progress from the space
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- 28. Key developments 1.Mapping seasonaldynamics of rice paddy, rice fallows and rice fallow vegetation dynamics from 2000 onwards using Machine Learning and Google Earth Engine 2.Multi-sensors data fusion to map at different scales and improve accuracy of the maps and database 3.Developed ODK based Geotagging facility for e-Data collection and data aggregation for automate the classification 4.Developed trade off for RS based operationalization of rice fallow mapping 5.Integration of microwave data for overcoming clouds during peak growing season map the yield and related gaps 6.Mapping Lentil and Mung Bean suitability in rice fallows 7.Mapped rain water harvesting zones/site for supplemental irrigation for legumes in rice fallows during off-season 8.Developed geodatabase and web analytics- halfway towards building rice fallow information system for India http://geoagro.icarda.org/intensification/ GeoAgro, BigData & ICTs in Rice fallow intensification UAV data (15cm)
- 29. Mapping Rice-fallow inOdisha CGIAR Centers (ICARDA, IRRI, ICRISAT) Collective efforts l o w h i g h Source: ICRISAT, Gumma, M Source: IRRI, Chandna, P
- 30. Smart Farming SystemsPlatform for sustainable intensification On the fly demand driven query and cluster analysis Cadastral, Object & Pixel based Biophysical and socio-ecological Machine Learning Crop types, crop intensity, rotation, fallows, crop stress, AET-l8, soil moisture- Citizen-Science Cellphone feedback Direct Access and Markets/Business Precision decision delivery at farm scales and feedback Crowdsource, OA, Cloud Computing at Farm Scale Precision-Decision Citizen Science Community of Practices Farming Stakeholders EOSAWS AI NN RF ML Farm Specific Interventions Right Time Right Place
- 32. Thank You c.biradar@cgiar.org Production followsfunctions Building functional feedback system through integration of crops, trees and animals Chandrashekhar Biradar Head-Geoinformatics Unit Principal Scientist (Agro-Ecosystems)