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• W2735880304 abstract "No AccessJun 2017Using ICT for Remote Sensing, Crowdsourcing, and Big Data to Unlock the Potential of Agricultural DataAuthors/Editors: Josh Woodard, Mechteld Andriessen, Courtney Cohen, Cindy Cox, Steffen Fritz, Drew Johnson, Jawoo Koo, Morven McLean, Linda See, Tarah Speck, Tobias SturnJosh WoodardSearch for more papers by this author, Mechteld AndriessenSearch for more papers by this author, Courtney CohenSearch for more papers by this author, Cindy CoxSearch for more papers by this author, Steffen FritzSearch for more papers by this author, Drew JohnsonSearch for more papers by this author, Jawoo KooSearch for more papers by this author, Morven McLeanSearch for more papers by this author, Linda SeeSearch for more papers by this author, Tarah SpeckSearch for more papers by this author, Tobias SturnSearch for more papers by this authorhttps://doi.org/10.1596/978-1-4648-1002-2_Module15AboutView ChaptersFull TextPDF (1.7 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: Estimates that by 2050, the global population could reach 9 billion, with the growth concentrated in poorer countries, particularly the low-income countries of Sub-Saharan Africa. Agricultural productivity will need to double to meet everyone’s needs for food, so producers must increase yields and cropping intensity, improve livestock productivity, and quite possibly diversify their economic activities on and off of the farm, all of which can benefit from revolutionary changes in information and communication technology (ICT), including (1) using multispectral satellite images and energy surface balance models to calculate crop and water productivity; (2) crowdsourcing supplier data via mobile phone; (3) combining gaming and crowdsourcing to identify and monitor cropland; (4) generating open access, spatially explicit data sets, and analyses for more productive farming and better livelihoods in Africa; (5) using the International Life Sciences Institute (ILSI) Crop Composition Database; and (6) using big data to provide localized weather and agronomic information to producers. ReferencesAlba, R, A Phillips, S Mackie, N Gillikin, C Maxwell, P Brune, W Ridley, J Fitzpatrick, M Levine, and S Harris 2010. “Improvements to the International Life Sciences Institute Crop Composition Database.” Journal of Food Composition and Analysis 23: 741–48. CrossrefGoogle ScholarBanham, Russ 2014. “Who Owns Farmers’ Big Data?” Forbes Brand Voice. http://www.forbes.com/sites/emc/2014/07/08/who-owns-farmers-big-data/. Google ScholarBilbao-Osorio, Beñat. 2014. The Global Information Technology Report 2014: Rewards and Risks of Big Data. World Economic Forum, Cologny. http://www3.weforum.org/docs/WEF_GlobalInformationTechnology_Report_2014.pdf. Google ScholarBIS Research. 2014. Global Precision Agriculture Market Analysis & Forecast (2015–2022) Technology (VRA, Soil Mapping, Yield Monitoring, Precision Irrigation, Others), Components and Systems. http://www.researchandmarkets.com/research/fk898n/global_precision. Google ScholarBujoreanu, Luda. 2013. “The Power of Mobile: Saving Uganda’s Banana Crop.” Information and Communications for Development (blog). http://blogs.worldbank.org/ic4d/the-power-of-mobile-saving-ugandas-banana-crop. Google ScholarBurns, Matt. 2015. “3D Robotics Taps Qualcomm for $50M Series C and Mobile Tech.” TechCrunch. http://techcrunch.com/2015/02/26/3d-robotics-taps-qualcomm-for-50m-series-c-and-mobile-tech/. Google ScholarCAC (Codex Alimentarius Commission). 2003. “Guideline for the Conduct of Food Safety Assessment of Foods Derived from Recombinant-DNA Plants.” CAC/GL 45-2003, Rome. Google ScholarClark, Helen. 2014. “How Big Data Is Helping Farmers Save Millions.” GizMag. http://www.gizmag.com/big-data-crops-climate-change/34400/. Google ScholarConor, Anthony. 2014. “Benefits and Limitations of Crowdsourcing Agricultural Data.” AGROAM (blog). http://www.agroam.org/blog/benefits-and-limitations-of-crowdsourcing-agricultural-data/. Google ScholarCrawford, John, John Hutchins, Richard Tiffin, and Alistair Stott. 2015. “AIMS: Putting the UK at Heart of the Big Data Revolution in Agriculture.” AgriTech Strategy (blog). https://agritech.blog.gov.uk/2015/03/27/aims-putting-the-uk-at-heart-of-the-big-data-revolution-in-agriculture/. Google ScholarCrimson Hexagon. 2011. “Twitter and Perceptions of Crisis Related Stress.” UN Global Pulse. http://www.unglobalpulse.org/projects/twitter-and-perceptions-crisis-related-stress. Google ScholarCTA. 2015. “Satellites and Mobile Phones Improve Crop Productivity in Sudan.” http://www.cta.int/en/article/2015-06-17/satellites-and-mobile-phones-improve-crop-productivity-in-sudan.html. Google ScholarDawson, Ross, and Steve Bynghall. 2012. Getting Results from Crowds: The Definitive Guide to Using Crowdsourcing to Grow Your Business. 2nd ed. Essen: Advanced Human Technologies. Google ScholarEssiet, Daniel. 2015. “Combating Challenges of Poor Data-driven Agriculture.” The Nation (Nigeria). http://thenationonlineng.net/combating-challenges-of-poor-data-driven-agriculture/. Google ScholarEuropean Space Agency. 2014. “Mobile App Provides Weather Forecasts for Rwandan Coffee Farmers.” Phys.org. http://phys.org/news/2014-03-mobile-app-weather-rwandan-coffee.html#jCp. Google ScholarFahlgren, Noah, Malia Gehan, A and Ivan Baxter. 2015. “Lights, Camera, Action: High-Throughput Plant Phenotyping Is Ready for a Close-Up.” Current Opinion in Plant Biology 24: 93–99. http://www.sciencedirect.com/science/article/pii/S1369526615000266. Google ScholarFerster, Warren. 2014. “U.S. Government Eases Restrictions on DigitalGlobe.” Space News. http://spacenews.com/40874us-government-eases-restrictions-on-digitalglobe/. Google ScholarFreischlad, Nadine. 2015. “Drones over the Rice Paddy: Ci-Agriculture Brings Smart Tech to the Field.” Tech in Asia. https://www.techinasia.com/ci-agriculture-precision-farming-indonesia/. Google ScholarFung, Kaiser. 2014. “Google Flu Trends’ Failure Shows Good Data > Big Data.” Harvard Business Review. https://hbr.org/2014/03/google-flu-trends-failure-shows-good-data-big-data/. Google ScholarGartner. 2012. “Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support Big Data by 2015.” http://www.gartner.com/newsroom/id/2207915. Google ScholarGartner. 2013. “Gartner Survey Reveals that 64 Percent of Organizations Have Invested or Plan to Invest in Big Data in 2013.” http://www.gartner.com/newsroom/id/2593815. Google ScholarGilpin, Lyndsey. 2014. “How Big Data Is going to Help Feed Nine Billion People by 2050.” TechRepublic. http://www.techrepublic.com/article/how-big-data-is-going-to-help-feed-9-billion-people-by-2050/. Google ScholarGraziano da Silva, José. 2016. “Big Data, Small Farms.” Foreign Affairs. https://www.foreignaffairs.com/sponsored/big-data-small-farms. Google ScholarGreatrex, Helen, James Hansen, Samantha Garvin, Rahel Diro, Sari Blakeley, Margot Le Guen, Kolli Rao, and Daniel Osgood. 2015. “Scaling Up Index Insurance for Smallholder Farmers: Recent Evidence and Insights.” CCAFS Report 14, CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Copenhagen. https://cgspace.cgiar.org/bitstream/handle/10568/53101/CCAFS_Report14.pdf. Google ScholarGustafson, Thomas Andrew. 2013. “I’m Not Just Gaming, Ma! I’m Helping the World’s Farmers.” NPR. http://www.npr.org/sections/thesalt/2013/11/25/247210031/i-m-not-just-gaming-ma-i-m-helping-the-world-s-farmers. Google ScholarHalais, Flavie. 2015. “7 Challenges the Agriculture Sector Must Address to Unleash its Data Revolution.” Devex. https://www.devex.com/news/7-challenges-the-agriculture-sector-must-address-to-unleash-its-data-revolution-86310. Google ScholarHamm, Steve. 2011. “Watson on Jeopardy! Day One: Man vs. Machine for Global Bragging Rights.” A Smarter Planet (blog). http://asmarterplanet.com/blog/2011/02/watson-on-jeopardy-day-one-man-vs-machine-for-global-bragging-rights.html. Google ScholarHarvestChoice. 1995. Dynamic Research Evaluation for Management (DREAM) 3.1. Washington, DC: International Food Policy Research Institute (IFPRI) and University of Minnesota. http://hdl.handle.net/1902.1/18230. Google ScholarHarvestChoice. 2012. “Visualize HarvestChoice Indicators: MAPPR.” International Food Policy Research Institute, Washington, DC, and University of Minnesota, S Paul, t. http://harvestchoice.org/node/2258. Google ScholarHarvestChoice. 2014a. “AgriTech Toolbox.” International Food Policy Research Institute, Washington, DC, and University of Minnesota, S Paul t. http://harvestchoice.org/node/9034. Google ScholarHarvestChoice. 2014b. “HarvestChoice Data Services.” International Food Policy Research Institute, Washington, DC, and University of Minnesota, S Paul, t. http://harvestchoice.org/node/9025. Google ScholarHosenally, Nawsheen. 2012. “Mapping Trees for Food Security.” ICT Update 69. http://ictupdate.cta.int/Feature-Articles/Mapping-trees-for-food-security/ Google ScholarIDC. 2014. “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things.” EMC2. http://www.emc.com/infographics/digital-universe-2014.htm. Google ScholarJones, J W, G Hoogenboom, C H Porter, K J Boote, W D Batchelor, L A Hunt, and J T Ritchie 2003. “The DSSAT Cropping System Model.” European Journal of Agronomy 18 (3): 235–65. CrossrefGoogle ScholarKahneman, Daniel. 2011. Thinking, Fast and Slow. New York: Farrar, Straus & Giroux. Google ScholarKay, Alexx. 2001. “Artificial Neural Networks.” Computerworld. http://www.computerworld.com/article/2591759/app-development/artificial-neural-networks.html. Google ScholarKelly, Jeff. 2015. “Executive Summary: Big Data Vendor Revenue and Market Forecast, 2011–2026.” Wikibon. http://premium.wikibon.com/executive-summary-big-data-vendor-revenue-and-market-forecast-2011-2026/. Google ScholarLaney, Doug. 2001. “3D Data Management: Controlling Data Volume, Velocity, and Variety.” META Group (blog). http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf Google ScholarLavars, Nick. 2015. “MIT’s Crop-Saving Drones at the Ready.” Gizmag. http://www.gizmag.com/mit-crop-drones/37526/ Google ScholarLawler, Ryan. 2015. “Planet Labs Nabs $95 Million and a New COO to Cover the Earth with Flocks of Tiny Satellites.” TechCrunch. http://techcrunch.com/2015/01/20/planet-labs-95m/. Google ScholarLeclerc, Rob, and Melissa Tilney. 2015. “AgTech Is the New Queen of Green.” TechCrunch. http://techcrunch.com/2015/04/01/the-new-queen-of-green/. Google ScholarLetouzé, Emmanuel, and Patrick Vinck. 2014. “The Politics and Ethics of CDR Analytics.” Data-Pop Alliance White Paper Series. http://static1.squarespace.com/static/531a2b4be4b009ca7e474c05/t/54b97f82e4b0ff9569874fe9/1421442946517/WhitePaperCDRsEthicFrameworkDec10-2014Draft-2.pdf. Google ScholarMarr, Bernard. 2015. “From Farming to Big Data: The Amazing Story of John Deere.” Data Science Central (blog). http://www.datasciencecentral.com/profiles/blogs/from-farming-to-big-data-the-amazing-story-of-john-deere. Google ScholarMartin, Steven, W James Hanks, Aubrey Harris, Gene Willis, and Swagata Banerjee. 2005. “Estimating Total Costs and Possible Returns from Precision Farming Practices.” Crop Management. http://naldc.nal.usda.gov/download/11889/PDF. Google ScholarMcKinsey Global Institute. 2011. “Big Data: The Next Frontier for Innovation, Competition, and Productivity.” http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation. Google ScholarMcLaren, Robin. 2015. Managing Unintended Consequences of Democratizing Land Rights. http://www.fig.net/resources/proceedings/fig_proceedings/fig2015/papers/ts02b/TS02B_mclaren_7687.pdf. Google ScholarNelson, G C, H Valinb, R D Sands, P Havlík, H. Ahammad, D Deryng, J Elliott, S Fujimori, T Hasegawa, E Heyhoe, P Kyle, M Von Lampe, H Lotze-Campen, D M d’Croz, H van Meijl, D van der Mensbrugghe, C Müllerk, A Popp, R Robertson, S Robinson, E Schmid, C A Schmitz Tabeau, and D Willenbockel 2014. “Climate Change Effects on Agriculture: Economic Responses to Biophysical Shocks.” Proceedings of the National Academy of Sciences of the United States of America 111 (9). http://dx.doi.org/10.1073/pnas.1222465110. CrossrefGoogle ScholarPaynter, Ben. 2008. “Feeding the Masses: Data In, Crop Predictions Out.” Wired Magazine. http://archive.wired.com/science/discoveries/magazine/16-07/pb_feeding. Google ScholarPractical Action. 2012. Poor People’s Energy Outlook 2012: Energy for Earning a Living. Bourton, UK: Practical Action Publishing. http://practicalaction.org/ppeo2012. Google ScholarQuinn, Sara. 2014. “Creating a Community of Practice in Sub-Saharan Africa to Utilize Unmanned Aerial Vehicle-based Tools for Agricultural Development.” International Potato Center. http://cipotato.org/press-room/blogs/creating-practice-sub-saharan-africa-unmanned-aerial-vehicle-based-tools-agricultural-development/. Google ScholarReader, Ruth. 2015. “Google Ventures Leads $15M Investment in Big Data for Farmers.” VentureBeat. http://venturebeat.com/2015/05/19/google-ventures-leads-15m-investment-in-big-data-for-farmers/. Google ScholarRidley, W P, R D Shillito, I Coats, H Y Steiner, M Shawgo, A Phillips, P Dussold, and L Kurtyka 2004. “Development of the International Life Sciences Institute Crop Composition Database.” Journal of Food Composition and Analysis 17: 423–38. CrossrefGoogle ScholarRonanki, Rajeev, and David Steier. 2014. “Cognitive Analytics.” Tech Trends 2014. http://dupress.com/articles/2014-tech-trends-cognitive-analytics/. Google ScholarRosegrant, M W, S Msangi, C Ringler, T B Sulser, T Zhu, and S A Cline 2008. “International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description.” 42, International Food Policy Research Institute, Washington, DC. Google ScholarRosegrant, M W, J Koo, N Cenacchi, C Ringler, R D Robertson, M Fisher, C M Cox, K Garret, N D Perez, and P Sabbagh 2014. Food Security in a World of Growing Natural Resource Scarcity: The Role of Agricultural Technologies. Washington, DC: International Food Policy Research Institute. http://dx.doi.org/10.2499/9780896298477. Google ScholarSaporito, Patricia. 2014. “2 More Big Data V’s—Value and Veracity.” SAP Business Innovations (blog). http://blogs.sap.com/innovation/big-data/2-more-big-data-vs-value-and-veracity-01242817. Google ScholarSee, Linda. 2013. “Citizen Scientists Rival Experts in Analyzing Land-Cover Data.” http://www.iiasa.ac.at/web/home/about/news/PLOS_ONE__Citizen_scientists_rival_experts_in_analyz.en.html. Google ScholarSee, Linda, I, S McCallum, C Fritz, F Perger, M Kraxner, U D Obersteiner, N Baruah Mili, and N. R Kalitas 2013. “Mapping Cropland in Ethiopia Using Crowdsourcing.” International Journal of Geosciences 4: 6–13. http://dx.doi.org/10.4236/ijg.2013.46A1002. Google ScholarSicular, Svetlana. 2014. “Big Botched Data.” Gartner Blog Network. http://blogs.gartner.com/svetlana-sicular/big-botched-data/. Google ScholarSilversmith, Alex, and Drew Tulchin. 2013. “Crowdsourcing Applications for Agricultural Development in Africa.” USAID, Washington, DC. http://pdf.usaid.gov/pdf_docs/pa00j7p7.pdf. Google ScholarStorum, Doug. 2015. “Farmers Stay Aware with aWhere’s Weather Data.” Boulderopolis. http://boulderopolis.com/farmers-stay-aware-with-awheres-weather-data/. Google ScholarThomasson, Alex. 2015. “The Future of Farming: Drones, Robots, and GPS.” The Conversation. http://mashable.com/2015/03/22/farmers-drones-robots/. Google ScholarTsotsis, Alexia. 2013. “Monsanto Buys Weather Big Data Company Climate Corporation for Around $1.1B.” TechCrunch. http://techcrunch.com/2013/10/02/monsanto-acquires-weather-big-data-company-climate-corporation-for-930m/. Google ScholarUnited Nations, Department of Economic and Social Affairs, Population Division. (2015). World Population Prospects: The 2015 Revision, Key Findings and Advance Tables. ESA/WP.241. New York: United Nations. Google ScholarUSDA (U.S. Department of Agriculture). 2005. “Agricultural Aircraft Offer a Different View of Remote Sensing.” AgResearch Magazine. http://agresearchmag.ars.usda.gov/2005/mar/remote. Google ScholarWinston, Clifford. 2013. “Government Implementation of Large-Scale Projects: Government Failure, Its Sources, and Implications for the ACA Website Launch.” Brookings Institution. http://www.brookings.edu/research/testimony/2013/12/04-government-implementation-large-scale-projects-winston Google ScholarWorld Bank. 2014. “Big Data in Action for Development.” http://live.worldbank.org/sites/default/files/Big%20Data%20for%20Development%20Report_final%20version.pdf. Google ScholarWorld Bank. 2015. Big Data Solutions: Innovative Approaches to Overcoming Agricultural Challenges in Developing Nations by Harnessing the Power of Analytics. Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/26436. Google ScholarWorld Economic Forum. 2011. “Personal Data: The Emergence of a New Asset Class.” http://www3.weforum.org/docs/WEF_ITTC_PersonalDataNewAsset_Report_2011.pdf. Google ScholarWorld Economic Forum. 2015. “Data-Driven Development Pathways for Progress.” http://www3.weforum.org/docs/WEFUSA_DataDrivenDevelopment_Report2015.pdf. Google ScholarWorld Wide Web Foundation. 2015. Open Data Barometer Global Report. 2nd ed. http://barometer.opendataresearch.org/assets/downloads/Open%20Data%20Barometer%20-%20Global%20Report%20-%202nd%20Edition%20-%20PRINT.pdf. Google ScholarYou, L, S Wood, U Wood-Sichra, and W Wu 2014. “Generating Global Crop Distribution Maps: From Census to Grid.” Agricultural Systems 127: 53–60. CrossrefGoogle ScholarZoltner, John. 2015. “The Future Is Here with Sensors and Geospatial Analytics in International Development.” ICTworks. http://www.ictworks.org/2015/06/15/the-future-isearlier-here-with-sensors-and-geospatial-analytics-in-international-development. Google Scholar Previous chapterNext chapter FiguresreferencesRecommendeddetailsCited byBig Data Driven Smart Agriculture: Pathway for Sustainable Development View Published: June 2017ISBN: 978-1-4648-1002-2e-ISBN: 978-1-4648-1023-7 Copyright & Permissions Related RegionsAfricaRelated TopicsAgricultureInformation and Communication TechnologiesMacroeconomics and Economic Growth KeywordsCASE STUDYINFORMATION AND COMMUNICATIONS TECHNOLOGYICTCAPACITY BUILDINGAGRICULTURAL PRODUCTIVITYAGRICULTURAL POLICYSMALLHOLDER FARMSMOBILE COMMUNICATIONSCROP YIELDSCROP FORECASTINGCROWDSOURCINGACCESS TO INFORMATION PDF DownloadLoading ..." @default.
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