{"id":19742,"date":"2016-11-18T17:19:41","date_gmt":"2016-11-18T22:19:41","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/descartes-labs-predicting-farmers-fortunes-from-space\/"},"modified":"2016-11-18T17:19:41","modified_gmt":"2016-11-18T22:19:41","slug":"descartes-labs-predicting-farmers-fortunes-from-space","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/descartes-labs-predicting-farmers-fortunes-from-space\/","title":{"rendered":"Descartes Labs: Predicting Farmer\u2019s Fortunes from Space"},"content":{"rendered":"

There\u2019s arguably no more important industry to humanity than agriculture: it feeds us, employs one-third of the global workforce [1], and contributes 3 trillion dollars to global GDP [2]. Given the prominence, significant effort and money are invested in accurately predicting seasonal crop output. In the United States, traders, insurers, lenders, and many others utilize crop forecasts to support the agricultural financial ecosystem. Until recently, these players relied almost entirely on monthly production reports issued by the US Department of Agriculture (USDA) to glean information about future supply and demand. The USDA compiled this data by surveying samples of farmers around the country and using statistical techniques to extrapolate domestic-level estimates [3]. While these estimates were decent (usually less than 5% error) [4], unexpected weather events or other anomalies between monthly reports reduce accuracy and provide inaccurate signals to the market.<\/p>\n

Enter Descartes Labs<\/strong><\/p>\n

Descartes Labs processes satellite imagery of US farmland (3.1 million square miles) [5] using neural networks and cloud computing horsepower to provide free<\/em> crop yield forecasts on a weekly basis. This methodology has proven to be more accurate than the USDA survey and provides real-time price signals that improve overall market efficiency. In fact, the Descartes Lab forecast is so accurate (1% error relative to actual production) it is effectively measuring the weight of every corn kernel in America to an error of plus or minus 3 milligrams [4]. Descartes\u2019 first harvest was in 2015 when it offered free state-by-state and country-wide corn yield forecasts on a weekly basis. In 2016, it expanded the forecast to include soy production and offered a further county-by-county breakdown of yield [5].<\/p>\n

Disruptive Business Model<\/strong><\/p>\n

While Descartes can promise customers accuracy, its business model seems unlikely to be profitable at first glance; releasing a free<\/em> weekly report has little top-line value. However, this free morsel is only to entice customers to pay for additional premium services. Currently, paying subscribers can have access to crop forecasts updated every two days enabling speculators to take positions based on data not yet publicly available. More notably, Descartes offers customizable forecast solutions that allow paying customers to apply proprietary machine-learning algorithms and staff expertise to their own big data questions (see\u00a0below for detail) [6].<\/p>\n

\"process2\"<\/a><\/p>\n

Operational Enablers<\/strong><\/p>\n

The operational backbone that supports Descartes\u2019 customer promise relies on the combination of three emerging digital technologies:<\/p>\n

    \n
  1. Artificial neural networks [7]<\/li>\n
  2. Cheap cloud-based processing horsepower [8]<\/li>\n
  3. Proliferation of small, cheap, earth-observation satellites [9]<\/li>\n<\/ol>\n

    Descartes utilizes these technologies in concert to produce superior agricultural forecasts. They have also developed a standardized forecast development process (shown below) with the potential to be applied broadly to other commercial applications.<\/p>\n

    Descartes Labs Forecast Methodology [6]<\/p>\n

    \"process-pic\"<\/a><\/p>\n

    If this technology can be used to monitor changes in crop yields, what is preventing it from being retooled to assess construction activity in China or human migration patterns out of Syria? The white space for earth-observation AI applications is massive and as costs for satellite images and cloud computing space fall, this operational model is extremely scalable given the low marginal cost of new forecasting applications.<\/p>\n

    The Future of Earth Observation<\/strong><\/p>\n

    Despite the seemingly boundless potential for this technology, Descartes will face several key challenges in the future. First, the frequently refreshed satellite imagery that their models rely on are controlled by only a few companies like DigitalGlobe and Planet [10]. What is stopping them from using their monopoly power to squeeze Descartes\u2019 margins? Furthermore, what is preventing these companies or another capable start-up from simply replicating what Descartes has done? To get ahead of these challenges, I recommend that Descartes consider the following recommendations to expand capabilities and protect market position:<\/p>\n

      \n
    1. Go Global \u2013 today\u2019s markets are global and what happens to the wheat harvest in Russia can drastically impact US markets (see Great Grain Robbery<\/a> [11]). Furthermore, while less accurate the USDA forecast provides adequate transparency into US markets \u2013 Descartes can make a bigger impact by expanding to more opaque regions.<\/li>\n
    2. Go Upstream \u2013 farming inputs like water can have a major impact on crop yields. Descartes should factor in mountain snow pack and reservoir levels into its yield forecast.<\/li>\n
    3. Democratize \u2013 Descartes should open their API to a larger audience free of charge. Gaining users and crowdsourcing their capabilities will generate amazing applications for this technology beyond the reach of a resource-limited Descartes team that can still be monetized with creative revenue generation mechanisms.<\/li>\n<\/ol>\n

      The Greatest Descartes?<\/strong><\/p>\n

      Just as Rene Descartes redefined the way we look at and think about the world, Descartes Labs is using digital technology to revolutionize the global marketplace for goods and ideas. In a world with more transparency and efficient markets, we\u2019re all better off.<\/p>\n

      784 words<\/p>\n

       <\/p>\n

      Sources<\/strong><\/p>\n

      1)\u00a0\u00a0\u00a0\u00a0\u00a0 \u201cLabour.\u201d www.fao.org\/docrep\/015\/i2490e\/i2490e01b.pdf.<\/p>\n

      2)\u00a0\u00a0\u00a0\u00a0\u00a0 \u201cAgriculture, Value Added (% of GDP).\u201d Agriculture, Value Added (% of GDP) | Data<\/em>, data.worldbank.org\/indicator\/NV.AGR.TOTL.ZS.<\/p>\n

      3)\u00a0\u00a0\u00a0\u00a0\u00a0 \u201cUnderstanding USDA Crop Forecasts.\u201d doi:10.15417\/1881.<\/p>\n

      4)\u00a0\u00a0\u00a0\u00a0\u00a0 \u201cDescartes Labs Raises 5M to Make Agricultural Predictions with Deep Learning.\u201d Venture Beat<\/em>, venturebeat.com\/2015\/11\/17\/descartes-labs-raises-5m-to-make-agricultural-predictions-with-deep-learning\/.<\/p>\n

      5)\u00a0\u00a0\u00a0\u00a0\u00a0 \u201cSeeing Corn with Satellites.\u201d The Atlantic<\/em>, Atlantic Media Company, www.theatlantic.com\/technology\/archive\/2016\/08\/seeing-corn-with-satellites\/495149\/.<\/p>\n

      6)\u00a0\u00a0\u00a0\u00a0\u00a0 \u201cDescartes Labs: Solutions.\u201d Descartes Labs<\/em>, www.descarteslabs.com\/solutions.html.<\/p>\n

      7)\u00a0\u00a0\u00a0\u00a0\u00a0 \u201cHow Imaging Technologies Are Changing the World.\u201d Forbes<\/em>, Forbes Magazine, www.forbes.com\/sites\/truebridge\/2016\/05\/06\/how-imaging-technologies-are-changing-the-world-part-1\/#77ca3eaa712b.<\/p>\n

      8)\u00a0\u00a0\u00a0\u00a0\u00a0 Lynley, Matthew. \u201cDeep-Learning, Image-Analysis Startup Descartes Labs Raises $3.3M After Spinning Out Of Los Alamos National Lab.\u201d TechCrunch<\/em>, 1 May 2015, techcrunch.com\/2015\/05\/01\/deep-learning-image-analysis-startup-descartes-labs-raises-3-3m-after-spinning-out-of-los-alamos-national-labs\/.<\/p>\n

      9)\u00a0\u00a0\u00a0\u00a0\u00a0 Wieczner, Jen. \u201cTraders\u2019 New Edge: Satellite Data.\u201d Fortune<\/em>, 22 Dec. 2015, fortune.com\/2015\/12\/23\/satellite-images-big-data\/.<\/p>\n

      10)\u00a0 \u201cA Sudden Light.\u201d The Economist<\/em>, The Economist Newspaper, 10 Aug. 2016, www.economist.com\/technology-quarterly\/2016-25-08\/space-2016<\/a>.<\/p>\n

      11)\u00a0 \u201cThe Great Grain Robbery: Lessons Learned from Earth Imaging’s Early History.\u201d Earth Imaging Journal Remote Sensing Satellite Images Satellite Imagery<\/em>, eijournal.com\/print\/column\/defense-watch\/the-great-grain-robbery-lessons-learned-from-earth-imaging%E2%80%99s-early-history.<\/p>\n","protected":false},"excerpt":{"rendered":"

      A Los Alamos-based startup is disrupting the market for the oldest industry on earth: agriculture.<\/p>\n","protected":false},"author":2269,"featured_media":19743,"comment_status":"open","ping_status":"closed","template":"","categories":[28,50,346,452],"class_list":["post-19742","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-agriculture","category-cloud-computing","category-machine-learning","category-satellites"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/digitization-challenge-2016\/","yoast_head":"\nDescartes Labs: Predicting Farmer\u2019s Fortunes from Space - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/descartes-labs-predicting-farmers-fortunes-from-space\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Descartes Labs: Predicting Farmer\u2019s Fortunes from Space - 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