Analyst Report: Big data and big agriculture

1 Summary

The opportunities brought about by data collection and analytics have touched every market, from health care to retail. And while the farm may not be the first place people consider as a prime target for IT and cloud services, opportunities clearly exist. In fact farms are heavily reliant on small improvements in operational efficiencies and processes in order to increase crop yields, manage risk, and create greater profit. This is particularly true for large-scale agribusiness where commodity crops are involved and small process adjustments have large impacts in terms of production.

This report reviews a host of data driven services, which can have a valuable role to play on the farm.

Key findings include:

  • As the global population increases, weather volatility grows, and petroleum dependent agriculture is increasingly sensitive to fossil fuel pricing, there will be more incentives to leverage new technology to increase crop yields and manage risk.
  • Opportunities for big data applications in agriculture include benchmarking, sensor deployment and analytics, and using better models to manage crop failure risk.
  • Key challenges for companies entering the market include proving the effectiveness of data centric technologies to improve yield as well as building trust with farmers.



Thumbnail image courtesy: Fuse/Thinkstock

2 Introduction

The global population will reach 9 billion by 2050 and the United Nations’ Food and Agriculture Organization has said that food production must increase by 60 percent by then to meet demand. In addition to the food requirement, there is a significant reliance on basic resources like phosphorous and petroleum. One 40-pound bag of petroleum based fertilizer, for example, requires 2.5 gallons of gasoline as a feedstock.

Monsanto’s $930 million acquisition of agriculture analytics firm The Climate Corporation got the attention of investors. Climate Corp.’s cofounders had been early Google employees who saw an opportunity to use online tools to improve the delivery of crop insurance as well as create models for better risk management. And with this major deal on the books, VCs are now taking a closer look at the opportunities in agriculture.

The concept of “precision agriculture” is not new. It includes the use of technology to monitor, map, and respond to variability in crop yields. GPS has helped enable precision agriculture by allowing for easy geo mapping to be combined with increasing amounts of data that includes variables ranging from pH to moisture levels to yield. Securely collecting and correlating this data is the beginning of using a variety of techniques to improve crop yields and ultimately improve farmers’ bottom line.

Agfunder News estimates that by the middle of 2014 there had been about $400 million invested in 35 agtech deals. That number may sound very small but it’s considerably larger that the $100 million of VC that flowed to agricultural technology companies in 2012. Estimates of the agricultural products industry in the U.S. hover around $300 billion with 30 percent of product being exported. In California alone agriculture is a $46 billion industry, and the roll out of mobile broadband is making most farms connected. It’s very early stages in this industry with slowly growing investment. But there are considerable opportunities given the sheer scale of the agricultural market. These technologies break down in an application specific manner.

3 Data applications

To get a handle on how data analytics, sensors, cloud services, and new software platforms for agriculture are affecting agribusiness, it’s helpful to consider some of the applications currently being deployed. They include:

Advanced Sensor Technology: The range of sensors that are available for deployment in an agricultural field is diverse, ranging from the basics like temperature and humidity to chemical composition measures collecting data on nitrogen oxide and ethanol levels. Sensor startup Libelium, with presence in Europe and North America, noted in 2013 that 18 percent of its sensor sales were in agriculture. Sensor technology has a multitude of payoffs that include precision monitoring of fertilizer and fungicide levels to optimize crop yields as well as risk mitigation that results from monitoring when temperature and humidity levels reach dangerous levels for crops. By adding analytics to sensor data, opportunities also exist to further optimize prescription farming by having continuous data on how a field is responding to a protocol.

Benchmarking: While sometimes overlooked, the ability to understand how one’s farm is performing either against a neighboring farm or even comparatively across a large-scale farm can have value to the farmer. This is particularly true in commodity crop growth like corn and soybeans where knowing, for example, that parts of your farm are only in the 30th percentile in yield can aid in focusing resources and making adjustments. The big proponent of this technology is Farmlink, a startup headed up by former Sprint PCS CEO Ron Lemay that collected $40 million in venture capital this year. The key here is being able to access accurate data on farm yields at a highly granular level across vast areas. The quality of farm data across large swaths of area, sometimes thousands of miles, is a debated issue as that data is being used to guide planting decisions. Farmlink is able to get its data from systems it has installed on combines, which are essential to harvesting on large-scale farms.

Agricultural management software: As farmers and farms get connected, software management systems have emerged. They address key farm needs like accounting, wireless linking of farm managers, operators, and machines, visualization and mapping of fields, maintenance tracking, and scheduling. Established industry leaders like John Deere have software tools in the market, in addition to companies that are specializing in ERP products for agriculture.

Insurance/risk management: Climate Corp.’s innovation was the addition of better data science to managing the risk of crop failure, the number one concern for farmers. The insurance products the company offers attempt to make risk management specific to field location, soil type, and desired yields and, most importantly, assesses the most probable risks on a given farm, be they heat stress or freeze. These types of tools gain increasing value in a world of greater weather volatility and are an example of how data science can be used to develop better risk models by crunching decades of historical data on weather patterns.

Farm analytics: Software-only analytics firms now have tools that take sensor data from the farm, combine it with outside data like weather data and aerial imaging, to give farmers information on what’s going on in their fields. Startup Farm Intelligence is one such company in this area that manages about a million acres. Its product is delivered via cloud services, running on Amazon EC2 with an additional cloud storage provider. Providers in the analytics space typically offer software platforms that provide digital imagery along with crop analysis that can include prescriptions for restoring underperforming areas of a farm.

4 Market challenges

When it comes to data and farmers, the key issue is trust. Most startups in this space continually communicate that they are on the side of the farmer and that they are driven by the primary purpose of supporting farmers to increase crop yields. A recent Wall Street Journal article chronicled the core challenge of bringing big data to the farm. Farmer fears are mainly competitive: that their farming techniques and strategies will be shared with competitors or giant seed companies like Monsanto, which have historically poor relationships with growers.

Consider that farmers are being asked to turn over enormous amounts of granular data on their operations, in some cases to suppliers that have a major impact on their expenses. This is a practice many businesses in other sectors would likely balk at. Secondary concerns include data ending up in the hands of commodity traders, which could negatively affect futures contracts, which determine the price of commodity crops. While many of these fears may seem overblown, they reflect the history of corporate relationships with farmers in the U.S. They are a market challenge that must be addressed. We’re likely to see some sort of industry-wide security standards for housing farm data in the cloud as an attempt to protect farmers.

Most independent startups argue that because they don’t have alliances with any of the industry leaders like Monsanto, John Deere, or DuPont, they avoid conflict of interest and don’t try to sell farmers products other than the data services themselves or the hardware sensor systems. This position could become less tenable, however, if, as seems likely, those industry leaders acquire data service startups as part of their drive for new products to roll out. For big companies eager to sell new products to farmers that could be packaged alongside core businesses like farm machinery or seed, there’s an attractiveness to software services, particularly if those services can work alongside a core product to make it even more effective.

Providing a clear ROI, measured largely in crop yields and failure avoidance, is another aspect of the trust issue. While farms are now wired and open to software services and communications tools, building confidence with them to try new products will take time, particularly given how tight their margins are.

Most startups and established companies in the agricultural technology space focus on the quality of their measurements as there is some skepticism about how good any data is, be it from in-field sensors or imaging data from unmanned aerial vehicles. The quality and usability of this data will be debated and perhaps questioned by farmers. Proving that measurements taken on farms, be they yield or moisture levels, are consistently reliable will need to be in place to further gain customer trust.

5 Future opportunity

Monsanto estimates that data services for farmers could increase global crop production by $20 billion per year. It has a clear interest in promoting the market but even increasing an average of ten bushels per acre of corn results in magnitude revenue gains globally for farmers. And as companies accumulate years of historical data that combines not just information on planting prescriptions but the performance of the analytics services themselves, the technologies should incrementally improve.

Similar to big data strategies in other areas, what’s so attractive about applying analytics to agriculture is that these are capital light strategies that are often cloud based. That means that in many cases a service can be rolled out with significantly lower levels of investment. This is key for an industry that has had more difficulty rolling out new products, whether it’s new seed technology or farm infrastructure, due to the lengthy research and development costs involved and the long time horizons. As with other IoT strategies, how far a company can get without major hardware investment in sensor technology to gain access to reliable data is an issue. But the improvements in sensors and declining connectivity costs suggest that either way there will be value for the farm in data analytics and software tools.

6 Key takeways

  • As the global populations swells, weather volatility increases, and demand for food production increase, a market opportunity exists for software analytics services that can increase crop yields and manage risk in agriculture.
  • Data applications for the farm include advanced sensor technology and analytics to optimize field conditions/yield, risk management/insurance tools, benchmarking analysis, and software management systems/ERP.
  • Market challenges include building trust with farmers, ensuring data privacy and security, and ensuring quality data. Look for industry leaders to move into selling software services to farmers, through in house development of tools as well as acquisitions.
  • While capital light strategies will continue to be attractive due to the low expense of cloud based software services, an opportunity also exists in hardware deployment packages that provide integrity of data and build trust with farmers.

7 About Adam Lesser

In addition to being a lead Analyst for Gigaom Research, Adam Lesser is an analyst for Blueshift Research, a San Francisco based investment research firm dedicated to public markets. He focuses on the intersection of technology and energy consumption, and has cleantech expertise in green IT, the share economy, the smart grid, and renewable energy generation.

His career began as an assignment editor for NBC News in New York where he worked on both the foreign and domestic desks. In his time at NBC, he covered numerous stories, including the Columbia shuttle disaster, the DC sniper, and the 2004 Democratic Convention. He won the GE Recognition Award for his work the night of Saddam Hussein’s capture. Between his time at NBC News and Blueshift, Adam spent two years studying biochemistry and working for the Weiss Lab at UCLA, which studies protein folding, which has implications for diseases like Alzheimer’s and Cystic Fibrosis.

8 About Gigaom Research

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9 Copyright

© Knowingly, Inc. 2014. "Big data and big agriculture" is a trademark of Knowingly, Inc. For permission to reproduce this report, please contact