Whitepaper: Agricultural Systems + Data Outlook 2Q14

1. Whitepaper: Agricultural Systems + Data Outlook
The Data Guild, 2014­02­20
How can data be leveraged to make food production and distribution systems more responsive,
resilient, and efficient?
An ecosystem of agricultural data has been quietly evolving, and is rapidly becoming a vital
component of global food security. The data rates and variety are vast: remote sensing via small
satellites, sensor networks in the fields, tractors­as­drones, and more. Many issues implied by
this category of data, however, are quite subtle and in some cases counter­intuitive. Given that
this field is relatively new and not particularly organized yet, key learnings may be adapted from
other sectors where large­scale data and analytics have already played a transformational role:
finance, intelligence, e­commerce, telecom, energy, etc.
We examine both key questions and the evolving vendor landscape for agricultural data in the
context of supply chain analysis, defining nomenclature for components of the ecosystem and
identifying key issues for consideration. Ultimately, this paper is at best an early draft for a much
longer and more comprehensive study: it provides a rubric for analyzing the complexities of
agricultural data, along with examples for the identified categories.
Impact
Farming represents the single largest employer globally, as the primary livelihood for 40% of the
world’s population. There are more than 500 million small farms worldwide , most of which are 1
family farms that rely on rainfed agriculture. The global domestic product for agriculture was 2
nearly $15 trillion in 2013 and the agricultural real estate in the U.S. alone is valued at over $2 3
trillion. The impact of these figures needs to be considered in the context of two factors:
resource consumption and production asymmetries.
In terms of resource consumption, recognize that 70% of the world’s freshwater resources goes
toward agriculture . This figure is estimated to reach 89% by 2050. Meanwhile, soils are being 4 5
1
“Small farms: Current Status and Key Trends”, Oksana Nagayets, Future of Small Farms (2005), p. 355
2
Agriculture, value added (% of GDP), The World Bank (2014)
3
National Agricultural Statistics Service, USDA (2014)
4
FAO Aquastat
5
UN Water Facts and Figures (2013)
Agricultural Data (Q2 2014) The Data Guild Page 1

2. depleted at a 10­40% faster rate than they are replenished. Within the United States, 90% of 6
cropland is currently losing soil faster than its sustainable replacement rate , and that represents 7
a very large capital loss. High annual rates of soil depletion and salinization, together with
increasing cycles of flooding and drought, place enormous stresses on the entire agricultural
system. The stakes are high, but much can be accomplished to mitigate looming issues by the
effective use of data and analytics.
In terms of production asymmetries, recognize that more than 80% percent of all agricultural
holdings measure less than two hectares: these are smallholder and family farms . Overall, 8
family farms account for more than 98% of all farms, and more than 56% of global agricultural
production . While corporate farms tend to predominate in areas of high potential yield, the 9
smallholder farms are stewards in marginal lands . Their highly specialized knowledge sustains 10
production as resource challenges escalate. For example, smallholder farmers typically use
innovative technologies to conserve resources – ranging up to 30­60% water use efficiencies in
some regions . Moreover there are cascading economic effects: each US$1 of farming income 11
in Asia creates an additional US$0.80 in non­farming sectors. Along with that, microfinance
should not be overlooked as a driver for local acceptance of new technologies.
The wealthy nations tend to maintain or increase their consumption of natural resources, while
exporting their footprints to producer nations which are typically poorer. For example, European
and North American populations consume a considerable amount of virtual water embedded in
their food imports, by more than a 200% multiple .12
Key Trends
Trends within the ecosystem can be identified by exploring the following questions:
● Who are the stakeholders in this system?
Certainly the farmers and their vendors and buyers play central roles. Which other actors
have substantial impact on the flows of data?
6
“Soil Erosion: A Food and Environmental Threat”, David Pimental, Environment, Development, and
Sustainability (2006)
7
Changes in Average Annual Soil Erosion by Water on Cropland and CRP Land, 1992 –1997,
Natural Resources Conservation Service, USDA (2000)
8
2000 World Census of Agriculture, FAO (2010)
9
“Food Tank By The Numbers: Family Farming”, Danielle Nierenberg, et al., Food Tank (2014)
10
ibid., Pimental (2006)
11
ibid., Nierenberg (2014)
12
UN Water Facts and Figures (2013)
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3. ● Are there potential feedback loops within the “supply chain” of agricultural data –
spanning from farm sensors to aggregate market metrics – that could be leveraged by
new business models?
For example, the emergence of feedback loops involving machine data plus algorithmic
modeling in the late 1990s propelled early successes in e­commerce to evolve 13
increasingly sophisticated web apps for improving customer experience online:
Amazon’s product recommendations, Google’s search results, eBay’s product auctions.
In farming, more high­quality, granular telemetry could provide an opportunity to address
risk is new ways and allow insurers to offer new, more affordable products. Better risk
mitigation, in turn, can open the door to new credit and capital investment in local
markets.
● Are there strategic points within the system where open standards could substantially
improve interoperability and problem­solving?
Lack of agreed­on standards and protocols in agricultural settings has hampered the
pace of innovation. Examples from other domains are instructive. For example, the
emergence of the HTTP protocol and the HTML markup language in the early 1990s
greatly accelerated applications on the Internet. A more recent example is the explosion
of Arduino­based sensors and Smart Home devices built to integrate with protocols such
as Z­Wave and Zigbee.
● Are there niches within the data ecosystem that are noticeably under­ or over­subscribed
in terms of investor and/or vendor attention?
On the one hand, the over­subscribed portions of the system will mostly likely undergo
consolidation where some firms acquire competitors, as others get rolled­up. On the
other hand, the under­subscribed portions – particularly around key friction points in food
production system – indicate opportunities for new business to emerge.
● What are the relationships between natural and data ecosystems?
Consider how data flows relate to energy flow, since farming is essentially a form of
highly optimized energy capture and storage.
● At which points in data workflows does human decision­making breakdown?
13
Statitistical Modeling: The Two Cultures, Leo Breiman, UC Berkeley (2001)
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4. Instead of replacing human intuition, how can machine learning help augment human
judgement? For example, people who have domain experience can make expert
decisions when the input dimensions are limited to 4­7 variables. In higher dimensional
spaces, human intuition fails and machine learning techniques become essential for
generalized insights.
Also, are there cultural constraints to consider, e.g. advising hog farmers to grow
popcorn or suggesting that farmers of one village should cooperate with next. What is the
social context to be understood and leveraged?
● Are there ways in which linked data­driven food production and distribution can assist
smaller farms to compete with large corporate farms? Are there perhaps new kinds of
co­ops possible here? Could we imagine essentially bespoke farming in small, but
focused on newly profitable niches and better­aggregated local demand – enabled via
large­scale analytics?
There are analogies with how Google (AdWords, etc.) disrupted entrenched advertising
giants to created entire new markets for a wide variety of e­commerce firms.
● To what extent do attitudes and cultural norms among farmers themselves affect how
new technologies are integrated into practice? How will this affect the pace of innovation
in the space?
Too often, the techno­centric, almost utopian, view of technologists assumes that key
stakeholders will welcome new technologies with open arms. In farming, as in many
other corners of society, this presumption of adoption is naive at best. And this is not just
among farmers themselves, but seed companies, wholesalers, and a host of other key
constituents in the food system who have optimized their businesses and livelihoods to
the system as it exists today. Disruption here is not simply a matter of replacing
techniques and technologies. Technologists and investors interested in this space would
do well to think of how the points discussed here fit into specific cultural landscapes ­
and how those landscapes are changing.
A Systemic Perspective
Taking a perspective of the system as a whole, there are clearly points where data is typically
produced, transacted, consumed, exchanged, aggregated, reported, etc. While the data
interactions and flows between various vendors are complex, some generalizations can serve to
categorize the vendor landscape.
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5. In a simple case, a cascade through six stages describes the larger ecosystem for data,
spanning from farm sensors to aggregate market metrics:
Data Collection
At the first stage, which we label as data collection, a variety of sensors and processors collect
high resolution data at the lowest level resolution. There are a variety of different categories for
data collection and an ever­growing field of vendors at this stage. The following lists attempt to
show examples for each category of data collection:
● remote sensing
○ orbital: space station imaging
■ UrtheCast
○ orbital: small satellites
■ Planet Labs
■ Skybox Imaging
■ Satshot
○ high­altitude: atmostats, airships
■ JP Aerospace
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6. ■ Titan Aerospace
○ low­altitude: aerial imaging, aerostats, drone orchestration, etc.
■ TerrAvion
■ HoneyComb
■ PrecisionHawk
■ Raven Aerostar
■ Skycatch
● tractor telemetry
○ John Deere FarmSight, Apex
○ AgLeader SMS
○ Trimble FarmWorks
● farm robotics
○ Blue River
● sensor networks
○ localized weather
■ WeatherHawk
■ Ambient Weather
○ water usage
■ Hortau
■ PowWow Energy
■ Agronode
○ nutrient testing
■ Solum / Monsanto
○ pest management
■ Semios
■ Dolphin Engineering
● direct data entry
○ much of the most relevant data is entered manually by farmers
● inferential sensors
○ as in soft sensors14
● import from other sources (government, semi­public agencies, etc.)
○ weather
14
“Design of inferential sensors in the process industry: A review of Bayesian methods”, Shima
Khatibisepehr, Biao Huang, Swanand Khare, Journal of Process Control (Nov 2013)
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7. ○ local rainfall
○ soil distribution
○ pest/disease spread
○ water allocations
○ snowpack variance/evaporation water cycle
○ soil compaction
○ hazards: pipelines, cables, underground irrigation
A number of issues beleaguer this data collection stage, including:
● Poor communications infrastructure in rural areas
○ lack of adequate cell coverage in rural areas (depends on the region)
○ satellite upload temporarily blocked by cloud cover and other weather events
○ co­ops among neighboring properties share towers, where overlap is possible
● Serious privacy concerns
○ see below in “Drivers: Privacy and Security Issues”
● Data quality
○ lack of calibration, high variance on devices (need for maintenance, etc.)
○ additional factors that explain variance in yield map results15
● Data Silos: Vendors must surface metadata to help overcome problems of data silos on
farms. Here, standards could play a key role in spurring innovation broadly, and creating
new synergies between players in the market. Specifically, support for popular geospatial
formats, data import/export, and effective licensing that does not impede data
aggregation downstream are all key needs.
The field of sensor design in general is undergoing a rapid evolution. For example, self­powered
sensors from Piezonix can function continuously by scavenging energy. Arduino and other
hardware platforms have opened up new capabilities for rapid prototyping and small
form­factors, even into the hands of hobbyists – which is a particular boost for entrepreneurs.
Meanwhile, National Instruments has a large market share for production of sensors, and much
of its market among design engineers is outside of the U.S.
Mobile, low­altitude data collection methods such as drones and aerostats may help augment
the remote sensing from higher altitudes – in other words, fill in gaps on demand, provide high
resolution baseline measures, etc. These could help augment communications where cell
coverage is sparse. Then again, use of such equipment may create negative reactions.
Increasingly, consumer­grade mobile devices provide substantial platforms for the data
collection required in agriculture. Examples include Project Tango from Google, used for high
resolution 3D mapping – or for that matter, the widespread use of smartphones and tablets by
15
Yield Monitors and Maps: Making Decisions, Larry Lotz, Ohio State (1997)
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8. farmers out in the fields. This trend is expected to continue, as specialized devices converge
into the general category of consumer mobile.
Clearly, this part of the vendor landscape is becoming crowded. There are definite needs for a
wide variety of data sources and sensor types. Even so, the tendencies of early vendors does
not support a wide playing field in the long term. On the one hand, many if not most of these
vendors attempt to “own” data and push their feature sets far up the technology stack. On the
other hand, without effective interoperability those vendors are creating data silos. Farmers must
focus on operations, of which data+analytics only comprises a portion. Demand will compel
interoperability to some extent: a similar effect was observed among early Internet vendors as
Web 2.0 practices drove adoption of open standards. Meanwhile consolidation is inevitable, with
the larger Ag players such as Monsanto and John Deere and more traditional IT vendors such
as IBM, Cisco, and arguably also Google in a good position to carve up market share.
Addressing the telecom connectivity issues specifically, note the tension emerging from multiple
drivers:
● Increasing pervasiveness of low­cost sensors
● Increasing instrumentation of almost all equipment
● Demand for near real­time data collection and analytics
These factors will continue to push the data collection issues that are already stressed. Note
that sophisticated sensor networks (e.g., from NI) tend to have embedded prognostics, such
that computation can be pushed out to the edge – using partial aggregates and other
computational techniques. A general trend in large­scale data analytics for the Internet of Things
(IoT) will be to push as much processing out to the field as possible. That becomes necessary 16
for real­time stream processing, and also to help reduce data rates – which meanwhile continue
to grow substantially.
Probabilistic approximation techniques for data streaming (compressed sensing) may also
become quite useful here. Potentially, much can be adapted from state of the art open source
projects that address mission­critical data infrastructure for low­latency use cases. See two
examples from Twitter:
● Summingbird by Twitter engineers: Oscar Boykin, Sam Ritchie, et al.
● Add ALL the Things: Abstract Algebra Meets Analytics, Avi Bryant (formerly Twitter)
16
Industrial Internet: Pushing the Boundaries of Minds and Machines, Peter C. Evans and Marco
Annunziata, GE, (2012­11­26)
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9. Elastic Infrastructure
At the second stage, which we label as elastic infrastructure, where the collected data gets
uploaded and stored – somewhere – so that it can be prepared for analysis. From a high­level
standpoint, better connectivity in rural areas is a key enabler of increased farm efficiency and
productivity. Opportunities are emerging for new kinds of mixed initiatives, including
public­private partnerships, that in improving these communications networks can also provide
multilateral benefits to farming communities. Such an infrastructure is akin to laying the
foundation for the next generation of precision farm production.
Vendors include:
● Google Earth Engine
● Marinexplore
● AmigoCloud
● SpaceCurve
● Agralogics
These vendors tend to have cloud­based implementations, and use SaaS or subscription
models for pricing, which in turn leverage more general IT vendors in the cloud space:
● Amazon AWS
● Google Compute Platform
● Rackspace Public Cloud
● Microsoft Azure
● HP Public Cloud
Additionally, wireless networks represent another kind of infrastructure vendor:
● Wise Networks
● Sensoraide
Ultimately these vendors will likely confront competitive pressure from more traditional network
vendors, e.g., Cisco Systems, Juniper Networks, etc.
Infrastructure vendors inherit the downstream issues generally encountered by data aggregator
businesses: privacy, metadata alignment, curation needs, effective licensing, tracking lineage,
import/export, etc. In regulated industries (Finance, Health Care, etc.) these issues must be
addressed directly – whereas in newer industries (e­commerce, social networks, etc.) such
controls are still in relative infancy. Ideally, good practices should be built in before regulation
appears.
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10. Note that infrastructure businesses tend to bundle data enrichment and analytics services in
addition to a core function of data transport and storage. So far, vendors tend to differentiate with
particular value­added specialties:
● time­series analysis and geospatial analysis
● metadata alignment / schema / lineage for a wide variety of data sets
● blending farm data with other external data (Open Data from gov sources)
● support for curation, addressing data quality issues introduced during collection
● allowing customers to create data products for resell
● managing interfaces (aka “app stores”) for third­party data products
● integrating mobile devices with service fleets
As data products continue to leverage machine learning, other important issues for elastic
infrastructure include:
● contingencies to upload data at scale via alternative channels
● data preparation at scale: imputed missing values, feature engineering, etc.
● ultimately, provide for queries, approximated metrics, etc., to feed analytics
● compression technologies
● data processing and computation at the edge (as noted above)
Backlash based on privacy concerns from farmers could ostensibly change infrastructure
strategies significantly. Also, privacy laws in different regions (e.g., EU) will have impact on data
policies. Both factors indicate eventual regulations in this stage of data infrastructure. The
traditional IT vendors have addressed these kinds of issues before many times; however,
start­ups may encounter difficult challenges advocating at that level of policy and government.
Focusing on the core problems of elastic infrastructure, most of the vendors do not pay enough
attention to the needs of data preparation (curation, cleaning, metadata alignment) prior to
serving data to the analytics downstream. Experience from other domains (e.g., ad­tech, social
networks) shows that the bulk of the work performed in data infrastructure at scale is in 17
clean­up prior to analytics use cases. Marinexplore is an exception in this case, providing
metadata alignment across a wide variety of data sources.
Another issue concerns data workflows on a farm. Generally there are teams of people, working
concurrently at different locations. There are important requirements for data to be updated
across the team in real­time. Given the scale of the data, this will require effective use of tiered
architectures, balancing what data preparation gets handled in the cloud versus on a mobile
device. AmigoCloud is an exception in this case, providing real­time updates among the mobile
devices used by a team on a farm.
17
Data Jujitsu: The art of turning data into product, DJ Patil, O’Reilly Radar (2012)
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11. Meanwhile, far too many of the data collection and analytics vendors attempt to provide their
own services for elastic infrastructure. Ultimately these get run on shared infrastructure (public
clouds) anyway. Note there has been a tendency for the large IT infrastructure vendors to move
up the technology stack, particularly in lucrative verticals. Again, agriculture represents a $15T
annual market globally. It is inconceivable that start­ups at this stage will not experience
significant economic pressure from the more traditional IT vendors for networking and storage.
Expect much consolidation at this stage, but also opportunities for disruption in key areas such
as compression and local computation.
Analytics
At the third stage, which we label as analytics, metrics get assessed or predicted for multiple
components that feed into agronomic models downstream. Note that the word “analytics” has a
variety of usages, ranging from reporting/dashboards out to real­time algorithmic modeling and
optimization. The usage in this stage in particular is more for predictive analytics.
Some examples include specialized analytics that are currently bundled with the corresponding
specialized sensors:
● water stress
● integrated pest management
● nutrient analysis
● localized weather
Again, most vendors currently attempt to own the data and the full technology stack. Specialized
sensors and analytics get bundled with infrastructure services – and yet without effective
interoperability and import/export this implies (and indeed, produces) much duplication. In turn,
this creates unnecessary extra costs for farmers and acts to impede innovation in the larger
ecosystem. We see similar cases downstream, where analytics for agronomic models have
overlap with market analytics, e.g. Agronometrics. Overall, the industry term "seed to sale" has
more than one connotation; however, in the context of data vendors it tends to describe features
to manage some aspect of analytics from planting through harvest and sales – likely at the
expense of features for interoperability.
Duplication of resources will tend to drive mergers and acquisitions over time, as vendors
consolidate their shareable components (elastic infrastructure) while combining their
specializations (sensor, analytics) into complementary packages. It is interesting that the large
vendors in agriculture data analytics – e.g., Monsanto and IBM – have apparently avoided
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12. bundling infrastructure and instead focus on specific data products such as risk metrics used for
crop insurance:
● Climate Corp / Monsanto
● IBM / Deep Thunder
An interesting targeted offering in this space is OlaSmarts, which bundles sensors, processing
and analytics into multiple verticals. One subsidiary focuses on precision agriculture, e.g., helps
farmers cut irrigation costs, particularly in water­constrained environments. It appears to be
positioned contra to most other strategies that focus on “seed to sale” and OSFA.
Another kind of vendor emerging in this category is represented by imaging analytics services,
e.g., Ceres Imaging, which leverage data from multiple sources to produce multiple kinds of data
products. Perhaps the realities of remote sensing have helped temper vendors’ speculations:
remote sensing data rates are very high, imaging algorithms are complex, and so this specialty
presents a highly skewed buy­to­build ratio. Based on the trajectories of data services in other
domains – ad­tech, security, finance, etc. – this is likely to become a more viable business
model than the “seed to sale” product attempts.
Keep in mind that effective analytics for agriculture data often implies integrating a wide range of
data sources. For an excellent discussion of this topic, see Sensor Fusion for Precision
Agriculture, Viacheslav I. Adamchuk, Raphael A. Viscarra Rossel, Kenneth A. Sudduth and
Peter Schulze Lammers (2011). Lessons from e­commerce reinforce this point. On the one
hand, data silos are not effective in the long run. On the other hand, multiple data sources get
leveraged to mitigate missing data, data quality issues, etc. Silos will tend to conflict with the
more effective agronomic modeling approaches that emerge over time. It stands to reason that
as the field matures, vendors will focus less on “seed to sale” product attempts and more on
improving predictive power by leveraging multiple data sources. Again, the larger vendors
appear to have recognized this point already.
The accuracy of analytics is sensitive to calibration issues . Calibration sites (i.e., research 18
farms) needed for technology development tend to require large­scale capital investments,
aggressive partnering, etc. In the near term, this implies more acquisitions by the large players:
Monsanto, IBM, etc. In the long term, services for “crowdsourcing” calibration sites will likely
emerge to allow more cost­effective R&D for start­ups.
Lessons from finance show that predictive modeling which has impact on large­scale capital
investments tends to be carefully audited and controlled. Concerns about data provenance and
model transparency get emphasized in analytics products. In machine learning there is a
perennial tension between model interpretability and predictive power. In other words, there are
18
“Yield Monitor Calibration Tips: Making The Most From Your Data”, Nathan Watermeier, Ohio State (2004)
Agricultural Data (Q2 2014) The Data Guild Page 12

13. design challenges for satisfying the common need to answer the “why” question for a particular
decision. Product managers insist on interpreting results from automation. Auditors require
accountability and controls for any modifications to critical decision­making systems. Executives
wish to have complex analytics summarized as a short list of rules for their organizational
learnings. Meanwhile, there is a constellation of reasons for feedback from analytics into
engineering in general, e.g., for improved feature engineering, or resource trade­offs – with the
latter being increasingly crucial to embedded use cases such as mobile devices and robotics.
Overall, the knowledge discovery process requires a measure of rightful skepticism about what
the machine is doing in making a particular decision. This is not an intuitive response in most
businesses, currently, and will increasingly become a pain point as the data rates and
dependence on analytics escalate. Consequently, the capital structure of corporate farms may
impede adoption of more advanced analytics. That could open opportunities for competition from
smaller farms and new kinds of data­intensive co­ops.
Other issues likely to be faced by analytics products include:
● model portability vs. lock­in (e.g., use of the PMML standard to migrate models)
● batch vs. real­time/streaming
● automation for feature engineering and model evaluation
That last point is particularly salient. Without interoperability, open standards, model portability,
etc., the proprietary analytics products and services effectively become static and
anti­competitive. Farmers and ag analysts lack the ability to conduct effective A/B tests or other
means of evaluation (e.g., tournaments) to compare analytics. That is (we hope) likely a
near­term artifact, and will change as vendors recognize the benefit of proving their analytics
directly with individual farmers’ data.
Another area of analytics that is potentially quite important are the efforts to support subsistence
farmers in other geographic regions, e.g., Agrepedia in Ethiopia. The point is that aggregate data
can be pulled off to the cloud, where predictive models can be run cost­effectively (e.g., pricing
models at harvest time) then provided to subsistence farmers via SMS. Of course, given the
normative level of connectivity in the rural areas of the U.S. today, such an approach could be
equally viable there.
Telecom providers tend to be powerful in many areas of the world, and services such as SMS
are relatively available and cheap. SMS can be adapted as data portals for cloud­based
infrastructure and simple, but effective, analytics services. This approach has excellent
implications, given that harvests in these regions get sold to intermediaries who tend to be
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14. aggressively extractive . Cloud­based services outside of a distressed region could help 19
disintermediate entire layers of corruption.
Farm Operations
At the fourth stage, which we label as farm operations, there are a variety of different functions
to manage, including:
● seed catalog selection
○ DuPont Pioneer
○ Yield Pop
● activity calendar
● weather – both short and long term predictions
● asset inventory
○ Trecker
● yield maps
○ Farmers Edge
○ Solapa 4
○ VitalFields
● livestock management
○ FarmerOn
● commodity price monitoring
○ FarmLogs
○ Agronometrics
○ HarvestMap
● accounting workflow
○ Granular
● contracts, deliveries
● harvest storage
An essential point of precision agriculture is that by combining analytics based on a variety of
data collected from sensors (including satellites, drones, etc.) along with field topography, farm
operation history, etc., the variability of crops at specific locations can be leveraged to improve
overall yield: modify the seed density, modify the inputs, etc. For example, consider the
statement from the acquisition of Climate Corporation by Monsanto:20
19
Many intermediaries buying harvests are effectively “loan sharks” who charge 50% interest rates, etc.
20
“Monsanto to Acquire The Climate Corporation, Combination to Provide Farmers with Broad Suite of
Tools Offering Greater On­Farm Insights”, Business Wire (2013­10­02)
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15. The companies estimate the majority of farmers have an untapped yield
opportunity of up to 30 bushels to 50 bushels in their corn fields, and they believe
that advancements in data science can help further unlock that additional value
for the farm.
Given current prices the delta in yield would be approximately US$200/acre for corn. Without 21
question, yield is the key performance indicator (KPI) on which corporate farms in the U.S. rely
most. Even so, yield is not necessarily the best metric of success for farmers overall. Outside of
the US, arguably the best interests of the smallholder and family farms are to optimize for return
on investment (ROI). While many point to the role of scientific advances for increasing crop
yields dramatically, it is important to note that these advances over the past two centuries have 22
come at the cost of disproportionately increased inputs (water, nitrogen, phosphorus, etc.) which
are the critical resources. Viewed through that lens, the precision estimates for aggregate yield
are perhaps most acutely in the context of financial traders.
Another issue is that farmers tend to want immediate access to operations data – analytics,
history, etc. – via their mobile devices. Even when the data is not needed immediately, it still
provides a comfort for adoption/learning curve as these technologies move into the field. That
places a stronger need on multi­tier architectures and trade­offs between cloud­based
infrastructure and mobile device capabilities.
While some of those vendors focus on specific operational concerns, other vendors pursue
One­Size­Fits­All (OSFA) strategies , attempting to encompass all the operations of a farm. 23
This strategy is akin to “seed to sale”, with related risks. Examples include:
● OnFarm
● Farm at Hand
Lessons from e­commerce indicate that OSFA approaches tend to collapse after the early
adopter phase wanes. Compelling solutions for farm operations will focus on interoperability,
well­defined interfaces, and the ability to accommodate “plug­ins” from alternative analytics
sources. Again, a similar effect was observed among early Internet vendors as Web 2.0
practices drove adoption of open standards.
21
http://www.quotecorn.com/
22
“Nasa­funded study: industrial civilisation headed for 'irreversible collapse’?”, Nafeez Ahmed, The
Guardian (2014­03­14)
23
“One Size Fits All”: An Idea Whose Time Has Come and Gone, Michael Stonebraker, Uğur Çetintemel,
ICDE Proceedings (2005)
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16. Overall, farm operations software is a likely point to emphasize recommendation services, i.e.,
feedback loops for consuming analytics products based on aggregated data. For example,
successful outcomes in one region may imply generalized learnings (recommendation systems)
in other similar regions. This value proposition may drive greater levels of interoperability, as
vendors attempt to monetize their aggregate data.
Also, this stage of farm operations provides a good point for evaluation of analytics, such as
model tournaments. History of predictions must be managed and leveraged. This implies even
greater concerns about data privacy and security – much more so than sensor data collection –
since aggregate data becomes more valuable to bad actors.
Distribution
The fifth stage, which we label as distribution, is also sometimes called “procurement”. More
traditional forms of supply chain analysis can begin to be applied at this stage:
● traceability: tracking containers, logistics, etc., via pallet RFID, etc.
○ Opara
○ TransVoyant
● direct market sales (disintermediation)
○ Plovgh
● shipping and storage costs
● accountability/sustainability reporting
Data becomes more “refined” as it aggregates downstream. Other forms of analytics at this
stage feed downstream: geospatial estimates of required inputs, aggregate yield, etc.
One poignant issue at this stage is that food processing (post­harvest) tends to use more fresh
water and energy resources than the farming. An emerging business model for traceability:
end­uses (Whole Foods, high­end restaurants, etc.) place a premium on accountable products,
so there's a pricing differential. Processors which are less accountable become suspect –
largely because insiders already know they are the major source of the problem re: massive
water waste, contaminants, etc. Effective monitoring, data collection, elastic infrastructure,
analytics, etc., are needed to ensure that the distribution stage is accountable for what gets
consumed. That implies another kind of feedback loop in the data flows: what are the
water+energy implications of a harvest after it leaves the farm?
Market Aggregation
Agricultural Data (Q2 2014) The Data Guild Page 16

17. The sixth stage, which we label as market aggregation, concerns the kind of agriculture data
that most people are already familiar with:
● global market analytics
○ Mercaris
○ Gro­Ventures (Africa focus)
● commodities trading
○ AgFlow
● market intelligence
○ Cleantech Group
○ Food Tank
○ Praescient Analytics
○ Palantir
○ Stratfor
● public policy
○ USDA
○ GODAN
○ FAO AQUASTAT
Traditionally this stage of agricultural data has been focused either on short­term opportunities
(commodities trading) or very high­level concerns from qualitative perspectives (policy making , 24
global food security, natural resource management).
Opportunities abound for leveraging feedback loops in the data, algorithmic modeling, aggregate
data services driving hyperlocal (per­block) recommender systems, etc. This is especially the
case as sensor networks become more pervasive and as remote sensing services continue to
provide better, higher­resolution data. A key point is to focus the data services so that markets
steer away from short­term extractive practices (hedge funds) and toward opportunities to apply
data to make food production and distribution systems more responsive, resilient, and efficient.
General Insights
An earlier question asked, who are the stakeholders in this system? We find a number of actors
who represent stakeholders in the flow of agricultural data, each of whom represent diverse,
sometimes conflicting, interests in the larger value chain:
● farmers
● corporate farms
● co­ops
● public/private partnerships, e.g., water districts
24
Of course, the impact of policy changes should be modeled and considered prior to implementation.
Agricultural Data (Q2 2014) The Data Guild Page 17

18. ● technology vendors
● shippers/storage
● wholesalers/distributors
● food processors
● end uses: groceries, restaurants, etc.
● public policy makers: USDA, CAP, etc.
● financial markets/traders
Which of these stakeholders require more transparency into the data flows? For example, do the
end use cases such as restaurants require traceability at the level of individual palettes, all the
way through food processors, shippers, etc., back to the origin at a farm? Does that need for
traceability conflict with legitimate concerns about data privacy, or could it open the door for data
security concerns and other abuses? In any case, we can use these identified stakeholders to
analyze the different issues identified for agricultural data.
Overall, the point of data flowing across these several stages is to generate actionable insights,
at very large scale, and in many cases with relatively low latency. That is a tall order, and voices
within agriculture lament the volume/velocity/variety of the data, and the “needle in a haystack”
effect of attempting to draw actionable insights from mountains of raw data.
Even so, it’s important to keep in mind that other verticals – e.g., finance and telecom – have
achieved this already for their own specific needs. Agriculture is known for relatively
conservative practices, with perhaps a 10­year cycle for adopting new technologies. To change
that aspect in any way, one must understand the root causes: among which uncertainty and
enormous risks dominate whole markets, local communities, and families. Farmers earn 40
paychecks in a lifetime, and there is little margin for error.
Even so, as the following drivers indicate, there are good reasons to accelerate key areas of
technology adoption. Some of the more conservative bias against new technologies may need to
be adjusted due to other looming priorities.
Driver: Drought Outlook
Circa 2014, the predominant issue being discussed in California (and hence, proximate to many
of the technology vendors) is drought. Variance in snowpack levels causes serious shortfalls in
water resource allocations via aqueducts – with obvious impact on farm operations now in crisis.
In addition, variance in the timing of the water cycle stress natural resources and infrastructure
throughout these connections, from snowpack to farm or food processor usage: reservoirs, river
ways, aquifers, levees, seawater incursion, etc. Attempts to control nature usually fail sooner or
Agricultural Data (Q2 2014) The Data Guild Page 18

19. later. For further details, see “The Emerging Science of Environmental Applications”, The Fourth
Paradigm, Jeff Dozier, William Gail (2009). In particular, two crucial factors have been missed in
the related science: mountain hydrology, and measuring/modeling the evaporation cycle.
Another complex issue is how applications of surface water (e.g., aqueducts, diverted rivers,
etc.) interact with groundwater usage in the context of aquifers. For example, in the California
Central Valley, there have been widespread examples of land subsidence. In conditions where
ground water pumpage rates exceed the recharge and surface water inflows, the structure of the
aquifer collapses : the rock falls in on itself, leading to sinkholes, damage to infrastructure, and 25
less water holding capacity.
Without better modeling of the water cycle, the impact of these variable factors on agronomic
models at scale causes serious problems with the effective use of agricultural data. It also
implies opportunities for researchers and entrepreneurs, as well as public/private partnerships
involving the larger vendors.
During an extended drought, much of the economics of agriculture shifts. The vendor landscape
will experience many changes. For example, growers for leafy greens will sell their water to
orchards. Both parties win: the vegetable farms realize greater revenue streams by arbitraging
water rights, and the orchards must preserve their capital investments. Therefore the priorities
for data use change dramatically. Also, reluctance to adopt new technologies is lowered as
farmers recognize existential risks to their businesses, and scramble for any potential remedy.
This is currently a major driver for data use at scale, and technology vendors may benefit from
the California drought, since many investors recognize the immediate business use cases for
technology solutions. Meanwhile, throughout the world there are cycles of drought and flooding
which must be addressed. Hopefully the experience in California, now prompting attention from
technology firms and investors in Silicon Valley, will have benefits globally.
An audience remark at a recent From Farms to Foodies industry forum summarized the 26
essential problem of water and other natural resources: “We must design systems to help
regenerate the soil, not be extractive: using technology from a regenerative standpoint, that is
the bigger challenge.” This has been a pervasive problem in agricultural data at the market
aggregate stage where emphasis has been placed on extractive use cases, e.g., hedge funds.
Driver: Privacy and Security Issues
25
“Ground­water availability in the United States”, Thomas Reilly, et al., USGS circular 1323 (July 2008)
26
Paul Dolan, Mendocino Wine Company
Agricultural Data (Q2 2014) The Data Guild Page 19

20. Entirely separate from the drought/flooding issues is a another constellation of concerns which
are almost as challenging. The accelerating use of agricultural data at scale – and with that, the
increased use of wireless networking, cloud computing, analytics vendors, etc. – brings into
question a number of privacy and data security issues. Two recent articles express these
themes succinctly:
Big ag companies could now control a data trove that presents privacy and business
risks to farmers who don’t want to share the secrets of their trade with rivals or the
government. – Businessweek27
At this point, digging into data could represent the next big step forward for U.S.
agriculture, but only if farmers feel safe taking the plunge. That’s why the Farm Bureau,
the country’s largest farm group, hashed out a new policy for sharing farm data in
January. It includes the right for farmers to delete their data whenever they want. – Iowa
Public Radio28
Brad Martin @Paia Corporation, an expert in embedded systems and hardware data security,
noted several points to be considering carefully about data privacy:
The self­motivated innate wisdom of farmers is apparent in their unwillingness to donate their
data for “big data” programs in which they are not certain to benefit proportional to their offering.
The past poor­neighbor behavior of industrial partners (c.f. seed patents) has led to a general
distrust of motives from those who own the data against those who would profit from using the
data (i.e., the industry).
Without the benefits of data sharing, each farmer would likely have lower yield and higher
production cost than an optimized analysis would tend to provide ("commons dilemma"). It is
somewhat less clear whether well­managed farms will themselves benefit on balance from the
overall improvement in yields and certainly unclear if they will obtain any benefit from increased
yields on other, competitive fields in the same market.
There may be a “slippery slope” option in which certain aspects of the farmer's data are willingly
exchanged for services rendered: a “free” crop analysis tool that keeps (and sells) related
records to third parties (anonymized, of course!). There may be a willing limit to the amount of
sharing.
27
“Farmers Press Agribusiness Giants for Data Security”, Shruti Singh, Jack Kaskey, Businessweek
(2014­01­23)
28
“Farmers Worry About Sharing Big Data”, Grant Gerlock, Iowa Public Radio (2014­02­18)
Agricultural Data (Q2 2014) The Data Guild Page 20

21. Once a farmer's information is merged into an aggregated database, it becomes part of a very
desirable target. We know that, given sufficient motivation, virtually no data system is immune to
compromise. This is not unlikely: several countries who are major agriculture trading partners
with the U.S. have substantial IT espionage programs that have proven effective against even
the world's most savvy network companies.
It's also not clear whether such large databases would be protected from government
interference. There's no current indication that USDA self­interest would lead to
micro­management of farm operations, but it's hard to imagine that compelling uses wouldn't be
found for databases of this type, leading to substantial risk to personal privacy for farms of all
sizes. Vendors will not knowingly allow uninformed compromise of their customer's/client's
databases by third parties.
More troubling is the possibility that these vendors – most of which provide cloud­based
aggregation/analytics services to farms – themselves become the targets of cyber attacks.
Other industries have seen cases in which innocuous vectors (common web exploits, spear
phishing) are used to gain access to a large population of special interest. If data services
become dominated by a small handful of software vendors, a targeted exploit surreptitiously
downloaded to many farms could be engineered to attack whichever software it encountered.
Once compromised, a farmer's data could then be uploaded to the Internet for the attacker's own
use.
It's simple enough to say, "keep your data safe, keep your systems updated, be careful about
viruses" but it's extremely difficult to do so in a comprehensive fashion. A highly­sophisticated
attack will not use any of the known and detectable mechanisms. Antivirus programs and web
filters won't work against them. Absent any specialized protection, the farmer who keeps data on
a computer that is not directly connected to the Internet will be best protected from attack and
loss.
Regardless of the extent of security precautions taken, it is clear that there will be bad actors
and there will be security breaches. The consequences will be significant, and the risk grows
with the scale of networked, semi­autonomous systems – as in every other industry in the world.
Therefore the industry must prepare for backlash from farmers, from the public, and ultimately
for increased regulation as a consequence. Those vendors who can take action proactively by
building security and privacy controls into their offerings will benefit.
This driver of privacy issues reinforces the inherently conservative nature of agriculture. As a
John Deere executive explained, “For the sake of individual data pieces, we are not going to
trade in a relationship we spent 175 years building.” Even so, the priorities of drought and other
major drivers in agricultural data pose a dilemma: where are the appropriate risks for adopting
Agricultural Data (Q2 2014) The Data Guild Page 21

22. new technologies? Ultimately the vendors need to become viewed as “honest brokers” by the
growers. For example, providing decision support software that recommends another vendor’s
seeds.
Is this an argument for privacy­preserving, distributed data mining? In other words, for farmers
exercising greater control of their data and getting paid for access to their data by outside
parties? Imagine if standards could be created that support distributed data mining where one
would be permitted to derive certain kinds of results from the data, but could not access the raw
data directly. Analogies exist in e­commerce, where ComScore, QuantCast, DataLogix, and
others aggregate data about consumers browsing on the Internet. On the one hand, our
browsers aren't exactly gold lodes, except for phishing or passwords, whereas e­commerce
firms have become prime targets. On the other hand, individual farm operations store enough
aggregate data and analytics to be valuable for attacks. Plus, the value grows with each stage
downstream.
Driver: Open Standards and Interoperability
The span of the agricultural data flowing through several stages – from sensor to market
aggregates – implies that no one player could possibly own all of the data, or all of the tech
stack. Consequently, there are distinct needs for interoperability to allow this field to grow. In
other words, unless vendors can find effective ways exchange data at critical points there will be
deadlocks in the overall system: data silos, limited degrees of sensor fusion, less predictive
modeling possible, etc.
As has been demonstrated in a variety of other verticals, interoperability is best achieved by
when vendors can agree to adhere to open standards. For example, the open standards HTTP
network protocol and HTML markup format allow for a wide variety of Internet browsers, web
servers, content, web services, etc. As it stands currently there is way too much fragmentation
in the flows of agricultural data. Platforms and open standards are needed to accelerate
innovation and help the field mature.
An open standards body was recently established for agricultural data, the Open Ag Data 29
Alliance (OADA). Their initial work includes a presence on GitHub with an open source code
repository. The stated principle of OADA is that “each farmer owns data generated or entered by
the farmer, their employees, or by machines performing activities on their farm.” Part of the
approach is to establish a common set of APIs on different cloud providers: ostensibly, farmers
could migrate their data between different providers. That is a start, and though it does not yet
begin to address many of the data security issues, the OADA is providing a forum for
29
“Group to Promote Open Data Standard”, Willie Vogt, Farm Futures (2014­03­12)
Agricultural Data (Q2 2014) The Data Guild Page 22

23. community discussions.
Moreover, OADA has substantial support from Monsanto, which has stated its aims to 30
integrate and assure data privacy:
The data created by a farmer, or generated from equipment the farmer owns or
leases, is owned by that farmer and should be easily managed.
Other interesting efforts toward these ends include:
● Standardized Precision Ag Data Exchange (SPADE) Project
● Spatial data infrastructures for precision farming data ­ standards and system design
criteria, Martin Weis (2007)
Compelling solutions for farm operations will focus on interoperability, well­defined interfaces,
and the ability to accommodate “plug­ins” from alternative analytics sources. Similar effects
were observed among early Internet vendors as Web 2.0 practices drove adoption of open
standards. In particular, it will be important for vendors to:
● surface their products’ metadata to help avoid potential data silos
● allow for data import/export between vendors, while propagating schema and lineage
● support popular geospatial formats, datetime formats, etc.
● use effective data licensing that does not impede data aggregation downstream
Even so, the capital structure of corporate farms may conflict with adoption of more advanced
analytics and interoperability. If so, that could open opportunities for competition from smaller
farms and new kinds of data­intensive co­ops.
One of the open standards that is becoming quite important for agricultural data is RFID, in terms
of traceability of farm products, accountability of processing and procurement, etc. From a public
policy perspective, this also provides the capability to reverse­engineer the procurement chain 31
in case of illness or contamination.
Arguably, there is an open standard used by analytics vendors in general, called PMML, which
could readily be used in agriculture. It provides for model portability, guards against vendor
lock­in, allows analytics to scale (e.g., on cloud­based infrastructure) independent of where the
models are trained, etc. Vendors providing analytics products and services would need to agree
on PMML for model import/export. That is likely to occur over time anyway as more traditional IT
vendors move into this space.
30
“Guiding Principles on Data and Privacy”, David Friedberg, The Climate Corporation
31
“RFID's Role in Food Safety”, Mark Roberti, RFID Journal (2013­07­29)
Agricultural Data (Q2 2014) The Data Guild Page 23

24. Driver: Funding Analysis
As recently as Q1 2012, the outlook for clean tech investments had been receding . Many of 32
the Silicon Valley venture capital firms backed away from agriculture. One notable exception is
Khosla Ventures, which has been consistently engaged in this area. They funded two recent
acquisitions by Monsanto Growth Ventures: Climate Corp and Solum.
Other predominant sources of capital investment include:
● family office investments
● strategic funds: Monsanto, Dow, BASF, etc.
● investment bankers
● challenge funds/incubators: Start­Up Chile, Africa Enterprise Challenge Fund
● crowdfunding: Kiva, AgFunder, Angel List
One geopolitical aspect becomes apparent in an analysis of the agricultural data vendors: a
large cohort of Ag data startups are based in Southern Hemisphere, and most of these have 33 34
been involved with Start­up Chile. So there is a competitive tension emerging between Monsanto
Growth Ventures and other strategic funds in the Northern Hemisphere (mostly Silicon Valley
since 2013) and incubators in the Southern Hemisphere.
This regional economic tension will likely be shadowed by public policy. For example, Mexico
recently ruled against allowing use of Monsanto GMO products . This tension echoes among 35
the influential buyers, e.g., Whole Foods has announced its intent to require GMO labeling by 36
2018. Mexico plays a unique role in the borderlands: it is within the Northern Hemisphere and
obviously sells much of its output to the United States, and yet politically and culturally it finds
resonance with other Latin American countries in the Southern Hemisphere.
This begs two questions. On the one hand, will other Silicon Valley venture capital firms rush
back into clean tech investments following the two recent (circa 2014Q2) successes of
Khosla/Monsanto? On the other hand, will national governments tilt the geopolitical playing field
by subsidizing incubators following the success of Start­Up Chile?
32
“The state of cleantech venture capital: what lies ahead”, Matthew Nordan, GigaOm (2013­03­27)
33
“Avance Proporción de Países Seleccionados Top 5”, slide 9, Start­Up Chile (2014)
34
AngelList “Agriculture Startups” (2014)
35
“Mexico Judge Bans Monsanto’s GMO Corn”, Devon Pena, Environmental and Food Justice (2013­10­11)
36
“Our Commitment to GMO Labeling”, Whole Foods Market
Agricultural Data (Q2 2014) The Data Guild Page 24

25. Conclusions
The following trends are in progress for each of the six stages:
Stage 1: Data Collection
● the needs of this stage are complex, but the vendor landscape is becoming crowded
○ implies much consolidation among startup vendors
○ new entrants face headwinds in the face of market fatigue
● remote sensing products tend to augment sensor networks
○ implies large­scale use cases for data fusion, i.e., cloud­based apps
○ startups that focus too much on one data source are probably doomed
● farm robotics and tractor instrumentation (mobile) will augment static sensors
● absent key learnings from other verticals, startups tend to repeat critical mistakes
○ creating data silos
○ desire to "own" the data and the tech stack
○ lack of promoting standards for interoperability
● data quality, communication, and privacy issues beleaguer vendors
○ implies that regulatory policy will emerge, enterprise incumbents may dominate
○ as public policy fails to respond, private solutions emerge
● computation and decisions/alerts are pushed to the edge of new sensor networks
○ sensors become extended computational resources that can take action
○ compression techniques, coupled with computational resources in low­power
packages create new opportunities to pervasive sensors nets that rely less on
always­on network connectivity
● farm operations use cases will drive toward more real­time processing
○ implies pushing computation out to the edge, as a truism throughout the larger
Internet of Things space
○ other verticals (finance, telecom, search) confronted this need already
○ in terms of open source strategy, look to Twitter for leadership
Stage 2: Elastic Infrastructure
● traditional IT infrastructure vendors will move into the space, edging out the startups:
○ economies of scale for networking, storage, cloud services, etc.
○ incumbents can navigate regulatory policy more effectively
○ their business tendency to move up the stack for lucrative verticals
● startups attempting a “seed to sale” strategy are mostly doomed
● successful startups will differentiate by focusing on specialized use of infrastructure:
Agricultural Data (Q2 2014) The Data Guild Page 25

26. ○ emphasize features that address ongoing pain­points, such as data preparation at
scale prior to analytics, e.g., data federation, clean­up, curation, metadata
alignment, etc.
○ edge their way into the subsequent stage of analytics by specialized use of
elastic infrastructure: time­series, geospatial, imaging, etc.
○ position themselves for acquisition by IT infrastructure incumbents
● Demand for better communications infrastructure grows
○ New opportunities for both established vendors and new entrants to fill gaps,
especially through strategic partnerships
○ Progressive communities establish public­private partnerships that include tax
incentives, financing, and other components that make buildout more
cost­effective
● North/South Hemisphere tension emerges
○ IT incumbents from the industrialized North displace business models for startups
predominantly based in the developing South
○ long product cycles in the North may benefit the relatively nimble startups in the
South, if local politics do not interfere
○ this does not imply a clear “winner” between the two
Stage 3: Analytics
● analytics products are not an end in themselves; they feed metrics into farm operations
○ misplaced emphasis at this stage poses additional risks for siloed strategies
○ analytics offerings that are tightly coupled to feedback loops with users in specific
workflows will edge out static dashboards
○ machine learning becomes a key factor use cases where local optimization and
customization provides measurable benefit
● platforms leverage the coming generation of low­power, high­computation sensors
networks
○ present new opportunities for efficient, highly­targeted analytics that rely less on
constant connection to the cloud
● "seed to sale" strategies drive startups to bundle infrastructure services with data
collection analytics
○ implies significant duplication of resources and extra costs to farmers
○ duplication costs drive acquisitions and mergers, plus “seed to sale” aversion
○ on the other hand, bundled services of multiple vendors (through strategic
partnerships) could succeed if the value of such a bundle is obvious
● large analytics vendors may avoid infrastructure plays
○ left to IT incumbents who typically pursue up the stack in lucrative verticals
● calibration is a major issue in practice, huge downsides for analytics innovation
○ requires large capital investments, aggressive partnering, etc.
Agricultural Data (Q2 2014) The Data Guild Page 26

27. ○ will drive acquisitions near­term by large analytics vendors
○ may drive "crowdsource" calibration services long­term
● as regulatory policy emerges, predictive analytics come under pressure
○ implies trade­offs in favor of accountability at the cost of accuracy
○ corporate farms may be too conservative to navigate those issues
○ potential opportunities for new kinds of data­intensive co­ops
● without attention to interoperability and standards, analytics products become too static
and limit adoption
○ crucial needs being missed: feature engineering, model portability, tournaments
Stage 4: Farm Operations
● One Size Fits All, related to "seed to sale", represents an anti­pattern for startup viability
○ the strategy tends to collapse after early adopter phase wanes
● farmers demand immediate access to data via mobile devices
○ natural response to grappling with technology learning curves
○ accentuates needs for cloud­based infrastructure and real­time processing
● business needs for recommendation services at this stage
○ drives need for feedback loops and data products from aggregate stages
○ will tend to track a similar market evolution in e­commerce
● technology giants focused on yield optimization
○ ROI is a better metric for most farmers’ success (outside of the U.S.); however,
that is even more difficult to model
○ yield increases have come at the expense of disproportionately larger increases
for inputs
○ focus on the precision estimates of aggregate yield presumably serves the
interests of financial traders more so than farmers overall
● data at this stage presents a more lucrative target for bad actors
○ this heightens concerns about privacy/security
Stage 5: Distribution
● traceability is a driver at this stage
○ implies new kinds of business opportunities
○ surfaces new issues for privacy/security and accountability
● processing consumes more water+energy resources than farms do
○ must ensure accountability for consumed resources
○ accentuates needs for monitoring, data collection, elastic infrastructure, analytics,
etc., in parallel to farm sensors
○ potentially a big sustainability win and a big economic opportunity as the costs of
water+energy increase
● what are the water+energy implications of a harvest after it leaves the farm?
Agricultural Data (Q2 2014) The Data Guild Page 27

28. ○ consumers will demand transparency
○ implies opportunities for feedback loops and data products
Stage 6: Market Aggregation
● traditional focus at this stage have been ineffective
○ short­term opportunities (commodities trading)
○ very high­level qualitative concerns (policy)
● major opportunities for leveraging feedback loops in the data
○ algorithmic modeling, aggregate data services, etc.
● refocus data services to steer market opportunities
○ steer away from short­term extractive practices (hedge funds)
○ apply data to make food production responsive, resilient, efficient
Outlook: Forced Asymmetries, Tail Wagging the Dog
Increasing variance in snowpack levels and rising rates for anthropomorphic evaporation in
wealthy regions (e.g., California) will stress local infrastructure which is already at crisis levels
(e.g., aqueducts and transportation). This will force even greater asymmetries in production as
well as in technology innovation – in terms of relatively wealthy versus poor regions, where the
latter increasingly gain the upper hand for technology and expertise. Of course, niches will
persist, such as local organic farms near metro areas in the U.S. Even so, some of the large
stakeholders have vested interests in undermining even these: technology giants (for political
momentum, homogenizing toward their agenda) and financial traders (surfacing risk through
exposed data, extracting capital).
In the wealthy regions: available water resources will be redirected to capital­intensive,
high­margin crops such as orchards, vineyards, and premium livestock to preserve capital.
Meanwhile, the productivity and long­term commercial viability of these properties is decreasing.
Some crops will push north as grow zones change. Other crops will be pushed toward imports
(e.g., Southern Hemisphere) or incentivize urban agriculture at scale. Extensive monocultures
(e.g., grain) become increasingly subject to systemic risks on several fronts.
As risks increase for capital­intensive crops in wealthy regions, this segment of farmers will
become more averse to technologies that open the door to potential privacy and security
breaches. They have far too much to lose, while multiple bad actors have too much to gain. In
particular, financial interests could engage in aggressively extractive practices at a scale that
would make the 2008 credit default swap crisis look small by comparison.
An essential tension is that technology giants will insist on owning the data collection, analytics,
Agricultural Data (Q2 2014) The Data Guild Page 28

29. operations, etc., required for precision agriculture – either through product features (likely
short­term approach, least likely success) or through mergers and acquisitions (most likely
long­term success). However, prerequisites for their long­term commercial success are
diametrically opposed to the realities of farmers’ concerns about security breaches. Large­scale
security breaches will occur, and there will be political backlash in the wealthy nations.
Corporate farms already at risk due to water shortages, soil depletion, etc., will become likely
targets for hostile actions from the financial sector, particularly in the U.S.
Another inherent tension is that the more vital agricultural production continually gets pushed to
poor regions, which are predominantly family farms and smallholder farmers. Increasingly, these
become the technology innovators and over time may develop the best practices for efficient 37
natural resource management.
An outlook of forced asymmetries emerges. Effectively, the relatively wealthy regions will
promote conditions ripe for highly volatile financial problems locally, while exporting their most
vital dependencies to poor nations – which must in turn make increasingly more effective use of
natural resources through technology innovation. Likely losers in this equation include corporate
farms (legacy practices, inefficient process, capital risks) and incumbent vendors who
misunderstand the role of data at scale. Likely winners in this equation include technology giants
who leverage interoperability in lieu of owning the technology stack (e.g., Monsanto) and
technology centers in the relatively poor regions (e.g., Start­Up Chile) who by definition must
now produce the true innovators and in turn will tend to have some of the greatest financial
leverage.
The authors acknowledge contributions by Brad Martin and Bill Worzel in the development of
portions of this paper, and also acknowledge many helpful suggestions by other members of
The Data Guild.
37
Trade and Environment Review 2013: Wake Up Before it is Too Late, UNCTAD (2013): “This implies a
rapid and significant shift from conventional, monoculture­based and high­external­input­dependent
industrial production toward mosaics of sustainable, regenerative production systems that also considerably
improve the productivity of small­scale farmers.”
Agricultural Data (Q2 2014) The Data Guild Page 29