Big Data & Big Opportunities

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Big Data & Big Opportunities

Dr. Matt Darr, Iowa State University

For a copy of this slide deck please send an email request to [email protected]

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• No intent to be critical of, or endorse, specific products/services

to the exclusion of others that fulfill similar functions.

• Mention of product/service name is for information purposes

only.

• This discussion is a snapshot of what is available today, and is

intended to generate positive momentum around the ag data space.

• We must recognize that everyone along the chain must derive

income from “AG BIG DATA” to be commercially viable.

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What is Big Data?

Big Data is data whose scale, diversity, and complexity

require new architecture, techniques, algorithms,

and analytics to manage it

and extract value and hidden knowledge from it.

Digital Agriculture is the new industry which is combining

large data sources with advanced crop and environment

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Is Big Data New?

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• Autosteering and swath control technology have driven strong ROI which has led to a proliferation of GPS technology on farm.

– This leads to ‘free’ machine data. – Typical ROI in Iowa:

• 3.3% Planting Overlap Error, $7.89/ac • 7% Tillage Overlap Error, $0.96/ac

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• Data Generation & Capture

– Yield Maps, Soil Fertility, Aerial Imagery, UAVs

– Wireless Data Transfer

• Data Warehouse

– Cloud Data Storage

• Prescription Agriculture

– VRA, Multi-hybrid Planting

• Probabilistic Decision

Management

– Nitrogen Modeling

– Weather & Soil Suitability Modeling

Segments of the Digital Agriculture Industry

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• Satellite Delivered: – 5m Resolution

– Timing can be limiting but more options are becoming available

• Contracted Flight: – 1m Resolution

– Typically can schedule images within a +/- 3 day window around target date

• Unmanned Aerial Systems (sUAS) – ~3 – 10 cm Resolution

– If weather permits scheduling can be within a few hours of target time

Data Generation: What Role Will High

Resolution Imagery Play?

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High Resolution Imagery in Agriculture

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High Resolution Imagery in Agriculture

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High Resolution Imagery in Agriculture

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Data Warehouse

Crop Consultant Seed/Fert Supplier Machinery Supplier Insurance Agent Landlord Internal Mng Team Grower Driven Entity Pooled Analysis

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Data Analytics: Field Example

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Data Analytics: Field Example

Hybrid B Hybrid A

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Data Analytics: Field Example

Hybrid B Hybrid A

170 Acre Field, Continuous Corn

Hybrid B Hybrid A 200 150 100 50 0 G ra in Y ie ld ( b u /a c ) ₁₃₁ 176

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24 Compaction from previous machine operations Variety A Variety B

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Big Data Field Example

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Big Data Field Example

Highly productive zone

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Big Data Field Example

Healthy Plants in Compacted Area

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Big Data Field Example

Healthy Plants in Compacted Area

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Big Data Field Example

Weak Plants in Compacted Area

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Big Data Field Example

Weak Plants in Compacted Area

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Producer Value:

Quantify the impact of production practices. 120 acres x 70% compacted x 15 bu/ac yield loss =

1,260 bu yield loss = $5,000+ Cost of imagery = $240

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• Over 2,500 on farm trials

since 2007.

• Conducted in cooperation

with grower partners.

• Data is available in a

non-identifiable form through the On-Farm network

website.

• Increasing scale of the

dataset allows for strong assessment of

performance trends.

Data Analytics: Aggregated Data

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Data Analytics: Value of Aggregated Data

Hundreds of data points for comparison across a broad range of geographic and

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Data Analytics: Value of Aggregated Data

Hundreds of data points for comparison across a broad range of geographic and

crop production boundaries.

What if every pass across the field with a machine was an On-Farm trial?

How fast could we progress

agriculture if this level of data was collected and shared broadly within

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• Incorporate probability of

events occurring, mainly

weather related.

• Utilizes extensive historical

data and weather

forecasting data to drive

model predictions.

• As the season progresses

real time weather data in

integrated into the model to

improve robustness.

Probabilistic Decision Management

Deterministic Model:

Outcome is a single value with no randomness, i.e. soil sample based fertility recommendations.

Probabilistic Model:

Outcome is a range of potential values that represent

environmental variability and can be used to manage risk.

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Big Data Policy Issues Receiving Major

National Attention

• Farmer ownership of data • Farmer control of data • Disclosure of data usage

• Farmer choice for use of data • Portability of data

• Security from misuse • No vulnerability to FOIA

Farmers express concern about all of these issues.

• Compatibility of systems • Protection of GPS

• Regulation of UAVs

• Use of aggregated data

• Consistency of agreements • Simple language

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Producer Surveys on Big Data

Skeptical of the New Technology – 65%

 The biggest concern is misuse of farm data by:

 Fear that it favors the large farmers.

 Prescriptions will recommend only some products, i.e., are biased.  It doesn’t work. Agriculture is too complex.

Neutral or Nuanced in Attitudes – 19%

 It has potential, but must be implemented carefully.

Embracing the New Technology – 16%

 The technology is here to stay. Let’s embrace it and make it work for us.  No one that is highly profitable today is doing it with only their own ideas and

crop data.

• The ATPs • Activist groups • Grain traders

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• Will my data be pooled with any other producers for large scale data

analytics?

– If so, who will have access to this data? Am I compensated for the value of my

production intellectual property in the development of improved services?

• How do you manage data ownership?

– The majority of “bank” providers will stress that producers own there data. – Focus on the fine print around “licensing”. Many “banks” stipulate a perpetual

royalty free license. This means they can use your data for free even if you close your account.

– Will copies of my data be retained within the “bank” if I close my account? • Who has access to my data? Can I redirect my data to third parties of my

choice?

• What type of security protocols are in place to protect against cyber hacking

and data piracy? How will the “bank” avoid a Target incident?

• Is the “bank” independent or are they also trying to provide agronomic or

business management services?

Questions to Ask Your

Data Services Provider

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Digital Agriculture Opportunities for

Providing Data Services

• Additional service opportunities and closer

connections with clients specifically around product selection and input timing.

• New business opportunities in data

warehousing, production benchmarking, profit/loss analysis, and data analysis.

• Opportunity to expand trust relationship with

customers.

• This industry represents new market in

agriculture. Solutions that off the best blend of value and simplicity will win.

• Ag retail already has the largest ag dataset.

This is an opportunity to extend the value of this data and lead this new industry.

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The Risk of the Status Quo

The new technology has the potential to change the way

farming is conducted and the way agronomic advice is

provided.

 The “balance of power” for agronomic advice may shift to the

seed / biotech companies from local ag retailers.

 New service opportunities will become available to those ag

service providers who are far-sighted.

 Growers who adopt and integrated advanced technology will

have increasing advantages for growth and profitability.

 Technologies that prove valuable may be become required risk

tools in order to access capital or insurance programs.

Adapted from Big Data Project Report. 2014. The Hale Group, Ltd.

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Big Data & Big Opportunities

Dr. Matt Darr, Iowa State University

The over-all point is that new technology will not necessarily replace old technology, but it will date it. By

definition. Eventually, it will replace it. It's like people who had black-and-white TVs when color came out.

They eventually decided whether or not the new technology was worth the investment.