Challenges and Opportunities of Field

Data ManagementMargaret Johnson

Creative Component

Spring 2016

Background

Fountain, MN

Family Michael – married for 5 ½ years

Sawyer – 3 ½

Levi - 2

Agriculture Background Grew up on a dairy farm

Worked on dairy farms throughout college

Continue to dairy farm with my husband and father-in-law

500 cows, replacement heifers, 1100 acres of corn and alfalfa

Education and Work History

2008 - Graduated from UW-River Falls

Bachelor of Science in Agriculture Marketing and Communications Minor: Dairy Science

2008 – Acquired position as Sales Associate for an Independent Seed Sales Representative

2009 – Branched into precision agriculture sales and service

2011 – Enrolled in MS Agronomy Program

2012 – Refocused only on precision agriculture services especially data management and soil sampling

Overview Introduction

Defining Big Data

Data Value

Available Technology for Decision Making

Data Managers

Data Collection

First Steps to Data Management

The Way Forward

Conclusion

Introduction

Data is generated in very large volumes

Growers are at a loss for how to translate the information collected

The creative component helps to answer two questions What are farmers generating and who can help them?

How do farmers begin data management and making it work for their operation?

“Big Data” Volume-

Information so large it challenges software, storage and analysis systems.

Velocity- Speed of information collected

Variety- Spectrum of data contributing to collection

i.e. Machine collected, weather, sensor data,

visual observation

“Big Data”

Veracity- Quality and usefulness of information

“Junk in = Junk out”

Variability- Methods of data collection and translating meaning

Complexity- Synchronizing and formatting the vast amount of data

Data Value

Precision farming was designed to cater to individual areas within a field known as site specific farming. Advancements in conservation, nutrient management,

pesticide use, profitability

Limited opportunities for yield advancements

Data analysis allows for site specific “actionable decisions”

Data Value Concerns

Ownership of aggregated data sets

Data acquisition i.e. differential pricing, crop market influence, targeted

product marketing, credit access influence, competitive advantage, regulatory compliance

Data collection Unmanned Aerial Vehicles

American Farm Bureau Federation is working to develop guidelines for data handling

Available Technology for Decision Making

52% of growers report they make their own management decisions

If reliable service is unavailable, farmers seek their own solutions

Services available through current suppliers and online Fees are driven based on level of service

Some available features of these decision making tools are: data management analysis functions, prescription writing, aerial imagery, nutrient management, etc.

Available Technology for Decision MakingTool Name Web Address Services

Farmers Business Networkwww.farmersbusinessnetwork.com

i.e. analyze individual management practices and compare to anonymous aggregated acres, data management, mobile app available

Encircahttps://encirca.services.pioneer.com/services/ encirca-yield-nitrogen-management/

i.e. nitrogen management, input selection, analysis, weather and soils

Climate Corporationwww.climate.com i.e. field level weather, scouting, field health and nitrogen advisor,

prescription writing

Farmobile LLCwww.farmobile.com i.e. data management, data visibility, data control and distribution

aWhere www.awhere.com i.e. field level weather forecasts, agronomic modeling, pest alerts

AgSolver www.agsolver.com i.e. manure management, field level profitability mapping, conservation planning, land prospecting

GeoVantage http://geovantage.com/ i.e. aerial imagery including Normalized Difference Vegetation Index (NDVI) images

Ag Leader SMS and AgFiniti www.agleader.com i.e. data management, analysis, prescription writing, mobile app, etc.

MyJohnDeerehttps://myjohndeere.deere.com

i.e. analyze machine and agronomic data, manage JD subscriptions, parts locator, etc.

Premeir Crop Systems www.premiercrop.com/ i.e. data analysis (field reports, grower reports, group data), nutrient and seeding prescriptions

360 Yield Center www.360yieldcenter.com i.e. crop sensing, nitrogen application hardware

FarmLogs www.farmlogs.com i.e. GDD tracking, nitrogen and crop health management, mobile app available, prescription writing, data collection tool

Data Managers

Agricultural Technology Providers (ATP)

Farmers lack resources for personal data management

Crop Consultants

Pioneers in collecting and analyzing field level data

Independent, unbiased

Local, personal experience in the field

Crop consultants work together to combat other service providers

Data Managers

Retailers Trusted advisors on farm, biased Broad network of agronomy resources Custom application presents the opportunity to collect field

data Precision technology experience

Application equipment is equipped with guidance systems

GPS soil sampling services provided

Field mapping with GIS

Yield monitor data analysis

Data Managers Retailers

Figure 2. Outline of the respondent’s involvement with managing farm-level data from customers to assist in decision making. (source: Erickson and Widmar, 2015)

Data Managers

Agricultural Companies

Yield Mapping

Monsanto’s The Climate Corporation

Reservations about services offered for a fee

Product performance can be collected and translated

i.e. seed varieties, machine performance (trade in values), nutrient application, etc.

Data Managers

Independent Data Management

Costly software, time, education

Data Management “Peer Groups”

Challenging to organize and find farmers willing

to participate

Grower Level Approach to Data Management

65% of farmers are still skeptical and or fearful of new technology

Finding a place to start with Data Collection

Data Management

Data Collection Yield monitoring technology introduced in the 1990’s

Today In cab monitors have many more capabilities i.e. steering, guidance, planting applications, remote

sensing, application, harvest

Upon purchase farmers should consider Technology operator

Current plans and future goals

User friendliness

Quantity of monitors needed

Data management options

Price

Available service providers

Data Collection

Calibrate equipment correctly

Keep technology updated Newest features and benefits

Easier support experiences

Simplified solutions for service situations

Conscientious operator Assisted steering provides ability to monitor other functions

of equipment

Accurate setup

Data Collection

Data Collection

Be persistent and consistent about warehousing all data generated from fields Custom operators, soil sampling data

Future diagnostics

Standardize Legends

Generate a back-up

First Steps to Data Management

Determine a method for management ATP, private, etc.

Consider principles applied to traditional crop management decisions i.e. water management, nitrogen management, hybrid

selection Grid soil sampling

Cost of nutrient savings can cover sampling costs Variable rate technology fertilizer plans Yield comparisons

Historical yield maps

First Steps to Data Management

Figure 5a. Normalized Yield Map (Johnson M., unpublished)

First Steps to Data Management Point overlay comparisons

Reduces timely weigh wagon comparisons

More area can be easily compared

“Yield by…” reports

i.e. seed variety, soil type, planting population, etc.

Table 2. Yield by variety report

(Johnson M, unpublished)

First Steps to Data Management

Keeping and storing data for future use

Compare hybrid/variety productivity

Enhance and maximize crop productivity

Compare and analyze harvest data for multiple years

Optimize soil nutrient levels

Minimize input costs and planning input purchases

First Steps to Data Management

Eliminates paper records

Streamlines record keeping i.e. insurance, mandated spray records

Simplifies data transmission Cloud based transfers

Instant data transfer

The Way Forward

University Education 60% of MN farmers under utilizing or not utilizing precision

technology Program designed for low level users

Entrepreneurial Companies Farmobile LLC

Collects all machine data through specialized transmitter

Creates a market place for agricultural data

Profit shares with farmers

The Way Forward

Education Many people in the industry have

a self taught education Retailers Precision agriculture technology dealers and service providers Data managers

Data Value Research Promotion to growers will be enhanced by providing feedback about how

data management will affect them financially Fast paced industry is a challenge

Conclusion

Choose data collection system that fits the operation and become confident and organized with its capabilities

Find a trusted reliable method of interpretation and analysis i.e. ATP, individual, web based management system

Target areas of production that are traditional management practice adjustments i.e. input selection, application rates, soil features

Remain up to date with current releases of precision agricultural technology advancements and opportunities

“Sometimes you win and sometimes you lose. The only time you lose is if you ignore the opportunity to learn” Matt Darr, Iowa State University Extension agricultural engineer (Gullickson, 2015)

Questions?

References Bedord, Laurie. 2015. Sifting Through Big Data. Successful Farming. February, p.40.

Bergman, Ryan., Darr, Matt. 2012. Big Data and Digital Agriculture. Iowa State University.

Bunge, Jacob. 2015. On the Farm: Startups Put Data in Farmers’ Hands; Farmmobile, Granular and Others are Developing Tools that Enable Crop Producers to Compile, Analyze their Own Information. Wall Street Journal. 31 August.

Carlson, B. M. 2014. Defining Programming Directions and Priorities with Respect to Water Quality and Row Crop Production. Journal of the National Association of County Agricultural Agents. Vol 7, Issue 2. Available at http://www.nacaa.com/journal/index. php?jid=407.

Erickson, Bruce., Widmar, David. 2015. 2015 Precision Agricultural Services Dealership Survey Results. Purdue University. Available at http://agribusiness.purdue.edu/files/resources/ 2015-crop-life-purdue-precision-dealer-survey.pdf.

Frieberg, D. 2014. Data Decisions: Share your farm data? Corn and Soybean Digest. 24 February.

Gullickson, Gil. 2015. Play Small Data. Successful Farming. Mid February, p.34.

Gustafson, Mike. 2014. Big Data and Agriculture. AgriMarketing. Vol.52(2). 24-25, 27.

Hegeman, R. 2014. Farming’s big data problem. Available at http://dfm.themorningsun.com/ article/farmings-big-data-problem/73b807d698e284a633c3dc0b714d11d9#. Associated Press.

IBM. 2012. What is big data? Available at http://www-01.ibm.com/software/data/bigdata/what-is-big-data.html

References Iowa AgState Group. 2014. The Digital Transformation of Row Crop Agriculture. Available at

http://www.iowacorn.org/documents/filelibrary/membership/agstate/AgState_Executive_Summary_0A58D2A59DBD3.pdf.

Kansas Farm Bureau. 2013. Commodity Focus: Crop Data Issues with Precision Agriculture. Available at http://www.kfb.org/Assets/uploads/images/commodities/2013-3-Oct17-DataPrivacyPrecisionAg.pdf. Kansas Farm Bureau, Manhattan.

Lamp, G. 2015. More Accuracy, More Profit. C Magazine. May/June. p. 6.

National Coordination Office for Space-Based Positioning, Navigation and Timing. 2013. GPS.gov. Available at http://www.gps.gov/applications/agriculture/. National Coordination

Office for Space-Based Positioning, Navigation and Timing, Washington D.C.

Potter, B. 2013. Enter the Drone Zone. Available at http://www.agweb.com/article/ enter_the_ drone _zone_NAA_Ben_Potter/. Farm Journal.

United States Department of Agriculture (USDA). 2010. Precision, Geospatial and Sensor Technologies. Available at http://www.nifa.usda.gov/nea/ag_systems/in_

focus/precision_if_crop.html. USDA, Washington D.C.