Precision Agriculture Adoption and

Profitability: Fact vs. Myth beyond Pretty

Maps

Terry Griffin, Ph.D., CCA

Associate Professor - Economics

Dept of Agricultural Economics and Agribusiness

2011 InfoAg, Springfield, IL

Precision Ag: On the one hand…

Information-intensive

• Field level data to

make decisions

• Requires additional

data and skill

• IPM, financial records

• Yield monitors

• Soil samples

• VR applications

Embodied-knowledge

• Information purchased in

the form of an input

• Requires minimal

additional data/skill

• Round-up Ready or Bt

• Automated guidance

• On-the-go

sensing:application

8.25

Soybean Farms, 2002

0 20 40 60 80 100

Other uses

Irrigation

Divide crop production

Negotiate new crop lease

Tile drainage

Conduct field experiments

Document yields

Monitor crop moisture

Percent of Farms

Without GPS With GPS

Source: USDA-ARMS Data, Griffin

2009

Cotton Farms, 2003

0 20 40 60 80 100

Other uses

Irrigation

Divide crop production

Negotiate new crop lease

Tile drainage

Conduct field experiments

Document yields

Percent of Farms

Without GPS With GPS

Source: USDA-ARMS Data, Griffin

2009

Corn Farms, 2005

0 20 40 60 80 100

Other uses

Irrigation

Divide crop production

Negotiate new crop lease

Tile drainage

Conduct field experiments

Document yields

Monitor crop moisture

Percent of Farms

Without GPS With GPS

Source: USDA-ARMS Data, Griffin

2009

GPS Adoption by Service Providers

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1998 2000 2002 2004 2006 2008 2010 2012

Perc

en

t of

dea

lers

Lightbars Auto Guidance Biotech soybeans

Source: GPS adoption - Whipker and Akridge, 2011

Biotech soybean adoption - USDA NASS

Data Handling – Yield Data

• Begin with “best” data possible

• Remove erroneously measured yield data

– Yield monitor not always able to measure properly

• Adjust observation location

– Flow delays, start & end pass delays

• USDA-ARS Yield Editor (v. 2.0 at InfoAg)

– Takes about 30 minutes

• Yield data correction is becoming automated

Data Handling

Unprocessed yield data

Filtered yield data

Unprocessed yield data

Filtered yield data

Evolution of Spatial Analysis

1. “Eyeballing” of printed maps (non-quantitative)

– Subjective and misleading

– GIS as the ending point

2. Numerical analysis (quantitative)

– Averaging by district, zone, county, other

3. Statistical analysis (quantitative)

– Regression, ANOVA

4. Spatial statistical analysis (quantitative)

– Adapted from

epidemiology, criminology, geography, regional

economics, agriculture

Reasons “Eyeballing” Not Sufficient

• A common analysis for precision ag data is

“eyeballing” maps to identify patterns

• The human brain is good at finding patterns

– Even when they are there or not

Data Analysis

Similar or different colors?

Excessive background “noise” can mask differences.

A

B

C

D

But what happens when “noise” is correlated?!?

Spatially correlated “noise”

Adapted from Chad Godsey, Oklahoma State University

Steps in On-farm Experiments

Planning Implement Collect Data Analyze Data

Interpret, Make

Decisions, Evaluate

Most effort is applied in the Implementation stage

However, all stages are equally important and warrant effort

e.g. as strong as weakest link of a chain

Data Data Information

Steps in On-farm Experiments

Planning

Implement

Collect DataAnalyze

Data

Interpret, Make

Decisions, Evaluate

Most effort is applied in the Implementation stage

However, all stages are equally important and warrant effort

e.g. as strong as weakest link of a chain

Steps for Field-Scale Research

Design

• Determine relevant question to ask/test

• Design & implement experiment

Data collection

• Record actual experiment (as-applied, include off-target details)

• Collect yield monitor and other site-specific data

Data use

• Manage &Analyze data

• Make production farm management recommendation

• Implement Decision and Evaluate

Progression of How Farmers use Computers

Record keeping & whole-farm management

Precision agriculture

Analyzing data one field at a

time

Step Manual Automated

Design Field-specific On-the-go experiments being

developed

Implement Requires effort Variable-rate controllers & GPS

guidance

Data collection Yield monitor

calibration

Yield monitors &

on-the-go sensors

Analyze Time-consuming &

expensive

Automated data cleaning & spatial

analysis

Decision making Mostly manual Strides being made in community

analysis

Ongoing GPS-NT Projects

Photo Source: Beeline

• Quality of life, less fatigue

– Improved family dynamics and social relations?

• Impact on rural household

– Farmers with neck or shoulder problems remain active

– Adverse affects from working in middle of night?

• GPS &VR controllers to implement on-farm trials

• Whole-farm benefits of GPS adoption

GPS-NT Quotes from Farm Families

• “I wouldn’t want to do without it”

– Farmer

• “I stay up worrying at night because my

husband and son are working”

– Wife of Farmer

• “Even with suffering from neck problems, I

can operate farm equipment for several more

years with GPS guidance”

– Senior adult farmer

The best use of technology?

Photo Source: Unidentified

Future of Precision Ag

• Data side of precision becoming more automated

– Analysis and software conducted in „cloud‟

• Value of single farm data is finite to that farmer

– Greatest value occurs in pooled community analysis

Precision Ag Website Resources

• PrecisionTalk on AgTalk

– http://talk.newagtalk.com/

• PrecisionAg Network

– http://www.precisionagnetwork.com/

• Alabama Precision Ag

– http://www.alabamaprecisionagonline.com

• Purdue Site Specific Management Center

– http://www.purdue.edu/ssmc

• University of Arkansas

– http://www.aragriculture.org/precisionag/

Terry Griffin

Associate Professor - Economics

501.671.2182

tgriffin@uaex.edu

http://www.aragriculture.org/precisionag/