Reference Strips and Precision Sensors for Nitrogen Management

1. Precision Agriculture Seminar
February 24, 2015
Boise, ID
Reference Strips
and Precision Sensors
Olga Walsh
Assistant Professor, Cropping Systems Specialist
Parma Research & Extension Center
University of Idaho

2. Presentation Outline:
1. Precision Ag (PA) benefits to
producers (specific example)
2. Level of PA adoption
3. Future of PA
4. Yield goal vs Yield potential
5. How sensors work
6. Reference strips
7. Misconceptions
8. Idaho research

3. Precision ag: Specific benefits
to producers
next few slides from Mr. Robert
Blair (2009 Precision Ag Farmer of the Year
2011 Eisenhower Fellow in Ag
2012 McCloy Fellow in Ag
Fourth-generation farmer Kendrick, Idaho)

4. Variable Rate Technology (VRT)
 VRT includes computer controllers and
associated hardware to vary output of fertilizer
and chemicals.
 Utilize application map and GPS info to control
the hardware that varies the application rate.
 Yield-goal/map based systems
 Yield-potential/”on-the-go” systemsRT200, 6 GreenSeeker
sensor system
Sense and spray on the go

5. Benefits of VRT
• Sensor data + Algorithm = Fertilizer Recommendation
• Manage areas instead of whole field
• Precise input placement – As needed where needed
• Environmentally friendly
• 20-25% Cost savings

6. 2009 NITROGEN HIT A RECORD HIGH
N cost was $.80 - $.90 per pound
100 pounds of N cost between $80 to $90 per acre
N costs on 500 acres of winter wheat at $.85/lb
at a 100 lb rate totaled:
$42,500

7. USING THE $42,500 TOTAL, COST
SAVINGS ON 500acres:
5% - $2,125
10% - $4,250
15% - $6,375
20% - $8,500

8. APPLICATION
 Guidance
/Steering
 VR Fertilizer
 Seed Control
 Auto Boom
NET
SAVINGS
 2.5%
 21%
 5%
 5%
PERCENT
SAVINGS
 $10,137
 $17,850
 $2,716
 $4,600
TOTAL PRECISION AG SAVINGS = $35,303

9. Current Idaho projects
 Systems for Improving Water and
Nitrogen Use Efficiency in Spring
Wheat (WUE and NUE)
 Precision Sensing for Improved
Wheat Production (N, wheat varietal
nurseries, diseases, pests)

10. Level of Precision Ag Adoption

11. PRECISION AG ADOPTION IN THE
U.S.
20% Adoption
amongst farmers
80% Adoption
amongst service
providers

12. WHY ONLY A 20% ADOPTION
RATE FOR FARMERS
 Average Age of
Farmers
 Understanding the
Technology
 Capital Outlay
 Lender’s Position
 Landlords/Others
Involved

13. Future of Precision Ag

14. Unmanned Aerial Systems -
Drones From military/security forces to precision agriculture.
 The U.S. Congress has mandated the Federal
Aviation Administration (FAA) incorporate drones into
national airspace by Sept. 30, 2015
 Next 10 years: annual spending on drones will
increase by 73%; worldwide spending - $89 billion;
U.S. will account for 62% of the research and
development
Multirotor Ready to Fly Kit
X4 - Aerial Precision Ag.
Ready to Fly Skyjib X4
Titanium Film Kit – Aerial
Systems International

15. Robotics
 Chemical Applications in Orchards
 Mechanical Weeding
 Autonomous Tractors
Blue River Technology - $3.1
million funding to develop
agricultural robots to kill
weeds and thin out plants
Vision Robotics Corporation
– harvesting robots

16. Yield goal vs Yield potential

17. Nitrogen Use Efficiency
 N is a key nutrient limiting crop production
 N use efficiency (NUE) is only about 40%
 About 60% of applied N is lost via
volatilization and plant loss, run off and leaching,
immobilization and denitrification =>
 60% of funds growers invest in N fertilizer is lost
 Reference strips and crop sensors help to
accurately estimate crop yield potential and crop’s
responsiveness to N mid-season.

18. Yield Goal vs Yield Potential
• Yield Goal:
Average yield for past 5 years + 30% (just in
case we have a good year)
Based on past (historical data)
Uses average N rates
• Yield Potential:
Estimated using in-season data
Based on current crop nutrient status
Precise N rate (crop- and site-specific)

19. Yield Potential Varies Year to Year
0
20
40
60
80
100
120
140
160
180
200
1940 1950 1960 1970 1980 1990 2000 2010 2020
“Maximum Attainable
Yield” (Yield Goal)
Actual
Harvested
Yield
Taylor, 2009
5 times in 70 years, harvested wheat yield = yield goal
Should we fertilize for maximum yield every year?
Alternative to Yield Goal - Yield Potential

20. How Sensors Work

21. Crop Sensors
hollandscientific.com; topconpositioning.com; nue.okstate.edu; agleader.com

22. Why use crop sensors?
 Feed the crop, maintain the soil
 Nitrogen = fuel for plant growth and
development
 To achieve highest efficiency – need to provide
the crop with exactly how much it needs (no
more and no less)

23. Why use crop sensors?
 Sensors = plant fuel gages, tell us:
How much the crop needs to reach yield
potential
How much the crop received already from the
soil (residual N, mineralization, rainfall …)
 Do we always add the same amount of fuel
no matter what?
 Should we apply the same amount of N
every year to every field?
E F
1/2
E F
1/2
E F
1/2

24. Potential and Response
 Research showed that both crop yield potential
AND crop response to applied N changes:
 year to year (temporal variability) and
 field to field (spatial variability)
 To get good estimates of N fertilizer demand,
both the crops ability to respond to
additional N and the grain yield potential
must be known.

25. Sensor-Based N Rate
1. We need
a lot
2. We don’t need
much
3. We need
a little1. High Yield
Potential,
plants some-
what deficient
in N
2. Very high
Yield Potential,
almost
adequate N
nutrition
3. Low Yield
Potential,
plants are very
deficient

26. Reference Strips

27. Nitrogen Reference Strips
Reference Strip Conrad, MT
 Everything is relative and understood in
comparison
 The easiest way to assess nitrogen status –
establish a non-limiting N strip and compare
it to the rest of the field

28. Help, My Strips Did Not Work!
 I can’t see my Reference Strip in my field,
everything looks the same
 It worked! – Enough N was delivered to the
crop “for free”
 Your crop probably will not benefit from addition
of N fertilizer
 Aren’t you glad you did not apply that high N rate
to the whole field/farm?
www.blog.iastatebk.com, 2013

29. N Reference Strips
STEP-BY-STEP:
 Establishing N Reference Strips every year
 Apply starter fertilizer at seeding
 Evaluate the Strip vs the rest of the field
 Make N fertilizer decisions
www.noble.org, 2013

30. CIMMYT, Mexico
300 lb N/ac 0 lb N/ac
Large-scale on-farm studies

31. Sensor Basics
• Emits light and measures reflectance from plants
• Red light is used for photosynthesis (absorbed)
• NearInfrared light – not enough energy, not
used (reflected)
• Sensor reading - Similar to a plant physical
examination
Light
generation
Light signal
Light
detection
? Calculate
NDVI
“Sensor”
www.nue.okstate.edu, 2014

32. Sensor Basics
• Sensor can detect:
 Plant Biomass
 Plant Chlorophyll
 Crop Yield
 Water Stress
 Plant diseases, and
 Insect damage
 Sensors are used by agronomists, breeders, plant
pathologists, weed scientists, crop consultants,
growers
Light
generation
Light signal
Light
detection
?Calculate
NDVI
“Sensor”
www.nue.okstate.edu, 2014

33. red
redNIR
NIR
30%50%
60% 8%
NDVI = (NIR-red)/(NIR+red)
Sensor detects the amount of light reflected from
the crop and calculates NDVI Tubana, 2007
NDVI = 0.76
NDVI = 0.25

34. What the GreenSeeker sensor “Sees”
The vigor of the leaves
and
the ratio of plant to soil
affect NDVI values
N-Tech Ind., 2009

35. N-Tech Ind., 2009
What the GreenSeeker sensor “Sees”

36. Most Common Misconceptions
About Sensors

37. Misconceptions
Misconception 1: GreenSeeker is a
Nitrogen Sensor
Facts:
Nitrogen leaf content is not a good predictor of
yield potential
GreenSeeker is a biomass sensor
Biomass/color is highly correlated with yield
potential
Gerhardt, 2009

38. Misconceptions
Misconception 2: “I can see variability
with my eyes—I don’t need a sensor”
Facts:
We can see macro variation in a field, but not
subtle changes
We cannot remember where the variations are
and to what degree
A flat rate is typically not the optimal answer to
variability
Gerhardt, 2009

39. Misconceptions
Misconception 3: “I can do variable rate N
with historic data—I don’t have the time
nor the need for an in-season device.”
Facts:
In certain seasons, 1 year of quality in-season
data can be more valuable than 10 years of
historic data
Averaging information has bias that will limit the
high end and over estimate the low end
In wet years hills do best, in dry years the lower
areas do best Gerhardt, 2009

40. Misconceptions
Misconception 4: “If you give me your
yield goal, I’ll tell you how much nitrogen
to apply”
Facts:
“Yield Goal” fertility is a not the best approach
economically and environmentally
You can’t estimate mineralization rates, residual
N, or lost N without in-season information(ex.
Nitrogen Rich Strip vs. Farmer Practice)
Gerhardt, 2009

41. Idaho research

42. Sensor-Based Work in Idaho
Pre-Season Variable Rate Nitrogen in Potatoes
 Cook, Hopkins, Ellsworth, Bowen, and Funk
(University of Idaho, Idaho Falls and Twin Falls)
 2 growing seasons (2003-04), 5 fields, Eastern ID
Objective: To compare traditional and sensor-based
variable rate fertilization
Results:
 Support the concept of variable rate N
application in potato production
 The recognized increase in yield and quality
more than compensated for the increased cost of
this method of variable rate N fertilization

43. Sensor-Based Work in Idaho
In-Season Variable Rare N in Potato and
Barley Production Using Optical Sensing
Instrumentation (2004)
 Bowen, Hopkins, Ellsworth, Cook, and Funk
(University of Idaho at Idaho Falls and Twin Falls)
Objective: To evaluate the use of optical sensing
instrumentation to help manage in-season N for
potato and malt barley
 1 growing season, 4 potato fields, 5 barley fields
Results: Sensors can be used to prescribe variable
N rates to malt barley at jointing and to potatoes
prior to row closure

44. A research assistant for Dr. Bryan Hopkins
(currently a soil scientist at Brigham Young University, Utah)
evaluates sugarbeet canopy health using a
GreenSeeker, Idaho
Sugar Producer, 2013

45. Thank you!
Olga Walsh
Assistant Professor, Cropping Systems Specialist
Parma Research & Extension Center
University of Idaho
(208) 722-6701 (ext 218)
owalsh@uidaho.edu
Blog: Idaho Crops & Soils –
www.idcrops.blogspot.com
Follow us on Twitter: https://twitter.com/IDCrops

46. From sensor to N Rate
 Established Reference Strip (at seeding)
 Compared to the rest of the field (sensed at tillering)
 Now we know: yield potential and crop
responsiveness to N
 How do we determine the needed N rate?
 Need a formula (algorithm) to translate
sensor readings into N recommendation

47. Algorithms
•Accurate mid-season fertilizer Nitrogen
recommendations based on NDVI
•26 algorithms
•> 10 crops and
•> 20 regions (US and worldwide)
•15% increase in NUE
•Savings: $10 - $20/ac
online since 2002

48. N-Rate Calculator on-line
www.nue.okstate.edu

49. N-Rate Calculator on-line

50. N-Rate Calculator on-line

51. Sensor-Based Approach Recognition
 Named “the most revolutionary approach in
a century to fertilizing crops” - the U.S.
Department of Agriculture
 Voted "the best and the brightest developed
throughout the world for the agricultural,
food, and biological systems industries" -
American Society of Agricultural Engineering