Optical sensors like the GreenSeeker can be used to optimize nitrogen application rates. The nitrogen rich strip technique involves applying extra nitrogen to a strip to indicate crop need. Sensor readings from the strip are used to calculate a response index and estimate yield potential. This information feeds algorithms to determine the optimal nitrogen rate. Producers using these techniques can increase nitrogen use efficiency while maintaining yields, saving on input costs. The approach has been adopted worldwide in major cropping systems and countries.

1. Optical Sensing for
Nitrogen Management
Sulochana Dhital, PhD Student
Oklahoma State University
Stillwater, OK

2. Mt. Everest
N
S

4. 0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
THOUSANDTONS
YEARS
US Consumption of Nutrients
Nitrogen (N) Phosphate (P2O5) Potash (K2O)

5. Ammonia Cost and Natural Gas Price
Source: NYMEX Henry Hub, Fertecon, PotashCorp (August, 2014)

6. Nitrogen Use Efficiency (NUE)
World Nitrogen Use
Efficiency (NUE):
~33%
(Raun and Johnson,
1999)

7. Dead Zone, Northern Gulf of Mexico
For 2014 August 1, the
area of Hypoxic zone is
13,080 sq.km (5,052 sq.
miles) is below 5 year
average.
Source: Nancy N. Rabalais
http://water.epa.gov/type/watersheds/named/msbasin/images/hypoxia_size_2013_
lg.jpg

8. Dead Zone, Northern Gulf of Mexico
Source: http://www.gulfhypoxia.net/overview/
Excess nitrogen flowing down
the Mississippi each year is
estimated to be worth
$750,000,000 (Science, Malakoff,
1998)

9. Hypoxia Worldwide Problem
Source: http://www.gulfhypoxia.net/overview/

10. Challenge
Increase Nitrogen Use Efficiency
Decrease input cost
Increase production/Yield

11. Nitrogen Application
 Nitrogen: required in highest quantity.
 Only preplant application have lowest NUE.
 Top-dress or side-dress mid-season N applications can
increase NUE (>50%).
 Fall N application has higher risk of N loss.
 Spring application can minimize risk and optimize profitability
regardless of tillage (Vetsch and Randall, 2004).

12. Nitrogen related facts
 Optimum N rate and NUE changes from year to year.
y = 0.0199x + 5.7962
R² = 0.29
y = -0.0114x + 6.116
R² = 0.11
0
5
10
15
20
25
0 50 100 150 200 250
YIELD,MGHA-1
OPTIMUM N RATE, KG HA-1
Central Great Plains, (1958-2010)
High N Check 0N

13. Free Environmental N: Total Atmospheric N
Source: National Atmospheric Deposition Program, 2014

14. Soil organic matter, N supply
The pattern of supply of N made available
through net mineralization of soil organic
matter N at three sites in Ireland.(Humphreys
et al., 2002)

15. Current Oklahoma State University Approach
 Nitrogen Rich Strip
 OSU GreenSeeker Sensors/Pocket Sensors
 Variable Rate Applicator
 Sensor Based Nitrogen Rate Calculator
 Wheat and Corn Algorithm
 Web-Based N recommendations
 Grain Protein Optimizer
 Ammonia Loss Calculator
 GreenSeeder Hand Planter

16. Nitrogen Rich Strip
 An area of field with high rate of nitrogen or non-N
limiting conditions.
 40-50 pounds N/acre, over the average rate.
 10 ft wide, 300 ft long.
 Simple, affordable.
 Starting from preplant application to 30 days planting
wheat.

17. Nitrogen rich Strip
 Compare N Rich with farmer practice (visual difference)
 Sensors to calculate needed N
 Mid-season N application
 Approach helps determine N coming from the
environment
 Minimize environmental damage from excess N

18. N Rich Strips

19. Conrad, Montana
N Rich Strip tells You:
Yes, I need Nitrogen: If you see strip.
No, I do not Need any: If you don’t see Strip.

20. Cow pox, farmers field
Source:www.osunpk.com

21. OSU GreenSeeker Sensors/Handheld Sensors
(1992-2002)
 Measure crop vigor through Normalized Difference
Vegetative Index (NDVI).
 NDVI is calculated using the equation.
NDVI = NIR ref – red ref / NIR ref + red ref
 NDVI values range from 0-0.9.
 Values near 0.9 are likely non limiting N/healthy plant,

22. OSU GreenSeeker Sensors/Pocket Sensors
(1992-present)
 OSU Commercial release GreenSeeker™ in 2002.
 Optical Sensor/active sensor based technology
 Emits near infrared and red light which is reflected by
the crop
 Pocket sensors are more affordable, portable and lower
cost (500 $)

24. Comparison of NDVI reading
N rich strip Farmers Field

25. GreenSeeker Sensor

26. GreenSeeker Handheld Sensor

27. Variable Rate
Technology

30. Sensor Based Nitrogen Calculator
(SBNRC)
 GreenSeeker NDVI data (wheat, corn, other crops).
 Yield potential for a crop is identified using NDVI and
planting date (can then compute GDD)
 INSEY = NDVI (each date) / (GDD) days from planting
Where GDD= Growing Degree Days from planting
 Guides producer to apply the optimum N rate

33. Sensor Based Nitrogen Rate Calculator
United States Outside the States
US Grain Belt-Winter Wheat
US-Spring Wheat-Rainfed
US Grain Belt-Corn(Rainfed and Irrigated)
S.Australia
E.Australia
Mexico-Spring Wheat-Rainfed
Brazil
Bermudagrass-Forage
Wheat-Forage-Pasture
Great Plains, Kansas-Sorghum
Minnesota, Ohio- Corn
North Central-Cotton
South West Irrigated-Cotton
Argentina
Canada- Spring Wheat, Canola
India-Rice, Spring Wheat
Kenya Rice-Dominion Farms
Colombia-Corn
Zimbabwe-Corn

34. Nitrogen Fertilization Algorithm
 YP0: Estimate grain yield potential using NDVI and
cumulative GDD
 RI :N Responsiveness estimated using NDVI in the N
Rich Strip and NDVI in the farmer practice or check
 CV: Coefficient of variation determined from NDVI
sensor readings collected in each plot

35. Response Index
 Yield response to additional N changes
 N responsiveness = Response Index (RI)
 RI = Grain yield (Highest N rate or N rich Strip)/Grain yield
(Check 0-N)
 In season RI : Highest NDVI / NDVI from check
 N response (RI) changes each year

36. Response Index
Long term Wheat
experiment, Lahoma OK

37. Response Index
Long term Corn
Experiment (1971-2010)
Mead, Nebraska

38. Yield potential and nitrogen response
are independent
(Arnall et al., 2013, Agron J. 105:1335-1344)

39. Optical sensor based Winter Wheat
Algorithm
 INSEY = NDVI/(days where GDD>0)
 YP0 = 590exp(INSEY*258.2)
 RI = 1.69(NDVI168kgN/NDVI28kgN) - 0.7
 YPN = YP0 * RI
 N rec = ((YPN-YP0) * Grain N)/ fertilizer N efficiency
 Assumptions:
 Grain N = 2.39%
 Fertilizer efficiency = 50%
 Max Yield = (local cap determined by the producer)

40. Optical sensor based algorithm for
Corn N fertilization
OSU Maize Algorithm
 YP0=1291*(EXP(NDVI/Sum of GDD*2649.9) V8 to V12
 RI = NDVI- N Rich Strip/ NDVI – Farmer Practice
 YPN = YP0 * RI
 N Rate = ((YPN – YP0)* 0.0125)/expected fertilizer use
efficiency

41. Extension: Oklahoma
 More than 500, 000 acres in Oklahoma use N rich Strip
 Producers have adopted this technology after seeing
the success of their neighbors
 Use of GreenSeeker sensor has been a key in Oklahoma
 Saving 10 $/acre (Either cutting N rate or gaining yield
by increasing N rate)

42. United States
 N rich strip used for different crops rice, cotton, wheat,
corn soybean etc.
 Louisiana Kansas, Nebraska, Arizona, Iowa Montana
Arkansas, Missouri etc. more than 35 US states are using
GreenSeeker sensing technologies.

43. Around the world
 NGO’S effective for the extension
 CIMMYT (Mexico, India, China,)
 CIAT,CARE, CGIAR
 OSU Nitrogen Fertilization Algorithm (Wheat and Corn)
are used in Argentina, China and India
 USAID: Grant allowed GreenSeeker Sensors to be
delivered in China, India, Turkey, Mexico, Argentina,
Pakistan, Uzbekistan, and Australia.

44. Mexico

45. Mexico
2011-2012

46. The GreenSeeker Sensor Calibration program in
Mexico

47. N savings Using GreenSeeker In Yaqui
Valley, Mexico
 Average wheat yield 2011-2012 : 7.2 t/ha
 Average N rate 250 kg/ha
 Savings: 68 kg N/ha
 13.5 pesos/kg N= 918 pesos/ha ~ 70$/ha
 While maintaining the same yield
 By 2012 = 4000 ha

48. Australia farmer using Green Seeker
Australia

49. Africa

50. India

51.  N Rich Strips guides to additional N application
 Yield potential can be predicted in corn, wheat,
and rice (biomass produced per day).
 Response to applied N is variable from year to year
and can be predicted.
 N rate changes from year to year.
 Nutrient removal is tied to yield level.
 Need to account freely available N in our N rate.
Conclusion