Variable Rate Technology in Wheat

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Variable Rate Technology in Wheat
www.dasnr.okstate.edu/precision_ag
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1993
Dr. Marvin Stone adjusts the fiber optics in a
portable spectrometer used in early bermudagrass
N rate studies with the Noble Foundation, 1994.
Dr. John Solie, Dr. Marvin Stone, and Dr. Shannon
Osborne collect sensor readings at ongoing
bermudagrass, N rate N timing experiments with
the Noble Foundation in Ardmore, OK. Initial
results were promising enough to continue this
work in wheat.
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Variable N rates using an inverse N-rate, NDVI
scale were applied. N rates were cut in half
with no differences in grain yield compared to
fixed rates. Grain N uptake levels using VRT
across a 70 meter transect were less variable
when compared to the fixed rates (left).
1994
John Ringer and Shannon Osborne collected sensor
readings and later applied variable N fertilizer
rates based on an initial bermudagrass algorithm.
Initial algorithms used to spatially treat N
deficiencies in wheat and bermudagrass employed
an inverse N Rate-NDVI scale.
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Samples were collected from every 1 square foot.
These experiments helped to show that each 4ft2
in agricultural fields need to be treated as
separate farms.
1995
Extensive field experiments looking at changes in
sensor readings with changing, growth stage,
variety, row spacing, and N rates were conducted.
New reflectance sensor developed.
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Collaborative Project with CIMMYT Variety
Selection/Yield PotentialSpring Wheat 1995
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CIMMYT
Date
Location
Personnel
Objectives
Feb, 1997
Ciudad Obregon
TEAM-VRT
Discuss potential
collaborative work
Jan, 1999
Obregon Texcoco
Steve Phillips, Joanne LaRuffa,
IRSP 98, refine INSEY, 2-
Wade Thomason, Sherry Britton,
wheel tractor and wheat
Joe Vadder, Gordon Johnson,
bed planter design
John Solie, Dick Whitney
Sep, 1999
Texcoco
Erna Lukina
IRSP 98, use of EY as a
selection tool
Aug, 2000
Texcoco
Marvin Stone, Kyle Freeman,
IRSP 99, applications of
Roger Teal, Robert Mullen,
INSEY, sensor design
Kathie Wynn, Carly Washmon,
for plant breeding
Dwayne Needham
Jan-Mar 2001
Ciudad Obregon
Kyle Freeman
Joint collaboration on
200-03530 NRI Grant
Apr 2001
Ciudad Obregon
Kyle Freeman
Wheat harvest
July 2001
El Batan
Jagadeesh Mosali, Shambel MogesMicah Humphreys,
Paul Hodgen,Carly Washmon
Wheat harvest
Apr 2002
Ciudad Obregon
Paul Hodgen
NASA Grant
TOTAL
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In, March, 1996, first variable rate N applicator
demonstrated to the public
1996
Relationship between total forage N uptake and
NDVI was used to apply variable N rates in turf.
Indices developed where we could detect
differences in N and P, independent of one
another. For wheat, numerator wavelengths
between 705 and 735, and denominator wavelengths
between 505 and 545 proved to be reliable
predictors of N and P uptake. In bermudagrass,
the index 695/405 proved to be reproducible from
one season to the next.
Evaluation of management resolution at 3 locations
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1997
In 1997, our precision sensing team put together
two web sites to communicate TEAM-VRT results.
Since that time, over 20,000 visitors have been
to our sites. (www.dasnr.okstate.edu/precision_ag)
www.dasnr.okstate.edu/nitrogen_use
The first attempt to combine sensor readings over
sites into a single equation for yield prediction
A modification of this index would later become
known as INSEY (in-season estimated yield), but
was first called F45D.
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Cooperative research program with CIMMYT. Kyle
Freeman and Paul Hodgen have each spent 4 months
in Ciudad Obregon, MX, working with CIMMYT on the
applications of sensors for plant breeding and
nutrient management.
1998
Cooperative Research Program with Virginia Tech
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1999
Applications of indirect measures of electrical
conductivity were evaluated in several field
experiments. This work aims to identify added
input variables to refine the in-season
prediction of yield.
TEAM-VRT entered into discussions with John
Mayfield concerning the potential
commercialization of a sensor-based N fertilizer
applicator for cereal crops.
Increased yields at lower N rates observed at
Covington. Using the in-season response index
(RINDVI), we were able to project responsiveness
to applied N, which changes from location to
location based on climatic conditions specific to
each parcel of land, and that changes on the same
land from year to year.
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Discovered that the N fertilizer rate needed to
maximize yields varied widely over years and was
unpredictable in several long-term experiments.
This led to his development of the RESPONSE INDEX.
2000
Predicted potential response to applied N using
sensor measurements collected in-season.
Approach allowed us to predict the magnitude of
response to topdress fertilizer, and in time to
adjust topdress N based on a projected
responsiveness.
RI Harvest
RI NDVI
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2001
N Fertilizer Optimization Algorithm (NFOA) 1.
Predict potential grain yield or YP0 (grain yield
achievable with no additional N fertilization)
from the grain yield-INSEY equation, where INSEY
NDVI (Feekes 4 to 6)/days from planting to
sensing (days with GDDgt0) YP0 0.74076 0.10210
e 577.66(INSEY) 2. Predict the magnitude of
response to N fertilization (In-Season-Response-In
dex, or RINDVI). RINDVI, computed as NDVI from
Feekes 4 to Feekes 6 in non-N-limiting fertilized
plots divided by NDVI Feekes 4 to Feekes 6 in the
farmer check plots (common fertilization practice
employed by the farmer). The non-N limiting
(preplant fertilized) strip will be established
in the center of each farmer field. 3. Determine
the predicted yield that can be attained with
added N (YPN) fertilization based both on the
in-season response index (RINDVI) and the
potential yield achievable with no added N
fertilization, computed as follows YPN (YP0)/
(1/RINDVI) YP0 RINDVI 4. Predict N in the
grain (PNG) based on YPN (includes adjusted yield
level) PNG -0.1918YPN 2.7836 5. Calculate
grain N uptake (predicted N in the grain
multiplied times YPN) GNUP PNG(YPN/1000) 6.
Calculate forage N uptake from NDVI FNUP 14.76
0.7758 e 5.468NDVI 7. Determine in-season
topdress fertilizer N requirement (FNR)
(Predicted Grain N Uptake - Predicted Forage N
Uptake)/0.70 FNR (GNUP FNUP)/0.70
Work with wheat and triticale plant breeders at
CIMMYT, demonstrated that NDVI readings could be
used for plant selection
Engineering, plant, and, soil scientists at OSU
release applicator capable of treating every 4
square feet at 20 mph
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Winter Wheat24 locations in Oklahoma1998-2001
Spring Wheat4 locations in Ciudad Obregon,
MX2001
Soft White Winter Wheat7 locations in
Virginia, 2001
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Oklahoma
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(No Transcript)
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100 lb N/ac
45 bu/ac, 2.5 N in the grain
75 lb N/ac
N uptake, lb/ac
50 lb N /ac
days with GDDgt0?
October February June 0 120 240 days
INSEY Rate of N uptake over 120 days, gt ½ of
the total growing days and should be a good
predictor of grain yield
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In-SeasonEstimatedYield (INSEY)
NDVI at F5

days from planting to F5, GDDgt0
Good predictor of final grain yieldRequires
only one sensor readingWork over different
regions/biotypes
Units N uptake, kg ha-1 day-1 where GDDgt0
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• Fertilize whole field with 40 lbs N/ac preplant
• Before exiting the field, apply one strip with 80
  (non-N-limiting)

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Why is it important to know the RI for a field?
C.V. 31
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So, whats in it for the farmer?
Ave Loss/ac/yr 9.77
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RINDVINDVI-N-non-limiting/NDVI-farmer check
RINDVI and RIHARVEST
1998
1999
RI Harvest
2000
2001
RI NDVI

• Strong correlation between RINDVI (vegetative
  stages) and RI HARVEST
• Accurately predict the crops ability to respond
  to N
• RINDVI may refine whether or not N should be
  applied, how much, and expected NUE

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YPMAX
YPN
YP0
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YPMAX
YP0
YPN (RI2.0)
YPN (RI1.5)
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Predict RI Predict YP0 Predict YPN based on
RI Fertilizer N GNUPYPN GNUPYP0/0.7
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