Increasing Cereal Nitrogen Use Efficiency Using Sensor Based Technology
W.R. Raun, J.B. Solie, M.L. Stone, K.W. Freeman, K.L. Martin, R. Teal,
B. Arnall, B. Tubana, C. Byungkyun, K.B. Morris, K. Girma, S. Moges, and C. Mack
Department of Plant and Soil Sciences
Department of Biosystems and Agricultural Engineering
Oklahoma State University
044 N. Ag. Hall
Stillwater, OK 74078
Email: wrr@mail.pss.okstate.edu
Tel: 405 744-6418
The world nitrogen use efficiency for cereal production has been reported to be
near 33% (Raun and Johnson, 1999). In general, one of the most common methods of
determining preplant fertilizer N rates has been the use of yield goals whereby farmers
averaged yield over the last 5 years and then added 30% to establish the goal. Preplant N
rate was determined by using a multiplier of their estimated yield goal. For example in
winter wheat, preplant N rate was determined by multiplying the yield goal in Mg/ha or
metric tons/ha times 33. In other words, for every ton of wheat in the yield goal, 33 kg
N/ha needed to be applied. For corn, this value is 19 kg N/ha for every ton in the yield
goal. The problem with yield goals is that they are seldom correct (Johnson and Raun,
2003). If we could predict the yield prior to planting, yield goals would would be
reasonable. Unfortunately, they simply don’t work. Modifications to the yield goal
calculation have included subtracting the amount N (as NO3-N and NH4-N) in the
preplant soil test from the final amount of N to be applied based on yield goals. In this
regard, it is no surprise that only 33% of all N applied for cereal grain production is
actually removed in the grain. The other 67% is lost. Yes, lost. Pathways for this
fertilizer N loss include plant gaseous loss as NH3, surface volatilization, denitrification,
leaching, and runoff. Immobilization isn’t even included in this list because it isn’t a
pathway for “loss”, if it is mineralized at a later time and becomes available to the plant.
Returning to the yield goal concept, if the yield levels could be reasonably
predicted, we know that harvested wheat, corn, and rice have average amounts of N in the
grain that are relatively consistent. For example, world averages for wheat, rice, and corn
grain are 2.13, 1.23, and 1.26% N. If we knew ahead of time what the yield was going to
be, we could easily compute how much N would be needed by multiplying the known
%N in the grain, times the expected yield. For example, if a 2000 kg/ha yield level was
expected for wheat, we would multiply 2000 kg/ha * 0.0213 which equals 42.6 kg N/ha
removed in the grain. Our preplant fertilizer N rate would then theoretically be 42.6/0.33
(33% nitrogen use efficiency) or 129 kg N/ha. The trick is knowing the true yield
potential, and the actual N use efficiency. In this regard, if both values are known, this
approach is actually quite reasonable. However, to date, no accurate method of
predicting grain yield ahead of time has been established, nor have the expected N use
efficiencies been determined, Reported research has shown that neither variable is
consistent from one year to the next, even in the same field.
Do farmers produce the same yields in the same field from one year to the next,
planted at the same time, using the same hybrid/variety and same preplant fertilization
rates? Absolutely not! Why? The reason that they don’t produce the same yields in the
same fields from one year to the next is because temporal variability in crop growth and
yield has an incredibly large impact on yield levels. What is temporal variability? This
is basically the combined environmental effects of when and how much rainfall was
received, ambient temperatures, when it was hot, when it was cold, and how this
rainfall/temperature dynamic interacted with topography and inherent soil properties of
each and every single 0.4m2 area in every field. This temporal variability has an
incredibly large impact on the quantity of N needed, and it is precisely this temporal
variability that cannot be predicted from one year to the next.
But, what if I could reasonably predict grain yield levels from mid-season
growing conditions, and the relative demand for fertilizer N based on mid-season
observations? This is an exciting prospect as it implies that if I consider the growing
conditions for the first half of the season, I should better predict crop yield midway
through the growing cycle than I can prior to planting. This ability might assist in
refining mid-season fertilizer N rates. Of course this has to be an improvement upon
predicting N rates before I even plant a seed in the ground.
Our work has always considered the use of yield goals to be a step in the right
direction, simply because this method should work if in fact yields could be predicted.
That it is virtually impossible to do this prior to planting is troublesome, but was not
totally discouraging. Work in the early 1990’s focused on using sensors for mid-season
prediction of yield levels. Following many years of work in Oklahoma and Mexico,
Raun et al., (2001) demonstrated that actual yield levels could be predicted from midseason NDVI measurements taken from winter wheat. This methodology was later
improved whereby they noted that one NDVI sensor reading (using the GreenSeeker
sensor sold by NTech Industries, Inc., www.ntechindustries.com) taken between Feekes
growth stage 5 to 7 (Large, 1954), divided by the number of days from planting to
sensing where GDD ((Tmin + Tmax)/2 – 4.4°C) were more than 0, was a good prediction
of actual yield. This work showed that over many locations and years, actual grain yields
could indeed be predicted in fields planted at different times, sensed and different times
and where climatic conditions were vastly different. This yield prediction model was
embraced and a method for refining mid-season fertilizer N rates subsequently pursued,
while simultaneously developing yield prediction models for other crops (Figures 1 and
2).
8.0
7.0
30 Locations, 1998-2003
YP0 = 0.254e324.4 INSEY
6.0
Grain yield, Mg/ha
YP0 + 1Std Dev = 0.359 e
R2=0.54
324.4 INSEY
5.0
4.0
3.0
2.0
1.0
0.0
0
0.001
0.002
0.003
0.004
0.005
INSEY
0.006
0.007
0.008
0.009
0.01
PKNP 1998
PKSN 1998
TPSN 1998
PKNP 1999
222 1999
301 1999
EFAA 1999
801 1999
502 1999
PKNP 2000
222 2000
301 2000
EFAA 2000
801 2000
502 2000
HNAA 2000
PKNP 2001
222 2001
301 2001
EFAA 2001
801 2001
PKNP 2002
222 2002
301 2002
EFAA 2002
801 2002
HNAA 2002
502 2003
222 2003
EFAA 2003
Figure 1. Relationship between INSEY (in-season estimated yield) or NDVI divided by
the number of days from planting to sensing where GDD >0, and actual wheat grain
yield. (dashed line is solid line + 1 standard deviation) to better reflect “yield potential”
or outer edge of observations.
For corn, the most accurate methods of predicting yield have combined NDVI and
measured plant height (Figure 2). Height can now be indirectly measured using simple
sonar devices (http://www.dasnr.okstate.edu/nitrogen_use/NEW_work.htm). Yield
prediction models are now available for winter wheat, spring wheat, sorghum, corn, and
bermudagrass (http://www.dasnr.okstate.edu/nitrogen_use).
12000
Efaw V7-8
Perkins V7
10000
Dry yield, kg/ha
Efaw V10-11
Perkins V9-10
8000
Efaw R1
Perkins VT-R1
6000
4000
2000
0
0
50
100
150
200
NDVI*Height (cm)
Figure 2. Relationship between the multiple of NDVI and by plant corn height and actual
corn yield levels.
Predicting yield is but one-half of the N rate equation. The other half requires the
ability to predict whether or not a response to applied N will be encountered and the
magnitude of that response. Johnson and Raun (2003) evaluated responsiveness to
fertilizer N in a long-term fertilizer trial in western Oklahoma that was started in the early
70’s. The same rates have been applied to the same plots every year for over 30 years.
They found that in some years, the check plot (not having received any N for more than
30 years) produced near maximum yields (Figure 3). How could this happen? What was
noted was that in unseasonably warm and wet winters, enough N was mineralized from
soil organic matter and N deposited in rainfall to produce near maximum yields. In other
words, there were years where even a check plot (0-N applied for over 30 years) would
not respond to applied fertilizer N.
Yield, kg/ha
4500
Lahoma-502, Wheat grain yield of check plots (No N), 1971-2002
4000
0-N kg/ha
3500
112 N kg/ha
3000
2500
2000
1500
1000
500
RI
0
5 1971
4
3
2
1
0
1971
1974
1977
1980
1983
1974
1977
1980
1983
1986
Year
1989
1992
1995
1998
2001
1986
1989
1992
1995
1998
2001
Year
Figure 3. Grain yields from 1971 to 2002 in plots receiving annually applied N at 112 kg
N/ha and plots that have never received fertilizer N, and the response index (yield from
the 112 kg N/ha plot divided by the check or 0-N kg N/ha).
How will I know if I have a year where no response to fertilizer N applications are
expected and/or years where a big response to fertilizer N is expected? Work by Mullen
et al. (2003) showed that NDVI sensor readings taken mid-season (Feekes 5 in winter
wheat) from the non-N limiting plots divided by sensor readings from the check (0-N or
farmer practice, termed the response index or RI) were highly correlated with the grain
yield from the non-N limiting plots divided by the grain yield in the check (0-N or farmer
practice). This was incredibly powerful because it showed that you could determine if a
response to added N could be realized (mid-season), and size of the response.
Combined, the response index (potential responsiveness to mid-season fertilizer
N) and yield potential could be used to identify accurate mid-season fertilizer N rates.
The yield potential achievable with no additional fertilizer could be calculated using
figure 1. The yield potential achievable if added N fertilizer was applied was determined
by multiplying the yield potential identified using INSEY (Figure 1) times the response
index. The mid-season fertilizer N rate was calculated by subtracting the expected grain
N removed if no fertilizer N was applied from the expected grain N removed if fertilizer
N was applied, divided by an expected efficiency factor (60 to 70% for topdress N
applied foliar). Expected grain N removed (with & without fertilizer) employs regional
averages. For example, in winter wheat, the %N in winter wheat grain averages 2.35%.
Following the establishment of this mid-season method of determining fertilizer N
rates, numerous field trials were conducted to test the concept. Averaged over locations,
nitrogen use efficiency was improved by more than 15% when N fertilization was based
on optically sensed INSEY, determined for each 1m2 area, and a response index (RI)
when compared to traditional practices at uniform N rates (Raun et al., 2002). One of the
keys to this work was recognizing both temporal variability and spatial variability. A
critical component of this work was knowing that each 1m2 area needed to be sensed and
treated independently (Solie et al., 1999 and Raun et al., 1998).
Most recently, the above algorithm for winter wheat has been modified for corn.
Both are currently employed on commercial applicators sold by NTech Industries, Inc.
(www.ntechindustries.com). The combined effects of this technology have been
increased nitrogen use efficiency, and increased profitability. Field trials conducted since
2001 have shown an average increase in profit of $43 per hectare (both in increased
yields and decreased fertilizer N rates) when using the variable N rate applicators
developed for winter wheat (www.dasnr.okstate.edu/nitrogen_use). Similar results have
been demonstrated in corn trials conducted jointly with Dr. Jim Schepers (USDA-ARS,
Lincoln, NE)
http://www.dasnr.okstate.edu/nitrogen_use/Nitrogen_Conference2003/Corn_Research.htm. Most
recently, field trials testing the variable N rate applicator in wheat have shown similar results to
the field trials reported on the NUE web site (www.dasnr.okstate.edu/nitrogen_use)(Tables 1-3).
At all threee sites, NUE’s were the highest when no preplant N was applied and the N rate
determined using the nitrogen fertilization optimization algorithm (NFOA) and N applied using
the OSU-NTech variable N rate applicator. The same NFOA that is employed on-board the
variable N rate applicator can be used on-line using our web-based Sensor Based Nitrogen Rate
Calculator (SBNRC) at http://www.soiltesting.okstate.edu/SBNRC/SBNRC.php. At 2 of the 3
sites, profits were maximized by applying a preplant rate of 50 kg N/ha and using the NFOA to
determine the appropriate topdress N rate. The topdress rate using either the NFOA or SBNRC
did not exceed 34 kg N/ha at any of the sites, yet farmers in this region generally apply more than
60 kg N/ha topdress (late February to late March). Applying more N topdress did not result in
increased grain yields at those sites where this comparison was possible. Overall this work is
supportive of using the NFOA for optimizing wheat grain yields and minimizing N rates and the
adverse effects that excess N can have on our environment.
Table 1. Wheat grain yield response to fertilizer N applied using variable N rate strategies and
fixed preplant rates, Chickasha, OK, 2004.
Treatment
1
2
3
4
5
Preplant
N Rate
kg/ha
0
135
0
50
0
Topdress
N Rate
kg/ha
0
0
27
12
29
Total
N Rate
kg/ha
0
135
27
62
29
Method
Grain
Yield
kg/ha
1510
All N pre
2631
Var. NFOA 2152
Var. NFOA 2377
Flat NFOA 1855
NUE
%
20
57
32
28
Gross
Revenue
$/ha
194
264
262
271
222
6
50
13
63
Flat NFOA 2647
42
305
7*
0
100
100
Flat Topdress 1781
6
174
Sensor CV at F5 = 12%
______________________________________________________________________________
NFOA – nitrogen fertilization optimization algorithm – fertilizer N applied to individual 0.4m2
areas using the mechanized variable N rate applicator, recognizing spatial variability.
Flat rate – fertilizer applied uniformly to the entire treatment, independent of spatial variability.
Gross Revenue determined using $52 USD per ton of wheat and $0.62 per kg N.
*from adjoining resolution trial (B. Tubana)
Table 2. Wheat grain yield response to fertilizer N applied using variable N rate strategies and
fixed preplant rates, Efaw, OK, 2004.
Treatment
Preplant
Topdress Total
Method
Grain
NUE
Gross
N Rate
N Rate
N Rate
Yield
Revenue
kg/ha
kg/ha
kg/ha
kg/ha
%
$/ha
1
0
0
0
2105
270
2
135
0
135
All N pre
2712
11
274
3
0
25
25
Var. NFOA 2724
60
337
4
50
9
59
Var. NFOA 2693
23
313
5
0
34
34
Flat NFOA 2587
34
314
6
50
16
66
Flat NFOA 2724
22
314
Sensor CV at F5 = 16%
______________________________________________________________________________
NFOA – nitrogen fertilization optimization algorithm – fertilizer N applied to individual 0.4m2
areas using the mechanized variable N rate applicator, recognizing spatial variability.
Flat rate – fertilizer applied uniformly to the entire treatment, independent of spatial variability.
Gross Revenue determined using $52 USD per ton of wheat and $0.62 per kg N.
Table 3. Wheat grain yield response to fertilizer N applied using variable N rate strategies and
fixed preplant rates, Lake Carl Blackwell, OK, 2004.
Treatment
Preplant
Topdress Total
Method
Grain
NUE
Gross
N Rate
N Rate
N Rate
Yield
Revenue
kg/ha
kg/ha
kg/ha
kg/ha
%
$/ha
1
0
0
0
3297
424
2
135
0
135
All N pre
4065
13
448
3
0
14
14
Var. NFOA 3650
57
461
4
50
11
61
Var. NFOA 4155
33
500
5
0
22
22
Flat NFOA 3519
26
440
6
50
14
64
Flat NFOA 4215
33
506
7*
50
34
84
Flat pre/top 4099
16
481
Sensor CV at F5 = 22%
______________________________________________________________________________
NFOA – nitrogen fertilization optimization algorithm – fertilizer N applied to individual 0.4m2
areas using the mechanized variable N rate applicator, recognizing spatial variability.
Flat rate – fertilizer applied uniformly to the entire treatment, independent of spatial variability.
Gross Revenue determined using $52 USD per ton of wheat and $0.62 per kg N.
* obtained from long-term trial 601 at LCB.
This work has been extended one step further. In addition to determining NDVI
from each 1m2 area and projecting yield potential and prescribed fertilizer N rates for
each and every 1m2 area in producer fields, we are currently testing a method whereby
the statistical properties of each 1m2 are considered as well. By determining the
coefficient of variation (CV) of sensor measurements from each 1m2 area, we can assess
what kind of variability exists and as a result accurately estimate uniformity of plant
stand and growth. When CV’s are low, plant stands are good and plant growth is
uniform, and the potential to achieve excellent responses to additional fertilizer N
increases. When CV’s of sensor measurements are high, plant stands are poor, and
responsiveness to added fertilizer N diminishes. This technology is possible today, but is
not presently on board the commercial applicators being sold by NTech.
The technology to variably apply N fertilizers in real time based on crop needs
has been developed and is available commercially. An algorithm has been derived that
calculates N fertilizer application rates based on the crops yield potential and the
response to additional fertilizer. Extensive field tests have demonstrated the validity of
the algorithm and the performance of the sensors/applicator. We have every reason to
believe that this technology will work as well in South America as it does in North
America.
References
Johnson, G.V., and W.R. Raun. 2003. Nitrogen response index as a guide to fertilizer
management. J. Plant Nutr. 26:249-262.
Large, E.C. 1954. Growth stages in cereals. Illustration of the Feekes Scale. Plant Pathol. 3:128129.
Mullen, R.W., Kyle W. Freeman, William R. Raun, G.V. Johnson, M.L. Stone, and J.B. Solie.
2003. Identifying an in-season response index and the potential to increase wheat yield with
nitrogen. Agron. J. 95:347-351.
Raun, W.R., J.B. Solie, G.V Johnson, M.L. Stone, R.W. Whitney, H.L. Lees, H. Sembiring and
S.B. Phillips. 1998. Micro-variability in soil test, plant nutrient, and yield parameters in
bermudagrass. Soil Sci. Soc. Am. J. 62:683-690.
Raun, W.R., and G.V. Johnson. 1999. Improving nitrogen use efficiency for cereal production.
Agron. J. 91:357-363.
Raun, W.R., G.V. Johnson, M.L. Stone, J.B. Solie, E.V. Lukina, W.E. Thomason and J.S.
Schepers. 2001. In-season prediction of potential grain yield in winter wheat using canopy
reflectance. Agron. J. 93:131-138.
Raun, W.R., J.B. Solie, G.V. Johnson, M.L. Stone, R.W. Mullen, K.W. Freeman, W.E.
Thomason, and E.V. Lukina. 2002. Improving nitrogen use efficiency in cereal grain production
with optical sensing and variable rate application. Agron. J. 94:815-820.
Solie, J.B., W.R. Raun and M.L. Stone. 1999. Submeter spatial variability of selected soil and
bermudagrass production variables. Soil Sci. Soc. Am. J. 63:1724-1733.