Variable Rate Liquid Nitrogen
Application for Cotton and Corn
PRODUCTION
AUTHORS:
 C. G. Bowers, Jr., Professor, Bio. & Ag. Engineering
 G. T. Roberson, Associate Professor, Bio. & Ag. Engineering
 D. K. Cassel, Professor, Soil Science
 G. C. Naderman, Associate Professor, Soil Science
 Cavell Brownie, Professor, Statistics

Abstract
Variable rate technology was used in a cotton and corn
rotation to apply a liquid nitrogen solution (33% N) based
on yield and soil maps to evaluate precision agricultural,
variable rate application production versus conventional
field averaged, constant rate application production. A
variable rate, liquid nitrogen applicator was designed and
assembled to apply a 30% liquid nitrogen solution at
layby based on the soil and yield variability in a 30+- acre
field. Both cotton nitrogen use efficiency for each soil
series and maximum yield 25-foot grids, from either year
1998 or 2000 yields, were utilized to generate a nitrogen
application map. Nitrogen application and yield data
were used to compare the two production systems for
three-tillage treatments typically used in eastern North
Carolina. Initial results for cotton nitrogen application in
years 2000 and 2001 showed that approximately the same
rates were applied on the average for both production
systems. For year 2000, the cotton yields for variable
rate nitrogen application on strip-till with wheat cover
were significantly higher than the field averaged constant
rate nitrogen for strip-till. There was no difference in
nitrogen treatments with the chisel/disk treatment. For
clean-till, subsoil, the yield for constant rate application
was significantly higher than for the variable rate
application.

Material & Methods
An interdisciplinary, replicated field study was designed for
1999-2003 to compare precision agriculture, variable rate
production to conventional field-average, constant rate
production in a cotton and corn rotation in eastern North
Carolina. This field study is located on a 30+- acre sandy loam
field at the Center for Environmental Farming Systems (CEFS) at
Goldsboro, North Carolina. The experiment was designed for
seven treatments randomized within 11 replications across the
field. The treatments are (1) Chisel/disk with standard nitrogen
(CDSN), (2) Chisel/disk with variable rate
N (CDVN), (3) Wheat cover crop, no till with
standard N (WCNTSN), (4) Wheat cover crop,
strip till with standard N (WCSTSN), (5) Wheat
cover crop, strip-till with variable N (WCSTVN),
(6) Clean-till, subsoil with standard N (SSSN),
and (7) Clean-till, subsoil with variable rate N
(SSVN).

Introduction
Biological &
Agricultural
Engineering
Precision agriculture is being adopted to optimize crop yields,
minimized their associated costs and reduce environmental
impact of crop inputs on water quality. Precision agriculture
management is provided through variable-rate application of
inputs such as lime, fertilizers, pesticides, seeds and tillage.
Variable rate technology is based on determining soil variability
from georeferenced soil sampling, determining yield variability
and potential yields from georeferenced yield monitoring and
soil series, using agronomic recommendations to generate
variable-rate application maps, and utilizing variable rate
technology to apply inputs. This system will enable optimal
potential yields to be reached and minimize environmental
impacts.

Objectives
 To measure soil and yield variability in a 30+-acre sandy
loam field and create yield potential maps.
 To conduct a 4-6 year, field study that compares precision
agriculture, variable rate production to conventional, field
averaged production for a cotton and corn rotation.
 To compare chisel, strip-till and subsoil tillage operations
within the two production systems.
Figure 1. Experimental Plan for Precision Agriculture Study
Standard agronomic practices
were used to determine the
inputs for lime, seed, fertilizer
and pesticide application rates.
Nitrogen efficiencies were
determined
for each soil series, and yield
potentials determined from 1998
and 2000 yields. Georeferenced
application rates were
calculated for the variable rate
application of a 30% nitrogen
solution using the soil nitrogen
efficiencies and yield potentials.
This application map was then
used with GPS and control
software to provide variable rate
application of nitrogen at layby
of the crop.
Figure 2.
Liquid
Nitrogen
Applicator
with Variable
Rate Technology Equipment

RESULTS
The process for generating the 2001 variable rate, nitrogen
application map for cotton is shown below. The nitrogen
efficiency soil series map (Figure 3) was generated using
MapInfoTM by georeferencing a graphical soil series map and
assigning efficiency numbers to each soil series using Red
Hen Systems MapCalcTM. Yield maps (Figure 4) were
generated with Ag. Leaders SMS Basic and Red Hen Systems
MapCalcTM. The application map shown in Figure 5 was
calculated for each 25-foot square grid in the field using the
equation given below. The start-up nitrogen was 2.5 gallons
of 30% nitrogen solution per acre.
[(Max Yield x Lint Percent x Nitrogen Efficiency – Start-up
Nitrogen) / (3.252 lbs. N/gallon 30% N solution)]
Table 1. Comparison of Average Layby Nitrogen
Application Rates
Year
Variable Rate
Constant Rate
(gallons 30% N solution/acre)
2000*
2001
12.8
14.4
12.9
16.5
Note: Rates were slightly lower than normally recommended
because of residual nitrogen in the soil from unharvested corn
due to a hurricane.
Yields were harvested and recorded with an AgLeaderTM
prototype yield monitor for cotton in
2000. The cotton yield map is shown
in Figure 6 and was generated with
SMS BasicTM and MapCalcTM using
inverse square weighting of the
nearest 6 points and smoothing
with +/- 1 standard deviation. The yield monitor had a
cumulative error of 1.6% with the individual treatment load
errors varying from –6.6% to +7.7%. Actual seed cotton
weights varied from 2,580 to 4,000 pounds for treatments.
Figure 3. Cotton Nitrogen Efficiencies for Soil Series
Figure 6. Cotton Yield Map for 2000
Using the cotton yield data for treatments and replications, a
standard analysis of variance was done to compare treatments
using SAS’s GLM. Results of this analysis are given in Table 2.
Table 2. Comparison of Treatments Using ANOVA and
Least Squares Means
Figure 4. Maximum Potential Cotton Yield Based on
1998 and 2000
Treatment
CDSN
CDVN
SSSN
SSVN
WCSTSN
WCSTVN
WCNTSN
MeanYield LSMean
(Lbs. seed cotton/ac)
1961.5
1897.3
2085.7
1704.7
1467.7
1699.6
1285.9
LSD
Grouping
ab
b
a
c
d
c
e
Note: Means without the same letter in common differ
significantly using the protected lsd procedure at a
significance level .05.
Figure 5. Cotton Layby Nitrogen Application Map
for 2001
Average layby nitrogen application rates for years 2000 and
2001 are in table 1 for variable and constant rates. The
variable rate range was 6.4 to 45.2 gallons 30% N solution per
acre in 2001. The two average application rates are
practically equal. It is planned to use the GLEAMS model to
estimate nitrogen loss to the environment and verify if
variable rate application reduces environmental impact. The
liquid N applicator was calibrated and had a 0.6% error at the
median rate of 25.8 gallons per acre in 2001.
The variable rate nitrogen application on the wheat cover,
strip-till produced significantly higher yields than the standard
nitrogen. The chisel/disk tillage treatments were the same for
both nitrogen rate application methods. The standard nitrogen
application for the clean-till, subsoil treatment gave
significantly higher yields than the variable rate nitrogen
application.
Biological &
Agricultural
Engineering
The estimated yield map of Figure 7 was determined by
spatially removing preliminary estimates of treatment
effects from the yield map of Figure 6. Observations of
the color trends between Figures 6 & 7 and the soil
series map of Figure 1 generally agree.
Figure 7. Estimated Yield Potential for Field From
2000 Cotton Yield Data
Finally, a spatial analysis was made using SAS PROC
MIXED with an isotropic exponential covariance
structure to compare treatments using predicted yield
from a treatment regression analysis of yield data of
Figure 6 and the estimated yield potential of Figure 7.
Results are shown in Figure 8. On the average, these
results are the same as the treatment ANOVA results
shown in Table 2. Expectations were that the higher
yielding soils, which had more nitrogen applied with
variable rate application, would have produced higher
yields. Cotton yields for 2001 will be used to further
evaluate treatments and yield spatial variability.

Conclusions
 A variable rate, liquid nitrogen applicator was
designed and assembled to apply a 30% liquid
nitrogen solution at layby based on the soil and
yield variability in a 30+- acre field.
 Nitrogen application and yield data were
used to compare precision agriculture, variable
rate liquid nitrogen application with field
averaged, constant rate nitrogen application for
three-tillage treatments.
 Initial results for cotton in years 2000 and 2001
showed that approximately the same rates were
applied on the average for both nitrogen rate
production systems across the three tillage
treatments.
 the three tillage treatments, ANOVA and
spatial statistical analysis show that strip-till
with variable rate nitrogen had higher yields
than constant rate nitrogen, that clean-till
subsoil with constant rate nitrogen produced
higher yields than variable rate nitrogen, and
that chisel/disk tillage had no yield difference.
 Cotton yields for 2001 will be used to further
analyze the treatments and variability.

Soil Physical
Properties
Figure 8. Spatial Variability of Treatment Yields.
Biological &
Agricultural
Engineering

Acknowledgements
Funding for this research was provided by Cotton Inc., North Carolina Agricultural Research Service, North Carolina Department of
Agriculture, and Deere Inc. Equipment, donated or loaned, came from John Blue Company (Squeeze pumps) and AgLeader (Prototype
cotton yield monitor). The authors also acknowledge Todd Markham (Agricultural Research Technician, NCSU), Charles Collins
(Engineering Research Technician, NCSU) and Clark Adams (Student, NCSU) for their technical help in this study and the Center for
Environmental Farming Systems (NCDA) for land and equipment used in this study.