Daryl B. Arnall, Brenda S. Tubaña, Starr L. Holtz, Kefyalew Girma
and W.R. Raun
ABSTRACT
Although nitrogen use efficiency (NUE) of
small grains is well documented at 33% world wide, there has been little
research relating NUE to yield factors. This study examined the
relationship between NUE and the response index at harvest (RIHARVEST)
of winter wheat (Triticum aestivum L.). Yield data from a
long-term fertility study established at Lahoma, Oklahoma in 1971 was
used to explore the relationship. In this report, six N rates at
non-limiting levels of P and K were evaluated. Regression analysis
showed a positive relationship between NUE and RIHARVEST for
all years across all N rates (r2 = 0.37). But this
relationship was improved (r2 = 0.45) when both RINDVI
and RIHARVEST were included in the model. The linear
relationship between NUE and RIHARVEST was significantly
improved, when yield data and corresponding NUE were separated according
to the annually applied fixed N rate. As the N rate increased the
resulting slope of the relationship between NUE and RI was reduced.
These analyses also demonstrate that temporal variability in NUE exists
and that NUE can be predicted.
Keywords: Nitrogen
Use Efficiency, Response Index, winter wheat, Normalized Difference
Vegetation Index, Nitrogen
INTRODUCTION
As the cost of nitrogen (N) fertilizer
increases and profit in winter wheat (Triticum aestivum L.)
production is put at risk, scientists are challenged to develop N
management strategies that guarantee increased N use efficiency (NUE).
Baligar et al. (2001) concluded that increased NUE in plants is vital to
enhance the yield and quality of crops, reduce N inputs and improve
soil, water and air quality. Farmers either under- or over-fertilize,
both of which will result in profit loss. Reduced N application will
cause reduction in head size, poor grain fill and reduced tillers per
plant resulting in decreased grain yield (Barley and Naidu, 1964; Syme,
1967; Halse et al., 1969; Pearman et al. 1978). On the other hand, over
fertilization of N may not always result in additional grain yield but
instead can increase N losses (Raun and Johnson, 1995; Reddy and Reddy,
1993; Kanampiu et al., 1997) and subsequent environmental
contamination.
Winter wheat producers in the Great Plains
generally apply N fertilizer either once before planting or in split
applications (Kelley, 1995). A mid-season split application of N
fertilizer provides room for adjusting rates according to crop growth
thus maximum utilization of fertilizer is expected (Mascagani and Sabbe,
1991; Boman et al., 1995). This N management strategy can be further
improved by applying mid-season N rates based on what the crop needs to
maximize yield and minimize input costs.
An approach to maximize NUE currently in
use at Oklahoma State University is the mid-season fertilizer N rate
based on predicted yield potential. Early work showed that the
normalized-difference-vegetative-index (NDVI) could be used to estimate
green biomass (Tucker, 1979). Raun et al. (2001) established an equation
to predict crop yield potential (YP0) using an in-season
estimate of yield (INSEY), an index determined by dividing NDVI sensor
readings collected mid- season by the total number of positive growing
degree days (GDD>0) from day of planting to sensing. The GDD>0 is
calculated as Tmin+Tmax/2 –4.4ºC. They also reported that the
coefficient of determination (R2) between actual grain yield
and INSEY from six out of nine locations across a two-year period was
83%. Raun et al. (2005) stated that application rates should be based on
removal of predicted yield potential of the crop. The average N
concentration in wheat grain is 2.39% thus, the removal amounts can be
estimated based on a projected yield. Raun et al. (2005) discussed that
the estimation of N fertilizer requirement is based on components other
than just predicted yield potential. Estimating temporally dependent
responsiveness to applied N is also an important component of the
algorithm since the amount of N mineralized year to year can be
dramatically variable. Johnson and Raun (2003) presented data from a
long-term fertilizer trial (established in the early 1970s) in which
there were years that the check plot (0-N applied for over 30 years)
produced near maximum yields and thus the demand for fertilizer N was
small. The response index (RI) concept was introduced to describe the
crops likeliness to respond to applied fertilizer N (Johnson and Raun,
2003). Recognizing its importance in precise estimation of N rate,
in-season RI was incorporated in the algorithm which Mullen et al.
(2003) showed can be estimated early in the season using NDVI sensor
readings. Another important component of the algorithm is the
coefficient of variation. Normalized difference vegetation index (NDVI)
readings from good stands yet nutrient deficient can be different from
poor stands of nutrient enriched wheat. The coefficient of variation
(CV) from NDVI readings can be used to predict early season plant stands
(Arnall et al., 2006) and thus was incorporated into the algorithm for
estimating mid-season N rate (Raun et al., 2005).
The current algorithm employed at Oklahoma
State University uses the predicted yield potential (YP0) and
estimated response index (RI) from NDVI readings to predict yield
potential when N is applied (YPN) and simultaneously altered
using the CV (Raun et al., 2005). Nitrogen fertilizer rate is then
determined by dividing the difference in grain N uptake of YPN
and YP0 by the NUE. The NUE factor used in the algorithm is
estimated through historical data. Knowing that NUE is affected by soil
properties, efficiency of crops, climate, and chemical species of
fertilizer used, mycorrhiza, and others (Baligar and Bennett, 1986a and
b; Fageria, 1992; Hauck, 1985), adjusting NUE in-season has the
potential to refine the present algorithm. A thorough examination of the
relationship with NUE and grain yield from previous data and
understanding how the behavior of this relationship is affected by
dynamic factors present in the field are essential steps that have not
been assessed in the course of improving NUE.
A study by Ellen and Spiertz (1980) showed
that fertilizer NUE determined using wheat grain yield, changes with
time and rate of application. While grain yield and N content of cereal
grain crops increased with applied N (Simonis, 1987; Raun and Johnson,
1995), higher N rates generally result in decreased NUE values.
In this study, data collected from a
long-term continuous winter wheat trial at Lahoma Research Station were
used to examine the relationship between NUE, and response index of
harvested grain to nitrogen fertilizer (RIHARVEST) and the
response index of NDVI (determined from mid-season NDVI measurements) to
nitrogen fertilizer RINDVI.
MATERIALS AND
METHODS
Nitrogen use efficiency was
calculated from grain yield data collected from a long-term experiment
established in the fall of 1970 under conventional tillage on a well
drained, deep and moderately permeable Grant silt loam (fine-silty,
mixed, thermic Udic Arguistoll) soil located at the North Central
Research Station near Lahoma (36.42°
N and 97.87°
W at an altitude of 396 m a.s.l.), OK. The average annual rainfall at
Lahoma is approximately 800 mm. The treatment structure of the entire
experiment consists six N rates (0, 22, 45, 67, 90 and 112 kg N ha-1),
five phosphorus (P) rates (0, 5, 10, 14 and 20 kg P ha-1) and
two rates of potassium (0 and 56 kg K ha-1) arranged in a
randomized complete block design (RCBD) with four replications. In this
report, the six N rates at fixed levels of 20 kg P ha-1 and
56 kg K ha-1 (non-limiting P and K) were evaluated. Nitrogen,
P, and K were applied as ammonium nitrate (34% N), triple super
phosphate (20% P) and potassium chloride (83% K), respectively. Plots
are permanent from year to year and received fixed rates of N, P and K
every year. Individual plots are 4.9 m wide and 18.3 m long. Winter
wheat was planted for 34 continuous years in 25.4 cm wide rows at
seeding rates of 67 kg ha-1. In some years the seeding rate
was increased to 110 kg ha-1 to account for expected poor
germination and reduced tillering due to dry soil condition or late
planting date.
Over the period of the trial, wheat
varieties have changed following practices of the local producers. The
variety 'Nicoma' was planted from 1971-1974, 'Triumph 64' from 1975-1976
and 1978, 'Osage' in 1977 and 1979, 'TAM W-101' from 1980-1991, ‘Karl’
from 1993-1994, ‘Tonkawa’ from 1995-1998, ‘Custer’ from 1999-2004, and
‘Overley’ from 2005 to the present. Preplant fertilizer was broadcast
and incorporated in late August to mid September. Planting dates ranged
from mid September to late October with the harvest dates ranging from
the first of June to mid July.
Grain was harvested from the center 2 m of
each plot using a conventional combine. Grain yield from each plot was
determined and a sub-sample was collected for total N analysis. Grain
samples were dried in a forced air oven at 66 oC, ground to
pass a 140 mesh sieve (100 μm), and analyzed for total N content using a
Carlo-Erba NA 1500 automated dry combustion analyzer, using the methods
outlined in Schepers et al. (1989).
In general, NUE is defined as the total
plant N divided by the amount applied (e.g. Liang and Mackenzie, 1994).
However in this study, we calculated NUE as uptake efficiency (the
difference of N uptake in the treated plot and N uptake in the 0-N
check) divided by the total applied N rate.
Response index of harvested grain was
determined as the ratio of yield of fertilized and check plot for each N
rate in each year. Response index of NDVI was calculated for the yearly
NDVI data since 1998 as the ratio of NDVI of N fertilized and
unfertilized check plot for each N level. Normalized Difference
Vegetation Index was taken with a GreenSeeker® Hand Held active
(self-illuminated) optical sensor (NTech Industries, Inc.). The sensor
uses a patented technique to measure crop reflectance in the red (650 ±
10 nm) and near infrared (770 ± 15 nm) bands and to calculate NDVI (Raun
et al., 2005b; Stone et al., 2005).
Linear regression analysis between RIHARVEST
or RINDVI and NUE was performed for each level of N (n= 34
for RIHARVEST versus NUE and n= 30 for RINDVI
versus NUE) and all N rates combined. The analysis included a test of
significance of slope and intercept from the linear regression for the
different N rates for both RIHARVEST versus Nitrogen Use
Efficiency and RINDVI versus NUE regressions. NUE was also
regressed on both RIHARVEST and RINDVI. This
regression also included collinearity analysis using the Variance
Inflation Factor (VIF) diagnostics tool (Neter et al., 1990). All
statistical data analyses were performed using the General Linear Model
(GLM), Regression (REG) and Mixed (MIXED) procedures in SAS (SAS Inst.,
2001).
RESULTS AND DISCUSSION
Varieties were changed eight times over
the years in this study. Preliminary analysis of yield among varieties
revealed no significant differences. Thus the change of cultivar over
years did not have an impact on NUE and RI data. Each year the best
available variety was used. Figure 1 presents wheat grain yield data
(1971 to 2005) from three treatments: 0-20-56, 67-20-56, and 112-20-56
kg N-P-K ha-1. The yield of the check plot ranged from 0.7 to
2.7 Mg ha-1 while the 112 kg ha-1 N rate ranged
from 1.4 to 5.9 Mg ha-1. The yields for the 67-20-56
treatment ranged from 1.7 to 5.1 Mg ha-1. However, the data
communicates a more profound problem of N management i.e. annual N
demand being highly variable. Raun et al. (2005a) conceptualized the use
of yield potential which addressed the limitations imposed by temporal
and spatial variability in N decision management. It was discussed that
among the components of the functional algorithm used to estimate N rate
is NUE. Currently, the sensor-based nitrogen rate calculator (http://www.soiltesting.okstate.edu/SBNRC/SBNRC.php)
allows users to input NUE values with the suggested range being from 0.5
to 0.7.
Yield is a function of the combined
effects of different factors during crop growth and development. These
factors change every cropping season affecting the availability of N and
as a result, there were years (1971, 1978 and 1982) where plots that
received no N fertilizer obtained the same grain yield as those plots
which received 112 kg N ha-1 (Figure 1). Alternatively in
1987 and 2002, grain yield was doubled when N was applied at a rate of
112 kg N ha-1 over that of the check.
It can be seen that the 67 kg N ha-1 often met or
exceeded the yield of the 112 kg N ha-1 (Figure 1). This
indicated that the 112 kg N ha-1 rate was at times excessive
leading to a lowered NUE.
Averaged over N rates, the relationship
between RIHARVEST versus NUE (Figure 2) and RINDVI
versus NUE (data not shown) were not strong (coefficient of
determination, r2 =0.37, p<0.05 and 0.20, P>0.1,
respectively). When the RIHARVEST versus NUE relationship was
assessed by each N rate, a positive linear relationship was observed
(Figure 4-8), but the r2 values were significantly improved
to as high as 0.72 with increased N rates. Similarly, the r2
of the relationship between RINDVI and NUE increased with N
rate (r2= 0.03, 0.16, 0.21, 0.27 and 0.28 for 22, 45, 67, 90
and 112 kg ha-1 N rates, respectively). The test of slope and
intercept however revealed that none of the two parameters were
significantly different between any two N rates. When NUE was regressed
on RIHARVEST, the increase in NUE due to N application
diminished as the N rate increased. This was verified by the negative
correlation between slope values (unit increase in NUE for every unit
increase of RI) obtained from the regression models and N rate (Figure
9). Nitrogen use efficiency is a measure of N uptake per unit of N
applied and since grain yield does not proportionately increase with N
applied, further increase in N rates would only reduce NUE.
Alternatively, the regression of NUE on RINDVI
for 9-year data did show an increasing slope with increased N
rate. The NDVI, while a good indicator of biomass production, has
moderate (r2= 0.5) association with final grain yield but it
is good for estimating in-season biomass production and thus forage N
uptake.
The regression of NUE on both RIHARVEST
and RINDVI revealed that inclusion of both variables resulted
in r2 of 0.45 when data was combined over N rates for 1998 to
2006 data (Figure 10). The model was given by: NUE = -13 + 21*RINDVI
+ 21*RIHARVEST. The collinearity diagnostics revealed that
the VIF was 1.15 for both predictor variables indicating little
collinearity between predictor variables. This shows that the
contribution of each predictor in explaining the variability in NUE was
real although the contribution of RINDVI was small when it is
added to the model as a first or second predictor variable. Despite the
fact that RIHARVEST was better related to NUE than RINDVI,
the inclusion of both improved the prediction of NUE.
CONCLUSIONS
The use of the RI is a powerful tool to
predict NUE because RI has the ability to measure crop responsiveness to
N-fertilizer. The RI encompasses the effect of environmental factors
such as temperature and moisture which highly influences N
transformations and directly affects crop growth. A linear relationship
exists between RIHARVEST and NUE with r2 of 0.37.
But this relationship was improved (r2 = 0.45 when both RINDVI
and RIHARVEST were included in the model.
The relationship between RIHARVEST
and NUE was significantly improved when data was analyzed by each N
level. The estimation of NUE mid-season as a function of RIHARVEST
is therefore achievable using the equation shown in Figure 2 or from the
individual N rates. When NDVI measurement is available the estimation
can be even more reliable. However, further research needs to be
conducted to define the relationship between NUE and RIHARVEST
when N is applied top-dress and when N application is split between
pre-plant and topdress applications.
The relationship established between RIHARVEST
and NUE and the ability to collect the RINDVI measurements
in-season using GreenSeeker™ Hand Held sensors has opened another
opportunity to customize the functional algorithm based on factors
controlling final yield and NUE.
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