Published May 23, 2019

Soil Physics & Hydrology

Numerical Modeling of Nitrate in a
Flood-Irrigated Pecan Orchard
Esmaiil Mokari*
Manoj K. Shukla

Dep. of Plant and Environmental
Sciences
New Mexico State Univ.
Las Cruces, NM 88003

Jirka Šimůnek

Dep. of Environmental Sciences
Univ. of California
Riverside, CA 92521

Jorge L. Fernandez

Dep. of Plant and Environmental
Sciences
New Mexico State Univ.
Las Cruces, NM 88003

Pecan [(Carya illinoinensis (Wangenh.) K. Koch] is an important specialty
crop in New Mexico. This research quantifies soil water and soil nitrate-nitrogen (NO3–N) (mg L−1 of soil) variations with depth, root NO3–N (kg ha−1)
uptake, and NO3–N (kg ha−1) balance for the 100-cm soil profile during
two growing seasons in a flood-irrigated pecan orchard. Nitrate-nitrogen
was determined six times during the growing seasons of 2015 and 2016. The
HYDRUS-1D model was used to optimize the water flow parameters using
measured volumetric soil water content (q). Model calibration and validation
for NO3–N included the optimization of reaction parameters for nitrification and denitrification of each soil layer. The results showed that the model
simulated q well (0.44 £ d [index of agreement] £ 0.73) at different depths
during both calibration (2009) and validation (2010) periods. Generally,
HYDRUS-1D simulated soil profile NO3–N concentrations that were correlated with measurements at all depths during both years. Total root NO3–N
uptake showed a significant increase of 72% in 2016 compared with 2015.
The NO3–N balance showed that ?40% of applied NO3–N per year wasdenitrified, which was the main contributor to the NO3–N loss from the soil
profile during both years. Nitrate-nitrogen leaching below the soil profile was
32 and 26% of applied NO3–N in 2015 and 2016, respectively. The fertigation rate was much higher than the plant demand during both years, and it
should be decreased to reduce NO3–N losses.
Abbreviations: DOY, day of year; Ea, actual evaporation; Ep, potential evaporation; RLD,
root length density; SCF, soil cover fraction; Ta, actual transpiration; TDR, time-domainreflectometry; Tp, potential transpiration.

PCore Ideas• Pecan is a vitally important specialty crop in the southwestern
United States.
• No study is available on modelingtemporal variability of NO3–N transport in pecan orchards.
• Hydrus-1D predicted well water andNO3–N variability by depth for aheavy textured soil.
• NO3–N balance identified denitrification contributing to major loss.
• Nitrogen management in pecan orchards should take into account on
and off years to decrease leaching.
Soil Science Society of America Journal

ecan [Carya illinoinensis (Wangenh.) K. Koch] is a vitally important specialty
crop in New Mexico, which is one of the largest producers of pecans in
the United States. Mature pecan trees require a lot of water to grow, with
evapotranspiration ranging between 1000 and 1300 mm per season under flood
irrigation (Miyamoto, 1983). Irrigation water applied to mature pecan orchards in
the Mesilla Valley, NM, has fluctuated from 1870 to 1940 mm per season (Sammis
et al., 2004). Increasing acreage under pecan orchards in the arid southwestern
United States has put pressure on available water resources. Moreover, the amountof fresh water for irrigation is becoming scarce because of low precipitation, highevaporation rates, and continued drought in the southwestern United States.
Water availability for irrigation can be a major deterrent to pecan productivity, andmore attention to water conservation is needed.
Nitrogen (N) is an essential element for plant growth, and pecan needs N fertilizer during the nut enlargement and nut filling stages (Byford, 2005a). Nitrogen
application rates are usually much higher than rates for other nutrients (Wells, 2013),
which increases the risk of N leaching in irrigated pecan orchards. The recommended rate of N fertilizers is ?200 kg ha–1 (Byford, 2005b). Excess N fertilization in irSoil Sci. Soc. Am. J. 83:555–564
doi:10.2136/sssaj2018.11.0442
Received 19 Nov. 2018.
Accepted 10 Feb. 2019.
*Corresponding author (emokari@nmsu.edu).
© 2019 The Author(s). Re-use requires permission from the publisher.

rigated fields contaminates groundwater (Böhlke, 2002; Cepuder
and Shukla, 2002) because NO3–N is a weakly absorbed ionthat moves quickly through soil (González–Delgado and Shukla,
2014; Spalding and Exner, 1993). Leaching of NO3–N to groundwater is affected by type of irrigation system (Sharma et al., 2012a)
and by soil texture, and it can be high in arid and semiarid areas
such as southern New Mexico, especially in surface-irrigated areas
with sandier soils (Al-Jamal et al., 1997; Sharma et al., 2012b).
However, not many accounts are available of NO3–N leaching from irrigated fields in southern New Mexico. For example,
Sharma et al. (2012b) reported that NO3–N loadings below theroot zone under furrow-irrigation systems were highest for onion(Allium cepa L.), followed by chili pepper (Capsicum annuum L.)
and cotton (Gossypium spp.).
Measurements of water and NO3–N in pecan orchards arelimited because they are time- and labor intensive and because thecost of instrumentation and analysis can be high (van der Laan et al.,2010). On the other hand, solute transport in and out of the rootzone can be simulated using a variety of numerical models. Amongthese models are the Nitrate Leaching and Economic Analysis
Package (Shaffer et al., 1991) and the Agriculture Production
Systems Simulator (McCown et al., 1996). These models can also
provide deeper insight into the transport behavior as well as leaching of the applied chemicals and fertilizers toward the groundwater table with irrigation. Several successful applications of the
HYDRUS model are also available in the literature (e.g., Dabach
et al., 2015; Deb et al., 2011b; Kandelous and Šimůnek, 2010;
Ramos et al., 2011; Skaggs et al., 2010; Tafteh and Sepaskhah,2012). The HYDRUS model has been applied in diverse scenariosof different land uses and management systems as well as for different irrigation and fertigation practices (e.g., Crevoisier et al., 2008;
Deb et al., 2015; Ebrahimian et al., 2011, 2013; Hanson et al.,2006; Kurtzman et al., 2013; Levy et al., 2017; Phogat et al., 2014;
Ramos et al., 2012; Siyal et al., 2012). For example, Li et al. (2015)
applied HYDRUS to optimize fertilizer management practices in a
direct-seeded rice field and reported high performance of the model in simulating the N transport and transformation. Additionally,
Turkeltaub et al. (2018) used the HYDRUS-1D model to investigate recharge and nitrate transport through the deep vadose zoneof the Loess Plateau and reported future vulnerability of groundwater to contamination at a regional scale.
Monitoring volumetric soil water content (q) and N variations, which are key factors in crop productivity, is essential for
gaining a deeper understanding of soil–plant–atmosphere–water
relations. Simulations can provide additional information, suchas on N leaching, which may cause groundwater contamination.
However, there are no studies reporting NO3–N leaching in irrigated pecan orchards of southern New Mexico, and most available studies are only for lighter-textured soils. To the best of ourknowledge, there are only two studies that focused on modelingwater fluxes in the root zone of a flood-irrigated pecan tree (Debet al., 2011a, 2013b). This research was therefore conducted (i) to
determine soil water and NO3–N dynamics within and below theroot zone, (ii) to simulate water and NO3–N variations and root
556

NO3–N uptake using HYDRUS-1D, and (iii) to compute theNO3–N balance during two growing seasons in a pecan orchard.

Materials and Methods
Study Site

Field measurements of q were performed from April to
December 2009 and from March to December 2010 in a pecan orchard located at the Leyendecker Plant Science ResearchCenter of New Mexico State University. The site is 14.5 km
south of Las Cruces (32°11´ 56.66˝N, 106° 44´ 30.50˝W) at anelevation of 1174 m asl. The area of the orchard is 1 ha, withseven rows of 30-yr-old ‘Western Schley’ pecan trees planted ina rectangular pattern (29 trees in each row). The row and tree
spacings in the orchard were 7 and 8 m, respectively. The average tree height, trunk diameter at breast height, and tree crownwidth were 10.9 ± 0.2, 0.7 ± 0.0, and 7.1 ± 0.5 m, respectively.
Tree canopy was divided into four quadrants; in one of the quadrants, time-domain reflectometry (TDR) sensors with an accuracy ±2% (CS 616; Campbell Scientific, Inc., Logan, UT) were
installed to continuously record diurnal q under the canopy (2-m
distances from tree trunk in the no tilled part) at depths of 10,
20, 40, 60, and 80 cm. Other quadrants were used for identifying
root distribution and soil physical and chemical properties.
The orchard has been under similar management regarding
tillage operations since 2007. The tillage operations include chiseling at 40-cm depth once per year before first irrigation, disking
at 10- and 25-cm depths four times per year, and cultivating at 5and 10-cm depths two to three times per year, followed by landleveling. All tillage operations were done outside of the canopy
area between tree rows. The orchard is flood irrigated, and a totalof 5, 6, 9, and 10 irrigations were applied at the site during 2009,2010, 2015, and 2016, respectively. In 2009 and 2010, pecans
were subjected to water stress treatments, and that is why only
five irrigations were made in 2009 and six in 2010. The irrigation was scheduled approximately once every 20 d from the beginning of the growing season until June. After that, it was done
once every 15 d until mid-October. The fertigation was scheduled by the farm manager. To determine volume of water per application, the rate of inflow was multiplied with the duration ofpumping. The groundwater table was 7 m below the soil surface,
and the sources of irrigation were surface water and groundwater.
The soil is classified as Harkey (coarse-silty, mixed, calcareous,thermic Typic Torrifluvents)-Glendale (fine-silty, mixed, calcareous, thermic Typic Torrifluvents). Nitrogen and zinc are thetwo nutrients most often required by pecan trees annually; thetype and amounts of fertilizers applied are given in Table 1.

Numerical Modeling
The HYDRUS-1D model (version 4.16.0110) was used
to simulate the one-dimensional movement of soil water andsolutes in variably saturated porous media (Šimůnek et al.,2016). The orchard was flood irrigated; therefore, it is reasonable to use the HYDRUS-1D model. However, for this study,
we have used average root length density (RLD) from three difSoil Science Society of America Journalferent quadrants of a pecan tree. The HYDRUS-1D model uses
the Richards equation (Eq. [1]) to predict the redistribution ofwater in soil:∂q=∇( K ∇ H )− S r [1]∂twhere q is the volumetric soil water content (cm3 cm–3), K is theunsaturated hydraulic conductivity (cm d–1), H is the hydraulichead (cm), Sr is a sink term that represents the volume of water
removed from a unit volume of soil per unit time attributable
to plant water uptake (cm3 cm–3 d–1), Ñ is the spatial gradient operator, and t is time (d). The van Genuchten–Mualem
functional relationships are used as follows (Mualem, 1976; van
Genuchten, 1980):
q s −q r
q(y) =  q r +y < 0 ; q s y ≥ 0 [2][1(|
a v y|n )]m+K=(y) K s S el [1−(1− S e1/m )m ]2 [3]where qs is the saturated q (cm3 cm–3), qr is the residual q (cm3
cm–3), y is the soil water pressure head, av is the reciprocal of
the air entry y (cm–1), m is 1 – 1/n (n > 1), n is the pore size
distribution index (unitless), Se is the effective saturation (unitless) given as Se = [q(y) – qr]/(qs – qr), 1 is the pore-connectivity
parameter (unitless), and Ks is the saturated hydraulic conductivity (cm d–1). Root water uptake was simulated according toFeddes et al. (1978), with the sink term (Sr) accounting only for
the water stress. The uncompensated root water uptake model
was used (with a wc value of 1) (Šimůnek and Hopmans, 2009).
The HYDRUS-1D model provides simulations of multiple solutes, which can be either independent of each other orlinked using the first-order degradation (or hydrolysis) pathway,
which can be applied to N species. The solute transport equation describes advective-dispersive transport in the liquid phaseand diffusive transport in the gaseous phase. In this study, urea,
NH4–N, and NO3–N were the N species considered in simulations. The following set of equations is used in HYDRUS-1D topredict movement of N species:∂ q c1=∇.(q D∇ c1 )−∇.( qc1 )−m a q c1 − S r c1 [4]∂t∂ q c 2 ∂ s2
+r=∇.(q D∇ c 2 )−∇.( qc 2 )−
∂t
∂t
[5]
m vol q c 2 −m nit q c 2 +m a q c1 − S r c 2∂q c3=∇.(q D∇ c 3 )−∇.( qc 3 )−m dnit q c 3 + m nit q c 2 − S r c 3 [6]∂twhere c1, c2, and c3 are liquid phase concentrations (mg cm–3) of urea,
NH4–N, and NO3–N, respectively; D is the dispersion coefficient(cm2 d–1); q is the volumetric flux density (cm d–1); r is the bulk density of the soil (g cm–3); s2 is the adsorbed concentration of NH4–N(g g–1), which is a linear function of c2 using the distribution coefficient of NH4–N (kd); ma is the first-order rate constant (d–1) representing hydrolysis of urea to NH4–N; mvol is the first-order rate conwww.soils.org/publications/sssaj

Table 1. Dates of irrigation, fertigation, and amounts in thepecan orchard during 2009, 2010, 2015, and 2016. During
each irrigation, 13.29 cm of water were applied.
Irrigation/
fertigation

Type

14 May 2009
urea (46% N)
21 June 2009
urea (46% N)
2 Aug. 2009ammonium sulfate (21% N)
2 Sept. 200910 Oct. 2009
7 Apr. 2010
urea (46% N)
27 May 2010
urea (46% N)
22 June 2010
urea (46% N)
18 July 201023 Aug. 2010
ammonium sulfate (21% N)
7 Oct. 2010
23 Mar. 2015
21 Apr. 2015
urea (46% N)
17 May 2015
urea (46% N)
9 June 2015
urea (46% N)
28 June 2015
23 July 201523 Aug. 2015
ammonium sulfate (21% N)
15 Sept. 20158 Oct. 2015
21 Mar. 201612 Apr. 2016
UAN† (32% N)
19 May 2016
UAN (32% N)
7 June 2016
UAN (32% N)
23 June 201617 July 201612 Aug. 2016
28 Aug. 2016
21 Sept. 2016
UAN (32% N)
14 Oct. 2016
† Urea-ammonium nitrate.

N
Total
applied applied N–– kg ha−1 ––
51.75
103.5
35.5
–
–
190.75
129.4
51.75
103.5
–
35.5
–
320.15
–
129.4
51.75
103.5
–
–
35.5
–
–
320.15
–
118.8
118.8
118.8
–
–
–
–
118.8
–
475.2

stant (d–1) representing volatilization of NH4–N to NH3–N; mnit is
the first-order reaction rate constant (d–1) representing nitrification of
NH4–N to NO3–N; and mdnit is the first-order reaction rate constant(d–1) representing denitrification of NO3–N.

Measurements and Model Inputs
Soil physical properties, including particle size, bulk density,and saturated hydraulic conductivity, are presented in Table 2.
Diurnal variations of q at five depths (10, 20, 40, 60, and 80 cm)
were measured using five TDR sensors installed horizontally. Theq values were recorded every 10 min using a CR10X datalogger
connected to an AM16/32B multiplexer. Measured q values obtained by TDR sensors were calibrated by the gravimetric method(Deb et al., 2013a). For HYDRUS-1D modeling, the average
RLD was obtained by depth from the northwest, southwest, andsoutheast quadrants of the pecan canopy. The rooting depth and
RLD (cm root cm–3 of soil) distribution for this flood-irrigatedpecan orchard were reported in Deb et al. (2013a). Briefly, there
were no roots below the 80-cm soil depth. The RLD was higher at557

Table 2. Soil physical properties at the study field.
Soil depth

Particle size distribution
Sand

Silt

Clay

cm
–––––––––––––––––––––––– % ––––––––––––––––––––––––
0–20
22.84 ± 1.92
51.00 ± 1.47
26.16 ± 0.71
20–40
10.84 ± 1.29
59.00 ± 1.29
30.16 ± 0.82
40–60
49.34 ± 12.99
37.25 ± 10.88
13.41 ± 3.59
60–10037.84 ± 11.52
51.00 ± 10.74
11.16 ± 2.00
† Saturated hydraulic conductivity.

0- to 40-cm depths than at the 40- to 80-cm depths. Root lengthdensity accounts for 60 to 74% of the total RLD within the 0- to
40-cm depth. Replicated soil samples were collected 4 to 5 d after
the scheduled irrigation from canopy area. The NO3–N (mg kg–1
of soil) (EPA 353.2) was measured six times (February, June, andOctober in 2015 and 2016), with three sample replications. Usingbulk density, NO3–N in mg kg–1 of soil was converted to mg L–1(of soil). Measurements of NO3–N were performed at five depths(10, 30, 50, 70, and 90 cm). Initial values of the van Genuchten
soil water parameters, including qs, qr, av, and n, were estimatedusing the neural network prediction module in HYDRUS-1Dfor each soil depth interval. The initial value of Parameter l was
assumed to be 0.5 (Mualem, 1976).
Regarding solute transport parameters, urea and NO3–N
were assumed to be present only in the dissolved phase (kd =0 cm3 g–1). Ammonium-nitrogen was considered to adsorb to
the solid phase using a kd value of 3.5 cm3 g–1 for all soil depths(Hanson et al., 2006). The longitudinal dispersivity was considered equal to one-tenth of the profile depth for all soil depth
intervals (Cote et al., 2003; Hanson et al., 2006; Phogat et al.,2012). Molecular diffusion was neglected because it was considered negligible relative to hydrodynamic dispersion (Deb et al.,2015; González-Delgado and Shukla, 2014). The first-order decay coefficient ma for urea was considered to be 0.38 d–1 for all
soil depth intervals (Hanson et al., 2006). The nitrification anddenitrification rates were initially set to be the same at all soil
depth intervals (mnit = 0.2 d–1 and mdnit = 0.02 d–1) and were
adjusted for each soil depth interval according to observed data.
Volatilization of NH4–N and subsequent NH4–N transport bygaseous diffusion were neglected in this study. Under flood irrigation, urea is reported to be washed into soils and is not available
to be nitrified significantly (Hu et al., 2008). The last term in Eq.
[4], [5], and [6] represents passive root nutrient uptake, which is
directly coupled with root water uptake by convective mass flowof water. Unlimited passive uptake of NO3–N was consideredby specifying the cmax value larger than dissolved simulated concentrations, which allowed all dissolved nutrients to be taken up
by plant roots with root water uptake (Šimůnek and Hopmans,
2009). For root water uptake, the piece-wise model of Feddes etal. (1978) was chosen. All critical y values for a deciduous fruitfrom the HYDRUS-1D database were used in this study (Deb etal., 2011a, 2013b).
The HYDRUS-1D model requires separate values of potential evaporation (Ep) and potential transpiration (Tp) withtime. The soil cover fraction (SCF) was determined monthly in
558

Bulk density

Ks†

Mg m–3
1.53 ± 0.04
1.28 ± 0.05
1.24 ± 0.08
1.11 ± 0.05

cm min−10.001 ± 0.000
0.001 ± 0.001
0.0174 ± 0.0108
0.0097 ± 0.0028

the pecan orchard (Samani Majd et al., 2013). Meteorologicalparameters were taken from a climate station located at the
Leyendecker Plant Science Research Center. The HYDRUS-1Dmodel calculated the daily reference evapotranspiration (ET0)based on the Penman–Monteith equation and then divided it
into Ep and Tp using measured SCF. The Feddes et al. (1978)
reduction function reduces Tp to actual transpiration (Ta), andthe absolute value of the minimum allowed pressure head at thesoil surface (hCritA) value limits Ep to actual evaporation (Ea).
Model performance was assessed using the following quantitative measures (Shen et al., 1998; Willmott, 1981):
RMSE ==
d 1.0 −

ΣiN=1 ( Si − M i ) 2
N



[7]

ΣiN=1 ( Si − M i ) 2[8]ΣiN=1 (| Si − M avg | + | M i − M avg |) 2

where d is the index of agreement between measured and simulated values, N is the number of paired measured and simulated
values, Si is the ith simulated value, Mi is the ith measured value,
Mavg is the average of measured values, and RMSE is the meandifference between measured and simulated results.

Initial and Boundary Conditions
The initial water content was based on observed q(0.33 cm3 cm–3) for all soil depth intervals. The initial NO3–N
and NH4–N concentrations were determined separately foreach depth interval based on measured data before the start of
the growing season. The transport domain was considered tobe urea free at the beginning of the fertigation simulation. An
atmospheric boundary condition with a potential surface waterlayer was considered at the soil surface for water flow, definedby potential evaporation, potential transpiration, and rainfall. In
this study, the soil profile was assumed to be 100 cm deep, andthe water table was 7 m below the soil surface. Therefore, theboundary condition at the bottom of the transport domain was
assumed as free drainage. The top and bottom boundary conditions for solute transport were set as “Concentration Flux BC”
and “Zero Concentration Gradient,” respectively.

Results and Discussion

HYDRUS-1D Calibration and Validation for
Water Flow
All measured q values in 2009 were used to calibrate waterflow in HYDRUS-1D and to obtain optimized water flow parameters by inverse modeling. The HYDRUS-1D model uses
Soil Science Society of America Journal

Table 3. Optimized parameters of the calibrated flow andnitrogenspeciestransport
model.
Material
no.
1
2
3
4

Water flow parameters†

Depth
n
Ks
qr
qs
interval
3−3cm– cm cm –cm d−1
0–20 0.09 0.46 1.11
5
20–40 0.1
0.42 1.11
5
40–60 0.15 0.34 1.4
25
60–100 0.24 0.37 1.4
25

N reaction
parameters‡
mnit

mdnit

—— d−1 ——
0.13
0.015
0.11
0.017
0.22
0.006
0.21
0.002

† qr, residual volumetric soil water content; qs, saturated volumetric soil
water content; n, pore size distribution index (unitless); Ks, saturated
hydraulic conductivity.
‡m nit, first-order reaction rate constant representing nitrification of
NH4–N to NO3–N; mdnit, first-order reaction rate constant representing
denitrification of NO3–N.

the Marquardt–Levenberg optimization algorithm (Marquardt,
1963) to optimize VG parameters, which is an effective method
for nonlinear least squares fitting (Kool et al., 1985, 1987; van
Genuchten, 1981). The model calibration started with optimizing water flow parameters for Material 1, followed by parameters
for sequentially deeper soil materials. The parameters that wereshown to be the most sensitive were optimized for each soil layer(Table 3). Water flow parameters were optimized using measured
q values for a 247-d period from day of year (DOY) 91 throughDOY 337 (1 April–3 December) in 2009 for each material separately. HYDRUS-1D were validated using measured q values fora 233-d period from DOY 132 through DOY 364 (12 May–30
December) in 2010. During HYDRUS-1D calibration and validation, model simulations of daily average q at depths of 10, 20,
40, 60, and 80 cm were statistically compared with measured values using the above-mentioned quantitative measures.

Model Performance during the Calibration andValidation for Water Flow
Figures 1 and 2 illustrate differences between measured
and simulated daily mean q at five depths (10, 20, 40, 60, and 80cm) during the calibration (DOY 91 through DOY 337, 2009)and validation (DOY 132 through DOY 364, 2010) periods.
Generally, there was good agreement between measured andsimulated q during both calibration and validation periods (in thecalibration period in particular). For instance, during the calibration period, RMSE fluctuated between 0.03 and 0.04 cm3 cm–3,and d fluctuated between 0.57 and 0.73 (Fig. 1). During the validation period, RMSE varied between 0.04 and 0.06 cm3 cm–3,and d varied between 0.44 and 0.66 for different soil depths(Fig. 2). Other studies also reported good agreement between
measured and predicted q (Abbasi et al., 2003, 2004; Deb et al.,2013b; Ebrahimian et al., 2011, 2012; Saito et al., 2006). The
HYDRUS-1D model simulated both rapid rises in q immediately
after irrigation (Table 1) and gradual declines during drying periods. Model-predicted qs matched well (0.01 £ RMSE £ 0.03)
with measured values at all depths except for 60 and 80 cm during
the calibration period (Fig. 1) and 40 and 60 cm during the validation period (Fig. 2). However, some underpredictions during
the validation period at the depth of 60 cm were likely associatedwith the soil water retention behavior of the heavy-textured soil
as well as measurement errors associated with sensors. Differences
between simulated and measured q were also reported by Abbasi
et al. (2004) and Deb et al. (2012, 2013b), among others.

HYDRUS-1D Calibration and Validation for
Solute Transport
Calibration and validation of the solute transport model iscomplicated because several parameters need to be simultaneous-Fig. 1. Temporal variations in the simulated and measured q at
different soil depths during the calibration time period from day ofyear (DOY) 91 (1 Apr. 2009) to DOY 337 (3 Dec. 2009).

www.soils.org/publications/sssaj559

Fig. 2. Temporal variations in the simulated and measured q at
different soil depths during the validation time period from day ofyear (DOY) 132 (12 May 2010) to DOY 364 (30 Dec. 2010).

ly specified. Optimized water flow parameters (Table 3) and measured NO3–N concentration profiles during 2015 were used forcalibration. In this study, nitrification and denitrification parameters were adjusted and optimized for each soil depth separately(Table 3). Remaining parameters, listed in the Measurements
and Model Inputs section, were obtained from published studies(Cote et al., 2003; Hanson et al., 2006; Phogat et al., 2012). The
measured NO3–N concentration profiles during 2016 were used
for model validation. The RMSE and d values were calculated
from measured and simulated NO3–N concentration profiles toassess the performance of the model input parameters.

ing both years (Fig. 3). Because root nutrient uptake occurs in therooting zone, NO3–N below the rooting zone could leach to thegroundwater. To the best of our knowledge, there are no studies on
simulating NO3–N concentrations in pecan orchards. However,the HYDRUS-1D model showed good agreement between
the measured and simulated NO3–N in bare soil (Wang et al.,2010). The HYDRUS-1D model was reported to perform wellon simulating water and N leaching in furrow-irrigated rapeseed
and maize under different rates of fertilizer applications (0.094 £NRMSE £ 0.11 for deep percolation and 0.14 £ NRMSE £ 0.18
for NO3–N leaching) (Tafteh and Sepaskhah, 2012).

Model Performance during the Calibration andValidation for Solute Transport

Root NO3–N Uptake

Simulated and measured depth distributions of NO3–N concentrations during two growing seasons, which represented calibration and validation periods, are presented in Fig. 3. Generally,
Fig. 3 illustrates that the optimized set of solute transport/reaction
parameters considered in model simulations (Table 3) was reasonable and applicable for simulating the N transport and transformations in the pecan orchard. Fluctuations in the measured
and simulated NO3–N concentration profiles at specified times
showed that HYDRUS-1D simulated well (0.36 £ d £ 0.79)
the patterns of NO3–N concentration profiles for both 2015
and 2016 (Fig. 3). Predictions of depth distributions of NO3–N
concentrations were the best at all depths during the calibration
period of June 2015, with d = 0.74 (Fig. 3b). However, simulated
NO3–N values had a relatively low d value and high RMSE during the validation period of June 2016 (Fig. 3e). The model simulated very well (0.99 £ RMSE £ 5.16) NO3–N concentrations
below the rooting zone (?60 cm for pecan) in all months dur560

Simulated cumulative Ea, Ta, and cumulative root NO3–N
uptake during two growing seasons are shown in Fig. 4. Actual
evaporation and Ta were almost similar during both years due
to similar irrigation schedules (Fig. 4). Root nutrient uptake wasbased on the assumption that all uptake was passive, through theroot water uptake pathway only. Therefore, an increase in Ta
caused an increase in root NO3–N uptake (Fig. 4). A comparisonof root NO3–N uptake in 2015 and 2016 showed an increase of?27 kg ha–1 in 2016, which was in agreement with ?48% more
N fertilizer applied in 2016 than 2015 (Table 1). More N uptake
was reported for rapeseed and maize at higher N application rates(Tafteh and Sepaskhah, 2012). The growing season of 2016 was
the alternate bearing or “off ” year, when a higher fertilizer application and soil N increased root NO3–N uptake in June 2016
(Fig. 4b; DOY 154–183). A 48% increase in N fertilizer application (Table 1) resulted in a 72% increase in NO3–N uptake in2016 compared with that in 2015 (Fig. 4; Table 4).

Soil Science Society of America Journal

Fig. 3. Simulated and measured NO3–N concentration profiles for days of soil sample collections during the calibration (a, b, c) and validation (d,e, f) periods of 2015 and 2016, respectively.

The N demand of pecan is high in June during the nut enlargement period and stays high during the subsequent nut filling stages(Acuña-Maldonado et al., 2003). The timing of fertilizer applications
influences N absorption and partitioning as well as nut yield; therefore, fertilizer application during the entire growing season should betaken into consideration. The first N application should be done before the bud break because absorption apparently takes place during
dormancy, followed by rapid N absorption during the shoot and leaf
development (Acuña-Maldonado et al., 2003). A 5-yr study assessedin pecan orchards showed that applying just 125 kg ha–1 N per year
(less than one-third the average N rate applied in our study) led toroughly 80 kg ha–1 of total N uptake (Acuña-Maldonado et al., 2003).
The high N uptake efficiency could be explained by the differencein the type of irrigation system (drip irrigation vs. flood irrigation).
Obviously, fertilizer management is more efficient in drip irrigationcompared with flood irrigation.

ious processes. Table 4 shows simulated cumulative components of
the NO3–N balance (kg ha–1) across the 100-cm soil profile during
2015 and 2016. The two inputs of NO3–N were from applicationsof NO3–N fertilizers and nitrification of NH4–N to NO3–N as aresult of urea fertilizer applications (Table 1). From Table 4, all components of NO3–N outputs were different between years because ofdifferences in the amount and type of fertilizer applications (Table
1). Denitrification had a large contribution to the NO3–N loss
from the soil profile in both years, accounting for ?40% of applied
NO3–N each year (Table 4). Nitrate-nitrogen leaching accounted
for 32 and 26% of applied NO3–N in 2015 and 2016, respectively.
To reduce NO3–N leaching, more frequent but lighter applicationsof N fertilizers are highly recommended in flood-irrigated orchards.
The soil NO3–N storage increased on average by 14.15 kg ha–1 during both years. Total NO3–N balance errors with HYDRUS simulations were <1% during both years.

NO3–N Balance

Conclusions

The importance of the NO3–N balance is to gain deeper understanding about fertilizer efficiency and fertilizer losses due to var-

www.soils.org/publications/sssaj

The HYDRUS-1D model was used to optimize selectedwater flow and solute transport parameters needed to simulate561

Fig. 4. Simulated cumulative water fluxes (total actual evaporation and transpiration) (cm) and cumulative NO3–N (kg ha−1) removed from theflow domain by root uptake during the time periods day of year (DOY) 1 (1 January) to DOY 365 (31 December) in (a) 2015 and (b) 2016.

depth distributions of soil water and NO3–N, root NO3–N uptake, and the NO3–N balance during two growing seasons in aflood-irrigated pecan orchard. In general, the model simulationsof temporal variations of q matched well with the corresponding
measurements at various depths, especially during the calibration
period (0.57 £ d £ 0.73). The model was calibrated and validated for the NO3–N transport, and nitrification and denitrification parameters were optimized for each soil depth interval.
Concentrations of NO3–N simulated by HYDRUS-1D agreed
well with corresponding measurements in most depths, especially in deeper depths, during both years, with d > 0.5 during thecalibration period. Total root NO3–N uptake was 72% higher
in 2016 than in 2015 due to excessive N fertilizer applicationsin 2016 compared with 2015. Leaching of NO3–N below therooting zone (60 cm) indicated a potential of groundwater pollution by NO3–N leaching. The NO3–N balance for the 100cm soil profile corresponded well with differences in the amountand type of the fertilizer applications. Denitrification accounted
for most of the NO3–N loss (40% of NO3–N input each year)

from the soil profile. An average of 29% of applied NO3–N wasleached during both years. This research demonstrated that, todecrease NO3–N leaching, N management strategies should
consider the alternative bearing of pecan and adjust fertilizer application rates accordingly.

Acknowledgments

This work was supported by the Nakayama Endowment, National
Institute of Food and Agriculture, New Mexico Water Resources ResearchInstitute, and Bureau of Reclamation. The authors thank Frank Sholedice
for editing the manuscript.

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–1.62
–0.24

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