To meet increasing demands, tea plantations are rapidly
expanding in China. Although the emissions of nitrous oxide (N2O) and
nitric oxide (NO) from tea plantations may be substantially influenced by
soil pH reduction and intensive nitrogen fertilization, process model-based
studies on this issue are still rare. In this study, the process-oriented
biogeochemical model, Catchment Nutrient Management Model –
DeNitrification-DeComposition (CNMM-DNDC), was modified by adding tea-growth-related processes that may induce a soil pH reduction. Using a
dataset for intensively managed tea plantations at a subtropical site, the
performances of the original and modified models for simulating the
emissions of both gases subject to different fertilization alternatives and
stand ages were evaluated. Compared with the observations in the early stage of
a tea plantation, the original and modified models showed comparable
performances for simulating the daily gas fluxes (with a Nash–Sutcliffe index
(NSI) of 0.10 versus 0.18 for N2O and 0.32 versus 0.33 for NO), annual
emissions (with an NSI of 0.81 versus 0.94 for N2O and 0.92 versus 0.94
for NO) and annual direct emission factors (EFds). For the modified
model, the observations and simulations demonstrated that the short-term
replacement of urea with oil cake stimulated N2O emissions by
∼62 % and ∼36 % and mitigated NO emissions
by ∼25 % and ∼14 %, respectively. The
model simulations resulted in a positive dependence of EFds of either
gas on nitrogen doses, implicating the importance of model-based
quantification of this key parameter for inventory purposes. In addition, the
modified model with pH-related scientific processes showed overall
inhibitory effects on the gases' emissions in the middle to late stages during a
full tea plant lifetime. In conclusion, the modified CNMM-DNDC exhibits the
potential for quantifying N2O and NO emissions from tea plantations
under various conditions. Nevertheless, wider validation is still required
for simulation of long-term soil pH variations and emissions of both gases
from tea plantations.
Introduction
Tea (Camellia sinensis (L.) Kuntze), as a perennial cash crop, has been widely cultivated
long-term in the tropical and subtropical regions of the world, with nearly
90 % of the global tea harvest area currently located in Asia and over
50 % of that located within China (http://www.fao.org/faostat/, last access: 9 May 2019). To
maximize the economic benefits, especially in China, tea production has
expanded intensively, mostly through the conversion of arable uplands, rice
paddies and forests into tea plantations (e.g., Xue et al., 2013; Yao et
al., 2015). For instance, both the total harvest area and production
dramatically increased by 166 % (from 1.09×106 to
2.90×106 ha) and 253 % (from 6.8×105 to
2.40×106 Mg), respectively, from 2000 to 2016
(http://www.fao.org/faostat/).
As a leaf- or bud-harvested crop, nitrogen is the key nutrient for yield. Thus,
high tea yields are largely supported by the intensive application of
nitrogen fertilizers. The nitrogen inputs amount to 450–1200 (mean: 553) kg N ha-1 yr-1 in the primary areas of tea cultivation in China (Han
et al., 2013), which is much higher than the recommended doses of 250–375 kg N ha-1 yr-1 (Fu et al., 2012; Hirono and Nonaka, 2012, 2014;
Hou et al., 2015; Li et al., 2016; Tokuda and Hayatsu, 2004; Yamamoto et
al., 2014; Yao et al., 2015, 2018). This intensive nitrogen application
results in superfluous reactive nitrogen remaining in the soil. The
excessive reactive nitrogen induces the high potential for nitrous oxide
(N2O) and nitric oxide (NO) emissions, thus leading to the detrimental
consequences of global warming and air pollution.
The tea plant has been well known as one of the very few families tolerant
to high levels of aluminum ion (Al3+) and thus can
grow well in acidic soil (Taylor, 1991). Mature leaves of the tea plant may
contain up to 30 g Al kg-1 on a dry-weight basis (Matsumoto et al., 1976)
without experiencing Al toxicity (Morita et al., 2008). Part of the tissue
Al returns to the soil through plant trimming, thus leading to Al
accumulation in the surface soil of a tea plantation. In addition, the Al under
an acidic condition can be recombined with the organic matter derived from
root exudation. This process further facilitates the accumulation of Al in
the upper soil layer of a tea plantation (Lin and Yang, 2014). The former
mechanism of Al accumulation in surface soil almost does not occur for the
absolute majority of plant families that much more weakly absorb of Al
than tea plants do (Taylor, 1991; Matsumoto et al., 1976). Hence, the soil pH of
tea plantations decreases with the increased stand age jointly due to the
processes of (i) acid release by root exudation and (ii) hydrogen ion
(H+) production in the hydrolysis of the accumulated
Al3+ from residue decomposition in surface soil.
The high nitrogen doses combined with the decreased soil pH may further
promote the production of the harmful nitrogenous gases through both
microbial processes (e.g., nitrification and denitrification) and
nonbiological mechanisms (e.g., chemodenitrification; see, e.g., Chen et al.,
2017; Fu et al., 2012; Yao et al., 2018), especially for low pH. A
number of field studies have demonstrated that much more N2O and NO
were emitted from tea plantations than from sites in other upland fields (e.g.,
Akiyama et al., 2006). In China, the N2O and NO emissions from tea
plantations are 16.6 and 14.9 kg N ha-1 yr-1 on average,
respectively (Fu et al., 2012; Han et al., 2013; Yao et al., 2015, 2018). In 2013,
for instance, the N2O emissions from tea plantations in China accounted
for more than one-tenth of the national total emissions of this gas from
croplands and contributed to 85 % of the total N2O emissions from
global tea plantations (Li et al., 2016). To alleviate the negative impacts
on environmental quality and human health, organic fertilization has been
strongly recommended in China and was adopted in nearly 4.5×104 ha of tea fields by 2011 (Han et al., 2013). Application of organic
fertilizers in tea fields can improve soil fertility (e.g., Han et al.,
2013), while stimulating N2O emissions but mitigating NO release (Yao
et al., 2015). Therefore, investigating the impacts of replacing synthetic
nitrogen fertilizer with organic manure and the effect of stand ages on the
emissions of N2O and NO from tea plantations are necessary for
understanding the mechanisms of nitrogen cycling and effectively mitigating
the emissions of both of the nitrogenous gases from tea fields.
First-hand information of N2O and NO emissions can be obtained from field experiments, but such experiments can be time and labor intensive. Modeling approaches based on sufficient validation have been proposed to
overcome the limits of field measurements (e.g., Chen et al., 2017). Because
process-oriented biogeochemical models such as DNDC (e.g., Li, 2000),
LandscapeDNDC (e.g., Haas et al., 2012), WNMM (e.g., Li et al., 2007) and
CNMM-DNDC (Zhang et al., 2018) are generally designed following the basic
theories of physics, chemistry, physiology and biology, they are expected to
be widely applicable under various climates, soils, land uses and field
management practices. These models, in principle, can facilitate the
understanding of the interactions among various processes, identify gaps in
current knowledge, and temporally and spatially extrapolate the results from
experiments (Chen et al., 2008). Among these models, the Catchment Nutrient
Management Model – DeNitrification-DeComposition (CNMM-DNDC) model is one of the
latest versions of the DNDC. CNMM-DNDC was established by incorporating the
core carbon and nitrogen biogeochemical processes of DNDC (including the
processes of decomposition, nitrification, denitrification and fermentation)
into the hydrological framework of the CNMM, and it therefore inherited the
features from both CNMM and DNDC (Zhang et al., 2018). CNMM-DNDC was
established to solve a common bottleneck problem of most biogeochemical
models, i.e., the inability to simulate the lateral flows of water and
nutrients. This solution potentially enables the model to identify the best
management practices of intensive cropping systems. In its initial
validation in a catchment with calcareous soils and complex landscapes, the
CNMM-DNDC performed fairly well in simulating ecosystem productivity
(represented by crop yields in croplands), hydrological nitrogen losses by
soil leaching and nitrate discharge in streams, and emissions of gaseous
carbon (carbon dioxide and methane) and nitrogenous gases (N2O, NO and
ammonia) from different lands (forests and arable lands cultivated with
maize, wheat, rapeseed or paddy rice; Zhang et al., 2018). However, the
scientific processes of soil pH reduction due to tea growth are still lacking representation in the CNMM-DNDC. This gap may induce significant biases in simulating the
fluxes in both nitrogenous gases from tea fields, especially for long-term
prediction, because soil pH is the key factor regulating N2O and NO
emissions from the soil (e.g., Chen et al., 2017; Yao et al., 2018).
Therefore, the authors hypothesize that adding the missing scientific
processes which lead to soil pH reduction into the internal model program
codes can improve the performance of the CNMM-DNDC in simulating the
N2O and NO emissions from tea plantations with different stand ages.
Filling the gap in the model is especially necessary for predicting the long-term emissions of both gases from tea plantations.
To test the above hypothesis, the authors conducted a case study using a
unique experimental dataset, which was obtained by Yao et al. (2015, 2018)
in a tea plantation with field treatments of fertilization alternatives and
stand ages. The aims of this case study were to (i) attempt to fill the gap
in the CNMM-DNDC through the addition of the processes that may induce soil pH
reduction due to tea growth; (ii) compare the performances of original and
modified models in simulating N2O and NO emissions; and (iii) evaluate
the modified model performance in simulating the direct emission factors
(EFds) of different annual nitrogen doses and the N2O and NO
emissions affected by the short-term replacement of a widely applied synthetic
nitrogen fertilizer (urea) with a typical organic manure (oil cake), taking into account
the stand ages within the early stage (1–6 years) of a new tea plantation.
Materials and methodsIntroduction to the field site and experimental treatments
The field site (32∘7.37′ N, 110∘43.18′ E; 441 m above sea level) selected for this modeling case study is located
in Fangxian, Hubei province, China. The field site is subject to a
northern subtropical monsoon climate, with annual precipitation of 914 mm
and a mean air temperature of 14.2 ∘C in 2003–2011 (Yao et al.,
2015). Two plots at the field site were involved in this study, encoded as
T08 and T12. Both plots had been consecutively cultivated in the long term with paddy rice in summer and upland crops (or drained but
fallowed) in winter until tea seedlings were transplanted in March 2008 for
T08 and March 2012 for T12. Conventional fertilization practices had been
adopted in both plots. A typical synthetic fertilizer (urea) was regularly
applied at 450 (150 in autumn and 300 in spring) kg N ha-1 yr-1 (encoded as T08-UN and T12-UN). To determine the annual
EFd (the fraction of the applied fertilizer nitrogen released in the
form of N2O or NO within the 1-year period after fertilization) of
either gas and to investigate the effects of short-term synthetic fertilizer
replacement by organic manure on N2O and NO emissions, eight spatially
replicated subplots were randomly set in either T08 or T12: four for the
control without nitrogen fertilizer applied (NN) and the others for the exclusive application of organic manure (OM) in 2013 (only T08) and 2014
(both T08 and T12). Each daily flux was inferred from the single measurement
based on five gas samples from a 30 min enclosure of a static opaque chamber
between 09:00 and 11:00 (China standard time). Oil cake, one of the most widely
applied organic manures in the subtropical regions of China, was exclusively
used to amend the OM subplots to fully replace the urea. Nitrogen doses with the urea application were adopted for the subplots of UN. The
NN and OM treatments were encoded as T08-NN, T08-OM, T12-NN and T12-OM.
T08-NN and T08-OM were adopted consecutively in 2 full years (from October
2012 to March 2014) and T12-NN and T12-OM in 1 full year (from October
2013 to March 2014). The organic manure in dry weight contained 7.1 %
nitrogen and 43.3 % carbon. The topsoil (0–15 cm depth) of the T08 and
T12 plots had a loamy texture measured in 2013, and detailed information
is provided in the Supplement (Table S1). The soil pH
at the time of tea seedling transplanting was 6.0 (Yao et al., 2018).
Irrigation was adopted following the typically regional management practice.
Daily fluxes of N2O and NO, topsoil (5 cm) temperature, and surface soil
(0–6 cm) moisture in water-filled pore space (WFPS) for each field
treatment were observed over 2 full years for T08-NN, T08-UN and T08-OM
(from mid-September 2012 to mid-October 2014) and 1 full year for T12-NN,
T12-UN and T12-OM (from mid-September 2013 to mid-October 2014). For more
detailed information on the field experiments and observed data, refer to
Yao et al. (2015, 2018) and Table S2.
Model modifications
In this study, the CNMM-DNDC was modified through (i) defining and applying
a soil pH regulating factor (fsph) on plant growth and (ii) adding two
processes that produce H+ and thus acidify soils
(Miao, 2015; Pang, 2014). These modifications were made to enable the model
to simulate the responses and feedbacks between tea growth and soil pH
changes.
Considering that the soil pH for tea growth is optimal within 5.0–5.4 and
suitable within 4.0–6.5 (Cao et al., 2009), fsph, a dimensionless
factor (0–1), is newly parameterized as a quadratic polynomial function
utilizing an average soil pH of 0–20 cm (spha) as its single
independent variable (Eq. 1). Based on Eq. (1), the value of fsph is
around 1.0 when soil pH is within 5.0–5.4 and is above 0.85 when soil pH
is within 4.0–6.5. However, the transient soil pH increase induced by urea
hydrolysis is not considered for affecting plant growth, because it can be offset
due to the soil-buffering effect within a few days. At each time step of the
simulation, the value of spha is updated. This parameterized factor is
introduced into the model to regulate photosynthesis and thus plant growth,
even though the modification to the model has not yet been calibrated or
validated due to a lack of sufficient field observations at the selected tea
fields.
fsph=-0.089spha2+0.947spha-1.51
The two processes newly introduced into the model to simulate additional
changes in the H+ concentration
(ΔH+), thus modifying soil pH,
are (i) ionization of the amino acids and other organic acids (HR) in root
exudates (Reaction 1) and (ii) hydrolysis of the Al3+
from the decomposition of tea residues due to the trimming (tea leaves and
young branches) or falling of old leaves (Reaction 2).
R1HR⇋H++R-R2Al3++3H2O⇋Al(OH)3+3H+
The ionization equilibrium of organic acids is formulated in Reaction (R1), wherein
HR represents the category of amino acids or other organic acids in root
exudates. Following Eqs. (2)–(4), the H+
concentration changes due to the ionization of these exudate-contained acids
(ΔH+ex; mol L-1) are
calculated by solving Eqs. (3)–(4) (analytical method), which
include the parameters of average ionization equilibrium constants for amino
acids (Kami; mol L-1) and the other organic acids (Korg; mol L-1) in root exudates and the molar concentrations of amino acids and
organic acids in the soil water (cami and corg, respectively; mol L-1). As the acid ionizations are thermodynamic processes, both
Kaim and Korg vary with soil temperature (T; ∘C). Their
values under various temperature conditions are given via the correction of
their constant values at 25 ∘C for both acids, i.e., Kaims=Korgs=1.75×10-5 mol L-1 (Fu, 1999), using a
temperature regulating factor, facid (Eqs. 5–6). The function form for
parameterizing facid (Eq. 7) was adapted from Li (2016). The molar
concentrations of the acids and H+ in the
soil water are calculated using Eqs. (8)–(10). In these equations, 10-4 is
a dimension adaptor (for each 3 h time step), h denotes the thickness of
each soil layer (m), SM stands for the soil moisture in volumetric water
content (m3 m-3), Mami and Morg represent the average
molar mass of amino acids (128 g mol-1) and the other organic acids
(119 g mol-1), respectively, in root exudates (Fu, 1999), aami and
aorg are the mass fractions of the two categories of acids in root
exudates (dimensionless), Ex is the root exudates in the soil layer (kg ha-1), sph′ denotes the soil pH, and cH(soil) is the H+ concentration corresponding to the most recently updated pH. At
each time step (3 h) of the model simulation, 6 % of the net primary
productivity is assumed to be released into the soil profile via root
exudation. This assumption was made by referring to the experimental data of
some other tree species (Miao, 2015). The Ex in the soil layer is determined
by portioning the exudate quantity according to the vertical distribution of
the root biomass in the soil profile of root depth.
2ΔH+ex=ΔH+ami+ΔH+org3Kami=ΔH+ami(cH(soil)+ΔH+ami)(cami-ΔH+ami)-14Korg=ΔH+org(cH(soil)+ΔH+org)(corg-ΔH+org)-15Kami=Kamisfacid6Korg=Korgsfacid7facid=0.81+0.0077T8cami=10-4h-1SM-1Mami-1aamiEx9corg=10-4h-1SM-1Morg-1aorgEx10cH(soil)=10-sph′
According to Reaction (2), the H+ concentration
changes due to the hydrolysis of Al3+ derived from
decomposition of tea plant residues (ΔH+res) are calculated by solving Eq. (11) (numerical method by Newton iteration). In this equation,
Kw ((mol L-1)2) and Kb ((mol L-1)3) denote the
water dissociation constant and ionization equilibrium constant of aluminum
hydroxide (Al(OH)3), respectively, and cAl(III) is the molar
concentration of Al3+ in the soil water (mol L-1). As both water dissociation and Al(OH)3 ionization are also
thermodynamic processes, their equilibrium constants (dimensionless) vary
with soil temperature and are thus determined following Eqs. (12)–(13), wherein
the values at 25 ∘C, i.e., Kws=1×10-14 (mol L-1)2 and Kbs=1.3×10-33 (mol L-1)3 for water and Al(OH)3, respectively (Fu, 1999), are
corrected by the factors fw and fb, respectively. The
parameterization for fw (Eq. 14) was cited from Li (2016), and fb
was parameterized by Eq. (15). For the calculation of cAl(III) in Eq. (16),
MAl denotes the molar mass of Al3+ (27 g mol-1), b the fraction of hydrolyzed Al(OH)3 (dimensionless), c the
Al content in tea residues (kg per kg dry matter) and Res the quantity
of tea residues in dry matter (kg ha-1). As the Al concentration in tea
leaves varied from 1.2 to 2.7 mg per g dry matter, the c value was set as
2.3×10-3 kg per kg dry matter (Hajiboland and Poschenrieder, 2015; Xu
et al., 2006).
11Kw3Kb-1=ΔH+res(cH(soil)+ΔH+res)3(cAl(III)-ΔH+res/3)-112Kw=Kwsfw13Kb=Kbsfb14fw=0.1945e0.0645T15fb=1.09-0.0037T16cAl(III)=10-4h-1SM-1MAl-1bcRes
Using the H+ concentration changes
calculated above, the soil pH most recently modified by the originally
existing processes, or at the last time step of the simulation, is further
updated by Eq. (17). The soil pH updated by Eq. (17) is used to update the
independent variable of Eq. (1) so as to provide an update of fsph.
sph=-lg(cH(soil)+ΔH+ex+ΔH+res)
For the processes newly added above, the unknown parameters, aami,
aorg and b, were calibrated in this study using the observed soil pH in
the T08 and T12 plots. The independent variables of T, h, SM and Res, as well
as the net primary production and the root biomass distribution in the soil
profile required to calculate Ex, are provided by the model simulations at
each time step.
The soil pH dynamics affected by the urea hydrolysis, soil buffering and
manure application have already been considered in the original CNMM-DNDC
(Table S3). The CNMM-DNDC with and without the above modifications is
hereafter referred to as the original and modified model, respectively.
Evaluation of model simulations for emissions of both gases
The model performances in simulating N2O and NO emissions from the tea
plantations were evaluated by comparing the simulations of the original and
modified models with the field observations. The required input of hourly
meteorological data (air temperature, precipitation, wind speed, solar
radiation, humidity) for years with gas flux measurements (2012–2014) were
obtained from the meteorological station at the field site, while those in
2008–2011 were adapted from the daily data at the nearby government
meteorological station (provided by the National Meteorological Information
Center, http://data.cma.cn/, last access: 5 October 2018) by referring to the diurnal patterns of the
hourly data observed and provided by the Shennongjia Station
(∼40 km south of the tea fields) of the Chinese Ecosystem
Research Network. The aforementioned observations were used for the required
inputs of soil properties (soil organic carbon (SOC), total nitrogen, mass fraction of clay, pH
and bulk density). The required inputs of field capacity and wilting point
(0.38 and 0.16, respectively, in volumetric water content) were calculated
by the pedotransfer functions used by Li et al. (2019). According to the
local survey, the initial biomass of tea seedling transplanting was set as
1500 kg dry matter (DM) ha-1. The harvesting of buds and the trimming of the
canopy were started in the fourth year after transplanting (YAT),
following the local conventional practices. The bud tea was harvested in
T08 from April to May and August to October in the fourth, fifth and
sixth YAT, with annual yields of 37.5–150 kg DM ha-1. The tea
plants were trimmed twice per year in June and November, and nearly 40 % of
the aboveground biomass was cut and left on the ground. The detailed
management practices during the gas measurement period were obtained from
Yao et al. (2015, 2018), which were also adopted during the remaining period
of simulation. The simulated soil profile (0–100 cm depth) was divided
into 20 layers. The thickness of each layer was 1, 5 and 10 cm for the top
10, middle 2 and other 8 layers, respectively. The time step of the simulation
was set at 3 h. The measured data (Yao et al., 2015, 2018) used for
evaluating the model performance included the topsoil temperature and
moisture and the daily fluxes of N2O and NO emissions from T08-NN,
T08-UN and T08-OM in 2012–2014 and T12-NN, T12-UN and T12-OM in
2013–2014 (Fig. 1).
Observed and simulated daily mean soil (5 cm) temperature, soil
(0–6 cm) moisture, nitrous oxide (N2O) and nitric oxide (NO) fluxes
from tea fields of different treatments by the original and modified models.
T08 and T12 represent the fields with tea seedling transplanting in 2008 and
2012, respectively. NN, UN and OM encode the no nitrogen applied and
fertilization with urea and oil cake, respectively. The gray and black
arrows indicate the dates of irrigation and fertilization, respectively. The
number under the arrow stands for the applied water amount in centimeters or fertilizer dose in kilograms of nitrogen per hectare. The vertical bar for each observation in panels (c)–(n) indicates
the standard error in four spatial replicates. The legend in panel (c) applies to all panels.
Investigation of fertilization and stand age effects on emissions of
both gases
In the field cases involved in this study, the short-term replacement of
urea with oil cake was implemented in the second (T12) or
fifth to sixth (T08) YAT following the land use change from long-term
paddy rice cultivation to perennial tea plantation. Based on the field
observations of N2O and NO emissions reported by Yao et al. (2015,
2018), the performance of the original and modified models in simulating the
effects of the urea replacement by oil cake was examined through the
comparison between the model relative bias (MRB) magnitudes and the
observational error indicated by the coefficient of variation (CV). An
absolute MRB (|MRB|) smaller than the 2 times the CV of the
spatially replicated observations, which represented the observational
uncertainty at the 95 % confidence interval (CI), was considered to
indicate a statistically satisfactory performance (Dubache et al., 2019).
For this examination, the urea replacement effects (Eur; %) on the
N2O and NO emissions and their relative observational errors
(εur; %) at the 95 % CI were calculated
using Eqs. (18)–(19). In both equations, Eo‾ and Eu‾ (in kg N ha-1 yr-1) denote the mean annual emissions of N2O
or NO from the OM and UN treatments, respectively, and δo and
δu (in kg N ha-1 yr-1) signify the
corresponding observational errors in 2 times the standard deviation (SD).
Equation (19) is analytically established according to Eq. (18) and following
the general error propagation theory. The observed data were directly cited
or adapted from Yao et al. (2015, 2018).
18Eur=100(Eo‾/Eu‾-1)19εur=100(Eu‾-2δo2+(Eo‾2Eu‾-4δu2)1/2/(Eo‾/Eu‾-1)
The virtual experiments were designed to evaluate the performance of the
original and modified models in simulating the annual EFds and to
investigate the effects of fertilizer nitrogen doses on EFds. For each
field treatment exclusively applied with urea or oil cake in 2013 or 2014,
virtual experiments with nitrogen addition rates varying from zero to 600
(with an interval of 50) kg N ha-1 yr-1 were carried
out. For each treatment, the gradient nitrogen doses were set only in the
experimental year but remained at 450 kg N ha-1 yr-1 in
the other year(s). The annual EFds (the fraction of the increased
fertilizer nitrogen input released in the form of N2O or NO within the
1-year period after fertilization) as a percentage for the nitrogen dose
gradients were simulated at each gradient with an interval (N50) of 50 kg N ha-1 yr-1, following Eq. (20), wherein E50+ and
E50- denote the simulated annual emissions of N2O or NO at the
higher and lower fertilizer nitrogen dose of the gradient, respectively.
EFd=100(E50+-E50-)/N50
The stand age effects on annual N2O and NO emissions in the early stage
(1–6 years) or over a full tea plant lifetime (35 years) of a plantation can be
investigated if the applicability of the model has been proven using available
observations at the field site. Acceptable model applicability can be
indicated by a smaller average |MRB| than 2 times the CV of the
spatially replicated observations. The effects of the stand ages during the
early stage (first to sixth YAT) or the full tea lifetime (usually
until approximately the 35th YAT in the region) can be investigated
using a virtual experiment. The tea plantation in this virtual experiment
was purely fertilized with urea at the conventional timings and with the conventional doses. Any
influencing factor other than stand age should be excluded from this virtual
experiment. To ensure the simulations of all the stand ages could be driven by
the same meteorological conditions as those under which the measured
data during the year-round period from 17 September 2013 to 9 October 2014 were collected, 35 independent scenarios were designed. Thus, the seedling
transplanting for the stand ages of 35, 34, …, 1 year was set to occur in
March of 1979, 1980, …, and 2013, respectively. The field management
practices for T08-UN would be set for each stand age scenario.
Statistics and method to quantify uncertainties
The statistical criteria used in this study to evaluate the model
performance include (i) the index of agreement (IA), (ii) the Nash–Sutcliffe
index (NSI), (iii) the determination coefficient (R2) and slope of the
zero-intercept univariate linear regression (ZIR) of the observations
against the simulations, and (iv) the MRB. The IA falls between 0 and 1,
with a value closer to 1 indicating a better simulation. An NSI value
(ranged from minus infinity to 1) between 0 and 1 shows acceptable model
performance, while closer to 1 is better. Better model performance is
indicated by a slope and an R2 value that are both closer to 1 in a
significant ZIR. The performance is regarded as acceptable if a significant
ZIR with its slope closer to 1 can be obtained or the |MRB|
on average is smaller than the 2 times the CV of replicated observations. The Akaike information criterion (AIC) is applied to evaluate the significance
of the multivariate linear regression. The additional independent variable
is significant when the value of the AIC decreases. For more details on these criteria, refer to Eqs. (S1)–(S5) in Table S4 in the Supplement.
The model simulation error (εs) indicated the simulated
bias diverging from the observation. It represented the total simulation
uncertainty and was made of the uncertainty due to the model insufficiencies
in scientific structure or process parameters (εmodel) and
that due to the uncertainties in input items (εinput; Zhang et al., 2019). For the investigation of stand age effects, the mean
relative εs and its random uncertainty (95 % CI) for
either gas were estimated as the mean and the 2 times the SD of the MRBs
relative to the observations for three stand ages (i.e., those in the T12-UN
and T08-UN fields in the second and fifth to sixth YAT). The relative
εs values for a gas were regarded to be equal among the
different stand age scenarios. The mean or the 2 times the SD of the relative
εs was converted to its absolute magnitude through
multiplying it with the product of an adjustment factor and the simulated
gas emission quantity. The adjustment factor was obtained from the model
validation of the three stand ages, which was estimated as the mean of the
ratios of individual observations to simulations. Since the uncertainties in
the model input items were known as random errors, the εinput was a random error. It was estimated using the Monte Carlo
test with Latin hypercube sampling (Helton and Davis, 2003) within the
uncertain ranges (95 % CI) of sensitive input items, which included the
soil properties (bulk density, pH, clay fraction, SOC and soil total
nitrogen content; e.g., Li, 2016), thermal degree days (TDD) for maturity
and nitrogen content in the different plant stages (seedling, early and
harvest stages). According to the measurement errors, the uncertain ranges
of the input items were 1.11–1.35 g cm-3 for bulk density, 5.6–6.4 for pH,
0.120–0.128 for clay fraction, 9.6–13.6 g kg-1 for SOC
content and 1.00–1.48 g kg-1 for total nitrogen content. The
uncertainties in the TDD and plant nitrogen content in the three stages
were assumed to be ±5 % of the default values, which were 2500 ∘C and 7.8, 6.8 and 6.0 g N kg-1 DM, respectively. A
uniform distribution for sampling was assumed in the Monte Carlo test, in
which the simulations were iterated until the mean of the simulated gas
emission quantities for all iterations converged to a certain level within the
tolerance of 1 %. The εinput at the 95 % CI was
presented as the double SDs of these iterated simulations.
If not specified, errors are presented hereafter at the 95 % CI.
In this study, the statistical analyses and graphical comparisons were
performed with the SPSS Statistics 19.0 (SPSS Inc., Chicago, IL, USA) and
Origin 8.0 (OriginLab, Northampton, MA, USA) software packages.
ResultsCalibration of modified model for soil pH simulation
Using the topsoil (0–15 cm) pH (6.0) prior to tea seedling transplanting
and the values of 5.4 and 5.0 measured in T12-UN and T08-UN, respectively,
in September 2013, each of the three parameters involved in the modified
model (Eqs. 8–9 and 16) was calibrated to 5.0×10-4 for
aami and aorg and 1.0×10-3 for b. The
simulations of the modified CNMM-DNDC with these calibrated parameters
resulted in topsoil (0–15 cm) pH values of 5.42 and 5.01 in the T12-UN and
T08-UN fields, respectively, in September 2013, which were consistent with
the observations. In contrast, the soil pH simulated by the original model
remained nearly constant (approximately 6.0) during the 6-year period,
despite the transient increases due to urea hydrolysis. Nevertheless, it is
still required to validate the simulations of the modified model in terms of the soil
pH changes due to tea growth using more field observations under different
conditions.
Model validation for soil environment and emissions of both gases
Both the original and modified models accurately predicted the seasonal
dynamics and magnitudes of topsoil temperature and moisture (Fig. 1a–b). The satisfactory model performance is indicated by the statistics
in Table 1.
Statistical evaluation of the original (Ori) and modified (Mod)
simulations on the soil temperature (ST), soil moisture (SM), nitrous oxide
(N2O) and nitric oxide (NO) fluxes as daily means, annual emissions
and annual direct emission factors (EFds).
VariablenMean observedMean simulated IA NSI Slope R2OriModOriModOriModOriModOriModST75614.816.416.40.980.980.920.920.910.910.950.95SM5040.510.540.540.710.71–0.27–0.270.930.93––Daily N2O110749.546.942.90.820.800.100.180.580.630.510.42Daily NO110731.229.827.40.840.800.320.330.680.740.510.41Annual N2O916.117.816.30.960.980.810.940.880.970.870.94Annual NO910.311.310.40.980.980.920.940.941.020.930.94EFds of N2O63.995.034.650.780.890.100.640.810.880.630.80EFds of NO63.052.952.770.890.850.500.381.011.080.500.51
Note that n is number of data pairs. For definitions of IA, NSI, slope and R2, refer
to Sect. 2.5 in the text.
The measured daily N2O and NO fluxes were highly variable across the
entire observation period (Fig. 1c–n). The original and modified models
generally captured the seasonal patterns of both gases for different field
treatments, even though the magnitudes of some peak fluxes were inconsistent
with the observations. In comparison, the original model generally
overestimated the peak emissions of both gases. The performances of both
models were similar and satisfying for the daily fluxes as indicated by the
comparable IA, NSI, and ZIR slope and R2 values (Table 1). For the
original model, three (NO) and five (N2O) of the nine individual
simulations for each gas showed |MRBs| larger than the
corresponding observed 2 times the CV, while the number of simulations with |MRBs| larger than
the observed 2 times the CV were four (NO and N2O) for the modified model
(Table S5). However, the statistics of both models still indicated
agreements for annual emissions, with the IA and NSI values of 0.96–0.98
and 0.81–0.94, respectively, for N2O and NO (Table 1). In addition,
the modified model improved the simulation of annual N2O emissions, with
higher IA, NSI, ZIR slope and R2 values of 0.98, 0.94, 0.97 and 0.94
(p<0.001), respectively (Table 1, Fig. 2). These results indicate
that the modified CNMM-DNDC can effectively simulate the daily and annual
emissions of both gases from the tested tea plantations. Additionally, the
modified model resulted in adjustment factors of 0.86 and 1.09 and relative
εs values of 17±20 % and -8±14 % for
the annual N2O and NO emissions, respectively, from the UN treatments and adjustment
factors of 1.00 and 0.97 and relative εs values of 0.2±24 % and 6±38 % for the N2O and NO emissions, respectively, from
the OM plots. These adjustment factors and relative
εs were used to estimate the absolute total errors in the
simulated emissions.
Comparison between the observations and simulations of annual
nitrous oxide (N2O) and nitric oxide (NO) emissions. The simulations
were provided by the original and modified models. The red or gray solid
lines illustrate the zero-intercept univariate linear regressions. The
vertical bars indicate the standard error in four spatial replicates. The
legend in panel (a) applies to all panels.
Effects of organic fertilization on emissions of both gases
According to the field observations, the short-term replacement of urea by
oil cake stimulated the annual N2O emissions by ∼62 %
(ranging between 35 % and 95 % or 5.3 and 13.7 kg N ha-1 yr-1) but simultaneously mitigated the annual NO emissions by
∼25 % (ranging between 12 % and 33 % or 2.4 and 6.0 kg N ha-1 yr-1). Based on the statistical analysis using
linear mixed models, both the stimulation and mitigation effects were
significant (p<0.05; Yao et al., 2015). The average relative
observational errors in these effects were ∼97 % (ranging
between 92 % and 106 %) for N2O and ∼73 % (ranging
between 60 % and 83 %) for NO (adapted from Yao et al., 2015, 2018; Table S6).
The simulated effects of the fertilizer replacement on annual N2O
emissions by the modified model showed stimulations by ∼36 % (ranging between 24 % and 49 % or 5.7 and 9.1 kg N ha-1 yr-1), with an |MRB| of ∼36 %
(ranging between 4 % and 56 %; Table S6). The |MRB| magnitudes
were significantly lower than the relative observational errors (p=0.02),
indicating consistency between the simulated and observed effects. The
inhibition effects of the fertilizer replacement on annual NO emissions were
about 14 % (varying between 1 % and 21 % or 0.1 and 4.1 kg N ha-1 yr-1) by the modified model except for some underestimation, which
indicated the consistency of effects between the simulations and observations
(Table S6). As these results suggest, the model with improvements in
scientific processes could simulate the effects of short-term replacement of
urea by oil cake on N2O and NO emissions in the early stage of the new
tea plantations.
Nitrogen dose effects on annual direct emission factors of both gases
As Figs. 3a–b and S1a–b show, the simulated annual emissions of either
gas varied nonlinearly with the nitrogen addition rate in the form of urea or
oil cake. Accordingly, for the modified model, the simulated annual EFds
of either gas at different levels of fertilizer doses increased linearly
with the urea addition rates (Fig. 3c–d) but nonlinearly with the
organic manure addition rates (Fig. 3e–f). In comparison with the
linear fittings for the manure treatment, the relationships were better
fitted the nonlinear curves, as indicated by the decreased AIC values (1.74
versus 1.72 for N2O and 0.53 versus 0.31 for NO). The simulations by
the original model showed similar results to those of the modified model
(Figs. 3c–f and S1c–f). The original and modified model simulations of
annual gas emissions for the two experimental nitrogen doses (zero and 450 kg N ha-1 yr-1) resulted in EFds significantly consistent
with the field observations for N2O (Fig. 4a). In comparison with the
original model, the modified model performed better in simulating the
EFds of N2O, increasing the IA from 0.78 to 0.89 and the NSI from 0.10 to
0.64 (Table 1). For NO, the simulated annual EFds by both models tended
to be positively related with the field observations (Fig. 4b), with
an acceptable IA of 0.85–0.89 and NSI of 0.38–0.50 (Table 1). These results
imply that, compared with the original model, the modified version with the
pH reduction processes added in this study could be applied to simulate the
EFds of either gas from tea plantations under different field
conditions.
Simulated annual emissions and direct emission factor (EFd) of
nitrous oxide (N2O) and nitric oxide (NO) from tea plantations with
early stand ages against nitrogen fertilizer doses. Data displayed in panels
(a)–(b) were simulated by the modified model and those in panels (c)–(f) by the
original (gray circle) and modified (blue circle) models. The legend in
panel (b) also applies to panel (a), wherein T08 and T12 represent the
plantations transplanted with seedlings in 2008 and 2012, respectively; UN
and OM indicate the fields consecutively applied with urea since tea
planting and short-term replacement of urea with oil cake, respectively; and
2013 and 2014 are the years with field observations of gas emissions. Each
vertical bar in panel (c)–(f) is the standard deviation of the EFds for
T08 in 2013 and 2014 and for T12 in 2013. Dashed lines are the lower and
upper uncertain bounds at the 95 % confidence interval for regression
curves. The legend in panel (d) also applies to panels (c), (e) and (f).
Effects of stand ages on emissions of both gases
The measured annual N2O and NO emissions from the T12-UN and T08-UN
fields in the second and fifth to sixth year ranged from 14.4 to 21.1
and 13.1 to 19.4 kg N ha-1 yr-1, with double CVs of ∼43 % (ranging from 9 % to 72 %) and ∼13 % (ranging from
6 % to 21 %), respectively (Yao et al., 2015, 2018). The original model
simulations of annual N2O and NO emissions showed an |MRB|
of ∼33 % (ranging from 6 % to 76 %) and ∼6 % (ranging from 3 % to 10 %), respectively, while |MRBs| of
the annual N2O and NO emissions were ∼17 % (ranging
from 11 % to 28 %) and ∼8 % (ranging from 1 % to 14 %) for
the modified model. The |MRB| on average for either gas (by
both models) was smaller than the 2 times the CV on average in the
observations. This evaluation indicates that the modified model with the new
processes could also reliably simulate the emissions of both gases under
different stand age conditions and therefore be applicable for investigating stand age
effects in the long term using a virtual experiment.
Comparison between observed and simulated annual direct emission
factor (EFd) of nitrous oxide (N2O) and nitric oxide (NO) by the
original and modified models from tea plantations. The vertical bar
indicates the standard error in four spatial replicates. The blue and red
lines illustrate the zero-intercept univariate linear regressions by the
original and modified models. Each simulated EFd is calculated from the
simulated emissions of two nitrogen addition levels (zero and 450 kg N ha-1 yr-1).
For the modified model, the simulated daily topsoil (0–15 cm) pH during
the early 6-year period basally declined gradually, with a temporary sudden
pulse immediately following the urea application events in either spring or
autumn (Fig. 5a). Although the simulated pH declined from the initial
value of 6.0 to less than 5.0, it was still higher than 4.5 which was the
threshold set in the model to trigger the chemodenitrification process.
Different from the slightly nonlinear changes in the simulated basal pH, the
simulated annual emissions of N2O and NO gradually increased with the
stand ages in the first 4 or 5 years but then decreased gradually.
The variation trend for the simulated annual emissions of either gas against
the early stand ages (1–6 years) could be fitted by a quadratic polynomial
equation instead of by a linear relationship as indicated by the decreased
AIC values for the nonlinear fitting as compared with that for linear
regression (-1.75 versus 0.66 for N2O and -3.67 versus 0.55 for
NO). Similar nonlinear relationships were also obtained for the simulations
by the original model (Fig. S2). As Fig. 5 indicates, almost all the
field observations in the fertilized fields fell not only generally within
the range of the uncertainty induced by the input items but also within the
upper and lower bounds of uncertainty (95 % CI) of the regressions.
Compared with the uncertainty induced by the inputs (εinput), the absolute values of the total model uncertainty
(εs) were much smaller, only accounting for 32 %
and 35 % of the εinput for N2O and NO,
respectively.
Simulated topsoil (0–15 cm depth) pH and annual emissions of
nitrous oxide (N2O) and nitric oxide (NO) against early tea stand ages
by the modified model. The solid lines are the polynomial regression
curves. Dashed lines are the lower and upper uncertain bounds at the 95 %
confidence interval (CI) for regression curves. Each pH datum is given as
the daily mean of eight diurnal simulations (3 h for each). The vertical bar
crossing each datum point in panel (b) or (c) represents the uncertainty (95 %
CI) induced by those of model inputs. Each box above panels (b)–(c) represents
total model error that was estimated by referring to the mean of model relative
biases (MRBs), with vertical bars representing the uncertainties (95 % CI)
estimated by referring to the double standard deviations of |MRBs|. The red circles and diamonds in panel (b)
and (c) represent
the observed emissions of N2O(b) and NO (c) from urea and organic manure treatments. The gray circles in panel (b) and (c) represent the simulation
by the original model.
Although the performances of both models in simulating the emissions of both
gases were comparable in early stand ages, the original and modified model
thereafter performed quite differently. The 35-year simulations demonstrated
that the above polynomial functions derived from the original model
simulation applied for both gases during the full tea lifetime; but those
functions derived from the modified model did not apply for the middle to late stand
stages (Fig. 6a). After the annual emissions of both gases simulated by
the modified model reached peak values, they decreased near linearly until
around the 15th YAT, when the chemodenitrification process was
triggered by the pH threshold (4.5) set in the model. Thereafter, the
emissions of either gas gradually increased by a very small annual increment
(Fig. 6a). Thus, the emissions of both gases simulated by the original
model were about 2 times those simulated by the modified model during the middle to
late tea stand ages. The εs of the simulation by the
modified model ranged from 2.11 to 2.89 and -1.63 to -0.78 for N2O
and NO, respectively (Fig. 6a), indicating the potential overestimation or
underestimation of either gas for 35-year simulations. Meanwhile, different
from the stable topsoil pH (except for the sudden pulse due to urea
hydrolysis) by the original model, the simulated basal pH of 0–15 cm by the
modified model continued to decrease, finally reaching 3.74 (Fig. 6b–c). In addition, the 35-year simulation showed that the negative
effects of soil pH on tea yield increased with the stand ages, resulting in
a reduction by 0.3 %–3.4 % (Fig. 6d). These results suggest that the
modifications by adding the processes regulating soil pH dynamics are
necessary for accurately quantifying the long-term emissions of N2O and
NO from tea plantations.
Simulated emissions of nitrous oxide (N2O) and nitric oxide
(NO) and topsoil (0–15 cm) pH of a urea-fertilized tea plantation against
stand ages over the full lifetime of a tea plant (35 years). Each box above panel (a)
represents total model error in the simulated emissions by the modified
model that was estimated by referring to the mean of model relative biases
(MRBs), with vertical bars representing the uncertainties at the 95 % confidence interval
estimated by referring to the double standard deviations of |MRBs|.
The given percentage of yield decline, simulated by the
modified model, was due to the effect of soil pH on tea growth.
DiscussionModel modifications
The modified CNMM-DNDC was hypothesized to reflect the general knowledge
that tea can grow in soils with a suitable pH within 4.0–6.5 (Cao et al.,
2009). But the transient increase in soil pH due to urea hydrolysis has no
impact on plant growth, as the soil pH could be recovered within a few days
due to the soil-buffering effect. Due to the lack of observed tea yields, the
parameterized impact of soil pH on tea growth could not be calibrated or
validated in this study, but virtual experiments showed increased yield
reduction with increasing stand age, implicating the intensified negative
effects on plant growth for older tea plantations. The newly added
scientific processes relating to pH reduction were calibrated using the
observed soil pH for different stand ages during the early stage of a tea
plantation. Although the simulations showed that the modified CNMM-DNDC with
the calibrated parameters could accurately reflect the basal soil pH
decline during the early years, validation was still missing due to a
lack of available independent observations of pH. However, the studies of the
tea plantations in Jiangsu and Anhui provinces showed that the average soil
pH (0–20 cm) decline rate was 0.06 pH yr-1 (Luo, 2006; Su, 2018).
For the simulation of 35-year tea plantation in this study, the calculated
average annual soil (0–20 cm) pH decline rate was close to the reports
with the value of 0.064 pH yr−1. Therefore, the consistent decline rate
indicates the modifications improve the scientific mechanisms of the
biogeochemical model which could be applied for long-term simulation. As the
actual soil pH would not decline constantly (Yao et al., 2018), the
validation of soil pH dynamics over a long time is still necessary. The
simulated annual emissions by both models were comparable in the early tea
stand ages, but those by the modified model were much lower in the middle to
late stages of tea lifetime. According to the modifications, the different
annual emissions of both gases should be primarily attributed to the soil pH
differences. Therefore, the proper simulation of soil pH decline for
long time increased the reliability of the simulated variation in annual
emissions even though validation of the differences was still missing due to
lack of field observations. Thus, further study is still needed to
confirm the general model applicability, especially for the simulations of
long-term yields, soil pH dynamics, and N2O and NO emissions from tea
plantations subject to different conditions.
Model performance
This study was the first study testing the original or modified models
against the measurements of N2O and NO emissions from a tea plantation.
The results showed that both the original and modified models accurately
captured the high temporal variations in daily N2O and NO emissions
driven by the application of fertilizers, stand ages and weather conditions
(Yao et al., 2015, 2018). Many previous studies did not report the R2 of
regressions between the observed and simulated daily fluxes of either gas,
usually due to poor model performance (Bell et al., 2012; Bouwman et al.,
2010; Butterbach-Bahl et al., 2009). Considering the large uncertainties in
field measurements as indicated by the SDs of the observations and the
complexity of the management practices, the performance of the modified
model for either gas was encouraging. Yao et al. (2015, 2018) obtained
significant revised hole-in-the-pipe (HIP) regressions for the observed
daily N2O plus NO fluxes as the dependent variable and the soil
ammonium plus nitrate concentrations, temperature and moisture as the
multiple independent variables. Compared with the R2 values of the
original HIP regressions fitting the daily observations, those of the
revised HIP model more than doubled and were up to 0.95–0.97 (Yao et al.,
2015, 2018). Similarly, the daily simulations by the modified model also
resulted in significant revised HIP regressions that showed more than
doubled R2 (0.48–0.55) in comparison with the values (0.01–0.12) of
the original HIP (Mei et al., 2011), despite the smaller determination
coefficients than those for the field observations. The improvements of the
revised HIP regressions by both observations and simulations were due to the
consideration of the temperature- and moisture-regulated effects of nitrogen
substrates for both nitrification and denitrification processes that produce
N2O and NO.
For the annual N2O emissions, the statistics of the modified model were
all better than the original model, indicating the modifications around soil
pH reduction improve the model performance in tea plantations. Thus, the
simulated corresponding effects of organic fertilization and EFds by
the modified model were more consistent with the observations. However, the
simulated annual NO emissions by the modified model were not much improved
in comparison with those by the original model. The underestimation (2.56 kg N ha-1 yr-1) and overestimation (3.29 kg N ha-1 yr-1) of the NO emissions in 2014 for T08-UN and
T08-OM, respectively, resulted in the significant underestimation of the
inhibition effects and increased model relative bias for the modified model.
The inhibited NO emissions were also partly attributed to the soil
heterotrophic nitrification (Yao et al., 2015), which is the direct
oxidation of organic nitrogen to nitrate without passing through
mineralization. However, the heterotrophic nitrification was not considered
in the model, which may result in the overestimated NO emissions in 2014 for
the manure treatments by both models. In addition, compared with the
original model, the underestimated NO emissions mentioned above were also the
key reason for the unsatisfactory simulation of EFds, which led to the
increment of the ZIR slope by 8 % (1.0 for the ZIR without T08-UN and 1.08
for the ZIR with T08-UN). Therefore, further study is still required for
validating the model performance in simulating NO emissions under different
fertilization conditions.
Contribution of the dominant process for emissions of both gases
The CNMM-DNDC model simulates the emissions of N2O and NO from
nitrification and denitrification separately and then sums them up to give
the overall emissions of either gas contributed by both processes (e.g., Li,
2016; Zhang et al., 2018). Some researchers have used the NO and N2O
molar ratio levels higher or lower than 1 to indicate nitrification or
denitrification as the dominant process for the emissions of either gas
(e.g., Yamulki et al., 1995). However, Wang et al. (2013) have indicated
that such criteria may not be applicable, as they commonly observed molar
ratios greater than 1 under strict anaerobic conditions with low to moderate
initial nitrate concentrations in a calcareous soil. This viewpoint could be
supported by the simulated major contributions of the denitrification
process by both models, accounting for 63 %–67 % and 59 %–62 % of the
annual N2O and NO emissions, respectively, for all the fertilized
fields. These larger contributions from the denitrification process could be
at least partially attributed to the hot and humid climate from April to
September, which resulted in favorable soil moisture and thus facilitated
the N2O and NO emissions. This explanation could be supported by the
simulated soil moisture and N2O emissions from the T08-UN treatment
with observations in 2 consecutive full years. The simulated daily soil
moisture falling in the range of 60 %–90 % WFPS appeared at a frequency of
only 40 % during the 2-year period. However, the simulated cumulative
N2O emissions (25.7 kg N ha-1) occurring on the days with such
relatively high moisture content accounted for 61 % of the total modeled
quantity of this gas (42.0 kg N ha-1). It is accepted that
nitrification generally dominates N2O production in soils with less
than 60 % WFPS (e.g., Chen et al., 2013). The dominant contributions of
denitrification to N2O and NO emissions by the simulations could also
be supported by previous experimental and modeling studies (Chen et al., 2017;
Zhang et al., 2017). However, direct validation of the simulations by the
original and modified model on the contributions of nitrification or
denitrification is still lacking, due to no available direct measurement of
N2O or NO emissions from either process. This challenge will need to be
overcome in future studies.
Effects of organic fertilization on emissions of both gases
For the tea plantations, the applied fertilizers and the retained nitrogen
in the soil are consumed by plant uptake, microbial processes and physical
losses through ammonia volatilization and nitrate leaching (e.g., Zhang et
al., 2015). Accordingly, changes in fertilizer types would affect the
nitrogen transformation from the fertilizer to the available forms for the
losses, thus altering the N2O and NO emissions (e.g., Deng et al.,
2013; Goulding et al., 2008; Skinner et al., 2014). Organic fertilization
has been widely encouraged in tea cultivation since it can reduce synthetic
nitrogen inputs into the biosphere while improving both soil fertility and
carbon sequestration (e.g., Skinner et al., 2014; Liang et al., 2011; Meng
et al., 2005). Yao et al. (2015) observed that short-term replacement of
urea with oil cake, which is characterized by a low carbon-to-nitrogen ratio,
stimulated N2O emissions to a large extent while inhibiting NO releases
to a relatively small extent. These observed effects were generally
simulated by the original and modified CNMM-DNDC, especially the increased
N2O emissions.
According to the model simulations, the stimulated N2O emissions were
jointly attributed to (i) the enhanced production of this gas, as well as
nitrate, in promoted nitrification and (ii) the enhanced production of this
gas in promoted denitrification. The promoted nitrification was due to less
ammonia volatilization derived from the organic nitrogen mineralization than
from the urea hydrolysis (∼1.0 versus 13 kg N ha-1 yr-1). The oil cake mineralization slowly produced ammonium, while the
deep placement of the fertilizer also inhibited ammonia volatilization. In
comparison, the urea hydrolysis quickly transformed the fertilizer nitrogen
form into ammonium within a few days following the fertilization event, when
the hydrolysis-derived pulse increase in soil pH (Fig. 5a) stimulated
ammonium loss by ammonia volatilization. The denitrification was promoted
not only by the improved supply of nitrate (as the primary nitrogen
substrate) from the promoted nitrification (Fig. S3) but also by the
enhanced activity of denitrifiers that have a very high affinity for the
carbon substrates provided by the organic manure decomposition (e.g., Li et
al., 2005; Skinner et al., 2014; Snyder et al., 2009). For the annual NO
emissions of the three paired OM-versus-UN cases, the modified model
resulted in a consistent decrease (1 %–21 %) due to the full urea
replacement by oil cake. The simulations showed that 0 %–44 % of the
decreases were ascribed to the promoted nitrification (Table S5), whereby
more nitrate was produced as the final product but less NO was produced as
the by-product. The remaining 56 %–100 % of the decreases, however, were
attributed to the promoted denitrification (Table S5), whereby more NO was
reduced to N2O (e.g., Meijide et al., 2007; Snyder et al., 2009;
Vallejo et al., 2006). Regarding the contributions of denitrification to the
overall N2O or NO emissions, the simulations showed no significant
effect of the full urea replacement by oil cake. However, validation of
this simulated insignificance is still lacking, because no direct
observations for the process contributions are currently available. Further
study is still needed to validate the model's performance in simulating the
contributions of nitrification or denitrification to the emissions of either
gas from tea plantations.
Effects of nitrogen fertilizer doses on direct emission factors of both
gases
Validation of the linear or nonlinear relationships for the urea or manure
treatments from the virtual experiment was still lacking, since there were no
available data from the experimental field site for the multiple fertilizer
gradients. Nevertheless, the relationships of the simulated EFds
against the nitrogen doses suggested that paired field observations of
fertilized and unfertilized treatments, or those of two largely different
nitrogen addition rates, as used in many field studies (e.g., Yao et al.,
2015, 2018), would yield greatly biased EFds of either gas from the tea
plantations, particularly creating a gross underestimation for moderate to
high nitrogen addition rates. This conjecture from the virtual experiment
was supported by two studies so far available for field observations of
N2O emissions from tea plantations treated with nitrogen dose gradients
(Han et al., 2013; Hou et al., 2015), even though similar literature
support for NO was still lacking. These experimental studies showed that the
EFd determined by the lowest nitrogen addition rates showed a 30 %
underestimation on average as compared with the value by the highest
nitrogen inputs (adapted from Han et al., 2013, and Hou et al., 2015).
Obviously, this study implicates the potential capacity of the modified
CNMM-DNDC as a robust tool to generate EFds of tea plantations subject
to different conditions, although it is still necessary to widely validate
the simulated EFds using field observations with multiple gradients
of nitrogen fertilizer doses.
Effects of stand age on emissions of both gases
Relative to the N2O and NO emissions in the second or sixth YAT,
more intensive emissions of both gases were observed in the fifth YAT
(Yao et al., 2015, 2018). These relatively intensified emissions were
thought to result from the comprehensive effects of increased soil nitrogen
and carbon availability for nitrification and denitrification as well as
reduced soil pH (Yao et al., 2018). For either gas, the observations in the
tea fields purely applied with either urea or oil cake most likely implied a
nonlinear trend in terms of stand ages, with the interannual maximum appearing
between the second and fifth YAT. This implication was supported by the
modified model simulations for a conventionally managed plantation over the
full lifetime of tea plant, in which the interannual maximum of N2O
emissions appeared in the fourth YAT when the initial harvest of tea bud
and the first canopy trim occurred. The increases in the early years were
mainly ascribed to the increasing root exudates and less-woody residues
returning to soil promoted by the tea plant growth. The simulated
interannual maximum emissions of N2O appeared in the year when basal
soil pH reached the threshold of about 5.0. The inhibition effect of pH on
microbial growth is intensified when soil pH is less than this threshold
(Fig. S4). The adopted pH-influencing mechanisms in the model mainly
induced the diminished annual emissions of N2O following the appearance
of the peak, because the emissions of N2O were associated with the
microbial production. In addition to the reduced microbial activity due to
low pH inhibition, the postmaximum declines in the annual gas emissions
against the stand ages were also attributed to the reduced availabilities of
nitrogen substrates for the microbial processes, due to (i) the higher
nitrogen demand for the tea growth stimulated by the multiple bud harvests
and two trims per year, as well as (ii) the too slow decomposition of woody
residues for old tea remaining on the ground surface. However, experimental
support is far less sufficient for these explanations on the variations in
gas emissions in relation to the stand ages in the early stage or during the full
lifetime of tea growth, and thus further studies are still required. In
addition, the smaller total model uncertainty, which only accounts for
33 % of the uncertainty induced by inputs, indicated that increasing the
reliability of the inputs of soil properties and plant growth parameters can
improve the model efficiency.
The decline of emissions following the peaks of both gases may not
continue throughout the entire lifetime of tea growth, as the process of
chemodenitrification would be triggered once the soil pH decreases to 4.5
or lower, thus promoting the emissions of either gas (e.g., Li, 2016;
Pilegaard, 2013). Such a conjecture was well supported by the virtual
experiment in this study, which demonstrated that the average soil pH
(0–15 cm) decreased to the threshold in the 15th YAT and continued to
decrease thereafter. In the model, the chemodenitrification process
occurring under the low soil pH (≤4.5) is assumed to transform a
portion of the NO produced in the microbial nitrification and
denitrification processes into N2O. Before the chemodenitrification
was triggered, the simulated microbial nitrification stably accounted for
∼36 % of the overall N2O and ∼41 % of
the overall NO emissions. When the chemodenitrification occurred, its
contributions to the overall simulated N2O emissions increased from
∼4 % to ∼8 % with increasing stand ages,
while the microbial nitrification and denitrification accounted for
∼34 % and ∼59 %, respectively. However,
these results of gas emissions from the virtual experiment still require
validation with field experiments in future studies.
Conclusions
To fill a gap in the process-oriented biogeochemical model, Catchment
Nutrient Management Model – DeNitrification-DeComposition (CNMM-DNDC), the effects of soil pH on tea growth and the processes that may
induce soil pH reduction due to root exudation and residue decomposition
during tea growth were added into the model in this study. Using the 2-year field
measurements in tea plantations at a subtropical site in central China, the
original and modified models were evaluated for simulating nitrous oxide
(N2O) and nitric oxide (NO) emissions from this important type of
agricultural ecosystem. Both the original and modified models showed
comparable performance for simulating the daily and annual emissions of
N2O and NO from the tested tea plantations at the early stage,
especially before the initial tea harvest and the first trim. The modified
model was further tested through simulating the emissions of both gases
affected by the short-term replacement of synthetic fertilizer (urea) with
organic manure (oil cake), gradient nitrogen doses of the two fertilizers and
different stand ages of new tea plantations. Both observations and
simulations demonstrated that short-term replacement of urea with oil cake
can largely stimulate N2O emissions and mitigate NO emissions. The
simulations by the modified model also showed linear relationships between
the direct emission factors (EFds) of either gas and the nitrogen
doses for tea plantations amended with synthetic fertilizer and nonlinear
relationships for those plantations applied with organic manure. These
relationships support the hypothesis that paired field observations of two largely different nitrogen addition rates, which have very
often been implemented in field studies, lead to significant biases for the
measured EFds of either gas from the tea plantations. These biases
particularly induce significant underestimations for the moderate to high
nitrogen doses that are typically applied by farms. The model simulations
also showed that annual emissions of either gas increase with stand ages
within the early stage of a new tea plantation and then gradually decrease
until they slightly increase again due to chemodenitrification triggered by
soil pH lower than 4.5. In conclusion, the modified CNMM-DNDC can reflect
the comprehensive influences of weather, soil conditions, plant nitrogen
demands and field management practices, thus showing potential to be a
powerful tool for investigating long-term emissions of N2O and NO from
tea plantations under specific field management alternatives at the site or
regional scale. Nevertheless, experimental data are still too scarce to
validate the model simulations of long-term soil pH changes and their
effects on the emissions and EFds of both gases from tea plantations.
To improve the robustness of the model for application in various tea
plantations, comprehensive validations using simultaneous field observations
are still necessary. The validations should include not only the variables
involved in this study but also others, such as the emissions of other
greenhouse gases (carbon dioxide and methane), volatilization of ammonia,
hydrological nitrogen losses by leaching and surface runoff, and temporal
changes in the soil organic carbon stock, which are urgently required.
Code and data availability
The model and input and output datasets can be obtained from the first author,
and all the observed datasets used in this study are available from the
coauthors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-6903-2020-supplement.
Author contributions
XZ and WZ contributed to developing the idea and enhancing the
science of this study. WZ improved the scientific processes of the
model, designed and implemented the model simulations and virtual
experiments, and prepared the manuscript with contributions from all
coauthors. ZY, CL, RW and KW designed and carried out
the field experiments. SL and SH collected and established the
input database for modeling. QZ and JS provided the climate data
observed in the field site.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional assessment of air pollution and climate change over East and Southeast Asia: results from MICS-Asia Phase III”. It is not associated with a conference.
Financial support
This research has been supported by the National Key
R&D Program of China (grant no. 2016YFD0800103), the National Natural Science
Foundation of China (grant nos. 41603075 and 41761144054) and the Chinese Academy of Sciences (grant no. ZDBS-LY-DQC007).
Review statement
This paper was edited by Qiang Zhang and reviewed by two anonymous referees.
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