Modelling of nitrification inhibitor and its effects on emissions of nitrous oxide (N2O) in the UK

Journal Pre-proof Modelling of nitrification inhibitor and its effects on emissions of nitrous oxide (N2O) in the UK

Yumei Li, Syed Hamid Hussain Shah, Junye Wang PII:

S0048-9697(19)36152-2

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136156

Reference:

STOTEN 136156

To appear in:

Science of the Total Environment

Received date:

30 November 2019

Revised date:

14 December 2019

Accepted date:

14 December 2019

Please cite this article as: Y. Li, S.H.H. Shah and J. Wang, Modelling of nitrification inhibitor and its effects on emissions of nitrous oxide (N2O) in the UK, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.136156

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© 2019 Published by Elsevier.

Journal Pre-proof

Modelling of nitrification inhibitor and its effects on emissions of nitrous oxide (N2O) in the UK Yumei Li1, 2, 3, 4*, Syed Hamid Hussain Shah2, Junye Wang2* 1 Key Laboratory of Computational Geodynamics, College of Earth and Planet Science, University of the Chinese Academy of Sciences, 19A Yuquan Rd, Beijing 100049, P. R.

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China

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2 Faculty of Science and Technology, Athabasca University, University Drive, Athabasca,

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Alberta T9S3A3, Canada

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3 Institute of Geology and Geophysics, Chinese Academy of Sciences, Beitucheng Western Road, Beijing 100029, P. R. China

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4 Molecular Fossil Laboratory, University of the Chinese Academy of Sciences, 380

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Huaibeizhuang, Beijing 101408, P. R. China

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* Corresponding author: [email protected] or [email protected]

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Journal Pre-proof Abstract Global food demand requires increased uses of fertilizers, leading to nitrous oxide (N2O) and nitrate leaching due to overuse of fertilizers and poor timing between fertilizer application and plant growth. Using nitrification inhibitors (NIs) can reduce the N2O emissions but the effectiveness of NIs strongly depend on environmental conditions, and their benefits have been limited due to less than optimal nitrogen rates, timing, quantity, and placement of NIs.

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Process-based modelling can be helpful in improving the understanding of nitrogen fertilizer

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with NIs and their effects in different environmental conditions and agricultural practices. But few studies of modelling NIs with application to agricultural soils have been performed.

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In this paper, we developed a sophisticated biogeochemical reaction process of NIs applied to

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agricultural soils, which account for the factions of NIs with fertilizer by combining the

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application rate, soil moisture, and temperature within the DeNitrification DeComposition (DNDC) framework. This model was tested against the data from two agricultural farms in

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Preston Wynne and Newark in the UK. The results agreed well with the measured data and

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captured the measured soil moistures and N2O emissions. In Newark, the average Mean Absolute Error of all blocks is 8.83 and 5.45 for ammonium nitrate or urea respectively while in Preston Wynne, 3.48 and 3.14. The results also showed that the warming climate can greatly reduce the efficiency of nitrification inhibitors, which will further amplify the greenhouse gas impacts. The modified DNDC model of nitrification inhibitor modules can reliably simulate the inhibitory effect of NIs on N2O emissions and evaluate the efficiency of NIs. This enables end-users to optimize the amount of NIs used according to the time and climate conditions of fertilizer application for increasing crop yield and reducing N2O emissions and provides a useful tool for estimating the efficiency of NIs in agricultural management.

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Journal Pre-proof Key words: DNDC model, nitrification inhibitor, nitrous oxide, winter wheat, grassland, DCD 1 Introduction As the world population grows, global demand for food is rising (FAO, 2018). Therefore, agriculture will be required to increase cereal, milk and meat production, either by increasing the amount of agricultural land used to grow crops or by enhancing productivity on existing

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agricultural lands through fertilizer use and irrigation (Tilman et al., 2002). Increasing

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agricultural production inevitably contributes to an increase in nitrous oxide (N2O) emissions

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as N2O emissions can originate directly from field-applied organic and inorganic fertilizers, and crop residue decomposition (Desjardins et al., 2010). In 2016, N2O from agriculture

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accounted for 75% of all emissions (Olivier et al., 2017). As the third largest greenhouse gas

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(GHG) in the atmosphere, the global warming potential (GWP) of N2O emissions is nearly

2007; WMO, 2016).

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300 times greater than Carbon Dioxide (CO2) in a 100-year time horizon (Bernstein et al.,

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Increasing grain production leads to an increased use of fertilizers in soils. This can cause environmental concerns due to overuse of fertilizers and poor timing between fertilizer application and plant growth (IFA, 2007; Lassaletta et al., 2014). Coskun et al. (2017a, 2017b) reported that only about 50% of the fertilizer nitrogen (N) applied to agricultural systems is up-taken by crops, and half of the N is lost to the environment, such as in nitrate leaching into groundwater or nitrate runoff to surface water, and N2O emissions. Furthermore, this increases costs of agricultural production due to the increased cost of higher quantities of fertilizer used. Nitrification inhibitors (NIs) can inhibit or delay the conversion of ammonium nitrogen in ammonia, ammonium-containing, or urea-containing fertilizers, to nitratenitrogen by nitrifying microbial activities in the soil (Ruser and Schulz, 2015). Del Grosso et 3

Journal Pre-proof al. (2009) studied the use of nitrification inhibitors and split fertilizer applications. They found that the NI with split fertilizer use can increase crop yields of ~6% but reduce fertilizer N losses of ~10% and GHG emissions of ~50%. Therefore, this shows that improving efficiency of synthetic fertilizer and urea applications to agricultural soils can mitigate N2O emissions and reduce fertilizer cost in agricultural production. Thus, NIs has the potential to play an important role in both economic and environmental benefits.

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Considerable effort has been made to study NIs efficiencies in reducing nitrate leaching

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to groundwater or suppressing nitrification N2O emissions from soil microbial nitrification when nitrogen fertilizer is applied to agricultural soils for a range of crops, soil types and

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agricultural practices. Lan et al. (2013) studied the efficiency of the nitrification inhibitor

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dicyandiamide (DCD, C2H4N4) in suppressing nitrification and N2O emission in paddy soils.

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They found that DCD reduced the nitrification rate and the amount of N2O in nitrification emissions. But, the effect of DCD gradually declined and could be negligible after 4 weeks.

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Lam et al. (2016; 2017) measured direct and indirect ammonia (NH3) and N2O emissions

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from two large vegetable farms that applied fertilizers with NIs using micro-meteorological techniques. They found that NI reduced effectively emissions from agricultural soils but could stimulate NH3 volatilization. McCarty (1999) reported that the effects of NIs on suppressing nitrate production were effective over a period of several weeks. Yang et al. (2017) showed that nitrogen use efficiency was controlled by NI and its effect on the nitrification ability of paddy soils, and N losses via denitrification. Lan et al. (2018) studied the effects of NIs on gross N nitrification rate, ammonia oxidizers, and N2O production under different temperatures in two pasture soils. They found that the inhibitory effects decrease as temperature increases from 15 to 35°C. Kelliher et al. (2008) analysed six sets of data from Smith (2007a) and Di et al. (2007). They showed that 4

Journal Pre-proof the inhibition of DCD decreased nitrogen emissions with increasing temperature when the soil temperatures were between 2.9 and 16.2°C. The percentage of the reduction in direct N2O emissions following the application of dairy cattle urine (controls) and urine with a DCD according to Smith et al. (2007a) and Di et al. (2007) (four other soils). Wu et al. (2017) and Di et al. (2014) investigated the effects of soil moisture on the efficiency of NIs. They found that higher soil water-filled pore space (WFPS) led to higher N2O and NO emissions, while N2 emissions were only detected at high WFPS of 80%. However, some studies show that the

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use of NIs may increase the share of ammonium nitrogen in soil and release it in the form of

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NH3 (Zaman and Nguyen, 2010, 2012, Qiao et al., 2015). Qiao et al. (2015) found an

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increased NH3 of 33–67% in 61 cases.

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Despite the considerable progress that has been made in laboratory and field experiments

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over the past decade, field experiments are spatially not continuous or scalable for a large region due to soil heterogeneity and climate change. As a result, fertilizer quality and

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efficiency with or without NIs are hampered considerably by timing, quantity, and placement

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of NIs. Although many process-based models, such as DNDC (Li et al., 1992), SPACSYS (Wu et al, 2007), WNMM (Li et al, 2005), and DAYCENT (Parton et al, 1998) have been developed to simulate N2O emissions, modelling of NIs application in soils is still in its infancy. Giltrap et al. (2010) proposed an empirical model of nitrification inhibition within the NZ-DNDC model. They used a simplified exponential function to simulate the effect of the biological degradation of DCD over time but were lacking consideration of the soil parameters and NIs amount. In reality, the effectiveness of an inhibitor depends upon many factors, such as its concentration in the soil, ratio of fertilizer to NI, soil moisture, and temperature (Wu et al., 2017; Coskun et al., 2017a; Lan et al., 2018). The half-life of DCD strongly decreased with increasing soil temperatures (Irigoyen et al., 2003; Kelliher et al., 2008). When the soil moisture level is higher (80%, WFPS), the N2O emissions from NIs 5

Journal Pre-proof applied to soil decreased with the temperature, and N2O emissions increased at low soil moisture conditions (Menéndez et al., 2012). In general, NIs are more effective in sandy soils (Gilsanz et al., 2016). Therefore, models of NIs should include soil properties, soil parameters and NIs quantity, coupled with both nitrification and denitrification in soil. Although, the effects of NIs on N2O emissions have been monitored and measured globally, we lack quantitative information on how, or if, the main drivers, such as ratio of fertilizer to NIs and soil properties, affect N2O emissions in agricultural soils as water and soil are two of

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the most important components in the interaction among NIs, water and nutrient cycle.

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Therefore, a model of NI biogeochemical processes should incorporate “real-world processes”

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of NIs in soil (Coskun et al., 2017a) and include integrated NIs-soil-nutrient dynamics.

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Several important issues remain to be addressed in the most widely-used process-based

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agroecosystem models, such as DNDC: (1) NIs kinetics, and (2) soil properties. The main objective of this study is to develop a model of NIs to incorporate key factors,

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such as soil properties, ratio of fertilizer to NIs, and soil parameters (i.e., temperature,

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moisture and pH) into the DNDC framework. The secondary objective is to calibrate and validate the NIs model compared to field data in the UK and evaluate the effect of NIs on N2O emissions. To the best of our knowledge, this is one of the first attempts of its kind in which a mechanistic model of NIs was used to estimate N2O emissions and to understand how the soil processes affect N2O emissions. We believe that this modelling tool can be used in other regions to refine the bottom-up emission inventories. 2 Method and Material 2.1 Nitrification model in DNDC 2.1.1 Nitrification process

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Journal Pre-proof According to Knowles (1982), the nitrification process is an enzymatic oxidation reaction of ammonia (NH4+) to nitrite (NO2-) where Nitrosomonas derive energy. Then the nitrite is converted by Nitrobacter to the nitrate (NO3-). The oxygen will be obtained from NH4+ to NO3- via NO2- as: 2NH4+ + 3O2 → 2NO2- + 4H+ + 2H2O and

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2NO2- + O2 → 2NO3-

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The converted rate of the NH4+ to NO3- via NO2- is limited by oxygen availability. If

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oxygen is unavailable during the nitrification, NO2- cannot be converted further to NO3- in the

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oxidization reactions.

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For urea, ammonia volatilization occurs when urea is applied to soils as follows:

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NH2 – CO – NH2 + H2O → CO2 + 2 NH3 Nitrification processes were formulized as follows (Li et al., 2000):

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Relative growth rate of nitrifiers: [𝐷𝑂𝐶]

𝐹

𝑚 𝜇𝑔 = 𝜇𝑀𝐴𝑋 (1+[𝐷𝑂𝐶] + 1+𝐹 ) 𝑚

where µg is relative growth rate of nitrifers, µMAX is the maximum nitrification rate, [DOC] is the concentration of dissolved organic carbon (kg/ha), and Fm is the moisture factor. Relative death rate of nitrifiers: 𝜇𝑑 = 𝑎𝑀𝐴𝑋 ∙ 𝐵𝑛 /(5 + [𝐷𝑂𝐶])/(1 + 𝐹𝑚 ) where µd is the relative death rate of nitrifiers, aMAX is maximum death rate for nitrifiers, and Bn is biomass of nitrifiers (kg/ha). 7

Journal Pre-proof Net increase in nitrifiers biomass: 𝜇𝑏 = (𝜇𝑔 − 𝜇𝑑 ) ∙ 𝐵𝑛 ∙ 𝐹𝑡 ∙ 𝐹𝑚 where Ft is temperature factor: 3.503(𝑇−34.33) ) 25.78

𝐹𝑡 = ((60 − 𝑇)/25.78)3.503 𝑒 ( 𝑅𝑛 = 𝑅𝑚𝑎𝑥 ∙ [𝑁𝐻4 + ] ∙ 𝐵𝑛 ∙ 𝑝𝐻

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where Rn is nitrification rate, Rmax is maximum nitrification rate, T is soil temperature and pH

N2O production from nitrification:

(1)

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where wfps is water filled pore space.

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𝑁2 𝑂 = 0.0006 ∙ 𝑅𝑛 ∙ 𝐹𝑡 ∙ 𝑤𝑓𝑝𝑠

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is soil pH value.

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2.1.2 Model of inhibitor process applied to ammonium nitrate or urea

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When nitrogen fertilizer and nitrification inhibitors are applied to soil, the relationship between nitrification and denitrification products is shown in Fig 1. Fig. 1 synthesizes the roles of fertilizers and nitrification inhibitors, the pathways, reactants and products of nitrification and denitrification, based on the results by Firestone and Davidson (1989), McCarty (1999), Arp and Stein (2003), Hénault et al. (2005) and Ruser and Schulz (2015). Generally, NIs can effectively inhibit N2O emissions in both arable and grasslands, whether ammonium nitrate or urea is applied to soils. However, different treatments have different inhibitory effects. In theory, ammonium nitrate contains nitrate, while urea has only two amino groups. This means that no matter how many NIs are added, even if nitrification does not occur at all, a part of N2O will be discharged when the nitrate ion is denitrified. 8

Journal Pre-proof Therefore, the N2O emissions applied with ammonium nitrate to soil is higher than that of urea with the same amount of nitrogen because of the denitrification. Thus, the effect of NIs on N2O emissions from ammonium nitrate is weaker than that of urea. The reaction equation is as follows: (NH2)2CO → [(NH4)2CO3] → 2 NH3 +CO2 + H2O Moreover, meteorological and soil factors, such as temperature and precipitation, play an

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important role in the NIs efficiency. Therefore, the contribution of NIs to the reduction of

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N2O emissions need to account for soil temperature and soil moisture.

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Giltrap et al. (2010) added an inhibitor factor in the nitrification processes, in which the

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efficiency of NIs (Neff) was correlated with the period of applied NIs. Neff represents the fraction of nitrification rate reduced by the inhibitor. Thus, the inhibited rate of nitrification is

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described as:

(2)

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𝑁𝑖 = (1 − 𝑁𝑒𝑓𝑓 )𝑁0

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where Ni is the inhibited nitrification rate and N0 is the nitrification rate without the inhibitor. They assumed that Neff is exponentially decayed with time as: Neff(t+1) = Neff (t) exp(

−ln2 t1

)

(3)

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where t1/2 is the half-life of the inhibitor in days. Eq. (2) and (3) insert Eq. (1): 𝑁2 𝑂 = 0.0006 ∙ (1 − 𝑁𝑒𝑓𝑓 ) ∙ 𝑅𝑛 ∙ 𝐹𝑡 ∙ 𝑤𝑓𝑝𝑠 2.1.3 Modification of inhibitor efficiency

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(4)

Journal Pre-proof In reality, the efficiency of NIs is affected by many factors, such as soil moisture, soil temperature, soil organic matter content, soil pH, and clay content (Di et al, 2007; Coskun et al., 2017a). Furthermore, NIs types, timing and applied quantities are important for the inhibitor efficiency. Therefore, the efficiency of the inhibitor can be formulized as follows: 𝑁𝑒𝑓𝑓 = 𝑓(𝑁𝐼𝑡𝑦𝑝𝑒 , 𝑁𝐼𝑎𝑚𝑜𝑢𝑛𝑡 , 𝑡, 𝑇, 𝑤𝑓𝑝𝑠, 𝑝𝐻)

(5)

where NItype is the type of inhibitors, NIamount is amount of the inhibitor applied to soils, t is

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days of the inhibitor applied to soils, T is the soil temperature, wfps is the soil moisture, and

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pH is the soil pH.

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Eq. (5) can be simplified as:

(6)

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𝑁𝑒𝑓𝑓 = 𝛼 ∙ 𝐹𝑎 ∙ 𝐹𝑑 ∙ 𝐹𝑇 ∙ 𝐹𝑤𝑓𝑝𝑠 ∙ 𝐹𝑝𝐻

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where Fa is the factor of the amount of the inhibitor applied to soils, Fd is the factor of the days after inhibitor applied, FT is the factor of soil temperature, Fwfps is the factor of soil

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moisture, FpH is the factor of the soil pH, and α is the coefficient.

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In this study, we used DCD and DMPP as examples to illustrate the NIs model. The application rate of DCD (DCDamount, kg N ha-1) with fertilizers and their effects on N2O emission (%) are calculated using Eq. (7): 𝑔𝑖 = 11.45 ∙ 𝐷𝐶𝐷𝑎𝑚𝑜𝑢𝑛𝑡 − 0.7129 ∙ 𝐷𝐶𝐷𝑎𝑚𝑜𝑢𝑛𝑡 2 + 0.0137 ∙ 𝐷𝐶𝐷𝑎𝑚𝑜𝑢𝑛𝑡 3

（7）

(0 ≤ DCDamount ≤ 30.0, r2 = 0.2080, n = 62, p<0.05）

where gi is N2O emissions reduction effected by the inhibitor, and DCDamount is the amount of DCD applied to soils, r is the correlation coefficient, n is the numbers of samples, and p is the probability value or significance. The low correlation coefficient is because nitrous oxide emissions themselves are variable (Fig. 2a) and so is the reduction of the fitness. 10

Journal Pre-proof Eq. (7) is a statistical regression of experimental data available from Ding et al. (2011), Majumdar et al. (2002), McTaggart et al. (1997), Zaman and Blennerhassett (2010), Cui et al. (2011), Jumadi et al. (2008), Ball et al. (2012), Macadam et al. (2003), Suter et al. (2010), Weiske et al. (2001), Di and Cameron (2002, 2003, 2006), Di et al. (2007), Di et al.(2010), Hoogendoorn et al. (2008), Qiu et al.(2010), Singh et al.(2009) and Zaman et al. (2009) (Table s1). It can be illustrated in Fig 2a.

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Similarly, the influences of DMPP with fertilizer on N2O emissions (%) are also related

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to the application rate (DMPPamount, kg N ha-1). When the data of DMPP by Suter et al. (2010) was under a range of 5 and 35℃, the temperature factor will be regressed later (Eq. 11). Here

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Suter et al. (2010) to regress Eq. (8) (Fig 2b):

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we consider the influence of applied rates of DMPP and use the data of DMPP at 25℃ by

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𝑔𝑖 = 31.312 ∙ 𝐷𝑀𝑃𝑃𝑎𝑚𝑜𝑢𝑛𝑡 − 2.0331 ∙ 𝐷𝑀𝑃𝑃𝑎𝑚𝑜𝑢𝑛𝑡 2

(8)

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（0 = < DMPPamount < = 11.5, r2 = 0.801, n=6, p<0.01）

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where gi is N2O emissions reduction affected by inhibitors, and DMPPamount is the amount of DMPP applied to soils.

From Eq. (7) and Fig. 2a, we can see that the relationship between N2O emissions and DCD application rates is a cubic curve when the amount is between 0 and 30 kg N ha-1. As DCD increases gradually from zero, the inhibition to N2O increases at first then plateaus. The relationship between N2O emissions and DMPP application rates is a quadratic polynomial (Fig. 2b). Similarly, the inhibition of N2O increases at first and then decreases as DMPP increases gradually from zero. Based on the above equations, the N2O emissions reduction due to the increase of NIs application rates can be calculated using Eq. (7) or Eq. (8) within DNDC. 11

Journal Pre-proof The factor of inhibitor amount, Fa, is described as: 𝐹𝑎 = 𝑔𝑖 /100

(9)

We define the factor of time (days) after DCD is applied, i.e., Fd is: 𝐹𝑑 = 𝐶𝑑 /C0 where Cd is the DCD concentration after DCD applied d days, C0 is the original DCD

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concentration (d =0). According to Kelliher et al. (2008), DCD degradation in soil can be

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formulized as:

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Cd =C0 ∙ 𝑒 (𝑘𝑑)

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where k is a degradation constant, d is the time (d) after DCD is applied. When soil average

𝐶𝑑 𝐶0

=

𝐶0 ∙𝑒 (𝑘𝑑) 𝐶0

= 𝑒 (𝑘𝑑)

(10)

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𝐹𝑑 =

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temperature is 25 ℃, for example, C0 =2.9, k = - 0.08 (Kelliher et al., 2008).

Soil average temperature (T, ℃) and its effects on N2O emissions (%) are regressed

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statistically using experimental data (Zaman and Blennerhassett, 2010; Di et al., 2007; Hoogendoorn et al., 2008; Simth et al., 2007; Lan et al., 2018) (Table S2). We have obtained the below equation (Fig 2c):

ℎ𝑇 = 73.376 − 1.7864 ∙ 𝑇 (5.0 = < T < = 35.0, r2 = 0.3187, n = 29, p<0.05）

where hT is N2O emission reduction effected by soil average temperature. The factor of soil average temperature, FT, is:

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Journal Pre-proof 𝐹𝑇

= ℎ𝑇 /100 = (73.376 − 1.7864 ∙ 𝑇)/100

(11) Hourly inhibited N2O flux (jwfps, µg N m−2 ∙h−1) using the DCD or DMPP treatments can be correlated with soil water-filled pore space (wfps, %) using data available from Liu et al. (2013), as follows: 𝑗𝑤𝑓𝑝𝑠 = exp (0.129 ∙ 𝑤𝑓𝑝𝑠 − 0.001 ∙ 𝑤𝑓𝑝𝑠 2 )

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(DCD or DMPP, p < 0.01)

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Hourly N2O flux (lwfps, µg N m−2 ∙h−1) without NI treatments are given by Liu C et al., as

-p

follows (2013) as:

Thus,

(p < 0.01)

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𝑙𝑤𝑓𝑝𝑠 = exp (0.167 ∙ 𝑤𝑓𝑝𝑠 − 0.002 ∙ 𝑤𝑓𝑝𝑠 2 )

0.01

i.e,

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𝐹𝑤𝑓𝑝𝑠

𝑗𝑤𝑓𝑝𝑠 =( ) 𝑙𝑤𝑓𝑝𝑠

𝐹𝑤𝑓𝑝𝑠 = exp (0.00001 ∙ 𝑤𝑓𝑝𝑠 2 − 0.00038 ∙ 𝑤𝑓𝑝𝑠)

(12)

The factor FpH can be formulized as: 𝐹𝑝𝐻 = 𝑝𝐻/7.0 (13) Insert Eq. (7), (8), (10), (11), (12) and (13) to Eq. (6), we obtain NIs efficiency of DCD as follows: 13

Journal Pre-proof 𝑁𝑒𝑓𝑓 = 𝛼 ∙ (11.45 ∙ 𝐷𝐶𝐷𝑎𝑚𝑜𝑢𝑛𝑡 − 0.7129 ∙ 𝐷𝐶𝐷𝑎𝑚𝑜𝑢𝑛𝑡 2 + 0.0137 ∙ 𝐷𝐶𝐷𝑎𝑚𝑜𝑢𝑛𝑡 3 )/100 ∙ exp (−0.08𝑡) ∙ (73.376 − 1.7864 ∙ 𝑇)/100 ∙ exp (0.00001 ∙ 𝑤𝑓𝑝𝑠 2 − 0.00038 ∙ 𝑤𝑓𝑝𝑠) ∙ (𝑝𝐻/7.0) (14a) Insert Eq. (8), (11), (12) and (13) to Eq. (6), we obtain NIs efficiency of DMPP as follows: 𝑁𝑒𝑓𝑓 = 𝛼 ∙ (31.312 ∙ 𝐷𝑀𝑃𝑃𝑎𝑚𝑜𝑢𝑛𝑡 − 2.0331 ∙ 𝐷𝑀𝑃𝑃𝑎𝑚𝑜𝑢𝑛𝑡 2 )/100 ∙

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𝐹𝑡 ∙ 𝐹𝑇 ∙ exp (0.00001 ∙ 𝑤𝑓𝑝𝑠 2 − 0.00038 ∙ 𝑤𝑓𝑝𝑠) ∙ (𝑝𝐻/7.0) (14b)

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-p

Finally, α was determined through optimization (=3.0) in Eq. (14a).

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When 0 = < DCDamount < = 30.0, and 5.0 = < T < = 35.0, it can be re-arranged as: 𝑁𝑒𝑓𝑓 = 3.0 ∙ (0.1145 ∙ 𝐷𝐶𝐷𝑎𝑚𝑜𝑢𝑛𝑡 − 0.7129 ∙ 𝐷𝐶𝐷𝑎𝑚𝑜𝑢𝑛𝑡 2 +

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0.0137 ∙ 𝐷𝐶𝐷𝑎𝑚𝑜𝑢𝑛𝑡 3 ) ∙ exp (−0.08𝑡) ∙ (0.73376 − 0.017864 ∙ 𝑇) ∙

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exp (0.00001 ∙ 𝑤𝑓𝑝𝑠 2 − 0.00038 ∙ 𝑤𝑓𝑝𝑠) ∙ (𝑝𝐻/7.0) (15)

Eqs. (14a, b) and (15) are new NI efficiency equations. After inserting them in Eq. (4), we can simulate NIs and their effects within the DNDC model. 2.2 Calibration and optimization of the modified DNDC Model In order to optimize the parameters of the NIs equation, data from two arable farms at Preston Wynne (Williams et al., 2017) and Newark (Thorman et al., 2017) in the UK, were selected as reference values. These sites are representative of typical climate and soil

14

Journal Pre-proof conditions in the UK agricultural regions and provide a suitable database for the calibration and validation of the DNDC model when environmental and management factors are changed. Data of soil moisture and N2O emissions from Newark were used for optimization and calibration of the coefficients of the NI model and data from Preston Wynne were used to verify the reliability of the model coefficients. The best fitness parameters were obtained through three statistical evaluations using the root mean square error (RMSE, Willmott and

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Matsuura, 2005) (Eq. 16), Mean Absolute Error (MAE, Willmott and Matsuura, 2005) (Eq.

1

𝑅𝑀𝑆𝐸 = ( ∑𝑛𝑖=1(𝑋𝑖 − 𝑌𝑖 )2 ) 1

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̅ ̅ ∑𝑛 𝑖=0(𝑋𝑖 −𝑋 )(𝑌𝑖 −𝑌)

𝑛 ̅ 2 ̅ 2 √∑𝑛 𝑖=0(𝑋𝑖 −𝑋) ∙√∑𝑖=0(𝑌𝑖 −𝑌)

(16)

(17)

(18)

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𝑟=

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𝑀𝐴𝐸 = 𝑛 ∑𝑛𝑖=1|𝑋𝑖 − 𝑌𝑖 |

0.5

-p

𝑛

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17), and the correlation coefficient (r, Taylor, 1990) (Eq. 18).

where X is simulated values at point i, 𝑋̅ is the average of the simulated values, Y is measured

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values at point i, 𝑌̅ is the average of measured values, and n is the number of samples. The soil moisture and N2O emissions were used to calculate their correlation coefficient, r, MAE and RMSE. The best fitness parameters were obtained by finding the maximum correlation coefficient and the minimum RMSE, through changing of one input parameter at a time. 2.2.1 Sites and treatments Newark is located in Nottinghamshire (Thorman et al., 2017) (Fig. 3). It was a farming area of managed grassland in 2011. The site locations, their soil characteristics, annual average climate data, and crop varieties are shown in Table 1. In Newark, two fertilizers, i.e. 15

Journal Pre-proof NH4NO3 and urea were applied to the grassland soil on Feb. 22 and March 21, respectively (Table 2). Preston Wynne is located in Herefordshire (Williams et al., 2017) (Fig. 3). It was a farm area, planting winter wheat in 2011(Table 1). N2O flux and soil moisture were measured from spring 2011 to spring 2012. NH4NO3 and urea were applied to the arable soil on March 26, April 26, May 2, respectively (Table 2).

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There were three replicated blocks, named block 1, block 2 and block 3 for each

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treatment in each case, except case C. Case C had two replicated blocks, i.e. block 2 and

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block 3. In Preston Wynne, 3 groups of soil moistures were measured in blocks 1, 2, and 3,

All

of

the

above

re

respectively, but only one group of soil moistures were measured in Newark. data

were

collected

from

the

website

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AEDA

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(www.environmentdata.org) and provided by Williams et al. (2017) and Thorman et al.

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(2017).

N2O fluxes were measured from headspace concentration of five static flux chambers (40

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cm wide × 40 cm long × 25 cm high) per block. During the enclosure, N2O gas diffused from soil. Before sampling, the chamber cover for at least 40 min and then a 50 ml syringe was used for withdrawing gas samples. Gas sampling was carried out between 10:00 am and 14:00 pm and a minimum of five samples were taken from each chamber at equal timeintervals of 15 min. Gas samples were analyzed using gas chromatography. N2O fluxes from the five replicated chambers per block were averaged. Cumulative fluxes were calculated using the trapezoidal rule to interpolate fluxes between sampling points (Shen et al., 2018a; 2018b). Further details can be found in Williams et al. (2017) and Thorman et al. (2017). 2.2.2 Input data and simulations

16

Journal Pre-proof The input data (Table 1) primarily comes from the measured values of local climate, vegetation and soil, such as: daily maximum temperature, minimum temperature, precipitation, soil organic matter content, nitrate nitrogen content, ammonium nitrogen content, soil pH value, soil clay content and soil density (Wang et al., 2012). Soil wilting point, soil saturated water content, soil porosity, and soil conductivity were not measured parameters at these sites. They were estimated based on the known soil

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parameters, soil properties and existing soil statistics and were set up for the default values

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range might be made to fit the experimental data.

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within the original model. Some adjustments for these parameters within a small reasonable

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2.2.3 Description of DCD

In this study, we used DCD as an example. DCD is a type of metal chelator, which can

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inhibit AMO (Subbarao et al., 2006) and is one of the most commonly used inhibitors

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because it is cheaper, less volatile and relatively soluble in water (Yang et al, 2016). As a strength inhibitor, it can depress NH3-oxidizing bacteria (AOB) (O’Callaghan et al., 2010; Di

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and Cameron, 2011). The first step of nitrification is catalyzed by the enzyme named ammonia monooxygenase (AMO; Hollocher et al., 1981; Bédard and Knowles, 1989; Hopper, 1997; McCarty, 1999). Thus, the emissions of N2O can be restrained. 3 Results and discussion In order to verify the validity of the model, we compared the dynamic changes of the measured soil water content and N2O emissions with the simulated results. 3.1 Soil moistures The modified DNDC model was used for simulations of soil water content at different depths. In Newark and Preston Wynne, soil moistures were measured at the depth of 0-10 cm. 17

Journal Pre-proof Fig. 4 shows a comparison between the measured soil moisture (red cross) and the simulated ones (green solid line). Soil moisture of 1 cm depth (Fig. 4, pink line of dash and dots) is taken as a reference. Fig. 4a displays the results in Newark and 4b is Preston Wynne. It can be seen that the DNDC model can closely simulate the dynamic trend of soil water content in two typical agricultural soils with two kinds of distribution models of precipitation in the UK by comparing the simulated and measured values of soil water at the two sites.

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However, the simulated values have larger fluctuations than the measured ones. In Newark,

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the soil moisture simulated by DNDC has a correlation with the measured values with the correlation coefficient (r) of 0.2773, RMSE of 0.1831, and MAE of 0.1538. Therefore, the

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simulated values are close to the measured values in this site and the fitness was as

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satisfactory as could be expected for a comparison of such a variable parameter.

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In Preston Wynne, the soil moisture simulated by DNDC has a better correlation with the measured values than in Newark with the correlation coefficient (r) of 0.4715. However,

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RMSE is 0.1514, MAE is 0.1165. It appears that the measured values and the simulated

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values are close. There is still a room for improvement in the coincidence between simulation results and real values of soil moisture. Because the simulated value of soil moisture is an important parameter for the simulation of N2O emissions, these small deviations of simulated soil moisture will be transferred to the simulation of N2O emissions, although it does not affect the overall calculations. This is a small system defect of DNDC, which should be improved in the future. It can be seen that the simulated results are lower that the measured ones at Newark in March. This can be explained as there are different patterns of precipitations and soil types at the two sites. The patterns of their precipitation are quite different at the two locations. In 2011, the precipitation of Newark and Preston Wynne were 348.8mm and 448.4mm, 18

Journal Pre-proof respectively. The precipitation in a single day can reach 12.4 mm in Preston Wynne, and the soil moisture can reach more than 0.8 in the winter. Furthermore, the clay content in Preston Wynne soil is 21%, and in Newark soil is 33%. The latter has a stronger water holding capacity, leading to a relatively stable soil moisture. The average soil moisture is 0.6585 in Newark, compared to 0.4495 in Preston Wynne. Comparatively, the range of soil moisture change in the Preston Wynne area is larger and it fluctuates frequently with rain and evapotranspiration. In growing seasons, especially in spring and autumn, it is relatively low

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precipitation, high evaporation and low soil moisture at Preston Wynne.

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3.2 Modelling of N2O emissions for application of fertilizer

In order to verify the validity of the model for the simulated results of N2O emissions

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after applying fertilizers, two kinds of fertilizers, i.e., ammonium nitrate and urea, were

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applied to the soils at Newark and Preston Wynne respectively. We compared the simulated and measured N2O emissions when no digestive inhibitors were added and only chemical

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fertilizers were applied (Table 2).

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3.2.1 Application of ammonium nitrate Fig. 5 shows simulated N2O emissions after applied ammonium nitrate at planting for grassland or winter wheat in different types of soil. In Case A of Table 2, ammonium nitrate (120kg N/ha) was be applied to the grassland soil in Newark. Simulated results of N2O emission is shown in Fig. 5a (block 1), and Fig. S1 a, c (blocks 2 and 3, duplicates of block1). In Case E of Table 2, ammonium nitrate (240kg N/ha) was applied to soil planted with winter wheat in Preston Wynne. Simulated results of N2O emissions is shown in Fig. 5 b (block 1), and Fig. S1 b, d (blocks 2 and 3, duplicates of block1). In both cases, the peak N2O emissions occurred during the first heavy rain within a few days after fertilization. All of the intense emissions occurred in spring. Ammonium nitrate 19

Journal Pre-proof was applied before N2O discharge. However, not every application of fertilizer can cause a large amount of N2O emissions. N2O emission peaks occur only when large-scale precipitation events occur within a few days after fertilization. In Newark, Case A (Fig. 5a and Fig. S1 a, c), ammonium nitrate (60kg N/ha) was applied to soil on February 22, 2011. According to simulated results, the peak N2O emissions occurred on February 22, 2011, while the measured results showed that this peak appeared

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two days later on February 24 with the 87 mm precipitation. It may be explained that low

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temperature and a lack of heavy rainfall delayed the dissolution of ammonium nitrate and

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denitrification of nitrate ions because the average temperature on that day was only 4.35℃. In Preston Wynne, Case E (Fig. 5b and Fig. S1 b, d), ammonium nitrate (100kg N/ha)

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was applied to the soil on May 3, 2011. According to simulated results, the peak of N2O

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emissions occurred on May 9, 2011 while the measured results showed that this peak appeared May 10 (No measurements were available from May 7 to May 9) after the rain of

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152mm precipitation in May 8.

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After fertilization at other times, N2O emissions increased only slightly because there was no significant precipitation. There was also no significant N2O emission peak. Similarly, after other precipitation events, because there is no large supply of nitrogen fertilizer, only a small amount of N2O emissions have been triggered and no N2O emission peak has occurred. It can be seen that N2O emissions from farmland are controlled by fertilization, temperature and precipitation combined. In Newark, Case A (Fig. 5a and Fig. S1 a, c), the correlation coefficient (r) between simulated and measured N2O emissions is 0.338, 0.1910, 0.1881, RMSE is 32.04, 29.01, 32.06 and MAE is 18.24, 16.40 and 16.15 in block 1, block 2, and block 3, respectively.

20

Journal Pre-proof Statistical indicators are not perfect, but the graphics show that the simulated results have the same trend as the measured values. Even the peak height of the two curves is almost identical. The high RMSE and low r values are due to the phase difference between the simulated and measured values. As mentioned above, Ammonium nitrate was applied on February 22, 2011. The peak of N2O emission simulated occurred on February 22, 2011 while the measured peak appeared on February 24. This phase difference does not affect the fact that

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the simulated value can capture the measured value in this case.

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In Case E of Table 2, ammonium nitrate (240kg N/ha) was applied in Preston Wynne (Fig.

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5 b and Fig. s1 b, d. In this case, the correlation coefficient (r) between simulated and measured N2O emission is 0.7336, 0.5813, 0.7362, and RMSE is 7.32, 10.56, 9.54 and MAE

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is 3.88, 4.76 and 4.38 in block 1, block 2, and block 3, respectively.

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The simulated results have the same trend as the measured values. But the peak height of

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the two curves is different. Simulated N2O emissions are higher than measured values, especially in May 10, 11, 12 and July 9. This may be due to that the model overestimates the

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impact of precipitation on N2O emissions. In this case, the average N2O emissions per day is lower, which leads to the RMSE being magnified and looks higher. In Case E of Table 2, the correlation coefficient (r) is high and MAE values are less than 5 g N ha-1. It shows that there is a strong positive correlation and approximation between simulated and measured N2O emission. The simulation results reproduce satisfactorily the measured values. Generally speaking, agricultural soils emit N2O (net sources, Chapuis-Lardy et al., 2007). Hence the modified DNDC model defaults to N2O emission lower limit of 0 g N ha-1 per day. However, in Newark and Preston Wynne, in a few days, N2O flux appears negative, that is, through absorption (consumption, Chapuis-Lardy et al., 2007). However, it is not clear 21

Journal Pre-proof whether these negative values are caused by real net absorption or observation errors. This makes the difference between the simulated and measured values of N2O flux larger in these days, leading to an increase of RMSE and MAE. 3.2.2 Application of Urea The modified DNDC model simulates the N2O emissions from urea applied when planting grassland or winter wheat in different types of soil. In case B of table 2, urea (120kg

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N/ha) was be applied to the grassland soil at Newark. Simulated N2O emissions are shown in

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Fig. 5 c (block 1), and Fig. S1 e, g (block 2 and 3). In Case F of table 2, urea (240kg N/ha)

-p

was applied to the winter wheat soil in Preston Wynne. Simulated N2O emissions are shown

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in Fig. 5 d (block 1), and Fig. S1 f, h (block 2 and 3).

In both cases, the peak of N2O emissions occurred in spring, after fertilization. Similar to

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case A and E, mentioned in chapter 3.2.1, adequate fertilizer, warm weather and sufficient

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precipitation all contributed to the emergence of N2O emission peaks. In case B, the correlation coefficient (r) between simulated and measured N2O emissions

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is 0.1931, 0.4270, 0.3248, and RMSE is 9.77, 7.50, 7.67 and MAE is 7.03, 4.84 and 5.40 in block 1, 2, and 3, respectively.

In Case F, the correlation coefficient (r) between simulated and measured N2O emissions is 0.7199, 0.7558, 0.5883, and RMSE is 4.23, 4.66, 4.65 and MAE is 2.95, 3.29 and 3.16 in blocks 1, 2, and 3, respectively. All of the figures show that the simulated results have the same trend as the measured values. Even the peak height of the two curves are close. The simulated N2O emissions compare satisfactorily to the measured values. 3.2.3 Comparisons of N2O emissions between ammonium nitrate and urea 22

Journal Pre-proof In Case E and F, the simulated N2O emissions have some peaks that don’t appear in the measured values. This is caused by the deposition of nitrogen in atmospheric precipitation. The measurement avoided the time when it was raining. These small peaks were missed by the measurements. Comparing the simulated curve with the measured values, it can be seen that the real values capture the tail after some N2O-peaks caused by rainfall. It can also be seen that the DNDC model compensates for the loopholes in the experiment and displays the

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real situation of the changes of N2O emissions in agriculture soil in the UK. When ammonium nitrate was applied, the maximum N2O emissions from grassland

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reached 142.23, 110.66, and 118.89 g N ha-1 per day (measured) in blocks 1, 2, and 3,

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respectively, with ammonium nitrate 120kg N/ha. While the maximum N2O emission from

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winter wheat field was only 26.23, 15.50, and 21.39 g N ha-1 per day (measured) in blocks 1,

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2, and 3, respectively, with ammonium nitrate 240kg N/ha, which was only about 1/6 of grassland. The DNDC model shows the difference well.

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When urea was applied, the maximum N2O emissions from grassland are 22.60, 32.77,

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and 21.83 g N ha-1 per day in block 1, 2, and 3 with urea 120kg N/ha, while the maximum N2O emission from winter wheat field was only 36.96, 36.63, and 25.32 g N ha-1 per day in block 1, 2, and 3 with urea 240kg N/ha, which was close to the values of grassland. Whether ammonium nitrate or urea were applied as fertilizer, both in grassland and winter wheat soil, the simulation results are close to the measured values. This shows that DNDC model is very effective for simulating N2O emissions with fertilization. The simulated results can predict the N2O emissions objectively, systematically and integrally. 3.3 Modelling of N2O emissions for application of NI In order to test our model, we simulated the N2O emissions after adding NI and compared it with the measured value on the basis of the validity of the original DNDC model. 23

Journal Pre-proof 3.3.1 Application of DCD with ammonium nitrate Fig. 6 a and b shows simulated N2O emissions after applied ammonium nitrate and DCD at planting grassland or winter wheat in different types of soil. In Case C of Table 2, ammonium nitrate (107kg N/ha) and DCD (13kg N/ha) was applied to the grassland soil in Newark. Simulated results of N2O emissions is shown in Fig. 6a (block 1), and Fig. S2 a, c (block 2 and 3, duplicates of block1). In Case G of Table 2, ammonium nitrate (220kg N/ha)

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and DCD (20kg N/ha) was applied to soil planted with winter wheat in Preston Wynne.

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Simulated results of N2O emissions is shown in Fig. 6 b (block 1), and Fig. S2 b, d (block 2

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and 3, duplicates of block 1).

In these two cases, when ammonium nitrate and DCD were applied together, the peak

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time of N2O emissions was the same as when ammonium nitrate was applied alone (Figs.5

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and S1), but the amount of N2O emissions decreased. When DCD was added, the effect of

measured values reflect this.

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nitrification inhibitor reduced the amount of N2O released by fertilizer. Both simulated and

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In the example of ammonium nitrate application, Case C, Newark's RMSE value is higher, while MAE value is lower. In Newark, the values of RMSE are 20.90, 13.52 and 19.29 in block 1, 2, and 3 while MAE value are 10.64, 7.12 and 8.57 in block 1, 2, and 3 (Fig. 6 a; S2 a, c). MAE value is acceptable, but RMSE value is high and R value is low. We already know that RMSE value magnifies singularities, so this result shows that the high value of RMSE value is caused by singularities. This is actually due to an error transmission caused by the phase difference mentioned in Section 3.2.1. Drought delayed the dissolution of ammonium nitrate and release of NH4+ and NO3- ions for several days. This led to the lag of an N2O emissions peak. Similarly, the phase difference caused by this delay leads to the decrease of r value. 24

Journal Pre-proof In Preston Wynne, the simulation results of the main peak are slightly different from the measured values. The values of RMSE are 5.16, 7.40 and 6.37 in block 1, 2, and 3 while MAE value are 3.37, 3.61 and 3.45 in block 1, 2, and 3 (Fig 6 b; S2 b, d). They are lower than values in Newark. This means that the synchronization between simulated and measured values is good. In both sites, the N2O simulated values and the measured values are close, and the peak

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shape of N2O emissions is consistent, while slight lags were due to drought in Newark, which

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indicates that the simulation results of DCD applied with ammonium nitrate are reliable.

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3.3.2 Application of urea and DCD

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Fig. 6 c and d shows simulated N2O emissions after applied urea and DCD at planting grassland or winter wheat in different types of soil. In Case D of Table 2, urea (107kg N/ha)

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and DCD (13kg N/ha) was be applied to the grassland soil in Newark. Simulated results of

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N2O emissions are shown in Fig. 6c (block 2), and Fig. S2 e (block 3, duplicate of block2). In Case G of Table 2, urea (220kg N/ha) and DCD (20kg N/ha) was applied to soil planted with

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winter wheat in Preston Wynne. Simulated results of N2O emissions is shown in Fig. 6 d (block 1), and Fig. S2 f, g (block 2 and 3, duplicate of block 1). When urea and DCD were applied together, the peak of N2O emissions occurred in the same days as when urea was applied alone (Fig.5 and s1). At the mean time, the amount of N2O emissions was less than the amount when urea was applied alone. Both simulated and measured values reflect that when DCD was added, the effect of nitrification inhibitor reduced the amount of N2O released by urea. In Newark, the measured values of N2O emissions are very low. The maximum value is only 7.08 and among them, 17 values are below zero in block 2. These low measurements may be related to soil heterogeneity to some extent. Because the N2O measurement location 25

Journal Pre-proof is fixed, it may lead to the systematic low values of this group. It makes the simulation much more difficult. Nevertheless, the peak shape of the simulated value is very similar to the measured value, except that the absolute value is higher than the measured value. It can be seen that the N2O emissions simulated by DNDC model is still effective. In Preston Wynne, all statistical indicators, including RMSE, MAE and R, are acceptable. The measured values at Preston Wynne is slightly higher than that in Newark with the

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maximum value of 10.04. Similar to the situation in Newark, there are 16 negative

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measurements in Preston Wynne. Meanwhile, in case H, the measured value is higher in winter in block 1, but the simulated value is lower. Considering that the measurement results

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of other parallel plots (block 2 and 3) are not as high, this may be due to the uncertainty of

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winter emission sources. It does not rule out the possibility that the measurement error

simulated and real values.

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increases due to the lower temperature. This also leads to statistical variations between

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Newark's RMSE value is slightly higher, while case H, Preston Wynne's RMSE value is

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smaller. The values of RMSE are 8.23, and 8.28 while MAE values are 4.91 and 5.98 in Newark in block 2, and 3 (Fig 6 c; S2 e). The values of RMSE are 4.86, 4.79 and 4.92 while MAE value are 2.97, 3.27 and 3.19 in Preston Wynne in block 1, 2, and 3, respectively (Fig. 6 c; Fig. S2 f, g).

Because many measurements are close to measurement errors (g N ha-1), this increases the difficulty of simulation. Nevertheless, in this study, the simulated values reproduce satisfactorily the measured values. This indicates that the simulation results of DCD applied with urea are reliable. 3.3.3 Analysis of DNDC model’s response to DCD applied

26

Journal Pre-proof After DCD application, the maximum daily emission of nitrous oxide from grassland was higher than that of winter wheat, but the difference was significantly reduced. The simulated results of the DNDC model are basically in agreement with the above situation, and the inhibition effect of DCD on N2O emissions is satisfactorily represented. In addition, climate during fertilization is also very important. The simulation results reveal that the amount of DCD needed varies with the time of fertilization. In May, for

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example, at Preston Wynne block 3, high precipitation and high DCD amounts applied led to

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a peak of nitrogen emissions caused by fertilization. At the same time, the inhibition effect of nitrification inhibitors was the strongest, with the inhibition rate of the highest daily

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emissions as high as 43%. But in March, because of the lower precipitation and little DCD

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effect on the N2O emission of about zero.

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applied, the rate of nitrification inhibitors added to fertilizer applications had an inhibition

Therefore, the application of the DNDC model can predict and optimize N2O emissions

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when nitrification inhibitors are applied, which is convenient for agricultural production to

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select the optimal amount and time of nitrification inhibitors applied according to climate conditions at fertilizer application. This can increase crop yield and reduce greenhouse gas N2O emissions as much as possible.

4 Application Forecast of Nitrification Inhibitors under the Background of Global Climate Change It is well known that, since the Industrial Revolution, global climate change has been dramatic, especially in the high latitudes of the Northern Hemisphere (Arrow, 2007). These variations exceed the variations of several past glacial-interglacial cycles (Petit, et al., 1999). Vimeux et al., 2001). Over the next few decades, intense climate change may inevitably

27

Journal Pre-proof affect the N2O emissions due to intensive applications of fertilizer and NI. Therefore, it is necessary to evaluate the effect of NIs under future climate models. 4.1 Optimization of nitrification inhibitor rate There is some inconsistency about the efficiency of NIs doses in many the experimental results. For example, Guo et al. (2014) recommend that DCD be used as NI in New Zealand at a dosage of 10 kg ha-1 while other scholars generally expressed that the DCD rate had little

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effect on the inhibition of N2O emissions. This is due to the complexity of the natural

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environment.

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We can use the modified DNDC model to evaluate the effects of different amounts of NIs

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on N2O emissions. In the soil applied urea, the N2O emissions decrease with increasing DCD from zero (Fig. 7). It can be seen that after fertilizer was applied to the soil on February 22,

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2011, N2O emissions increased immediately, but the increased amount was small until an

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increase of precipitation that led to a significant increase of N2O flux and the N2O emissions peak. As the precipitation continues to increase, the simulated N2O emissions value decreases,

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but the range of change tends to slow down (Fig. 7). The simulated values are almost the same when the amount of DCD is greater than 5.0kg N ha-1. This implies the lowest recommended amount of NI under current climatic conditions in this area. 4.2 Effects of Nitrification Inhibitors on N2O Emissions in the Context of Global Climate Changes The DNDC model can be used to predict how nitrification inhibitors will work differently in the future when temperatures rise in the UK. In order to facilitate calculation, we need only simulate the changes of temperature and precipitation. All simulation results are based on the following assumptions:

28

Journal Pre-proof (1). Except for temperature and precipitation, other climatic factors remain unchanged. (2). The crop yield remains unchanged. (3). The change of atmospheric carbon dioxide concentration and the effect of “carbon dioxide fertilization” were negligible. (4). The changes of temperature and precipitation are homogeneous, and the change of temperature and precipitation allocation pattern is not considered. For example, when the

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temperature rises, it is assumed that the daily temperature increases synchronously

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throughout the year. When precipitation increases, it is assumed that precipitation increases in

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proportion to the number of precipitations in the same year.

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On this basis, we assume the four scenarios (SC1 -SC4):

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SC1. Daily mean temperature (T) unchanged (ΔT=0℃), precipitation (P): (1) remains unchanged (ΔP=0), (2) increases by 10% (ΔP= 10%), (3) increases by 20% (ΔP= 20%), (4)

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decreases by 10% (ΔP= -10%), (5) decreases by 20% (ΔP= -20%) (Fig. 8 a).

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SC2. Daily mean temperature (T) rises by 1 degree Celsius (ΔT=1℃), precipitation (P): (1) remains unchanged (ΔP=0), (2) increases by 10% (ΔP= 10%), (3) increases by 20% (ΔP= 20%), (4) decreases by 10% (ΔP= -10%), (5) decreases by 20% (ΔP= -20%) (Fig. 8 b). SC3. Daily mean temperature (T) rises by 2 degree Celsius (ΔT=2℃), precipitation (P): (1) remains unchanged (ΔP=0), (2) increases by 10% (ΔP= 10%), (3) increases by 20% (ΔP= 20%), (4) decreases by 10% (ΔP= -10%), (5) decreases by 20% (ΔP= -20%) (Fig. 8 c). SC4. Daily mean temperature (T) deceases by 1 degree Celsius (ΔT= -1℃), precipitation (P): (1) remains unchanged (ΔP=0), (2) increases by 10% (ΔP= 10%), (3) increases by 20% (ΔP= 20%), (4) decreases by 10% (ΔP= -10%), (5) decreases by 20% (ΔP= -20%) (Fig. 8 d).

29

Journal Pre-proof The simulation climate and results of the above scenarios are shown in Table 3 and Fig. 8. 4.2.1 Fixed air temperature Fig. 8 shows that increasing or decreasing precipitation may lead to changes in N2O emissions with fertilizer and nitrification inhibitor applications when the temperature is constant. Fig. 8a shows the effect of NIs on N2O emissions when precipitation increases or decreases at fixed temperature. The simulation results show that the increase or reduction of

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precipitation had an obvious effect on N2O emissions after the application of fertilizer on

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March 21. Only when precipitation is reduced by more than 20% can N2O emissions be

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restrained. This is because, in this period, soil moisture (WFPS) is about 0.6, which is a water critical region. Increasing or reducing precipitation has a significant impact on soil moisture,

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leading to a greater impact on N2O emissions. An increase of precipitation leads to the

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increase of soil water content, promotes soil microbial activity, enhances nitrification and denitrification, and increases N2O emissions. On the other hand, with the increase of soil

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water content, nitrification inhibitors are leached more, their half-life becomes shorter and

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their content in soil decreases, which weakens their inhibition on the nitrification process and ultimately leads to an increase in N2O emissions. 4.2.2 The future warms like the Medieval Warm Period There is evidence that the temperature in the northern hemisphere will increase significantly over the coming years (e.g., Smith et al., 2007b, IPCC, 2007). Although there are great differences in the range and pattern of warming under different climate scenarios, it is the consensus of the many scholars (e.g., Overpeck et al., 2006) that the temperature will continue to rise in the future, as in the Medieval Warm Period (MWP, 1-2℃ over average, Bradley, 2003).

30

Journal Pre-proof The simulated results reveal that N2O emissions varies with the precipitation when the temperature rises (Fig. 8b, c). When the temperature increases, if the precipitation also increases, the nitrogen emissions will increase, and vice versa. If the precipitation decreases, the N2O emissions will also decrease. At first, an increase of temperature leads to the increase of soil temperature, which weakens the efficiency of nitrification inhibitors. Secondly, the soil temperature in the UK is relatively low. In low temperature areas, higher temperature enhances the activity of nitrifying bacteria, promotes the process of nitrification and

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denitrification, which leads to an increase in N2O emissions. At this time, if the precipitation

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decreases, the soil moisture decreases more, resulting in the inhibition of nitrification and the

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moisture and promote N2O emissions.

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reduction of emissions. In contrast, an increase in precipitation will lead to an increase of soil

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The relationship between N2O emissions and temperature means that, if the hypothesis of warming in the northern hemisphere holds, the role of nitrification inhibitors will be

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weakened. As an important greenhouse gas, N2O emissions will increase, which will further

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amplify the greenhouse effect and contribute to global warming. This is the positive feedback effect of the global climate system. This kind of positive feedback will lead to the gradual amplification of the greenhouse effect, which needs attention. 4.2.3 If the future climate turns into Little Ice Age We are now living in Holocene, an interglacial period (Tzedakis et al., 2009). According to the climate change regulation of the past (NGICP, 2004), the earth's surface temperature is decreasing continuously and will enter the next glacial period. Even in Holocene, climate is also volatile and uncertain (Bordbar et al., 2019). It is known that the sea surface temperature (SST) and mean annual temperature in the Little Ice Age over the interval 1400 to 1700 C.E.,

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Journal Pre-proof where the greatest cooling over the extratropical Northern Hemisphere continents decreased by more than 1 ℃ (Mann et al., 2009). The simulation results (Fig. 8d) show that if the temperature decreases by 1 ℃, the N2O emission increases with the increase of precipitation, and decreases with the decrease of precipitation. When the temperature decreases, the nitrogen emissions caused by fertilization decreases, and the role of nitrification inhibitor increases, which will reduce the nitrogen

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emissions. However, evapotranspiration also decreases with the decrease of temperature. If

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precipitation increases, the increase of soil moisture will be greater, which promotes the release efficiency of fertilizer and nitrification, and consequently leads to an increase of

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nitrogen emissions. In contrast, if the temperature decreases and precipitation decreases, the

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nitrification will be inhibited and the nitrogen emissions will be reduced.

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4. Conclusion

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Nitrification inhibitors (NIs) affect N2O emissions from agricultural soils by inhibiting nitrification of fertilizers. However, their efficiency is limited by environmental factors and

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agricultural practice. In this study, the integrated equation of the efficiency of nitrification inhibitors was established under different NI quantities and applied days, soil temperature, soil moisture and soil pH through statistical regression of a wide range of NIs field trials on N2O flux. Then we integrated the new model of NIs with the DNDC model. The new model was tested using field measurements of cropland and grassland from two sites in the UK. The simulated results are in good agreement with the measured data at the two sites and captured the measured soil moistures and N2O emissions. In Newark, the average Mean Absolute Error (MAE) of all the three blocks is 8.83 or 5.45 for ammonium nitrate or urea respectively, while in Preston Wynne, the average MAE is 3.48 or 3.14. This shows that the simulated results are reliable and robust for both ammonium nitrate and urea fertilizers applied to 32

Journal Pre-proof agricultural soils. This model can predict the efficiency of NIs under different environmental factors and agricultural practice. We examined the effect of climate change on the inhibition of N2O emissions by NIs to increase crop yield and reduce N2O greenhouse gas emissions. We simulated the effects of fertilizers with and without NIs on nitrogen emissions from agricultural systems under 20 possible climate scenarios in the future. It was found that a rainfall increase may lead to

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increased N2O emissions from agricultural soils in the UK, regardless of future temperature

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increases or decreases. If the northern hemisphere continues to warm in the future, the efficiency of NIs will be weakened, leading to an increase of N2O emissions. This can

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amplify the greenhouse effect, resulting in further warming of the earth's surface. This kind

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of positive feedback will lead to the gradual amplification of the greenhouse effect, which

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Acknowledgement

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needs attention.

This work was supported by the National Natural Science Foundation of China (NSFC)

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[grant number: 41272207; 41430531], the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number: XDA20040102; XDA05130402], the

Chinese

Academy of Sciences (CAS) Scholarship [grant number: 2017 No.65], and the Campus Alberta Innovates Program Research Chair [No. RCP-12-001-BCAIP). The authors would thank the ADAS UK Ltd and the website of AEDA (www.environmentdata.org) for the applied data of Preston Wynne and Newark. The authors would also thank the anonymous reviewer and the editor for their comments and suggestions to improve manuscript and Dr. Jincheng Shen for his discussion and Mr. Jim Sellers for English editing and proofreading.

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Fig. 1. A schematic of nitrogen fertilizers with inhibitors applied to agricultural soils. This figure synthesizes the roles of fertilizers and nitrification inhibitors, the pathways, reactants and products

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of nitrification and denitrification. NIs reduce the nitrification rate and the fraction of N2O in

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Fig. 4. A comparison between simulated and measured soil moisture: (a) Newark (NW) and (b)

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Preston Wynne (PW). Green solid line is the simulated soil moisture of the depth of 0-10 cm. Pink

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50 50.0 200 PW B3 U 150 30.0 30.0 r=0.5883 NW B3 U 100 10.0 -10 050 RMSE=4.65 200 r=0.3248 NW B1 AN -10.0 150 10.0 100 0 1/1/2011 5/1/2011 r=0.3378 9/1/2011 MAE=3.16 1/1/2012 50 RMSE=7.67 1/1/2011 5/1/2011 9/1/2011 1/1/2012 0 RMSE=32.04 -10.0 MAE=5.40 5/1/2011 9/1/2011 1/1/2012 MAE=18.24

N2O - flux (g N ha-1 ∙d -1)

10

(mm) precipitation -1 ∙ -1 N 2O - flux (g N ha d )

100

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150

precipitation (mm)

200

precipitation (mm)

NW B1 U r=0.1931 RMSE=9.77 MAE=7.03

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N2O - flux (g N ha-1 ∙d -1)

190 140 90 40 -10 1/1/2011

N2O - flux (g N ha-1 ∙d -1)

Fig. 5. Modelling of N2O emissions applied ammonium nitrate and Urea in Newark and Preston

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Wynne. 5a shows the simulated N2O emissions applied ammonium nitrate in block 1 of Newark (blocks 2 and 3 are shown in Fig. S1 a, c). 5b shows the simulated N2O emissions applied ammonium nitrate in block 1 of Preston Wynne (blocks 2 and 3 are shown in Fig. S1 b, d). 5c shows the

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N2O - flux (g N ha-1∙d -1)

N2O - flux (g N ha-1∙ d -1)

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b

simulated N2O emissions applied urea in block 1 of Newark (blocks 2 and 3 are shown in Fig. S1 e, g). 5d shows the simulated N2O emissions applied urea in block 1 of Preston Wynne (block 2 and 3 are shown in Fig. S1 f, h). PW: Preston Wynne; NW: Newark; B1: block 1; AN: ammonium nitrate; U: urea

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b

10.0 -10.0 10.0 1/1/2011

-10.0 1/1/2011

5/1/2011

9/1/2011

d

5/1/2011

200 PW B3 U 150 r=0.5883 NW B3 U 100 50 RMSE=4.65 r=0.3248 0 MAE=3.16 RMSE=7.67 1/1/2012

9/1/2011

MAE=5.40 1/1/2012

N2O flux(gN/ha/d) (gN/ N ha/ ha-1d) ∙ d -1) measuredN2O-flux(kg N2O-flux measured -1 ∙

-1

200 150 100 50 0

N ha d-1∙ d) -1) N(g (gN/ha/d) N ha simulatedN2O-flux(kg N2O-flux simulated N/ha/d) 2O flux(g Precipitation (mm) Prec.(mm)

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Fig 6. Modelling of N2O emissions applied DCD with ammonium nitrate or Urea in Newark and

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Preston Wynne. 6a shows the simulated N2O emissions applied DCD with ammonium nitrate in block 1 of Newark (blocks 2 and 3 are shown in Fig. S2 a, c). 6b shows the simulated N2O emissions applied DCD with ammonium nitrate in block 1 of Preston Wynne (blocks 2 and 3 are shown in Fig. S2 b, d). 6c shows the simulated N2O emissions applied urea in block 2 of Newark (block 2 are shown in Fig. S2 e, no block 1). 6d shows the simulated N2O emissions applied urea in block 1 of Preston Wynne (blocks 2 and 3 are shown in Fig. S2 f, g). PW: Preston Wynne; NW: Newark; B1: block 1; B2: block 2; AN: ammonium nitrate; U: urea.

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precipitation (mm)

30.0 30.0

precipitation (mm)

N2O - flux (g N ha-1 ∙d -1)

1/1/2012

N2O flux(gN/ha/d) (gN/ N ha/ ha-1d) ∙ d -1) measured N2O-flux (g N/ha/d) measuredN2O-flux(kg N2O-flux measured -1 ∙d-1-1) -1 N ha simulated N2O-flux (g N(g (gN/ha/d) N N/ha/d) ha ∙ d ) simulatedN2O-flux(kg N2O-flux simulated N/ha/d) 2O flux(g Prec.(mm) Precipitation (mm) Prec.(mm)

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9/1/2011

precipitation (mm)

c

50.0

N2O - flux (g N ha-1 ∙d -1)

5/1/2011

200 150 100 50 0

precipitation (mm)

1/1/2011

precipitation (mm)

10.0 -10.0 10.0 1/1/2011 -10.0 5/1/2011

200 PW B3 U 150 r=0.5883 NW B3 U 100 200 50 r=0.3248 NW B1 AN RMSE=4.65 0 150 100 MAE=3.16 50 r=0.3378 RMSE=7.67 5/1/2011 9/1/2011 1/1/2012 0 RMSE=32.04 MAE=5.40 9/1/2011 1/1/2012 MAE=18.24

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N2O - flux (g N ha-1 ∙d -1)

190 140 90 40 -10 1/1/2011

N2O - flux (g N ha-1 ∙d -1)

N2O - flux (g N ha-1∙d -1)

50.0

30.0 30.0

-p

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a

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Fig. 7. Effects of inhibitor on N2O emissions applied fertilizers

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Fig. 7 shows the effects of different amounts of nitrification inhibitors with urea on N2O emissions

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-p

predicted by DNDC model. Fertilizer and DCD applied on February 22, 2011.

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b

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a

d

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c

Fig. 8. Simulation of 20 scenarios with climate changes. 8a shows the effect of nitrification inhibitors

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on N2O emissions when temperature remains unchanged and precipitation increases or decreases. 8b shows the effect of nitrification inhibitors on N2O emissions when temperature remains unchanged

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and precipitation increases or decreases.

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Journal Pre-proof Table 1 Input data of Newark and Preston Wynne*

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Site Newark Preston Wynne Block number 1 2 3 1 2 3 Start Date (m / d / y) 2/22/2011 2/22/2011 2/22/2011 3/9/2011 3/9/2011 3/9/2011 End Date (m / d / y) 2/17/2012 2/17/2012 2/17/2012 3/5/2012 3/5/2012 3/5/2012 Longitude (West) -1.00 -1.00 -1.00 -2.78 -2.78 -2.78 Longitude (East) -0.73 -0.73 -0.73 -2.51 -2.51 -2.51 Latitude (South) 53.00 53.00 53.00 52.03 52.03 52.03 Latitude (North) 53.16 53.16 53.16 52.20 52.20 52.20 Winter Winter Winter Crop type grassland grassland grassland wheat wheat wheat Winter Winter Winter Previous crop type grassland grassland grassland oats oats oats Clay Clay Clay Soil texture clay loam clay loam clay loam loam loam loam Clay content (%) 33 33 33 21 21 21 Soil organic carbon 1.0 2.3 2.6 2.8 1.0 1.0 (kg C / kg) Soil dry bulk 1.24 1.30 1.30 1.30 1.24 1.24 3 density (g / cm ) Soil pH 7.0 7.5 7.2 6.5 6.5 6.5 Soil nitrate-nitrogen 1.49 1.27 0.07 1.55 1.89 2.01 content (mg N / kg) Soil ammonium0.5 nitrogen content 0.39 0.39 0.66 0.45 0.35 (mg N / kg) Soil wilting point 0.22 0.23 0.23 0.23 0.22 0.22 (wfps) soil saturated water 0.52 0.53 0.53 0.53 0.52 0.52 content (wfps) soil porosity 0.4454 0.4454 0.4454 0.476 0.476 0.476 soil conductivity 0.023 0.1324 0.1324 0.1324 0.023 0.023 (cm/min) soil moisture data 1 groups 3 groups measured * All of the data, except for Soil wilting point, soil saturated water content, soil porosity and soil conductivity were collected from the website of AEDA (www.environmentdata.org) and provided by the ADAS UK Ltd.

Table 2 Cases in Newark and Preston Wynne* Site Application date Fertilizer 50

kg N/ ha

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Case G

4/4/2011 5/3/2011 3/9/2011 4/4/2011 5/3/2011

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Case H

3/9/2011 4/4/2011 5/3/2011 3/9/2011 4/4/2011 5/3/2011 3/9/2011

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Case F

3/21/2011

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Case E

2/22/2011

-p

Case D

3/21/2011

Ammonium nitrate Ammonium nitrate Urea Urea Ammonium nitrate DCD Ammonium nitrate DCD Urea DCD Urea DCD Ammonium nitrate Ammonium nitrate Ammonium nitrate Urea Urea Urea Ammonium nitrate DCD Ammonium nitrate DCD Ammonium nitrate DCD Urea DCD Urea DCD Urea DCD

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Case C

2/22/2011 3/21/2011 2/22/2011 3/21/2011 2/22/2011

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Case B

Newark Newark Newark Newark Newark Newark Newark Newark Newark Newark Newark Newark Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne Preston Wynne

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Case A

60.0 60.0 60.0 60.0 53.5 6.5 53.5 6.5 53.5 6.5 53.5 6.5 40.0 100.0 100.0 40.0 100.0 100.0 34.0 6.0 93.0 7.0 93.0 7.0 34.0 6.0 93.0 7.0 93.0 7.0

* All of the data were collected from the website of AEDA (www.environmentdata.org) and provided by Williams et al. (2017) and Thorman et al. (2017) of the ADAS UK Ltd.

Table 3 Variation of daily mean temperature and precipitation No. of scenarios

Daily mean temperature rises (ΔT, ℃) 51

Precipitation rises (ΔP, %)

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0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 -1.0 -1.0 -1.0 -1.0 -1.0

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1-1 1-2 1-3 1-4 1-5 2-1 2-2 2-3 2-4 2-5 3-1 3-2 3-3 3-4 3-5 4-1 4-2 4-3 4-4 4-5

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0 10 20 -10 -20 0 10 20 -10 -20 0 10 20 -10 -20 0 10 20 -10 -20

Journal Pre-proof Highlights

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A sophisticated model of nitrification inhibitors (NIs) was developed for agriculture. The new model of NIs was integrated into DeNitrification DeComposition (DNDC). The integrated DNDC model was examined for urea and ammonium applied to soil. N2O emissions under 20 climate change scenarios were compared with NIs used. Precipitation has the greatest impact on N2O emissions of soil in England.

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    

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Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8