Estimation of greenhouse gas emissions from rice and annual upland crops in Red River Delta of Vietnam using the denitrification–decomposition model

Bui Thi Thu Trang; Mai Van Trinh

doi:10.1515/gps-2023-0187

Article Open Access

Estimation of greenhouse gas emissions from rice and annual upland crops in Red River Delta of Vietnam using the denitrification–decomposition model

Published/Copyright: February 27, 2024

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From the journal Green Processing and Synthesis Volume 13 Issue 1

Abstract

This study used the denitrification–decomposition (DNDC) model and ArcGIS 10.1 to calculate and quantify the greenhouse gas (GHG) potential from rice and annual upland crops in the Red River Delta of Vietnam. GHG emissions were monitored, analyzed, and calculated at experimental sites. The operating mechanism, sensitivity analysis of the parameters, calibration, and verification of the DNDC model for the GHG emission calculation were studied and performed, and a set of parameters was built. A good correlation between actual and simulated values was shown. From the data set of meteorological stations in and around the Red River Delta, the current land-use map, the topographic and soil map, a complex map of meteorology–soil–land use was built. Each unit of this map contains complete information about climate, soil, and crops as input data for modeling GHG emissions from crop production. From the spatial analysis and collected input data, GHG emissions were measured and calculated for the cultivated field of the Red River Delta (annual rice and upland crops) using the DNDC model. The model's outputs were used to build thematic maps on the distribution of global warming potential (CH₄, N₂O) for each unit of the complex map of climate, soil, and crops.

Keywords: DNDC model; greenhouse gas; emission; the Red River Delta of Vietnam

1 Introduction

Greenhouse gas (GHG) emissions from agriculture production have become a global problem. Significantly, it can be mentioned that Vietnam is one of the countries that depend on agricultural production as a prominent citizen’s livelihood. Vietnam's agricultural production is not only heavily affected by climate change but also causes climate change because it contributes to emissions.

Vietnam's National GHG inventory was implemented four times in 1994, 2000, and 2014, and most of the calculations of GHG inventory used the emission factors of IPCC Tier 1 [1] but not the country-specific factors (Tier 2 and higher). However, precise quantification of GHG emissions from rice and other crops is complex because of spatial variations in climate and soil, crops, and farming practices.

Besides using the closed-chamber method of direct field measurement or inventory emissions according to IPCC guidelines, countries, such as China, India, etc., have used the denitrification–decomposition (DNDC) model method combined with remote sensing to calculate the GHG emissions from rice farming. The application of a mathematical model quantifying GHG emissions is a possible solution to meet both technical requirements and emission calculation with high and stable accuracy [2].

DNDC model has modules to calculate CO₂, N₂O, and CH₄ emissions by day and simulate the nitrogen cycle in agricultural soil. DNDC has a structure to simulate relatively fully the biochemical and physicochemical processes in soil, as well as other environmental factors (temperature, precipitation, etc.) that affect the processes of formation and release of GHGs from the soil into the atmosphere. DNDC has proven its reliability in GHG calculations through studies in many countries around the world and is considered as one of the most comprehensive currently available tools to calculate and forecast GHG emissions from agricultural and forestry activities [3,4,5].

The DNDC model has been applied to estimate N₂O emissions and SOC fluctuations in agricultural cropping systems in the US since the early 1990s in the studies of Li et al. [5,6]. Bouwman et al. [7] used the DNDC model to calculate N₂O emissions from small livestock production systems. Cai et al. tested the DNDC model in GHG emission calculations in East Asia [8]. Then, Pathak et al. and Babu et al. [9] continued to perfect the DNDC model combined with remote sensing to calculate emissions from rice cultivation in India. Most of the changes focused on the addition of anaerobic biogeochemical equations, rice growth, and farming practice parameters [9,10].

Then, a series of studies of DNDC model applications to estimate the emission reduction potential of improved farming practices were done by Cai et al. and Li et al., [11,12,13]. In 2008, Fumoto and his colleagues continued to improve the DNDC model into a version of DNDC-Rice specifically for rice land. The enhancements allow DNDC to improve the accuracy of quantification and CH₄ emissions from rice fields under scenarios of climate, soil properties, farming practices, etc. and are used to determine the potential for CH₄ mitigation in Japan [14]. Smith et al. [15] studied the sensitivity of the parameters of the DNDC model and used the calibrated DNDC model to estimate the GHG emission factor in Canada. Zhang et al. used the DNDC model in combination with remote sensing to calculate emissions for 1.44 million hectares of rice cultivation in southern China [16]. In 2016, Zhang and colleagues applied the DNDC model to estimate N₂O emissions under different types of irrigation in vineyards in Ningxia, China [17]. Zhao et al. conducted sensitivity analysis, calibration, and verification of the DNDC model and then applied the model to suggest potential measures to reduce GHG emissions from rice fields in Shanghai, China [18].

Thus, it can be affirmed that the DNDC model has been continuously tested by CH₄ and N₂O monitoring data measured in the field and continues to be calibrated, perfected, and upgraded by researchers around the world. The application of the DNDC model to different rice farming systems in the United States, China, Thailand, India, Japan, and the Philippines has given very positive results. These studies have demonstrated that it is possible to use the DNDC model to estimate GHG emissions from rice fields at a large scale.

2 Methodology

2.1 Research framework

The research steps are shown in Figure 1.

Figure 1

Research framework.

Regarding the implementation process, first of all, the study has collected all data on bio-physical and socio-economic conditions of the study area, meteorology, soil, land use, crop, and farming activities in both spatial and non-spatial data of the Red River Delta.

The field measurements (to measure CH₄ and N₂O emissions) were performed in four provinces (Nam Dinh, Thai Binh, Hai Duong provinces, and Hanoi city) on Fluvisols soil, gray soil, saline soil, and thionic soil and planted some rice varieties of Huong Viet 3, BT7, TX111, DS1, LVN17, and BC15.

The spatial data of the whole Red River Delta were processed and edited for georeferences. Then, the layers of land map information and land-use status map are superimposed and combined with the information of meteorological stations in the region to create a combination map of meteorology–land–land use representation. Therefore, each polygon contains information on meteorology, soil, and cultivation in the format of the DNDC model as inputs for further GHG emission estimation process.

In the next step, the study uses the modified DNDC model to calculate CH₄ and N₂O emissions from rice and upland crops in the Red River Delta with different meteorological and soil conditions. The global warming potential (GWP) was estimated from CH₄ and N₂O emissions in terms of CO₂ equivalents, guided by IPCC (2006), with a ratio of 28 for CH₄ and 265 for N₂O. The polygons containing emissions of CH₄, N₂O, and CO₂eq are then presented on maps as thematic maps (using ArcGIS 10.0) of GHG emissions (Figure 1).

2.2 Methodology

2.2.1 Data collection

The spatial data include land-use map of the Red River Delta in 2015 received from the Vietnam General Department of Land Management and the Ministry of Natural Resources and Environment, and the soil map of the Red River Delta in 2016 received from the Soils and Fertilizers Research Institute, Vietnam Academy of Agricultural Sciences.

The soil data information includes soil types, soil layer thickness, and sand oil physical and biochemical properties received from the Soils and Fertilizers Research Institute. The crop data include crop varieties, phenology, seasonal crop calendar, farming techniques, fertilizers types, nutrient content, and application procedures.

The crop data include rice varieties and other annual crops; physiological and biochemical characteristics of rice and maize varieties; seasonal calendar; farming techniques (soil preparation, irrigation, fertilization, weeding, plant protection spraying); and types and characteristics of fertilizers (from documents, books, and scientific articles; information about varieties and results of variety testing, December 2017 statistical report of the Ministry of Agriculture and Rural Development [MARD]) [19].

Meteorological data were collected during the period 2010–2020 at 28 principal meteorological stations in the network of national monitoring of Viet Nam Meteorological and Hydrological Administration. These stations ensure measurements on a unified basis and serve for basic investigation. The information includes station coordinates, minimum and maximum temperature, sunshine hours, wind direction and speed, rainfall, and humidity in a daily resolution. The meteorological station’s locations are shown in Figure 2, and their information is presented in Tables S1 and S3.

Figure 2

Location map of meteorological stations.

2.2.2 Gas sampling and analysis

2.2.2.1 Rice varieties

The rice varieties at the experimental sites were regionalized by MARD and were Huong Viet 3, BT7, TX111, DS1, and BC15. All these five varieties have durations from 110 to 138 days and can be grown in two seasons in a year; spring rice has a longer duration than summer season rice. All five varieties have a high yield potential range from 5.5 to 7.5 tons·ha⁻¹ season⁻¹ (Table S2).

2.2.2.2 Experimental layout

Field measurements were set up on 10 selected sites (Table 1). The farming activities were practiced following the recommendations of local extension with adjustments for local conditions of weather, soil, and farming levels. In general, Spring rice started on February 5th and ended on June 15th, and Summer rice started on 24th June and ended in October.

Table 1

Amount of fertilizer applied at the study sites

Site	Province, soil type, and crop rotation	Rice varieties	Coordinates	Fertilizer applied (kg·ha⁻¹·crop⁻¹)
				Spring crop			Summer crop
				N	P₂O₅	K₂O	N	P₂O₅	K₂O
1	Ha Noi, Fluvisols, Spring rice and Summer rice	BT7	20°55′60″N; 105°50′54″E	95.4	65.0	70.0	82.8	55.0	60.0
2	Nam Dinh, Fluvisols, Spring rice and Summer rice	TX111	19°59′11″N; 106°8′5″E	95.4	65.0	70.0	82.8	55.0	60.0
3	Thai Binh, Fluvisols, Spring rice and Summer rice	DS1	20°24′18″N; 106°17′49″E	95.0	65.0	50.0	90.0	55.0	45.0
4	Thai Binh, Fluvisols, Spring rice, Summer rice, and Winter crop	BT7	20°24′54″N; 106°16′1″E	100.0	60.0	50.0	90.0	58.0	45.0
5	Ha Duong, Fluvisols, Thai Binh, Fluvisols, Spring rice, Summer rice, and Winter crop	BC15	20°3′8″N; 106°13′28″E	100.0	60.0	70.0	90.0	58.0	60.0
6	Nam Dinh, Solonetz, Thai Binh, Fluvisols, Spring rice and Summer rice	TX111	20°3′28″N; 106°13′1″E	100.0	75.0	70.0	104.9	47.0	72.0
7	Nam Dinh, Solonetz, Thai Binh, Fluvisols, Spring rice and Summer rice	TX111	20°13′59″N; 106°15′33″E	100.0	75.0	70.0	104.9	47.0	72.0
8	Thai Binh, Solonetz, Spring rice and Summer rice	Huong Viet 3	20°24′50″N; 106°34′35″E	100.0	75.0	50.0	95.0	50.0	45.0
9	Thai Binh, Thionic, Spring rice and Summer rice	BC15	20°45′74.23″; 106°38′5″E	127.0	48.0	80.0	104.9	47.0	72.0
10	Ha Noi, Gray, Spring rice and Summer rice	BC15	21°16′22″N; 105°53′30″E	120.0	75.0	50.0	110.0	55.0	45.0

In spring, land preparation was done from February 5 to 18, 2018; transplantation was done from February 08 to 20, 2018, with a rice population of 30–35 hills·m⁻², and harvest from June 02 to 11, 2018. In summer, land preparation was done from June 24 to 25, 2018, transplantation was done from 30 June 30 to July 02, 2018, with a rice population of 30–35 hills·m⁻² and harvest from October 17 to 27, 2018. Water management was similar to farming practices, in which the field was flooded 10 cm until the rice was matured, and then the field was drained (in spring, it was drained from May 20 to 30, 2018; in summer, it was drained from October 01 to 19, 2018 depending on each experimental site). After harvesting, the straw was removed, but the stubble was plow-incorporated into the soil.

Fertilizer was applied three times per season. Basal fertilizer uses 100% phosphorus fertilizer, 30% nitrogen fertilizer, and 30% potassium fertilizer. The first split at tillering was with 40% nitrogen fertilizer, and the second split was at panicle initiation; the second additional fertilizer was 30% nitrogen fertilizer, and the remaining 70% was potash fertilizer. The rate of fertilizer application was recommended by the provincial Departments of Agriculture and Rural Development and trained by the Agricultural Extension Centers of the provinces. This is also the fertilizer level we obtained from the survey results of 720 farmer households.

2.2.2.3 Gas sampling

The CH₄ and N₂O fluxes were determined using the techniques of static flux chamber and gas chromatography, following the methods of Rochette and Eriksen-Hamel (2008) [20]. The chamber consists of a permanently installed base unit (open bottom) and a removable top. A stainless steel base unit (45 cm length, 9 40 cm width, 9 40 cm height) with a water groove (5 cm in depth) on the top was placed 10 cm deep in the soil for 3 days before transplanting to avoid lateral gas diffusion. The removable top (45 cm length, 9 40 cm width, 9 9 cm height) covered six hills of rice, and the plant density inside the chamber was the same as that outside of the chamber (see www.climaviet.org). Floodwater was used to seal the plexiglass top to the base unit during gas sampling. A rubber septum, thermometer, and two mini-fans (12 V) were installed at the top of the chamber [21]. Pressure control (a plastic tube with 7.6 m length and 1.5 mm diameter) was also installed to maintain an equilibrium gas pressure between the inside and outside of the chamber and to minimize mixing of the internal chamber gases with the exterior atmosphere [22]. Removable wooden boardwalks were set up in the early stages of the rice season to avoid soil disturbances during gas sampling. Samples for analysis of CH₄ and N₂O were taken at the stages of transplant, tillering stage, stem elongation, panicle initiation, booting stage, flowering stage, milk stage, and dough stage.

2.2.2.4 Analysis of gas samples

Gas samples were analyzed by gas chromatography: CH₄ gas was determined by a flame ionization detector at 300°C and N₂O was determined by an electron capture detector at 350°C.

For determining the GHG emission, the intensity of CH₄ or N₂O emission (mg·m⁻²·h⁻¹) was calculated using the equation of Smith et al. [15].

2.2.3 Soil analysis

Soil samples were taken in the survey field at the rooting layer before the experiment. Soil sampling was carried out following the Vietnam standards: soil texture by a pipette (TCVN 8567:2010); soil pH by using a pH meter (TCVN 5979-2007); total OC by Walkley – Black (TCVN 9294:2012); total N by the Kjeldahl procedure (TCVN 7373:2004); total P by the colorimetric method (TCVN 8940:2011); total K by atomic absorption spectroscopy method (TCVN 8660:2011); cation exchange capacity (CEC) by the ammonium acetate extraction method; and available K₂O (TCVN8662:2011) and available P₂O₅ by Olsen (TCVN8661:2011).

2.2.4 Modeling

2.2.4.1 Model description

The DNDC model was used to simulate GHG emissions from rice cultivation and some other annual crops.

The DNDC model is a process-oriented computer simulation model of carbon and nitrogen biogeochemistry in agroecosystems. The model consists of two components: the first component, consisting of the soil climate, crop growth, and decomposition sub-models, predicts the soil temperature, moisture, pH, redox potential (Eh), and substrate concentration profiles driven by ecological drivers (climate, soil, vegetation, and anthropogenic activity). The second component, consisting of nitrification, denitrification, and fermentation sub-models, predicts emissions of carbon dioxide (CO₂), methane (CH₄), ammonia (NH₃), nitric oxide (NO), nitrous oxide (N₂O), and dinitrogen (N₂) from plant–soil systems. Classical laws of physics, chemistry, and biology, as well as empirical equations generated from laboratory studies, have been incorporated into the model to parameterize each specific geochemical or biochemical reaction. The entire model forms a bridge between the C and N biogeochemical cycles and the primary ecological drivers [23].

The input data of the model included meteorology (temperature, precipitation, wind speed, solar radiation, humidity); cultivation (seeds, time of sowing, harvesting, fertilizers, watering, crop management, weeds, etc.); and soil (soil type, pH, bulk density, hydraulic conductivity, clay content, OC content, etc.)

The output data of the model involved CH₄ and N₂O emissions per unit of cultivated area and other indicators related to OC, Eh, etc.

2.2.4.2 Sensitivity assessment of the DNDC model for the calculation of emissions

The input sensitivity analysis scenarios are prepared based on the climate, soil, and actual farming practices in the fluviol farming system in Thai Binh. Sensitivity analysis of the inputs is performed by changing a single input parameter within an observable range while keeping all other inputs at the original parameter (baseline). Input factors for sensitivity assessment include climate factors (temperature, precipitation, humidity), soil properties (organic carbon content in soil, clay fraction, pH, bulk density, and porosity), or farming practices (the amount of nitrogen fertilizer and manure used). The selected initial value is rice grown on fluviol soil in Nguyen Xa commune, Vü Thu district, Thai Binh province. The rice variety used in the experiment is the one that is widely grown in the area. The soil pH ranged from 4.8 to 5.0; soil texture values (%) were 19.96% (<0.002 mm), 49.38% (0.002–0.02 mm), and 30.66% (0.02–0.2 mm); the soil organic content was 0.18%; plant available phosphorus was 182.09 mg/100 g; and CEC was 26.75 cmol·kg⁻¹. Fertilizer was applied three times/season without application of manure, and the plow depth was about 20 cm. The irrigation mode was still mainly regular flooding. The average daily temperature and daily rainfall data were obtained from the Thai Binh meteorological station in 2018.

2.2.4.3 Calibration of the DNDC model for emission calculation

The estimated results of CH₄ and N₂O emissions by the DNDC model were compared with the measured ones at the experimental site in two rice seasons. The correlation between the two results was determined using the R ² [24] and Nash – Sutcliffe efficiency index (NSI) [25], as follows:

R 2 = ∑ i = 1 n ( O i − O ¯ ) ( P i − P ¯ ) ∑ i = 1 n ( O i − O ¯ ) 2 ∑ i = 1 n ( P i − P ¯ ) 2 2

NSI = 1 − ∑ i = 1 n ( O i − P i ) 2 ∑ i = 1 n ( O i − O ¯ ) 2

where i is the total number of tests, O _i is the actual measured value i, Ō is the mean of the actual measured value, P _i is the simulated value i that corresponds to O _i, and n is the total number of tests. The R ² value ranges from 0 to 1, representing the correlation between the actual measured value and the simulated value. The NSI value ranges from −∞ to 1, representing the match between the actual measured value and the simulated value on a 1:1 straight line. If R ² and NSI are less than or close to zero, then the results are considered unacceptable or of poor reliability. On the contrary, if this value is equal to 1, the simulation results of the model are perfect. The model is accepted when the R ² coefficient and NSI index are greater than 0.5.

The model parameters that can be calibrated are the bulk density, clay fraction, drainage efficiency, porosity, wilting point, field capacity, conductivity, rainwater collection index, soil salinity index, initial nitrate concentration at the surface soil, initial ammonium concentration at the surface soil, pH, and microbial activity index.

2.2.4.4 Model verification

The model was verified by comparing the GHG emission calculation results of the model with the experimental data set from the study of Mai et al. [26], which measured the field emissions at Thinh Long, Hai Hau, and Nam Dinh.

Similar to the content of model calibration, this research has calculated the value of the coefficient of determination R ² and the NSI to compare the fit between the calculated emission values by the model (after calibration) and observed values.

2.2.5 Spatial analysis

The DNDC model estimated GHG emissions for each polygon on the map unit. Then, ArcGIS 10.1 software was used to present all these results on thematic maps, the so-called maps of CH₄, N₂O, and CO₂eq emission.

2.2.6 Statistical analysis

The data and results of the conducted experiments after applying the DNDC model were processed using Microsoft Excel. GHGs were converted to CO₂eq with a factor of 28 for CH₄ and 265 for N₂O, according to IPCC 2014 [1].

3 Results and discussion

3.1 Physical and chemical properties of soils at experimental sites

Most of the measurement sites have organic and total N contents at high levels. For the organic content, because fluvisols outside the dyke are regularly accreted, the organic C was determined to be low of less than 1%; in contrast, due to fluvisols in low-lying areas being poorly drained and poorly mineralized, the organic C content was higher than 2.2%. Fluvisols with OC ranged from 0.9% to 2.61%; saline soils ranged from 0.4% to 2.29%; thionic soils were 3.3%, and gray soils were 1.23%. Thus, the organic content in thionic soils was much higher than that in the remaining three soil types. Additionally, most of the total N fluctuated in the range of 0.12%–2.7%.

At most of the study sites, phosphorus and available potassium were high, especially available phosphorus, which was very high. The soils’ CEC was medium to high, ranging from 12.6 to 26.7 cmol·kg⁻¹. Mainly, thionic soils with total phosphorus were in the middle and poor levels.

In terms of acidity, saline soil, fluvisols, and gray soil had minor acidic reactions; thionic soils had acidic reactions. The pH_KCl fluctuated as follows: fluvisols, 4.8–5.56; saline soils, 5.04–5.9; gray soil, 5.51; and thionic soil, 3.88.

The soil texture was classified according to three levels: clay, silt, and sand. Fluvisols mainly had silt particles: clay from 21.4% to 31.4%, silt from 54.2% to 57.2%, and sand from 14.4% to 21.4%. Soils varied from sandy to silt and clay, depending on the topographical conditions, the distribution distance of the soils from the river, and the distribution upstream, midstream, or downstream of the river.

3.2 Model sensitivity

3.2.1 Sensitivity of parameters for CH₄ emissions

Figures 3 and 4 show that the temperature was a parameter that greatly affected the level of CH₄ emissions. When the temperature fluctuated by 25%, 50%, and 75% of the initial value, CH₄ emissions increased or decreased sharply, ranging from 75% to 530%. This is consistent with Li et al. research because the microbial activity involved in the production of methane increased significantly with increasing and decreasing temperature. Changes in precipitation did not have much effect on CH₄ emissions. This result is conformable with the published studies of Sass et al. , Yagi et al. (1996), Adhya et al. (2000), and Lu et al. (2000) [3,27,28,29,30].

Figure 3

Sensitivity of parameters for CH₄ emissions.

Figure 4

Sensitivity of parameters for N₂O emissions.

Among rice cultivation practices, the amount of nitrogen fertilizer (urea) and the amount of manure are the two main farming activities that have a significant impact on seasonal CH₄ emissions. When the amount of nitrogen fertilizer is reduced by 25%, 50%, and 75%, CH₄ emissions decrease in the range of 0.12–14.25%. When the amount of nitrogen fertilizer is increased by 25%, 50%, and 75%, CH₄ emissions increase in the range of 0.13–0.47%. When the amount of manure is increased from no fertilizer to 1, 1.5, and 2 tons·ha⁻¹, the CH₄ emission rate increases from 10.55% to 15.37%. Cai et al. showed that the form of nitrogen fertilizer directly or indirectly affects CH₄ and N₂O emissions. The application of the ammonium sulfate nitrogen fertilizer helps reduce CH₄ emissions but increases N₂O emissions by 25–50% [11].

When the bulk density decreases to 75%, CH₄ emissions decrease sharply (90.15%), and when the bulk density increases to 75%, CH₄ emissions increase sharply (172.05%).

The clay fraction is the next sensitive factor. When the clay fraction is decreased by 25%, 50%, and 75%, CH₄ emissions increase by 18.09%, 53.41%, and 118.12%, respectively, and when the clay fraction is increased by 25%, 50%, and 75%, CH₄ emissions decrease by 7.89%, 11.45%, and 14.77%, respectively.

The fourth sensitive factor for CH₄ emissions is the microbial activity index. When the microbial activity index decreased in the range of 25–75% compared to the initial value, CH₄ emissions decreased in the range of 34.79–86.65%. In contrast, when the microbial activity index is increased within the range of 25–75% compared to the initial value, CH₄ emissions are decreased by 33.98–89.91%, respectively. Soil microorganisms play an important role in the production of CO₂, CH₄, and N₂O in most terrestrial ecosystems. However, the level of GHG emissions between the soil and atmosphere depends on various factors that affect the development of microorganisms, such as soil oxygen content, underground water content, soil temperature, soil mineral N, organic matter and pH, or farming practices such as manure and N fertilization, plowing of plant residues, and tillage methods. Tillage can also affect the activities of microorganisms and indirectly affect CH₄ and N₂O emissions from soil.

Organic matter content in soil is a relatively sensitive factor for CH₄ emissions due to the influence of methanogenic bacteria density. Many studies have shown that organic matter plays an extremely important role in the formation and emission of CH₄ as the starting material of CH₄ formation in soil. The stronger the decomposition process of organic matter in the soil, the lower the oxidation–reduction potential, creating favorable conditions for the formation of CH₄. The richer the soil is in organic matter, the more CH4 emissions there are [31,32]. Sensitivity analysis results show that when the initial OC content is increased from 25% to 75%, CH₄ emissions increase by 18.22% and 52.99%, respectively. On the contrary, when the initial OC content decreased to 25–75%, the amount of CH₄ decreased by 18.89% and 62.69%, respectively.

Porosity and conductivity of water are also factors that affect CH₄ emissions. CH₄ emissions decrease with water conductivity or porosity. CH₄ emissions increase with increasing water conductivity or porosity. The conductivity of water is an index that describes the total dissolved ions in a solution. The movement of these ions creates an electromagnetic current, also known as ion conduction. The conductivity of water is proportional to the temperature of the water.

Compared to the analyzed factors, pH, drainage efficiency, depth of water retention layer, field capacity, and initial nitrate concentration at surface soil might have little impact on the CH₄ emissions.

Density and clay rates were also sensitive factors. Some parameters that did not affect CH₄ emissions were the wilting point in moisture soils and the salinity index. These results were similar to those reported by Li et al. (2004), Wassmann et al. (2000), and Yagi et al. Among rice cultivation methods, nitrogen (urea) and manure were the two primary farming practices that significantly impacted seasonal CH₄ emissions [5,30,34,35].

3.2.2 Sensitivity of parameters for N₂O emissions

N₂O emissions were not much influenced by meteorological and precipitation factors. while temperatures oscillated by 25%, 50%, and 75% of initial values, N₂O emissions, respectively, increased by 3.59%, 12.92%, and 24.52% and decreased by 2.02%, 13.12%, and 24.01%. The reason is that microorganisms involved in the nitrification process decreased when the temperature decreased and increased significantly when the temperature increased. This result is consistent with the studies of Li et al. [3].

In addition, N₂O emissions were impacted significantly by the clay rate and the microbial activity index. The parameters drainage capacity, water movement speed, and initial ammonium content in the topsoil at the study sites experienced a lower influence on N₂O emissions from rice soils. The wilting point in moisture soil and salinity index were the unaffected factors.

The initial nitrate concentration at the surface soil is also a highly sensitive factor for calculating N₂O emissions. When the initial nitrate concentration increased at the surface soil (0.3 mg·kg⁻¹) to 25%, 50%, and 75%, the N₂O emission level increased by 29.56%, 59.52%, and 89.74%, respectively. On the contrary, when the initial nitrate concentration decreased at the surface soil by 25%, 50%, and 75% compared to the initial value, the N₂O emission level decreased by 28.94%, 56.72%, and 81.23%, respectively.

Similar to CH₄ emissions, bulk density is a very sensitive factor for N₂O emissions. Bulk density positively affects the increase in N₂O emissions.

Organic matter content in soil is the next most sensitive factor. Organic carbon plays an important role in the formation of N₂O and N₂ in the soil by affecting the density of bacteria participating in nitrification and denitrification processes. Carbon can stimulate the growth and activity of microorganisms and provide the necessary organic carbon for reducing agents. The development of soil microorganisms increases O₂ consumption, accelerating the formation of anaerobic conditions necessary for denitrification. Zou et al. (2004) also showed that when the organic C content in the soil increased, N₂O emissions were also increased. When the initial OC content increased from 25% to 75%, N₂O emission levels increased by 75.2% and 78.3%, respectively. On the contrary, when the initial OC content is decreased, the N₂O emission level decreases [33].

Some factors that also have a significant influence on N₂O emissions are field capacity, porosity, and depth of the water retention layer. Field capacity and porosity are directly proportional to N₂O emission levels, while the depth of the water retention layer is inversely proportional to N₂O emission levels. Hua et al. showed that wet rice soil has both an aerobic surface soil layer and a deep anaerobic soil layer. It is the structure of two distinct soil layers in close proximity that creates conditions for the simultaneous occurrence of nitrification and denitrification reactions participating in the release of N₂O. However, the nitrification process only dominates in the topsoil layer, while the denitrification process dominates in the deep soil layer during periods of high soil moisture. N₂O produced from denitrification is often greater than that from nitrification [34].

Regarding fertilizers, nitrogen levels were positively linearly correlated with N₂O emissions. The oscillation of the amount of N fertilizer applied to the soil by 25%, 50%, and 75% of the initial values increased or decreased N₂O emissions by 2.29% and 10.72%, respectively. When the manure was changed from 0 to 1–2 ton·ha⁻¹, the N₂O emissions increased sharply. The trends in this research are similar to those of Li et al. and Bouwman et al. (2002) [4,6,7].

3.3 Model calibration

The model coefficients were adjusted according to the measurement results at the experimental sites. After that, the CH₄ and N₂O emissions calculated by the DNDC model were compared with the measured data in the field. Based on the CH₄ and N₂O emission values measured in the field and calculated using the model to show the point distribution, the GHG emissions values were distributed close to the 1:1 line. Figures 4 and 5 show a good correlation between the actual and simulated values: R ² in spring and summer crops reached 0.86 and 0.79, NSI reached0.82 and 0.77 (for CH₄); R ² in spring and winter crops reached 0.62 and 0.69, and NSI reached 0.69 and 0.76 (for N₂O) (Figure 6).

Figure 5

Simulated and field-measured CH₄ emissions in the (a) spring season and (b) summer season.

Figure 6

Simulated and field-measured N₂O emissions in the (a) spring season and (b) summer season.

3.4 Model validation

Similar to the data of model calibration, this study has calculated the value of the coefficient of determination R ² and the NSI to compare the trend between the calculated emission values by model (after calibration) and observed values.

The results of model verification show the following: (i) comparing CH₄ emissions observed and modeled in the spring crop, NSI = 0.79 and R ² = 0.95; in the seasonal crop, NSI = 0.88 and R ² = 0.95; and (ii) comparing the observed and modeled N₂O emissions in the spring crop, NSI = 0.79 and R ² = 0.81; in the seasonal crop, NSI = 0.73 and R ² = 0.87. Thus, a good correlation between the field-measured values and the calculated values by the model was demonstrated. The model had a relatively high correlation (shown by the R ² and NSI asymptotically up to 1). The results are presented in Figures 7 and 8.

Figure 7

Simulated and field-measured CH₄ emissions in spring and summer seasons.

Figure 8

Simulated and field-measured N₂O emissions in spring and summer seasons.

3.5 Input data for the model

3.5.1 Meteorological data

The coordinates of meteorological stations in the region are shown in Figure 2. The climate characteristics of each station are shown by daily data, and the average meteorological parameters are shown in Table S3.

3.5.2 Current land-use map

Current land types in the study area are rice and other annual, urban areas, rural areas, and others, which are shown in Figure S1. The planted area of rice and annual upland crops in the Red River Delta are shown in Table S4.

3.5.3 Soil map in the Red River Delta

From the collected soil map, filtering and establishing the thematic map were carried out; thereby, a new soil map for the Red River Delta was formed with the main soil types being fluvisols, gray soil, saline soil, thionic soil, sand soil, sloping, peat soil, erosion, chalk soil, which is shown in Figure S2. The planted area of rice and annual upland crops by main soil types of the Red River Delta are shown in Table S5.

3.5.4 Complex map of meteorology–soil–land use in the Red River Delta region

From the coordinate data of meteorological stations, the current land-use map and the distribution map of the soil types, a complex map of meteorology–soil–land use (Figure 9) was built by using the overlay analysis method.

Figure 9

Complex map of meteorology–soil–land use in the Red River Delta region.

From the coordinate information of meteorological stations, the current land-use map and the soil map, a complex map of meteorology–soil–land use (Figure 8) was built using the overlay analysis method. Each unit of the complex map already contains information about the location of the land plot, meteorology, soil type, crop status, and area of the land parcel (cells).

The total area of annual rice and upland crops on four main types of land is 1,115.41 thousand hectares, in which the total area of four main types of land for rice cultivation is 727.87 thousand ha, and the total area of four main types of land for annual upland crops is 387.54 thousand ha.

3.6 GHG emissions from rice and upland crops in the Red River Delta

3.6.1 GHG emissions from rice cultivation

Based on the results of the DNDC simulation for each unit of the complex map of meteorological–soil–land use, thematic maps showing the distribution of CH₄ emissions from rice soil (Figure 10) and N₂O emissions distribution from rice soil (Figure 11) were established. The results show a high range of CH₄ emissions from 600 to 700 kg·ha⁻¹·year⁻¹. In coastal areas, CH₄ emissions range from 755 to 859 kg·ha⁻¹·year⁻¹. The CH₄ emissions were lowest in gray soils with an average of 250.57 kg·ha⁻¹·year⁻¹ and highest in thionic soils with an average of 802.74 kg·ha⁻¹·year⁻¹. Comparing the results between the measured and the DNDC model, we found a correlation between the content of soil organic matter and the soil and emissions. For instance, gray soil has lower organic matter content and lower emissions than other soil types, while thionic soil has the highest organic matter content and higher CH₄ emissions. This trend agrees well with the measurement trend and previous studies by Pandey et al., Tariq et al., and Mai et al. [35,36,37].

Figure 10

Map of CH₄ emissions from rice soil in the Red River Delta.

Figure 11

Map of N₂O emissions from rice soil in the Red River Delta.

N₂O emissions range widely from 0.55 to 2.0 kg·ha⁻¹·year⁻¹. N₂O emissions differ by the soil type; the gray soil has the lowest N₂O emission with a value of 0.667 kg·ha⁻¹·year⁻¹, and saline soil has the highest N₂O emission with a value of 1,389 kg·ha⁻¹·year⁻¹. There is a correlation between the soil nitrogen content and N₂O emissions. For instance, gray soil had lower nitrogen content and lower N₂O emissions, while thionic soil had high nitrogen content and the highest N₂O emission. This finding agrees with field measurement results and previous studies of Arjun et al. [38], Wassmann et al. [35], and Azeem et al. [37].

3.6.2 GHG emissions from annual upland crops in the Red River Delta

Based on the results of the DNDC simulation results for each unit on the complex map of meteorological–soil–land use, thematic maps showing N₂O emissions distribution from the annual upland crops soil (Figure 12) were established.

Figure 12

Map of N₂O emissions from annual upland crop soil in the Red River Delta.

The N₂O emission values on the map range from 1.2 to 1.35 kg N₂O·ha⁻¹·year⁻¹. The highest N₂O emission is concentrated around the Ha Noi center, with emission rates higher than 1.3 kg N₂O·ha⁻¹·year⁻¹, while the lowest emission points are distributed around the Hai Phong city and Thai Binh province with about 0.7–0.9 kg N₂O·ha⁻¹·year⁻¹.

Thionic soils had the lowest N₂O emissions (average: 0.723 kg N₂O·ha⁻¹·year⁻¹); in contrast, fluvisols witnessed the highest N₂O emissions (average: 1,957 kg·ha⁻¹·year⁻¹). The values ranged from 0.716 to 2,728 kg N₂O·ha⁻¹·year⁻¹.

3.6.3 GWP

N₂O emission and GWP from upland crops were estimated using IPCC (2014) guidelines and were then presented on a map (Figure 11) with a unit of kgN₂O·ha⁻¹·year⁻¹. N₂O emissions from these range from 17,500 to 20,000 kg CO₂eq·ha⁻¹·year⁻¹. In the coastal area, GWP ranges from 18,500 to 22,842.21 kg CO₂eq·ha⁻¹·year⁻¹ (Figure 13).

Figure 13

Map of GWP from rice in Red River Delta.

For annual upland crops, the calculated results show that the area around the Thai Binh station has the lowest emission (average: 260.41 kg CO₂eq·ha⁻¹·year⁻¹), followed by the area around the Phu Lien station (average emission of 274.81 kg CO₂eq·ha⁻¹·year⁻¹). The area around the Nam Dinh station has the highest emission (average 346.09 kg CO₂eq·ha⁻¹·year⁻¹). Total CO₂eq mostly fluctuates in the range of 310–355 kg CO₂eq·ha⁻¹·year⁻¹ due to high soil N content, light mechanical composition, and a large amount of N fertilizer. The result is shown on the emission map in Figure 14.

Figure 14

Map of GWP from annual upland crops in Red River Delta.

The map of GWP (total GHG emissions in CO₂eq) from rice and upland crop soil is presented in Figure 15. The map values show that the northern parts have a lower N₂O emission (average: 4,586.33 kg CO₂eq·ha⁻¹·year⁻¹) than the Southern parts (average: 22,842.21 kg CO₂eq·ha⁻¹·year⁻¹). The reason for that is the Southern parts have alluvial, saline, and alum soils, while the area around the Tam Dao station and Ha Nam station has only two types of soil: alluvial and gray. The monitoring and analysis results at the experimental sites show that gray soil has lower emissions than thionic soil, saline soil, and fluvisol soil. Besides, meteorological factors also affect the emission level, especially temperature and humidity. Lu et al. (2000) showed that a temperature increase of 15–25°C under 45-day anaerobic conditions significantly increased the decomposition of carbon in the soil [28].

Figure 15

Map of total GHG emissions (in CO₂eq) from annual rice and upland crops in Red River Delta.

4 Conclusion

This research monitored, analyzed, and calculated GHG emissions from rice crops at experimental sites in the Red River Delta. The DNDC model used to simulate GHG emission showed a good correlation between the observed and simulated values. The complex map of meteorology–soil–land use for the Red River Delta has been established from three single maps by overlaying operation. Each unit of this complex map contains information on climatic, soil, and crops, which uses input data for the DNDC model. Simulated results showed that CH₄ emissions ranged from 72.20 to 859.16 kg·ha⁻¹·year⁻¹, in which gray soils had the lowest CH₄ emission (average: 250.57 kg·ha⁻¹·year⁻¹) and thionic soils had the highest (average: 802.74 kg·ha⁻¹·year⁻¹). Similarly, N₂O emissions ranged from 0.306 to 2,247 kg·ha⁻¹·year⁻¹, and N₂O emission was the lowest (average: 0.667 kg·ha⁻¹·year⁻¹) in gray soils and highest (average 1,389 kg·ha⁻¹·year⁻¹) in saline soils. GWP in CO₂eq values are in the range of 17,500–22,842 kg CO₂eq·ha⁻¹·year⁻¹, which tends to be higher in the coastal area. For annual crops, N₂O emission ranged from 0.716 to 2,728 kg N₂O·ha⁻¹·year⁻¹, in which N₂O emission in thionic soils are lowest with an average of 0.723 kg N₂O·ha⁻¹·year⁻¹, while N₂O emission on fluvisols is highest with an average of 1,957 kg·ha⁻¹·year⁻¹. GWP in CO₂eq area fluctuates from 310 to 355 kg CO₂eq·ha⁻¹·year⁻¹. From these simulated results, we developed a thematic map on the distribution of GHG emissions (CH₄, N₂O, GWP) for each map unit representative of meteorological, soil, and land use conditions for the whole delta.

btttrang@hunre.edu.vn

Funding information: The authors state no funding is involved.
Author contributions: The authors contributed equally to this work.
Conflict of interest: The authors state no conflict of interest.
Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Appendix 1: DNDC Climate Data

Bac Ninh Meteorological Stations

No.	Month/day/year	Tmax(°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	15.4	11.7		3	0	64
2	01/02/2010	16.3	13.3		4	0	76
3	01/03/2010	15.1	12.9	3.8	7	0	88
4	01/04/2010	12.9	10	0	7	0	79
5	01/05/2010	11.4	8.3	0	5	0	85
6	01/06/2010	13	8.6	0	6	0	80
7	01/07/2010	12.9	9.9	0	5	0	82
8	01/08/2010	13.1	10.2	0.7	5	0	92
9	01/09/2010	14.9	10.9	0	6	0	72
10	01/10/2010	14.9	11.7		5	0.1	62
11	01/11/2010	13	11.4		6	0	63
12	01/12/2010	15.1	10.4	0.1	6	0	72
13	01/13/2010	19.2	8.5	0.2	5	8.1	76
14	01/14/2010	16.7	10.6		3	0	84
15	01/15/2010	15.5	13.5	0.1	3	0	93
16	01/16/2010	17.9	14.7	0	4	0	91
17	01/17/2010	16.9	15.7	1.7	3	0	90
18	01/18/2010	15.7	13.4		5	0	78
19	01/19/2010	15.8	12.6	0	4	0	85
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	16.5	14.3	2.5	4	0	92
3644	12/23/2020	21.2	15.3	1.1	3	0	94
3645	12/24/2020	25.7	18.6	0	4	0.6	88
3646	12/25/2020	27.5	21.1	0	5	5.3	84
3647	12/26/2020	26	17.7	0	6	1	79
3648	12/27/2020	18.3	14.7		5	0	67
3649	12/28/2020	20.8	15.5		5	1.7	67
3650	12/29/2020	24	16		3	6.8	76
3651	12/30/2020	24.3	18.7		4	2.1	80
3652	12/31/2020	22.8	18.8	0	4	0	81

Ha Nam Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	15.8	12.8		4	0	58
2	01/02/2010	17.3	13.8		3	0	76
3	01/03/2010	16.7	13.1	4.3	8	0	90
4	01/04/2010	13.4	10.4	0.4	6	0	88
5	01/05/2010	11	9.3	1.2	7	0	97
6	01/06/2010	12	9	1.4	4	0	90
7	01/07/2010	12.1	10.4	0.8	5	0	92
8	01/08/2010	12.2	10.9	0.6	5	0	98
9	01/09/2010	14.5	10.9	1.7	8	0	82
10	01/10/2010	14.9	12.2		4	0	66
11	01/11/2010	13.4	11.9		7	0	66
12	01/12/2010	14.7	10.9		9	0	72
13	01/13/2010	17.7	10.6		4	5.4	79
14	01/14/2010	16	12.8		4	0	86
15	01/15/2010	15.6	14.5		5	0	93
16	01/16/2010	17.7	15	0.4	4	0	95
17	01/17/2010	17.6	15.2	9.3	6	0	97
18	01/18/2010	15.7	14		6	0	82
19	01/19/2010	16.4	13.4		4	0	88
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	17.8	16.1	1.5	4	0	96
3644	12/23/2020	21.5	16.8	0.6	3	0.1	94
3645	12/24/2020	27.5	19.6		3	3.7	88
3646	12/25/2020	27.9	20.9		4	6.7	83
3647	12/26/2020	25.4	18.6	11.7	4	0.8	90
3648	12/27/2020	18.6	15.1	0.1	6	0	74
3649	12/28/2020	21.4	15.3		4	2.2	68
3650	12/29/2020	24	16.4		4	7	78
3651	12/30/2020	24.1	18.5	0.1	4	3.4	79
3652	12/31/2020	21.5	19.5	0.3	4	0	84

Hai Duong Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s)⁻¹	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	16	11.5		3	0	64
2	01/02/2010	17.1	13.5		3	0	72
3	01/03/2010	15.9	12	1.5	6	0	90
4	01/04/2010	13.2	10	0	5	0	82
5	01/05/2010	11.2	8.5	1	4	0	95
6	01/06/2010	12.1	8.8	0.4	6	0	84
7	01/07/2010	12.6	9.8	0.1	4	0	89
8	01/08/2010	12.7	10.3	0.5	5	0	95
9	01/09/2010	14.2	10.8	0.1	5	0	74
10	01/10/2010	14.6	11.4		4	0	59
11	01/11/2010	13.1	11		5	0	66
12	01/12/2010	14.6	10.4	0.1	6	0.2	74
13	01/13/2010	19.4	9.2	0	4	8	72
14	01/14/2010	17.7	11.6		3	0	83
15	01/15/2010	16.3	14	0	4	0	92
16	01/16/2010	19.3	14.8	0	4	0	89
17	01/17/2010	18.5	16.3	0	5	0	88
18	01/18/2010	16.3	13.9		4	0	76
19	01/19/2010	16	12.2	0	3	0	87
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	16.4	14.8	2.1	5	0	98
3644	12/23/2020	22.4	16	0.1	4	0.5	96
3645	12/24/2020	26.6	19.8		6	5	92
3646	12/25/2020	27.1	20.4		6	6.5	92
3647	12/26/2020	26	19.2		6	1.2	86
3648	12/27/2020	19.3	15.7		6	0	73
3649	12/28/2020	22.3	15.3		5	2.9	75
3650	12/29/2020	24.4	16.2		4	8.1	81
3651	12/30/2020	24.8	18		4	2.7	86
3652	12/31/2020	23.4	19	0	5	0	86

Hung Yen Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	15.9	12.6		5	0	54
2	01/02/2010	17.5	13.7		5	0	68
3	01/03/2010	16.1	12.9	1.4	6	0	89
4	01/04/2010	13.2	9.6		6	0	86
5	01/05/2010	10.6	8.5	0.4	4	0	97
6	01/06/2010	12.2	8.6	0.8	5	0	84
7	01/07/2010	12	9.8	0.5	4	0	94
8	01/08/2010	12	10.5	0.1	4	0	98
9	01/09/2010	14	10.8	0.7	6	0.2	75
10	01/10/2010	15	11.9		6	0.2	56
11	01/11/2010	13.2	11.4		6	0	62
12	01/12/2010	14.5	10.5		5	0	72
13	01/13/2010	18	9.7		5	7.6	77
14	01/14/2010	17	12.8		4	0	85
15	01/15/2010	15.7	14.3	0	4	0	96
16	01/16/2010	18.2	14.9	0	3	0	94
17	01/17/2010	17.4	16.2	0	6	0	88
18	01/18/2010	16.3	13.6		5	0	76
19	01/19/2010	16	12.3		3	0	88
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	16.6	15.6	1.6	5	0	98
3644	12/23/2020	21	16.3	0.4	5	0	96
3645	12/24/2020	26.6	19.7		5	3.2	94
3646	12/25/2020	27.4	21.3		7	8.4	84
3647	12/26/2020	26	19.2		5	2	86
3648	12/27/2020	19.2	15.7		6	0	73
3649	12/28/2020	21.2	15.4		5	2.2	75
3650	12/29/2020	24.1	16.3		5	7.6	82
3651	12/30/2020	23.3	18.5		5	2.2	86
3652	12/31/2020	21.2	19.2	0.1	5	0	92

Lang Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	15.6	12.8		3	0	56
2	01/02/2010	16.2	14		2	0	69
3	01/03/2010	15.3	13.4	2.7	5	0	91
4	01/04/2010	13.6	10.5	0	3	0	85
5	01/05/2010	10.7	8.7	0.6	3	0	93
6	01/06/2010	12.9	9	0.9	4	0	83
7	01/07/2010	12.5	10.1	0.3	3	0	87
8	01/08/2010	12.4	10.5	1.1	3	0	92
9	01/09/2010	15.2	11.2	0.2	6	0	76
10	01/10/2010	16	12.5		2	0.4	58
11	01/11/2010	14	11.9		5	0	61
12	01/12/2010	15.4	11.3	0.1	5	0	70
13	01/13/2010	18.1	9.8	0	3	6.2	73
14	01/14/2010	16.6	12.9		2	0	83
15	01/15/2010	15.5	13.9	0.6	2	0	95
16	01/16/2010	18	14.8	0.6	3	0	93
17	01/17/2010	17.5	15.7	4.8	6	0	91
18	01/18/2010	15.7	13.7	0	4	0	78
19	01/19/2010	16.9	13.1	0	4	0	84
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	17	15.4	1.3	5	0	90
3644	12/23/2020	20.7	15.5	1.1	2	0	94
3645	12/24/2020	26.7	19.5		3	2.1	88
3646	12/25/2020	28.3	21.4		5	6.8	78
3647	12/26/2020	26.4	19.4	0.1	6	0	80
3648	12/27/2020	19.4	16	0.2	4	0	64
3649	12/28/2020	19.5	16		3	0	64
3650	12/29/2020	23.5	16.4		3	4.8	74
3651	12/30/2020	23.6	18.2		3	1.7	76
3652	12/31/2020	22.9	20.1	0	2	0	77

Nam Đinh Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	16.3	12.5	—	4	0	62
2	01/02/2010	18.2	14.2	—	2	0	71
3	01/03/2010	16.6	12.3	3.10	5	0	92
4	01/04/2010	13	9.8	0.20	4	0	89
5	01/05/2010	10.6	9	0.90	4	0	98
6	01/06/2010	12	8.5	1.20	6	0	89
7	01/07/2010	11.8	10	1.00	4	0	97
8	01/08/2010	12.3	10.6	0.80	5	0	99
9	01/09/2010	14	10.6	0.60	6	0	82
10	01/10/2010	14.5	11.8	—	4	0.2	66
11	01/11/2010	13	11.4	—	5	0	70
12	01/12/2010	14.3	9.5	—	6	0	74
13	01/13/2010	18.6	9.4	—	5	7.1	80
14	01/14/2010	17.3	12.7	—	3	0	87
15	01/15/2010	16.1	14.4	0.10	4	0	94
16	01/16/2010	18.5	15	0.10	3	0	92
17	01/17/2010	18.4	16.2	0.20	4	0	93
18	01/18/2010	16.2	13.7	—	4	0	80
19	01/19/2010	16	12.7	—	3	0	90
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	17.6	16.1	0.2	4	0	97
3644	12/23/2020	26.5	16.7	0.1	4	2.3	89
3645	12/24/2020	27.7	19.9	0	5	4.5	89
3646	12/25/2020	27.6	21.1		5	8.6	86
3647	12/26/2020	26.5	20.6	0	5	1.6	86
3648	12/27/2020	21.6	16.3	0.1	4	0	70
3649	12/28/2020	22.5	15.1		4	1.5	69
3650	12/29/2020	25.7	16.1		4	8.5	78
3651	12/30/2020	24.3	18.6		4	1.3	81
3652	12/31/2020	21.5	19.1	0.1	4	0	89

Ninh Binh Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	16.2	12.6		3	0	54
2	01/02/2010	18.2	14.2		3	0	70
3	01/03/2010	16.8	12.5	3.7	4	0	91
4	01/04/2010	13.4	10.5	0.4	5	0	89
5	01/05/2010	11	9.6	0.5	6	0	96
6	01/06/2010	12	9.1	0.8	5	0	89
7	01/07/2010	12.4	10.1	0.2	4	0	92
8	01/08/2010	13	11.1	0.5	4	0	96
9	01/09/2010	14.3	10.7	0.6	6	0	81
10	01/10/2010	14.9	12.1		5	0	64
11	01/11/2010	13.4	11.6		5	0	65
12	01/12/2010	14.6	10.7		5	0	69
13	01/13/2010	18	10.9		3	4.7	76
14	01/14/2010	17.3	13.3		4	0	82
15	01/15/2010	16.1	14.5	0.4	3	0	92
16	01/16/2010	18.3	15	0.1	4	0	93
17	01/17/2010	17.8	15.7	2.4	3	0	95
18	01/18/2010	16.3	14.1		4	0	80
19	01/19/2010	17.3	13.6		3	0	86
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	17.6	16.7	0.2	3	0	97
3644	12/23/2020	27.4	17	0	5	5.6	88
3645	12/24/2020	27.6	19.7		5	5.2	88
3646	12/25/2020	27.4	20.6		6	9.2	84
3647	12/26/2020	25	21	0.2	4	0.7	89
3648	12/27/2020	21.6	15.4	1.4	6	0	79
3649	12/28/2020	22.6	15.5		4	2.9	70
3650	12/29/2020	25.5	16.7		4	7	76
3651	12/30/2020	24.1	18.8	0	4	1.2	79
3652	12/31/2020	21.3	19.5	0.6	4	0	92

Phu Lien Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s)⁻¹	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	15.9	10.9		3	0	67
2	01/02/2010	17	13		4	0	75
3	01/03/2010	15.6	12.5	5.9	5	0	93
4	01/04/2010	13.7	10.4	0.5	4	0	91
5	01/05/2010	12.1	10.4	1.2	4	0	98
6	01/06/2010	11.6	10	1.4	5	0	96
7	01/07/2010	12.6	10.5	0.5	4	0	98
8	01/08/2010	14.2	11.5	0.9	5	0	98
9	01/09/2010	13.3	10.6	0.2	5	0	86
10	01/10/2010	14.4	10.8		3	0	76
11	01/11/2010	13	10.6		4	0	78
12	01/12/2010	14.5	9.2	0	5	0	84
13	01/13/2010	18.3	8.9		5	9	78
14	01/14/2010	18.4	12.5		5	0	80
15	01/15/2010	16.2	14		5	0	91
16	01/16/2010	20	14.5		4	0	90
17	01/17/2010	21.1	16.8		5	0	86
18	01/18/2010	16.8	14.1		5	0	81
19	01/19/2010	16.2	13.4		4	0	90
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	18	16.5	0.1	3	0	99
3644	12/23/2020	24	17.3	0.2	4	2	96
3645	12/24/2020	25.5	19.2		4	3	94
3646	12/25/2020	26.2	19.8	0	4	5.7	94
3647	12/26/2020	25.5	19.4		5	4.1	92
3648	12/27/2020	19.4	15.7	0	5	0.1	82
3649	12/28/2020	24	15.2		4	4.5	80
3650	12/29/2020	25.3	15.5		6	6.2	83
3651	12/30/2020	25	17.7		3	3.4	89
3652	12/31/2020	21.6	18.6	0.2	5	0	90

Son Tay Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity °C)
1	01/01/2010	15	12		1	0	69
2	01/02/2010	15.5	12.7		2	0	80
3	01/03/2010	14.6	13	1.4	1	0	92
4	01/04/2010	13.6	10.3		1	0	86
5	01/05/2010	10.7	9.3	2.2	1	0	98
6	01/06/2010	12.5	9.1	1.5	1	0	93
7	01/07/2010	12.9	10.4	0.2	1	0	91
8	01/08/2010	12.4	10.5	2.2	2	0	96
9	01/09/2010	13.6	11	1.3	2	0	92
10	01/10/2010	15.5	12		1	0	77
11	01/11/2010	13.4	11.5		1	0	74
12	01/12/2010	14.9	10.6	0.5	2	0	84
13	01/13/2010	17	10		4	0.2	86
14	01/14/2010	15.7	13		1	0	87
15	01/15/2010	16	13.4	0.5	1	0	95
16	01/16/2010	17.5	14.6	0.4	1	0	97
17	01/17/2010	17	15.6	2.4	4	0	93
18	01/18/2010	15.8	13.8		2	0	87
19	01/19/2010	19.1	13		1	0	84
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	17.1	15.9	2	2	0	94
3644	12/23/2020	20.2	15.5	0.1	2	0	94
3645	12/24/2020	24	18.8	0.1	2	0.9	90
3646	12/25/2020	27.5	20.9		3	4.4	82
3647	12/26/2020	25.5	18.6	2.3	5	1.4	86
3648	12/27/2020	18.6	15	0.2	3	0	73
3649	12/28/2020	18.4	15.3		2	0	74
3650	12/29/2020	22.6	15		2	6.1	76
3651	12/30/2020	23.5	17.7		3	2.2	80
3652	12/31/2020	22.1	19.4	0.1	2	0	84

Tam Đao Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	7.6	5.2		4	0	80
2	01/02/2010	11	7.2	0.3	4	0	99
3	01/03/2010	12.4	7.1	2.7	11	0	100
4	01/04/2010	8.5	6.5	0.6	6	0	100
5	01/05/2010	8.7	7.6	0.9	4	0	100
6	01/06/2010	8.4	6.2	1.4	2	0	100
7	01/07/2010	9	6.7	0.5	4	0	100
8	01/08/2010	9.7	7.5	1.5	4	0	100
9	01/09/2010	7.6	4.5	1.5	12	0	92
10	01/10/2010	7.7	5.4	0.1	11	0	72
11	01/11/2010	6.2	5		5	0	86
12	01/12/2010	8.4	4.1	0.5	11	0.2	93
13	01/13/2010	9.5	5.7	0.1	3	2.5	97
14	01/14/2010	11.2	7.9		6	0	96
15	01/15/2010	13	10.3	1.5	7	0	100
16	01/16/2010	13.3	12	0.4	7	0	100
17	01/17/2010	13.3	9	1.7	11	0	100
18	01/18/2010	9.8	8	0.8	11	0	100
19	01/19/2010	11.4	8.7	0.1	6	0	99
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	15.6	14.6	2.4	4	0	96
3644	12/23/2020	16	14.6	3.4	4	0	97
3645	12/24/2020	17.8	15.6	0.2	5	1.2	98
3646	12/25/2020	18.1	16.7	0.7	6	0.7	97
3647	12/26/2020	18	13.4	4.1	11	0	95
3648	12/27/2020	13.4	9.7	0.3	11	0	88
3649	12/28/2020	13.8	9.6	0.1	4	0	93
3650	12/29/2020	17	12.8		4	2.9	93
3651	12/30/2020	16.4	13.9		4	0	96
3652	12/31/2020	16.7	15.3		8	0	86

Thai Binh Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity (°C)
1	01/01/2010	16.6	11.6		4	0	66
2	01/02/2010	18.1	13.6		3	0	75
3	01/03/2010	17	12.2	0.6	8	0	92
4	01/04/2010	13.7	10	0	5	0	90
5	01/05/2010	11.1	8.8	0.1	5	0	98
6	01/06/2010	11.9	8.8	1.3	7	0	90
7	01/07/2010	12	10.4	0.7	5	0	96
8	01/08/2010	13.2	10.7	0.5	5	0	96
9	01/09/2010	13	10.5	0.6	7	0	86
10	01/10/2010	14.7	11.4		5	0	67
11	01/11/2010	13	10.9		7	0	72
12	01/12/2010	14.2	8.9		9	0	80
13	01/13/2010	18.7	9.7		6	8	76
14	01/14/2010	18	12		3	0	86
15	01/15/2010	16.2	14.5	0	5	0	92
16	01/16/2010	19.2	15.2	0	5	0	92
17	01/17/2010	20	16.7	1	6	0	91
18	01/18/2010	17	13.5		5	0	82
19	01/19/2010	15.9	13		5	0	93
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	18.1	16.3	0.1	3	0	96
3644	12/23/2020	26.7	17.4		4	4.4	90
3645	12/24/2020	25.5	20		4	1.8	91
3646	12/25/2020	27.2	20.6		9	7.3	86
3647	12/26/2020	25.4	19.8	0	8	3.3	87
3648	12/27/2020	21.2	16.1		7	0	67
3649	12/28/2020	22.8	14.8		6	2.3	70
3650	12/29/2020	26.1	15.7		5	7	76
3651	12/30/2020	25	17.6		5	1.5	84
3652	12/31/2020	21.6	19		3	0	88

Vinh Yen Meteorological Stations

No.	Month/day/year	Tmax (°C)	Tmin (°C)	Rainfall (mm)	Wind speed (m·s⁻¹)	Sunshine hours (h)	Humidity (%)
1	01/01/2010	15.1	12.5		3	0	64
2	01/02/2010	15.8	13.4		3	0	75
3	01/03/2010	15.2	13.5	2.9	4	0	90
4	01/04/2010	14	11		5	0	82
5	01/05/2010	12.2	9.2	0.5	5	0	90
6	01/06/2010	12.2	9.7	1	4	0	92
7	01/07/2010	13	11	0.2	3	0	86
8	01/08/2010	13	11.2	2.5	3	0	95
9	01/09/2010	14	11.9	1.8	5	0	88
10	01/10/2010	15.8	12.4		3	0	74
11	01/11/2010	14	12		4	0	66
12	01/12/2010	15.3	11.2	0.1	5	0	82
13	01/13/2010	17.5	9.4	0.1	5	4	82
14	01/14/2010	16.2	11		3	0	84
15	01/15/2010	15.6	13.8	0.4	3	0	95
16	01/16/2010	17.9	15	1	3	0	92
17	01/17/2010	17.3	16.2	4.6	6	0	89
18	01/18/2010	15.1	12.5		3	0	64
19	01/19/2010	15.8	13.4		3	0	75
…	…	…	…	…	…	…	…
…	…	…	…	…	…	…	…
3643	12/22/2020	17.8	15.6	3	3	0	92
3644	12/23/2020	21.3	16.1	0.4	3	0	94
3645	12/24/2020	25	19.2	0.2	2	0.5	92
3646	12/25/2020	28.4	21.3		5	3.3	84
3647	12/26/2020	25.4	19.6	0.7	7	1.3	80
3648	12/27/2020	19.6	16.4		6	0	78
3649	12/28/2020	18.6	16		3	0	80
3650	12/29/2020	23.4	15.6		4	5.8	82
3651	12/30/2020	23.6	17.8		4	2.8	86
3652	12/31/2020	22.9	19.5		3	0	88

Appendix 2: Details of Meteorological Stations

No.	Name of meteorological stations	Coordinates
No.	Name of meteorological stations	X	Y
1	Son Tay	21°08′N	105°30′E
2	Lang	21°01′ N	105°48′ E
3	Ba Vi	21°06′ N	105°32′ E
4	Ha Đong	20°58′ N	105°46′ E
5	Vinh Yen	21°19′ N	105°36′ E
6	Tam Đao	21°28′ N	105°39′ E
7	Bac Ninh	21°11′ N	106°05′ E
8	Hai Duong	20°56′ N	106°18′ E
9	Chi Linh	21°07′ N	106°28′ E
10	Phu Lien	20°48′ N	106°38′ E
11	Hon Dau	20°70′ N	106°78′ E
12	Hung Yen	20°39′ N	106°03′ E
13	Thai Binh	20°27′ N	106°21′ E
14	Ba Lat	20°30′ N	106°55′ E
15	Ha Nam	20°33′ N	105°55′ E
16	Nam Đinh	20°24′ N	106°09′ E
17	Van Ly	20°15′ N	106°30′ E
18	Ninh Binh	20°14′ N	105°58′ E
19	Nho Quan	20°19′ N	105°45′ E
20	Cuc Phuong	20°14′ N	105°43′ E
21	Uong Bi	21°04′ N	106°75′ E
22	Bac Giang	21°27′ N	106°18′ E
23	Hiep Hoa	21°37′ N	105°97′ E
24	Viet Tri	21°18′ N	105°25′ E
25	Phu Ho	21°27′ N	105°14′ E
26	Kim Boi	20°70′ N	105°52′ E
27	Chi Ne	20°49′ N	105°67′ E
28	Lac Son	20°43′ N	105°39′ E

Appendix 3: Input Data Processing and Steps of Model Running

A. Climate Data

Data for the years from 2010 to 2020 at 28 traditional meteorological stations in the network of national monitoring of Viet Nam Meteorological and Hydrological Administration. These stations ensure measurements on a unified basis, serve for basic investigation. Collected data includes station coordinates, day’s maximum temperature (°C), day's minimum temperature (°C), total daily sunshine hours (h), wind direction and speed (m·s⁻¹), daily rainfall (mm), and humidity (%) (from Vietnam Meteorological and Hydrological Aministration). The sunshine hours per day is calculated and converted into units of solar radiation intensity (unit: Mj·m⁻²·day⁻¹). Meteorological data is processed, aggregated by year, each meteorological region and saved as *.txt. The data are sorted by day, in order from left to right as follows: day of year, maximum temperature (Tmax), minimum temperature (Tmin), precipitation (Rainfall), wind speed (Vmax), solar radiation intensity (Rn), humidity (Huminity). (Figure A1)

Figure A1

Example of climate data that is one of input data of DNDC model.

B. Soil Data

Soil data is collected and entered directly into the model, including: Bulk density, pH, OC, drainage efficiency, depth of water retention layer, field capacity, wilting point, clay fraction, conductivity, porosity, microbial activity index, soil salinity index, rainwater collection index, initial nitrate concentration at surface soil, initial ammonium concentration at surface soil, N, P₂O₅, K₂O (%), etc. (Figure A2)

Figure A2

Example of soil data that is one of input data of DNDC model.

C. Crop Data

Crop data is collected and entered directly into the model, including: seeds, physiological and biochemical characteristics; seasonal calendar; farming techniques; types and characteristics of fertilizers, etc. (Figures A3, A4, A5, A6)

Figure A3

Example of crop data that is one of input data of DNDC model.

Figure A4

Example of fertilization that is one of input data of DNDC mode.l.

Figure A5

Example of irrigation that is one of input data of DNDC model.

Figure A6

Example of flooding that is one of input data of DNDC model.

D. Steps of Model Running

Running DNDC model includes 04 main steps:

− Step 1: Enter the input parameters, including:

+ Import meteorological data: Go to "Climate" to create a file name, enter the coordinates of the study area and select the meteorological file of the study area;

+ Enter soil data: Go to "Soil" to select soil type, pH, SOC, NO₃, NH₄ ⁺, soil salinity, and some other parameters;

+ Input crop data: Go to “Croping” to enter data on tillage, crop, watering, fertilizer, flooding, etc. (Figure A7)

Figure A7

Example of step 1 – Importing meteorological data.

− Step 2: Save the model running file as *, dnd. (Figure A8)

Figure A8

Example of step 2 – Save the running file.

− Step 3: Run the model. (Figure A9)

Figure A9

Example of step 3 – Run the model.

− Step 4: View model results. (Figures A10 and A11)

Figure A10

Example of step 4 – View results.

Figure A11

Example of step 4 – View results.

Appendix 4 Conducting Research Pictures

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Received: 2023-09-19

Accepted: 2024-01-15

Published Online: 2024-02-27

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/gps-2023-0187

Keywords for this article

DNDC model; greenhouse gas; emission; the Red River Delta of Vietnam

Creative Commons

BY 4.0

Estimation of greenhouse gas emissions from rice and annual upland crops in Red River Delta of Vietnam using the denitrification–decomposition model

Article

Abstract

1 Introduction

2 Methodology

2.1 Research framework

2.2 Methodology

2.2.1 Data collection

2.2.2 Gas sampling and analysis

2.2.2.1 Rice varieties

2.2.2.2 Experimental layout

2.2.2.3 Gas sampling

2.2.2.4 Analysis of gas samples

2.2.3 Soil analysis

2.2.4 Modeling

2.2.4.1 Model description

2.2.4.2 Sensitivity assessment of the DNDC model for the calculation of emissions

2.2.4.3 Calibration of the DNDC model for emission calculation

2.2.4.4 Model verification

2.2.5 Spatial analysis

2.2.6 Statistical analysis

3 Results and discussion

3.1 Physical and chemical properties of soils at experimental sites

3.2 Model sensitivity

3.2.1 Sensitivity of parameters for CH4 emissions

3.2.2 Sensitivity of parameters for N2O emissions

3.3 Model calibration

3.4 Model validation

3.5 Input data for the model

3.5.1 Meteorological data

3.5.2 Current land-use map

3.5.3 Soil map in the Red River Delta

3.5.4 Complex map of meteorology–soil–land use in the Red River Delta region

3.6 GHG emissions from rice and upland crops in the Red River Delta

3.6.1 GHG emissions from rice cultivation

3.6.2 GHG emissions from annual upland crops in the Red River Delta

3.6.3 GWP

4 Conclusion

Appendix 1: DNDC Climate Data

Appendix 2: Details of Meteorological Stations

Appendix 3: Input Data Processing and Steps of Model Running

Appendix 4 Conducting Research Pictures

References

Supplementary Material

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3.2.1 Sensitivity of parameters for CH₄ emissions

3.2.2 Sensitivity of parameters for N₂O emissions