Nghiên cứu xây dựng phương pháp luận và tính toán dấu vết các bon cho sản phẩm lúa gạo tại vùng đồng bằng sông hồng tt tiếng anh

MINISTRY OF NATURAL RESOURCES AND
ENVIRONMENT

INSTITUTE OF METEOROLOGY, HYDROLOGY AND
CLIMATE CHANGE

DAO MINH TRANG

DEVELOPMENT OF A METHEODOLOGY FOR
CALCULATION OF CARBON FOOTPRINT OF
RICE PRODUCTS IN THE RED RIVER DELTA

Major: Climate Change
Code: 9440221

SUMMARY OF THE THESIS
CLIMATE CHANGE

Hanoi, 2019


The Thesis was completed at:
Vietnam Institute of Meteorology, Hydrology and Climate
Change

Supervisor: Assoc. Prof. Dr. Huynh Thi Lan Hương
Assoc. Prof. Dr. Mai Van Trinh
Reviewer 1:
Reviewer 2:
Reviewer 3:

The Thesis will be defended at Viet Nam Institute of
Meteorology, Hydrology and Climate Change at:

…hour….date…month…year
The dissertation is available at:
- National Library of Viet Nam
- Library of Viet Nam Institute of Meteorology, Hydroogy and
Climate Change


3

INTRODUCTION
1. RATIONALE
According to Viet Nam’s greenhouse gas (GHG) inventory result,
GHG emissions from agriculture has accounted for a large
proportion, of which methane emissions from rice cultivation
constituting 48-62%. Recently, Viet Nam has become one of major
rice exporting countries in the world. Increasing the export value of
the Vietnamese rice, including labeling low-carbon footprint, is
important, so that trade barriers can be overcomed if any in future.
The Red River Delta (RRD) is one of the two major regions for
rice production in Viet Nam. The implementation of the research on
“Development of a metheodology for calculation of carbon
footprint of rice products in the Red River Delta” is the basis for
the development of methodology and calculation of rice carbon
footprint in other regions. Based on the cresults, this study has
identified activities with high mitigation potential and proposed
prioritized mitigation options.
2. OBJECTIVES

(1) To develop a methodology for calculation of carbon footprint
of rice products in the Red River Delta;
(2) To calculate the rice carbon footprint for the pilot area of Phu
Luong commune, Dong Hung district, Thai Binh province;
(3) To propose mitigation options to reduce GHG emissions from
rice production in the study area.
3. SUBJECT AND SCOPE


4

The study subject was GHG emissions during the rice life cycle in
the spring and summer seasons according to three cultivation
methods: convention (CM), wide-narrow row (WNR) and system of
rice intensification (SRI). The pilot area was Phu Luong commune,
Dong Hung district, Thai Binh province. The study year was 2017.
The thesis calculated key GHG emissions during the rice life
cycle. In the up-stream processes, GHG sources included: electricity
generation for machinery operation and production of fertilizer, lime
and pesticide. In the "rice production" stage, GHG sources included:
methane emissions from rice cultivation; CO2 emissions from the
application of urea and NPK fertilizers; N2O emissions from
agricultural soils; lime application and diesel combustion for on-farm
operation of agricultural machinary. In the "post-farm" stage, GHG
emissions from transporting rice from fields to houses and on-field
burning of rice stubble and straw were taken into account.
4. ARGUMENTS OF THE DISSERTATION
The incorporation of the Life Cycle Assessment (LCA) of
the International Organization for Standardization (ISO) and the
2006 Guidelines for National Greenhouse Gas Inventories (GL 2006)

of the Intergovernmental Panel on Climate Change (IPCC) is a
suitable methodology for calculating carbon footprint of rice
products during its life cycle in the Red River Delta.
CH4 emissions from rice cultivation accounts for the largest
proportion in the carbon footprint; followed by that from electricity
generation and energy use for operation of agricultural machinery.


5

The expansion of WNR cultivation method in Phu Luong
commune is a potential mitigation option that also brings back
economic benefits, and hence should be prioritized.
5. SCIENTIFIC AND PRATICAL SIGNIFICANCE OF THE
DISSERTATION
This study has developed a methodology for calculating the rice
carbon footprint in the RRD based on which future studies can be
adjusted and developed to calculate for rice carbon footprint in other
rice production areas, such as the Mekong River Delta.
The identification of prioritized mitigation options will support
for the implementation of Viet Nam's NDC and contribute to
removing trade barriers if any in the future.
6. CONTRIBUTION OF THE DISSERTATION
▪ Develop a methodology for calculating carbon footprint of rice
products in the Red River Delta;
▪ Apply methodology for pilot calculation for the research area;
▪ Propose prioritized mitigation options during the rice life cycle,
contributing to the review and updatte of the NDC.
7. STRUCTURE OF THE DISSERTATION
In addition to Introduction and Conclusion, the thesis consists of

03 chapters. Chapter 1 presents an overview of studies on rice carbon
footprint and the research area. Chapter 2 presents the content and
research method. Chapter 3 presents the results of the research and
discussion.

The

appendix

includes

sample

questionnaires,

intermediate calculation results and images of the study area.


6

CHAPTER 1.
OVERVIEW OF STUDIES ON RICE CARBON FOOTPRINT
AND RESEARCH AREA
1.1. Overview of product carbon footprint

1.1.1. Definition of “carbon footprint”
“The quantity of GHGs expressed in terms of CO2-equivalent
(CO2e), emitted into the atmosphere by an individual, organization,
process, product, or event from within a specified boundary” [97].

1.1.2. Scope of product carbon footprint
The scope of carbon footprint includes: Tier 1 (on-site emissions),
Tier 2 (emissions embodied in purchased energy) and Tier 3 (all
other indirect emissions not covered under Tier 2) [30], [33], [106].

1.1.3. Guidelines on calculating product carbon footprint
1) Calculating product carbon footprint
One of the guidelines for calculating GHG emissions using the
activity-based approach is the IPCC’s GL 2006 [51]. The three
universally accepted PCF calculation guidelines are: Publicly
Available Specification (PAS) 2050 of the British Standards Institute
(BSI), the GHG Protocol of the World Resources Institute and the
World

Business

Council

for

Sustainable

Development

(WRI/WBCSD) [106] and ISO 14067.
2) Calculating carbon footprint of agricultural products
Guidelines for calculating carbon footprint of agricultural
products include: WRI/WBCSD’s GHG Protocol Agriculture
Guidance [95]; PAS 2050-1:2012 - Assessment of life cycle GHG
emissions from horticultural products of BSI [32] and FAO [47].

7
1.2. Overview of studies on rice carbon footprint

1.2.1. Sources of greenhouse gases emissions during rice life cycle
❖ Up-stream processes: Production of input materials (electricity,
fertilizer, lime and pesticides); Production, amortisation and
maintenance of agricultural machinery and equipment.
❖ Rice production: Diesel combustion for on-farm operation of
agricultral machinery; CO2 emissions from groundwater
extraction for irrigation; Methane emissions from rice
cultivation; N2O emissions from soils; GHG emissions from
lime application; CO2 emissions from urea application.
❖ Post-farm stage: Transporting rice from fields to houses; Onfarm burning of rice stubble and straw after harvest.

1.2.2. International studies on rice carbon footprint
A variety of studies applied the LCA of ISO such as Blengini and
Busto [28], Gan et al. [56], [57], Kasmaprapruet et al. [80], Xu et al.
[109]. Some studies combined LCA and the IPCC’s GL, such as
Farag et al. [48], Yodkhum and Sampattagul [110]. Few studies
calculated GHG emissions from the production of input materials.

1.2.3. Studies in Viet Nam on rice carbon footprint
Viet Nam has applied the IPCC’s GL to calculate the national
GHG emissions from agriculture in 1994, 2000, 2005, 2010, 2013
and 2014. Several studies used the LCA to assess the impact of rice
cultivation techniques such as Le Thanh Phong and Pham Thanh Loi
[15] and Le Thanh Phong and Ha Minh Tam [14]. Some other
studies used empirical methods to measure GHG emissions from rice

cultivation and agricultural soils such as: Nguyen Viet Anh and


8

Nguyen Van Tinh (23), Nguyen Huu Thanh et al. [17] and the
Institute for Agricultural Environment (IEA) [65].

1.2.4. Existing gaps in current research
Studies on rice carbon footprint are limited and didnot adequately
calculated GHG emissions during the rice life cycle.
1.3. Overview of the research area
Phu Luong Commune is located in the North of Dong Hung
district in Thai Binh province, with the area of 298 hectares for rice
cultivation, of which the cultivation area according to the
conventional method is 148 hectares, according to the WNR method
is 90 hectares and the SRI method is 60 hectares. The activities
during the rice life cycle in Phu Luong commune are typical for Thai
Binh province in particular and the Red River Delta in general.
1.4. Conclusion of Chapter 1
In Viet Nam, very few studies on rice carbon footprint were
conducted and most of them have not yet fully calculated the GHG
GHG emissions during the rice life cycle. The main methodology
used is LCA of ISO. Very few studies have calculated GHG
emissions from the production of input materials for rice production.


9

CHAPTER 2. RESEARCH CONTENT AND METHODS

2.1. Research content
▪

Overview of studies on rice carbon footprint on the world and in
Vietnam;

▪

Develop a methodology for calculating carbon footprint of rice
products in the Red River Delta;

▪

Calculate rice carbon footprint of the pilot area of Phu Luong
commune, Dong Hung district, Thai Binh province;

▪

Propose prioritized options to mitigate GHG emissions from
activities during the rice life cycle in the study area.

2.2. Research methods

2.2.1. Method of data collection and synsthesis
1) Method of collecting secondary data: is implemented on the basis
of inheriting, analyzing and synthesizing relevant data.
2) Method of field survey: The minimum sample size of “30” is
appropriate so the number of sample in the thesis is 30 farmer
households according to each cultivation method.
3) Method of expert consultation: interview managers at Phu Luong

Cooperative’s managers and experts in energy, agriculture and
transportation sectors.

2.2.2. Method of processing data
1) Calculate sample statistical features and estimate for population:
to exclude samples whose performance figures deviate significantly
from the average.
2) Use functions and tools in Excel to calculate, include: average
function, minimum value, maximum value, variance and sum.


10
3) IPCC’s GL 2006 was applied to calculate GHG emissions from
key activities during rice life cycle.
4) Process-based LCA approach of ISO was applied.
5) Matrix analysis method: was used to rank the priority of
mitigation options, based on the following criteria: mitigation
potential, mitigation cost, technology availability and co-benefit.
2.3. Conclusion of Chapter 2
In order to implement the four research contents, the thesis applied
the method of data collection and synthesis (through secondary data
collection, field survey and expert consultation) and the methods of
processing data, including calculating sample statistical features,
applying Excel functions and tools, using the GL 2006 of IPCC, the
LCA method of ISO and matrix analysis.
CHAPTER 3. RESULTS AND DISCUSSION
3.1. Methodology for calculating the carbon footprint of rice
products in the Red River Delta
The study applied the process-based LCA approach in
cooperation with the GL 2006 of IPCC [68], FAO [47] and COPERT

4 of EURO [86]. The calculation of GHG emissions from key
activities is based on equations in GL 2006 of IPCC, FAO (for
fertilizer, lime and pesticide production) and COPERT 4 of EURO 2
(transporting rice from fields to houses by motobikes). The
calculation process consists of 5 steps: (i) Selection of GHGs; (ii)
Determination of the scope for calculation; (iii) Data collection and


11

processing; (iv) Calculation of rice carbon footprint and (v)
Uncertainty analysis.
❖ Electricity generation for operation of agricultural machinery
and equipment:
Emissions𝐺𝐻𝐺 =Electricity consumption *EF electricity grid (Equa. 1)
where:
CO2 emission: The amount of CO2 emissions from electricity
generation (tCO2e);
Electricity consumption: The amount of electricity used in the
operation of agricultural machinery (MWh);
❖ Fertilizer production:
Emissions𝐺𝐻𝐺 = Application rate × EFfertilizer (Equa. 2)
in which:
Application rate: The amount of fertilizer applied (kg/ha);
EF

fertilizer:

Emission factor of fertilizer production by type of

fertilizer (kg CO2e/ kg of fertilizer).
❖ Lime production: Tier-1method of the GL 2006 was applied. The
GL 2006 default emission factor of lime production is 0.75 tonnes
of CO2 /ton of lime.
❖ Pesticide production
Emissions of CO2 e = Application rate × EFpesticide (Equa. 4)
where:
Application rate: Rate of pesticide application (kg a.i./ha);
EF pesticide: Emission factor of pesticide production (kg CO2e/kg a.i.).
❖ Methane emissions from rice cultivation


12
CH4 rice = ∑i,j,k(EFi,j,k * ti,j,k * Ai,j,k * 10-6 ) (Equa. 5)
where:
CH4 rice: Annual methane emissions (Gg CH4/year)
EFijk: Daily emission factor under i, j, and k conditions (kg CH4/m2/day)
Tijk: Cultivation period of rice under i, j, and k conditions (days)
Aijk: Annual harvested area under i, j, and k conditions (ha/year)
i, j, and k: different ecosystems, water regimes, type and amount
of organic amendments, and other conditions under which CH4
emissions from rice may vary.
k: Organic amount, classification of how many households apply
high, medium and low organic rates.
❖ Diesel combustion for the on-field operation of machinery
EmissionsGHG, fuel =Fuel consumption

×EF GHG, fuel (Equa. 6)

where:

Emissions GHG, fuel: GHG emissions by type of fuel
EFGHG, fuel: Default emission factor of a type of GHG by fuel type
(kg of gas/TJ). For CO2, it also includes a carbon oxidation factor
which is assumed to be 1.
According to FAO [46], the equations from Nemecek and
Kagi [92] were applied to calculate the amount of diesel:
Amount of diesel used a,b =fa ×ta ×MFCa,b × ddiesel (Equa. 6.1)
where:
Diesel-use

a,b

: the amount of diesel used for the machinary

operation (kg/ha);
fa : the frequency of the activity;


13

ta: the time required to do activity a on one hectare (hour/ha);
MFCa,b: Mean Fuel Consumption, the characteristic fuel
consumption for activity a with tractor b (liters/hour);
ddiesel : the density of diesel (kg per liter).
Fuel consumption=Diesel-use ×Calorific value diesel (Equa. 6.2)
where:
Fuel consumption: The amount of fuel combustion (MJ/ha);
Diesel-use a,b : The amount of diesel used for machinery operation (kg/ha);
Calorific value diesel : Calorific value of diesel (MJ/kg);
❖ Lime application

Emissions𝐶𝑂2−𝐶 = (M𝑙𝑖𝑚𝑒 *EFlime )+(Mdolomite *EFdolomite ) (Equa. 7)
where:
EmissionsCO2–C: The amount of C emissions from lime application
(tonnes of C);
M: The amount of lime or dolomite applied (ton);
EF: Emision factor (tC/ton of lime or dolomite).
❖ CO2 emissions from urea application
CO2 -C emissions = M ×EF (Equa. 8)
where:
CO2 – C emissions: Emissions of carbon from urea application
(tonnes of C/ha);
M: The amount of urea applied (tonnes of urea);
EF: Emission factor (tonnes of C/tonnes of urea).
❖ N2O emissions from agriculture soils
- Direct N2O emissions


14

N2ODirect-N = [(FSN+FAW +FBN + FCR)*EF1] + (FOS *EF2) (Equa. 9)
where :
N2ODirect-N: Emission of N2O in unit of Nitrogen (kg N/yr)
FSN: Annual amount of synthetic fertilizer nitrogen applied to soils
adjusted to account for the amount that volatilizes as NH3 and NOx
FAW: Annual amount of animal manure nitrogen intentionally
applied to soils adjusted to account for the amount that volatilizes
as NH3 and NOx
FBN: Amount of nitrogen fixed by N-fixing crops cultivated annually
FCR: Amount of nitrogen in crop residues returned to soils annually
FOS: Area of organic soils cultivated annually

EF1: EF for emissions from N inputs (kg N2O-N/kg N input)
EF2: EF for emissions from organic soil cultivation (kg N2O-N/ha-yr)
The conversion of N2O-N to N2O: N2O = N2O-N * 44/28.
- Indirect N2O emissions:
N2Oindirect-N = N2O(G)+N2O(L)+N2O(S) (Equa.10)
where:

KNK

N2Oindirect-N: Emissions of N2O in units of nitrogen
N2O(G): N2O emited from volatilization of applied synthetic
fertilizer and animal manure N, and its subsequent atmospheric
deposition of NOx and NH3 (kg N/yr);
N2O(L): N2O emited from leaching and runoff of applied fertilizer
and animal manure N (kg N/yr);
N2O(S): N2O emited from discharge of human sewage N into
rivers or estuaries (kg N/yr)


15
❖ Transporting rice from fields to houses by motorbikes
CO2 emissions = Distance traveled ×EFmotorbike (Equa.11)
where:
CO2 emissions: the amount of CO2 emissions from travelling by
motorbike (kgCO2tđ);
Distance traveled: The distance that motorbike travelled (km);
EFmotorbike: Emission factor of motorbike (kgCO2tđ/km).
❖ Burning rice straw after harves
Lfire =A ×MB ×Cf ×Gef ×10-3 (Equa. 12)
where:

Lburning : amount of GHG emissions from fire (tons of each GHG)
A: area burnt (ha);
MB : mass of fuel available for combustion (tonnes/ha);
Cf : combustion factor;
Gef : emission factor (g/kg d.m.).
The mass of fuel available for combustion (MB) or the output of
straw burning on the field (Qst) is estimated by the equation
according to Gadde et al. (2009):
Qst =Qp ×R×k (Equa. 12.1)
where:
Qst : The amount of straw burned on the field (tonnes)
Qp : The amount of rice (tonnes);
k : The ratio of straw burned on the field to total straw.
b) Calcultion of carbon footprint


16
According to the IPCC's 5th Assessment Report (AR5) (2013), the
GWP of CH4 is 28 and the GWP of N2O is 265.
CFs = ∑3i=1[GWP(tieri )]
CFy =

CFs
Grain yield

where:
CFs: the spatial carbon footprint (kg CO2e/ha);
CFy: the yield-scaled carbon footprint (kg CO2e/kg).
c) Uncertainly analysis
Uncertainty analysis was conducted based on Equations 3.1 and

3.2 in Volume 1 of GL 2006 of IPCC [68].
3.2. Greenhouse gas emissions from activities during rice life cycle

3.2.1. Electricity generation for operation of agricultural machinery
and equipment
Data on capacity (MWh), operation time (h/ha) and times of
operation (times/crop) of water pumps, electric fans and rice milling
machines were collected based on field survey data. The emission
factor of the Vietnamese electricity grid in 2017 was 0.864 tonnes of
CO2/MWh (Decision No. 330 /BDKH-GSPT dated 29 March 2019).
Table 3.6. Greenhouse gas emissions from electricity generation
for operation of agricultural machinery for rice cultivation
Unit: kgCO2e/ha
Source

Spring season

Summer season

CM

SRI

WNR

CM

SRI

WNR

Pumps

4120.9

2846.2

2846.2

3434.1

2371.8

2371.8

Electric fans

0.004

0.003

0.002

0.004

0.003

0.002

17

Rice milling
machines
Total

114.93

114.93

114.93

114.93

114.93

114.93

4235.9

2961.1

2961.1

3434.1

2371.8

2371.8

3.2.2. Fertilizer production
Emission factors for N, P2O5, K2O and NPK production are 3.63
kgCO2e/kg N, 0.13 kgCO2e/kg P2O5, 0.56 kgCO2e/ kg K2O and 4.59
kgCO2td /kg NPK, with respectively [84].
Table 3.8. Greenhouse gas emissions from fertilizer production
Unit: kgCO2e/ha
Spring season

Summer season

Source
CM

SRI

WNR

CM

SRI

WNR

N

526.35

457.68

655.14

513.77

450.21

640.20

P2O5

8.08

13.27

14.10

7.94

13.27

13.52

K2O

57.66

63.57

63.50

54.14

61.84

63.13

NPK

1250.6

1183.7

1002.4

1201.6

1183.7

957.30

Total

1842.7

1718.23

1735.17

1777.48

1709.03

1674.15

3.2.3. Lime production
According to the survey data, farmers only use lime for basal
fertilization. The emission factor of lime production is 0.75
kgCO2/kg lime according to GL 2006 [68].
Table 3.9. Greenhouse gas emissions from lime production
Unit: kgCO2e/ha
Source

Spring season
CM

Summer season

SRI

WNR

CM

SRI

WNR

Lime production 23.15 0.00

12.76

23.15

0.00

12.76


18

3.2.4. Pesticide production
The amount of pesticides sprayed for the Brown Planthowver was
0.2 kg/ha [65]. The thesis assumed that according to the conventional
method, farmers sprayed for the Brown Planthowver one time more
than SRI and wide-narrow row methods. The emission factor of
pesticide production was 25.5kgCO2e/kg kg a.i. [26]. The proportion
of active ingredients was assumed to be 25%.
Table 3.11. Greenhouse gas emissions from pesticide production
Unit: kgCO2e/ha
Source

Pesticide production

Spring season

Summer season

CM

SRI

WNR

CM

SRI

WNR

2.55

1.28

1.28

2.55

1.28

1.28

3.2.5. Methane emissions from rice cultivation
Data on cultivation areas and methods, rice varieties, and rice
growth duration were collected from questionnaire results. The
methane emission factor (kgCH4/ha/day) was calculated based on
IEA [57] and had a value of 2.50 (CM), 1.69 (SRI) and 1.61 (WNR
in the spring season and 3.36 (CM), 3.09 (SRI) and 2.69 (WNR) in
the summer season, with respectively.
Table 3.15. Methame emissions from rice cultivation
Unit: kgCO2e/ha
Crop

Cultivation method
CM

SRI

WNR

Spring season

7870.93

5765.76

5556.19

Summer season

10646.16

10110.03

8990.94


19

3.2.6. CO2 emissions from urea application
Data on the amount of urea and NPK fertilizer and type of NPK
was collected from the survey results. According to GL 2006, the

emission factor of urea application is 0.2 kgC/kg N.
Table 3.16. CO2 emissions from urea application
Unit: kgCO2e/ha
Spring season
CM

SRI

Summer season

WNR

CM

SRI

WNR

Basal application

63.45 38.12

55.85

62.49 38.12

55.09

First application

15.81 12.12

18.35

15.27 12.12

17.88

2.29

9.47

6.78

2.29

9.05

6.50

81.55 59.71

80.99

80.05 59.30

79.47

Second application
Total

3.2.7. N2O emissions from agricultural soils
The emission factor of N2O emissions (kgN2O-N/kg N) from
agricultural soils was calculated based on IEA [57] and had a value
of 0.00572 (CM), 0.00545 (SRI and WNR) in the spring season and
0.00648 (CM), 0.00534 (SRI and WNR) in the summer season. The
emission factor of direct N2O emissions from agricultural soils is
0.003 kgN2O-N/kg N according to the GL 2006.
Table 3.20. N2O emissions from agricultural soils
Unit: kgCO2e/ha
Sources
Direct
emissions

Spring season

Summer season

CM

SRI

WNR

CM

SRI

WNR

221.13

199.79

250.20

216.14

197.22

244.09


20

Indirect
emissions

200.20

163.41

204.63

251.04

153.96

190.55

Total

421.33

363.20

454.83

467.17

351.19

434.65

3.2.8. Lime application
EFdolomite is 0.13 (GL 2006).
Table 3.21. Greenhouse emissions from lime application
Unit: kgCO2e/ha
Source
Lime
application

Spring season

Summer season

CM

SRI

WNR

CM

SRI

WNR

14.71

0

8.11

14.71

0

8.11

3.2.9. Diesel combustion for on-farm operation of agricultural
machinery
Farmers used plowing machine 2-3 times/crop, combine
harvesters 1 time/crop and two rice milling machines 300 hours/crop.
Table 3.23. The parameters used for the calculation
Parameter
MFC (l/h)

Value

Combine harvestors

30.5 l/h

Plowing machines

14.80 l/h

Density of diesel (kg/l)

0.84 kg/l

Calorific value of diesel (TJ/Gg)

43.00

CO2

74100

N2O

178.80

EFdiesel (kgCO2/TJ)


21

Table 3.24. Greenhouse gas emissions from diesel combustion for

on-farm operation of agricultural machinery
Unit: kgCO2e/ha
Source

GHG

Plowing

Spring season

Summer season

CM

SRI

WNR

CM

SRI

WNR

CO2

1940

2058

2898.7

1986

2058

2858

machines

N2O

4.68

4.97

6.99

4.79

4.97

6.90

Combine

CO2

694.9

740.4

750.46

694

740.4

750.4

harvestors

N2O

1.68

1.79

1.81

1.68

1.79

1.81

Milling

CO2

3.82

3.82

3.82

3.82

3.82

3.82

machines

N2O

0.01

0.01

0.01

0.01

0.01

0.01

2646

2809

3661.8

2691

2809

3621.5

Tổng

3.2.10. Transporting rice from fields to houses by motorbikes
The average distance from fields to houses is 0.95 km. The
emission factor of motorcycles is 77.59 (g/km) according to
COPERT 4 of EURO 2 [86].
Table 3.26. Greenhouse gas emissions from transporting rice
from fields to houses by motorbikes
Unit: kgCO2e/ha
Spring season

Summer season

Sources

CM

SRI

WNR

CM

SRI

WNR

Rice transport

3.46

5.37

3.72

3.46

5.85

3.67

3.1.11. On-field burinng of rice stubble and straw after harvest
Most households incorporated the straw into soils in the spring
season and burned more in the summer season. The percentage of


22

rice stubble compared to the total rice residue is 0.32 and that of rice
straw is 0.68 [65]. The emission factor of N2O is 0.0185 kgCO2e/kg

of straw [68] and that of CH4 is 0.22397 [12]. FCO is 0.8 [26].
According to the survey, 15% of rice husk was incorporated into
soils, 15% was used for barn renovation and the remaining was
purchased and hence no emissions from rice husk burning.
Table 3.30. Greenhouse gas emissions from on-field burning or
rice stubble and straw after harvest
Unit: kgCO2e/ha
Sources
Rice stubble
Rice straw
Total

GHG

Spring season

Summer season

CM

SRI WNR

CM

SRI

WNR

N2O

4.98

0.0

5.10

36.67 26.37

29.95

CH4

72.1

0.0

73.85

530.8 381.7

433.5

N2O

0.00

0.0

0.92

17.29

9.28

13.51

CH4

0.00

0.0

13.37

250.3 134.3

195.6

77.1

0.0

93.24

835.1 551.7

672.6

3.3. Results of rice carbon footprint in Phu Luong commune

3.3.1. Rice carbon footprint in Phu Luong commune
The rice carbon footprint in the spring season was 2.88kgCO2e/kg
(CM), 2.32kgCO2e/kg (SRI) and 2.42kgCO2e/kg (WNR). In the
summer season, the results were 3.92 kgCO2e/kg (CM), 3.53
kgCO2e/kg (SRI) and 3.46 kgCO2e/kg (WNR). When comparing
with other international studies on rice carbon footprint which also
combined the LCA of ISO and IPCC’s GLs, the results of the thesis
are not much different with the standard deviation of 0.85.


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3.3.2. Uncertainty analysis
The uncertainty of rice carbon footprint in the spring season was
10.9% (CM), 10.5% (SRI) and 10.4% (WNR) and in the summer
season was 12.3% (CM), 13% (SRI) and 12.1% (WNR).
3.4. Proposing measures to reduce greenhouse gas emissions
The thesis proposed four mitigation options during the rice life
cycle in the study area in 2020-2025, including: M1. Expansion of
the application of the WNR cultivation method; M2. Reusage of rice
straw in the summer season to produce organic fertilizers; M3.
Production of biochar from rice stubble and straw in the spring
season and M4. Mixing biodiesel with conventional diesel oil at a
rate of 20% for the operation of agricultural machinery. Option M1
should be prioritized owing to its highest mitigation potential,
particularly 555.4 tCO2e (spring season) and 427.4tCO2e (summer
season) in 2025 compared to the Business-As-Usual scenario. This
option also brings an economic benefit of $49.4/tCO2e in the spring
season and $64.2/tCO2e in the summer season.
3.5. Conclusion of Chapter 3

Key GHG sources were: methane emissions from rice cultivation;
diesel combustion for on-farm operation of agricultural machinery;
electricity generation for irrigation and fertilizer production. Four
mitigation options were proposed: M1. Expansion of the WNR
method; M2. Reusage of rice straw in the summer season to produce
organic fertilizers; M3. Production of biochar from rice stubble and
straw in the spring season and M4. Mixing biodiesel with
conventional diesel oil at a rate of 20% for the operation of
agricultural machinery.


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CONCLUSIONS AND RECOMMENDATIONS
A. Conclusions
i) Metheodology
The thesis developed a methodology for calculating rice carbon
footprint, where the process-based LCA approach of ISO was mainly
combined with the GL 2006 of IPCC, FAO and COPERT 4 of
EURO 2. The calculation of GHG emissions from key activities was
based on the GL 2006. The equations for calculating GHG emissions
from the production of fertilizer, lime, and pesticide were derived
from FAO and that from transporting rice by motorbikes were based
on instructions in COPERT 4 of EURO 2, with respectively.
ii) Rice carbon footprint
Data were collected from interviews with 90 farmer households in
Phu Luong commune, of which 30 households for each cultivation
method: CM, SRI and WNR. The emission factor of methane
emissions from rice cultivation and N2O from agricultural soils in the
study area were calculated based on IEA’s empirical measurement

results. For activities that had no site-specific emission factor, the GL
2006 default values or those from relevant studies were applied .
Key GHG sources in the rice carbon footprint include: methame
emissions from rice cultivation; electricity generation for irrigation;
diesel combustion for on-farm operation of agricultural machinery
and fertilizer production. The rice carbon footprint in the spring
season were 17.2 tCO2e/ha (CM), 13.6tCO2e/ha (SRI) and
14.5tCO2e/ha (WNR) and those in the summer season were
20.09tCO2e/ha (CM), 18.08 tCO2e/ha (SRI) and 17.9 tCO2e/ha


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(WNR). By yield unit, the rice carbon footprint in the spring crop
was 2.88kgCO2e/kg (CM), 2.32kgCO2e/kg (SRI) and 2.42kgCO2e/kg
(WNR). In the summer season, the results were 3.92 kgCO2e/kg
(CM), 3.53 kgCO2e/kg (SRI) and 3.46 kgCO2e/kg (WNR).
The uncertainty of the result in spring season was 10.9% (CM),
10.5% (SRI) and 10.4% (WNR) and in the summer season was
12.3% (CM), 13% (SRI) and 12.1% (WNR). The uncertainty of
GHG emissions from non-mechanical sources was often higher than
that from mechanical sources. Successful pilot calculation in Phu
Luong commune has demonstrated the feasibility of the methodology
and hence it can be applied for other areas.
iii) Measures to mitigate rice carbon footprint
Based on the calculation results, the thesis proposed four
mitigation options, including: M1. Expansion of the application of
the WNR cultivation method; M2. Reusage of rice straw in the
summer season to produce organic fertilizers; M3. Production of
biochar from rice stubble and straw in the spring season and M4.

Mixing biodiesel with conventional diesel oil at a rate of 20% for the
operation of agricultural machinery. Option M1 should be a priority
because of its highest mitigation potential and economic co-benefits.
Based on this study, rice carbon footprint in other areas can be
calculated in order to identify appropriate mitigation options.
Therefore, the thesis had both both scientific and practical
significance, contributing to the removal of possible trade barriers to
rice exports in the post-2020 period and supporting Viet Nam in the
achievement of mitigation targets as declared in the NDC.