Production efficiency analysis of indigenous pig production in Northwest Vietnam
ISSN 2588-1299
VJAS 2020; 3(4): 882-891
Vietnam Journal
of Agricultural
Sciences
Production Efficiency Analysis of Indigenous
Pig Production in Northwest Vietnam
Tran Ba Tuan1, Tong Yin2, Tran Huu Cuong3, Nguyen Cong
Tiep4 & Sven König2
1North Vietnam College of Agriculture and Rural Development, Hanoi 134000, Vietnam
2Institution of Animal Breeding and Genetics, Justus Liebig University Gießen, Gießen
35390, Germany
3Faculty of Accounting and Business Management, Vietnam National University of Agriculture,
Hanoi 131000, Vietnam
4Faculty of Economics and Rural Development, Vietnam National University of
Agriculture, Hanoi 131000, Vietnam
Abstract
This research was conducted to investigate the production efficiency
of Ban pig production in northwest Vietnam between October 2016
and January 2017. Primary data obtained from 171 producers were
analyzed by applying cost-benefit analysis and stochastic frontier
production function. The benefit-cost ratio per litter was 1.24,
indicating that the enterprise was profitable. Compared to other
farms, the farms focused on farrow-to-finisher attained the highest
net return (EUR 213.71/litter), while inputs were used most
effectively by the mixed farms. The results from the Cobb-Douglas
production function revealed that labour, feeding costs, stocking
density, and pigpen structure had positive effects on the production
output. Additionally, farms with the phase of farrow-to-nursery
obtained less total revenue, while farms focused on the farrow-to-
finisher phase achieved higher production outputs than the mixed
farms. The level of technical efficiency for each farm ranged between
0.62 and 0.98, with a mean of 0.88. The number of live-born piglets
and depreciation cost had positive effects, whereas the nursery
interval had a negative impact on the technical efficiency. Ban pig
producers could increase technical efficiency by efficiently utilizing
available resources and improving managerial skills.
Keywords
Stochastic frontier, economic efficiency, indigenous pig, production,
North-west Vietnam
Introduction
Accepted: June 18, 2020
Accepted: October 7, 2020
The availability of different domesticated animals, e.g., pigs,
cows, sheep, and chickens, enriches the household economy because
livestock production is a significant element of livelihood strategies
Correspondence to
Vietnam Journal of Agricultural Sciences
882
Tran Ba Tuan et al. (2020)
production plays a substantial role in livestock
husbandry, and pork production accounted for
75.9% of the total meat production in 2013
ability to convert different kinds of rough feed,
maturity, a short generation interval, and
relatively small space requirements (Ezeibe,
2010). The pig sector is an integral part of
mixed farming systems (VAC) in Vietnam as a
way to utilize household leftover food and
agricultural byproducts. ACIAR-ILRI-CAP
pig sector contributes to about 14% of the total
household income and 25% of the total
household income from agriculture. Pig
production is an efficient way to improve protein
utilization because, compared to cows, sheep,
and goats, pigs grow faster and are more
Vietnam, one of the main development
opportunities for small holders under conditions
of increasing land scarcity and environmental
degradation is pig production.
to households, e.g., land, labour, and feed
produced from agricultural products. Therefore,
farmers have to utilize their resources as
efficiently as possible. Efficiency is one of the
crucial elements influencing productivity
growth, especially in agricultural economic
development, where resources along with
opportunities for developing and adopting
The term efficiency of a farm can be defined
as its ability to provide the largest possible
amount of output from a given set of inputs
(Palmer and Torgerson, 1999). The stochastic
frontier is a helpful tool to approximate
production efficiency in agriculture. This model
model is based on the concept of the production
function, depending on the relationship between
the set of inputs and the set of outputs in
production. From the classical perspective,
producers produce the maximum achievable
outputs based on a given technology and level
of inputs. This is the definition of the production
possibility frontier, which is identified by an
optimal relationship between inputs and
outputs, and the potentially sub-optimal
production activities of the producers are
modelled using the concept of technical
efficiency. The definition of technical
efficiency is the rate of the farm’s observed
production and the optimal level of production
at a given state of inputs and technology. The
level of technical efficiency of a farm is
identified by the relationship between the
detected production and potential production.
However, the stochastic frontier model’s major
weakness is its failure to provide an explicit
distribution assumption for the inefficiency
term (Sharma et al., 1999).
Pig production, especially of the indigenous
pig (Ban pig), significantly contributes to the
economic income, and social and cultural life for
most communities in northwest Vietnam. Ban
pigs can be readily converted into cash, which
helps meet the regular financial demands of the
households, such as expenditures for education
and spending on basic daily necessities (Hung et
al., 2017). As one of the cultural symbols, pork
from the Ban pig is also an important food supply
at traditional events. Furthermore, manure from
pig production is an excellent source of fertilizer
to enrich poor soils, and consequently, to
improve plant cultivation for farmers. However,
the development of Ban pig husbandry is
hampered by diseases and lack of village
veterinary staff, instability and fluctuations in
prices, inadequate technical assistance from
extension services, high-fat proportion, low
growth rate, low fertility, and poor infrastructure
facilities.
Over the last few decades, stochastic frontier
models have been widely used in agricultural
2016). Capturing random variables that are
beyond the producer’s control is the main
advantage of this model because the estimation is
Small-scale Ban pig production is a
widespread pork production system in northwest
Vietnam because of limited resources available
883
Production efficiency analysis of indigenous pig production in Northwest Vietnam
more in accordance with the potential output
et al., 2016).
district); and Che Cu Nha, Khao Mang, Pung
Luong (Mu Cang Chai district).
The pig farms with at least one sow at the
data collecting time were selected. The farms
were chosen with the assistance of field
extension officers from the Department of
Agriculture. Veterinary staff of the communities
were also included, especially in offering
logistics. Recently, no censuses of Ban pig
breeds have been carried out in the study areas.
Hence, snowball sampling was used to identify
the households that raised local pigs in the
communities. This method was used previously
in studies of Taenia solium cysticercosis
(Sikasunge et al., 2007). The veterinary staff
recognized the first few farmers who raised local
pigs, and afterwards, other farms were found
based on the information from the first farmers.
Several studies were conducted to
investigate productive efficiency in pig farming
systems using the stochastic frontier production
(2015), and Aminu & Akhigbe-Ahonkhai
(2017). However, indigenous pig breeds were not
considered in these studies. Hence, it is necessary
to analyze the socio-economic factors
influencing the economic efficiency of Ban pig
production and identify the most efficient source
from pig producers in the local farms.
Specifically, the objectives of the present
research were (1) to examine the costs and
returns from the perspective of local Ban pig
farmers; (2) to approximate the level of
technical efficiency for Ban pig producers; and
(3) to investigate the factors that influence the
technical efficiency of Ban pig farms in
northwest Vietnam. The findings in this study
should be applied in the studied areas to
improve the production efficiency of local
breeds.
Single-person interviews with a semi-
structured questionnaire were conducted for 180
native pig farmers distributed across nine
communities in the three divisions from
November 2016 to January 2017. The number of
farms was 69, 49, and 62 in Van Ho, Tan Lac,
and Mu Cang Chai, respectively, and they were
approximately in line with the proportion of the
total population size of pigs in each area in 2015
(GSO Vietnam, 2016). The main features
reflected in the interviews were socio-economic
characteristics, management practices, labour
use, availability of housing and equipment,
capital, and output. A preliminary assessment of
180 questionnaires led to deleting nine
respondents because crucial information was
missing for those respondents. As a result, 171
farms were kept in the analyses.
Materials and Methods
Study area and data collection
Fieldwork was conducted in three provinces,
Son La, Hoa Binh, and Yen Bai, which contain a
high density of commercial and local pigs, and
are stratified into the three main climatic zones
of the Northwest area. One district in each
province was selected after consulting with
official staff from the Department of Agriculture
(Van Ho district - Son La, Tan Lac district - Hoa
Binh, and Mu Cang Chai district - Yen Bai). In
every district, three communes were chosen
purposively based on the following two criteria.
First, the study areas were crucial and high-
intensity regions for Ban pig production. Then,
to reduce logistic constraints for data collection,
the selected regions were relatively easy to
access. Hence, the study areas were Chieng
Khoa, Van Ho, Long Luong (Van Ho district);
Phu Cuong, Phu Vinh, Dich Giao (Tan Lac
Data analysis
The survey data were analyzed using Stata
version 12. The stochastic frontier production
function model was applied to estimate the level
of technical efficiency and its determinants in
Ban pig production. The descriptive statistics of
the variables (Table 1), such as means, standard
deviations, frequencies, and percentages, were
used to interpret the socio-economic
Vietnam Journal of Agricultural Sciences
884
Tran Ba Tuan et al. (2020)
characteristics of the farmers, as well as the
inputs and outputs of the local farms.
(measurement error; environment, and diseases,
etc.), it follows a normal distribution with
N(0,σ2v); and ui is the one sided-error component
that reflects technical inefficiency and is
assumed to be independently and identically
Cost-benefit analysis
The cost-benefit analysis was applied to
approximate the farm net revenue for Ban pig
production systems. Theoretically, net revenue
(NR) is the difference between the total revenue
(TR) and the total costs (TC):
distributed as
a
half-normal distribution
N+(0,σ2u).
The technical efficiency (TE) of the ith
sample farm can be estimated by:
NR = TR − TC (1)
(
)
y
i
i
i
i
TEi =
=
F x ;β exp(v −u ) = exp(−ui) (9)
( )
F x ;β exp(v )
i i
Total cost is the accumulation of the entire
variable costs (VC) and fixed costs (FC) items:
∗
y
TE ranges from 0 to 1, indicating the
minimum to the maximum level of technical
efficiency.
TC = VC + FC (2)
The variable costs came mainly from the
feeding costs, labour costs, and health care costs.
Two functional forms, i.e., log-linear Cobb-
Douglas and Translog production forms, can be
applied for the stochastic frontier production
function. In this paper, the log-linear Cobb-
Douglas form was adopted. The model was:
Total revenue was identified as the total
amount of money that a producer received from
the sale of fattening pigs and piglets:
TR = ∑PnQn (3)
lny = β0 + ∑βjlnxij + βppPPstructi +
βD D1i + βD D2i + (vi − ui) (10)
where P is the price per 1 kg live weight of the
pig nth and Q is the live weight of the pig nth. The
total revenue came mainly from the selling of
nursery pigs, finisher pigs, and culled sows.
1
2
where yi denoted the total revenue of pig
production per litter (EUR); xij was the input
quantities jth used by the ith pig farm, including
labour (man/days), feeding costs (EUR), health
care costs (EUR), and stocking density
(animal/m2). PPstructi was the type of pigpen
structure (1 for temporary, 2 for semi-permanent,
and 3 for permanent); and D1i and D2i were
dummy variables of operation. For D1i, values of
1 were assigned for farms focused on the farrow-
to-nursery phase, and 0 was given for other
farms. For D2i, farms with the farrow-to-finishing
phase were assigned as 1, and the other farms
received a 0.
Gross margin (GM) is the total revenue
minus the total variable costs:
GM = TR − VC (4)
The return’s ratio was used to calculate the
amount of return on an investment relative to the
investment costs. It is presented by three
formulas:
Rate of returns (RR):
RR = NR/TC (5)
Gross ratio (GR):
GR = TC/TR (6)
The inefficiency model (ui) was specified as:
Benefit-cost ratio (BCR):
BCR = TR/TC (7)
ui = δ0 + δ1Agei + δ2Edui + δ3DisPL
+
i
δ4NurIni + δ5NumPig_Alivei + δ6CAP +
i
The stochastic frontier production function
δ7Vac_PLi (11)
The general stochastic frontier production
function can be defined as:
where Age was the age of the farmer (in
years); Edu was the educational level of the
household’s head (in school years); Dis_PL was
the disease frequency of the pigs (Dis_PL = 1, 2,
and 3 for none, low, and high disease
frequencies, respectively); NurIn was the nursery
interval (in days); Numpig_Alive was the
number of live-born piglets (in number); CAP
(
)
yi = f xi; β exp(vi − ui) (8)
where yi denotes the output of the ith sample
farm (i =1, 2, …n); xi is a vector of the inputs
used by the ith farm; β is a vector of the
parameters to be estimated; vi represents a
random error not under the control of the farmers
885
Production efficiency analysis of indigenous pig production in Northwest Vietnam
was depreciation (in EUR); and Vac_PL was a
vaccination for piglets (dummy).
total cost per litter was EUR 510.99, and the
average total revenue was EUR 634.64. The total
cost consisted of the variable costs and the fixed
costs, in which the average variable cost
accounted for 97%, and the fixed cost accounted
for 3%. Additionally, the feed costs variable was
the most important because it covered the highest
proportion of the variable costs of production
(78%), followed by the labour costs of 20%, the
health care costs of 1%, and other costs of 1%.
The parameters of the log-linear Cobb-
Douglas and inefficiency model were estimated
simultaneously by the maximum likelihood
method. The variables used in the model are
listed in Table 1.
Results and Discussion
Costs and returns for pig production
The average gross margin and the net
revenue per litter were EUR 139.97 and EUR
123.65, respectively. The rate of return on
investments in Northwest Vietnam was 0.24,
Table 2 shows the full information in detail
about the costs, returns, and profitability of Ban
pig production in the research area. The average
Table 1. Descriptive statistics (standard deviation: SD; minimum: Min; maximum: Max) for revenue, inputs, production management,
disease management, type of operation, and socio-economic information
Variable
Total revenues per litter
Total net revenue per litter
Labour
Unit
EUR
Mean
634.64
123.65
19.88
281.59
7.07
Std. Dev.
233.15
131.11
7.18
Minimum
278.60
-227.82
10
Maximum
1382.55
597.96
50
EUR
Man day
EUR
Feeding costs
157.56
7.27
69.63
0.00
980.35
39.80
3.00
Health care costs
Stock density
EUR
Pigs/m2
1.28
0.68
0.29
Pigpen structure
2.51
0.77
1.00
3.00
Farm location
1.95
0.85
1.00
3.00
Age
Years
School years
Days
41.02
5.72
10.11
4.09
24.00
0.00
65.00
12.00
240.00
10.60
82.11
3.00
Educational level
Nursery interval
138.13
7.47
40.28
1.10
60.00
5.20
Number of live-born piglets
Depreciation
Number
EUR
16.32
2.18
12.72
0.76
0.84
Disease frequency of pigs
Vaccination for piglets
Vaccination for sows
Farrow-to-nursery operation
Farrow-to-finisher operation
Times
1.00
0.28
0.45
0.00
1.00
0.46
0.50
0.00
1.00
0.62
0.49
0.00
1.00
0.16
0.37
0.00
1.00
Vietnam Journal of Agricultural Sciences
886
Tran Ba Tuan et al. (2020)
Table 2. Average costs, returns, and profitability of Ban pig production in EUR/Litter
Variable
Amount (EUR)
634.64
494.67
383.32
99.46
% of TC
Total revenues (TR)
Variable costs (VC)
- Feed
0.97
0.78
0.20
0.01
0.01
0.03
- Labour
- Medication
7.07
- Other costs
4.82
Fixed costs (FC)
Total costs (TC)
Gross margin (GM)
Net revenues (NR)
Return rate (RR)
16.32
510.99
139.97
123.65
0.24
Benefit-cost ratio (BCR)
Gross ratio (GR)
1.24
0.81
indicating that for every EUR 1 invested, EUR
0.24 was gained from pig production as a profit.
This result is lower than that of studies on pig
production in Ekiti State (Aminu & Akhigbe-
Adeyemo 2012), where the rates of return on
investments were 0.34 and 0.82, respectively.
The benefit-cost ratio of 1.24 depicted that Ban
pig production was a profitable business in
northwest Vietnam as it was greater than one.
The gross ratio was 0.81, implying that EUR 0.81
was spent for every EUR 1 return in the
production.
types of farms were 0.74, 0.78, and 0.85,
suggesting that for every EUR 1 return from the
pig production system, EUR 0.74, EUR 0.78, and
EUR 0.85 were spent by the farmers in the
mixed, farrow-to-finish, and farrow-to-nursery
farms, respectively. This implies that pig
producers from mixed farms used inputs more
efficiently than others.
Parameters of the production factors
Parameters estimated from the stochastic
frontier production function are presented in
Table 4. The results revealed that the coefficients
of most of the inputs were statistically significant
at different levels, except for the health care
costs. The coefficient of labour was positive with
a value of 0.07, which was significant at 10%.
This means that a 1% increase in the man-day
increased the total revenue by 0.07% when other
factors were kept constant. This finding is in line
with research on the profitability and technical
efficiency of pig production in Ekiti State
(Aminu and Akhigbe-Ahonkhai, 2017), and the
study on efficiency in small-scale pig production
The differences in costs, returns, and
profitability of pig production for farms among
three types of operations are presented in Table
3. The farms focused on the phase of farrow-to-
finish achieved the highest profit of EUR 213.71
per litter, while the profit decreased to EUR
75.46 per litter if farms only concentrated on the
phase of farrow-to-nursery. However, the rate of
return in the farms that mixed both phases (0.35)
was higher than the farms only undertaking one
phase, i.e., farrow-to-finish (0.28) or farrow-to-
nursery (0.17). Also, the gross ratios of the three
887
Production efficiency analysis of indigenous pig production in Northwest Vietnam
Table 3. Average costs, returns, and profitability of Ban pig production in EUR/Litter in farms focused on the phases of farrow-to-
nursery, farrow-to-finish, or a mixture of both
Variable
Total revenues (TR)
Variable costs (VC)
- Feed costs
Farrow-to-nursery
488.64
403.18
301.30
90.15
Farrow-to-finish
986.13
753.11
619.09
121.36
7.35
Mixed farm
786.91
561.20
439.88
109.56
6.89
- Labour costs
- Medication costs
- Other costs
7.06
4.67
5.31
4.87
Fixed costs (FC)
Total costs TC
13.80
19.32
21.25
416.98
85.46
772.43
233.03
213.71
0.28
582.45
225.71
204.47
0.35
Gross margin (GM)
Net revenues (NR)
Return rate (RR)
Benefit-cost ratio (BCR)
Gross ratio (GR)
71.66
0.17
1.17
1.28
1.35
0.85
0.78
0.74
coefficient of the feeding cost was 0.27, which
was significant at 1%, indicating that every 1%
increase in the feeding cost raised the total return
by 0.27%. The large elasticity of feed costs is an
indicator of the importance of concentrates in pig
production. These results conform to prior
et al. (2014). The health care costs had a negative
coefficient but was not significant, even at the
10% level.
revenue of pig production. The production
elasticity concerning the pigpen style was
positive with a value of 0.04 and significant at
5%, indicating that the better the pigsty
conditions, the higher revenue farmers earned
from pig production. The coefficient of farms
with the phase farrow-to-nursery was negative,
while farms focused on the farrow-to-finisher
phase had a positive coefficient, and both were
significant at the 1% level. The elasticity of -0.30
implied that the farrow-to-nursery farms tended
to get 0.30% less in total revenue than the mixed
farms. In contrast, higher output (0.10%) were
achieved for farms with farrow-to-finisher
compared with the mixed farms.
The elasticity of the stocking density was
0.03 and positively significant at the 10% level,
suggesting that a 1% increase in the stocking
density increased output by 0.03%. In contrast, a
significant negative coefficient of the stocking
density was reported in pig production in Akwa
of the stocking density coefficient in this study
suggested that increasing the stock of animals
tended to increase the timeliness of resources
used, as a result leading to an increase in
technical efficiency.
Determinants of technical efficiency in Ban
pig production
The estimated coefficients of the
inefficiency model are listed in Table 4. The
coefficient of the nursery interval (0.01) was
positive and significant at the 5% level,
suggesting that technical inefficiency effects
increased with an increase in the nursery interval.
Additionally, the type of pigpen and
operation had significant impacts on the total
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888
Tran Ba Tuan et al. (2020)
Table 4. Coefficient, standard error, and Z-value of parameters estimated from the stochastic frontier production function for Ban pig
production
Variable
Production function
Constant term
Parameter
Coefficient
Standard error
Z value
β0
β1
4.91***
0.07*
0.21
0.04
0.03
0.003
0.02
0.02
0.03
0.04
23.03
1.78
8.35
-0.77
1.66
2.09
-8.64
2.73
Labour
Feeding costs
β2
0.27***
-0.002ns
0.03*
Health care costs
Stock density
β3
β4
Pigpen structure
Farrow-to-nursery
Farrow-to-finisher
Inefficiency model
Constant term
βpp
βD1
βD2
0.04**
-0.30***
0.10***
δ0
δ1
δ2
δ3
δ4
δ5
δ6
δ7
1.06ns
0.004ns
0.001ns
0.02ns
0.01**
1.69
0.02
0.05
0.25
0.05
0.2
0.63
0.20
0.01
0.08
2.21
-3.96
-1.99
-1.10
Age
Education level
Disease frequency of piglets
Nursery interval
Number of live-born piglets
Depreciation cost
Vaccination for piglets
Log-likelihood function
-0.78***
-0.04**
-0.47ns
86.20
0.02
0.43
Note: *, **, and *** show statistical significance levels at 10%, 5%, and 1%, respectively; ns: no statistical significance.
Hence, every one-day increase in the nursery
interval declined output by 0.01% in Ban pig
production. The results also revealed that
technical inefficiency was reduced with an
increase in the number of live-born piglets per
litter. This suggests that the farms that had a
higher number of live-born piglets per litter
achieved a higher level of technical efficiency.
The variable depreciation had a coefficient of -
0.04 and was significant at the 5% level,
indicating that technical efficiency rose with an
increase in the expenditures for housing and
equipment in pig production. Consequently, this
points to the importance of investments in
accommodations in Ban pig production.
Interestingly, the coefficient of age and
education level of the household’s head in this
research was not statistically significant even at
the 10% level as reported in the previous studies
Akhigbe-Ahonkhai (2017).
Distribution of respondents by technical
efficiency
The specific level of technical efficiency for
pig producers obtained from the stochastic
889
Production efficiency analysis of indigenous pig production in Northwest Vietnam
frontier model is presented in Table 5.
Substantial differences in technical efficiency
scores among Ban pig producers were identified.
The predicted levels of technical efficiency for
each farm ranged from 0.62 to 0.98, with a mean
of 0.88, which agreed with the previous findings
(78%) operated at levels of technical efficiency
below 0.95, while 22% of the pig producers
achieved levels of technical efficiency from 0.95
to 0.98. This indicated that none of the pig farms
could produce on the frontier of the pig
reported that 8% of households were fully
efficient.
Conclusions
The profitability and technical efficiency of
Ban pig production in northwest Vietnam were
estimated using the cost-benefit analysis and the
stochastic frontier production function. Inputs
were utilized the most effectively by the mixed
farms, while farms focused on the farrow-to-
finisher phase attained the highest net returns.
The production inputs, e.g., feeding costs, labour,
stocking density, and pigpen style, contributed
positively to the output from the Ban pig
production. Farms focused on the farrow-to-
finisher phase achieved the highest economic
efficiency, while the farms with the farrow-to-
nursery phase had the lowest economic
efficiency.
The main factors that affected technical
efficiency in indigenous pig enterprises were
nursery interval, number of live-born piglets, and
the investments in housing and equipment. The
findings of the present study revealed that all of
the pig farms operated below the frontier
threshold. The results showed that on average,
the technical efficiency of Ban pig producers
could be raised by 12% through improving
managerial skills, housing, and equipment for pig
production. Therefore, the local extension
organizations should provide training courses on
pig production management for the breeders to
increase technical efficiency.
The average technical efficiency of pig
breeders was 0.88, suggesting that the Ban pig
farmers in northwest Vietnam were producing at
88% of their potential production levels.
Therefore, on average, the level of technical
efficiency could be increased by 12% for the Ban
pig producers through efficiently utilizing
available resources with the current state of
technology. The least efficient producer could
save costs by 35% (1-63/98) if production could
be as efficient as the farm with the maximum
technical efficiency.
Table 5. Efficiency distribution of the Ban pig producers
Technical efficiency level
0.62 - 0.80
Frequency
Percentage
30
60
44
37
17
35
26
22
0.81 - 0.90
0.91 - 0.94
0.95 - 0.98
Minimum efficiency
Maximum efficiency
Mean efficiency
0.62
0.98
0.88
Vietnam Journal of Agricultural Sciences
890
Research and Investment. 1: 48-54.
Acknowledgments
Førsund F. R., Lovell C. K. & Schmidt P. (1980). A survey
of frontier production functions and of their
relationship to efficiency measurement. Journal of
Econometrics. 13: 5-25.
We are thankful for the communities in the
research areas in Van Ho district (Son La), Tan
Lac district (Hoa Binh), and Mu Cang Chai
district (Yen Bai) for sharing their knowledge
on their pig farming and allowing us to work
with them.
GSO (General Statistical Office) (2016). Statistical
Yearbook Vietnam 2015. Statistical Publishing House,
Hanoi, Vietnam.
Hung P. V., Nga N. T. D. & Lapar L. (2015). Improving the
livelihood of small farmers in the pig value chain:
Experiences in the north of Vietnam. Regional seminar
on "Agriculture Development for the Mekong Delta:
How to Increase the Livelihood of Farmers", Can Tho
City, Vietnam, April 24, 2015. Hanoi, Vietnam:
Vietnam National University of Agriculture.
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