Xác định quy mô đất tối ưu trong sản xuất nông nghiệp của người dân đồng bằng sông cửu long tt tieng anh

MINISTRY OF EDUCATION AND TRAINING
CAN THO UNIVERSITY
---o0o---

NGUYEN LAN DUYEN

DETERMINING THE OPTIMAL FARM SIZE
IN AGRICULTURAL PRODUCTION OF HOUSEHOLDS
MEKONG DELTA

DISSERTATION
(ABSTRACT)
Major: Agricultural Economics
Major code: 9 62 01 15

Can Tho, 07/2020


The research has been finished at Can Tho University

Supervisor: Assoc. Prof. Nguyen Tri Khiem, PhD

Discussant 1: ..............................................................................................
Discussant 2: .............................................................................................
Discussant 3: .............................................................................................

The dissertation will be defended at the council of the school level at:
..........................................................................................................................
On: …….. hour…….. date …….. month……… year……….

Citing of this the dissertation in available at following the libraries:

- Learning Resource Center – Can Tho University
- Vietnam National Library

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LIST OF PUBLISHED PAPERS RELATED TO DISSERTATION
1. Nguyen Lan Duyen and Nguyen Tri Khiem (2018). Effect of Farm Size on
the Economics Efficiency of rice households Mekong Delta. Economic studies,
volume 2 (477), pp. 58 – 67.
2. Nguyen Lan Duyen and Nguyen Tri Khiem (2018). The relationship between
Farm Size and Land Productivity of rice farming households in the Mekong Delta.
Journal Science Ho Chi Minh City Open University, 61(4), pp. 57 – 66.
3. Nguyen Lan Duyen and Nguyen Tri Khiem (2019). Effect of Farm Size and
Labor Size on the Labor Productivity of rice households in the Mekong Delta.
Journal Science Ho Chi Minh City Open University, 14(1), pp. 68 – 78.
4. Nguyen Lan Duyen and Nguyen Tri Khiem (2019). Effect of Farm Size on
the Total Factor Productivity of rice households Mekong Delta. Journal of
Economics & Development, volume 265, pp. 82 – 92.
5. Nguyen Lan Duyen and Nguyen Tri Khiem (2019). The effect of Farm Size
on the Profit Ratio of rice households in the Mekong Delta. Can Tho University
Journal of Science, episode 55, Special issue on Economics, pp. 42 – 50.

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CHAPTER 1. INTRODUCTION
This chapter gives an overview of the research rationale, research objectives
and scope of the thesis.
1.1 Reason for research

Agricultural land area in Asia accounts for 20% of the world's total
agricultural land area, but the landholdings are very small (from 1-2 ha/household)
compared to the world average (3.7 ha/household) and the trend of small-scale
ownership is increasing (Pookpakdi, 1992). Vietnam's agricultural land area is 0.12
ha/person, only one sixth of the world average, equivalent to Belgium and the
Netherlands, higher than the Philippines and India, but lower than China and
Indonesia (OECD, 2015). Due to the industrialization that transfers agricultural
resources (such as labour and land) to the industrial sector, leaving less for
agricultural production (Dinh Bao, 2014). In agricultural production, industry or
services, producers are interested in many factors. One of the crucial factors
determining the success of production is the efficiency of production activities
(HQHDSX), or to use optimally resources to improve HQHDSX. In agricultural
production, land is a scarce factor (Hoque, 1988), a vital factor of production
(Adamopoulos and Restuccia, 2014) and is an especially irreplaceable means of
production (Pham Van Dinh and Do Kim Chung, 2004), therefore, producers need
to determine the optimal farm size threshold for investment in order to maximize
the efficiency of production activities.
However, at different stages of the economy, the farm size is different. In the
1960s, small scale was good and effective because of taking advantage of family
resources (labor, land, production tools, ...) but in the 1970s and 1980s due to the
process of urbanization and specialization. As an industrialization, attracting a large
number of rural laborers makes production more efficient on a large scale (Fan and
Chan-Kang, 2005). According to these researchers, by the 1990s, the application of
science and technology to production increased the land use intensity, thus
negatively affecting the land resources and the environment leading to production
not as effective as before.
The Inverse Relationship (IR) hypothesis between farm size and the efficiency
of agricultural production activities implies that small farm will be more effective
than households with large farm, discussed in countries with developed agriculture
in recent centuries, first in Russia (Chayanov, 1926), then India, the main studies

being done in Africa, Asia, and Asia Europe, Latin America, and even developing
agriculture countries support this relationship. The findings of the reverse
relationship are an empirical discovery that is so popular that advocates of small

1


farm agriculture have proposed agricultural strategies that favor small farm (Nkonde
et al., 2015).
However, there are also many studies disagreeing with the above hypothesis
and based on empirical evidence that have provided the opposite opinion, that largescale producers will be more effective than small farm producers or positive
relationship (PR) between farm size and the efficiency of production activities. This
is reflected in policies that support large farm (Srivastava et al., 1973; Khan, 1979;
Khan and Maki, 1979; Kevane, 1996; Adesina and Djato, 1996; Tadesse and
Krishnamoorthy, 1997; Dorward, 1999).
Thus, farm size can have an impact on the efficiency of agricultural production
in two dimensions, showing economies of scale and non-economies of scale (Hoque,
1988; Byiringiro and Reardon, 1996; Heltberg, 1998b; Dorward, 1999; Helfand and
Levine, 2004; Barrett et al., 2010; Henderson, 2015; Wickramaarachchi and
Weerahewa, 2018). From a producer standpoint, it is not possible for households to
decide whether to increase or decrease farm size when being unsure whether the
current farming size is in an economic or non-economic stage, because a wrong
decision will bring serious consequences affecting family livelihoods.
Over the various stages of the economy, most researchers measure or define
the efficiency of productive activities by land productivity when analyzing the
relationship between farm size and HQHDSX, and there are a few other researches
that replacing land productivity measure with technical efficiency or economic
efficiency. In Vietnam in general and the Mekong Delta in particular, this issue was
also studied by some economists but mainly inherited one of two measures (land
productivity or technical efficiency or economic efficiency). In recent years, Li et

al. (2013), Nkonde et al. (2015), Wickramaarachchi and Weerahewa (2018) combine
traditional measurement (land productivity or technical efficiency) with total
measures (labor productivity, capital efficiency and total factor productivity) to
comprehensively assess the efficiency of household production activities.
Agricultural policymakers who face difficult decisions in the choice of
agricultural structure must ensure that achieving two goals of growth and equity
(Khan and Maki, 1979) and contribute to stimulating rural growth and reducing
poverty (Lipton, 1993). On that basis, the Government of Vietnam issued a new
Land Law in 2013, increasing the area of agricultural production land to 03
ha/household in the Mekong Delta and the limit of receiving land use rights is not
more than 30 ha/household in the hope of increasing the efficiency of production
activities. However, reality has a two-way effect, which means that at certain farm
size when increasing the farm size will increase efficiency or sometimes reduce the
efficiency of production activities and vice versa.
On that basis, the thesis "Determining the optimal farm size in agricultural
2


production of households Mekong Delta" deeply analyzes the impact of farm size
on the efficiency of production activities through various measures (measures of
land, labor, capital, economic efficiency, management techniques and technology
improvement) to determine the optimal farm size threshold to maximize the
efficiency of production activities. At the same time, this result serves as a solid
scientific basis for the State to assert or identify the validity of land allocation Policy
in the 2013 Land Law and make a useful contribution to a more rational adjustment
of land Policy in the future, especially helping households use the farm size
appropriately to increase the efficiency of production activities, improve livelihoods
and contribute to the development of the economy.
1.2 Research objectives
To achieve the overall objective of the research that is determine the optimal

farm size in rice production of Mekong Delta households, with the following
specific objectives:
(1) Analyze the status of production and land use in rice production of
households in the Mekong Delta.
(2) Analyze the influence of farm size on the efficiency of rice production
activities of households in the Mekong Delta.
(3) Determine the optimal farm size in rice production of households in the
Mekong Delta.
(4) Proposing solutions to help use the appropriate farm size, contributing to
increase the efficiency of rice production activities for households Mekong Delta.
1.3 Scope of the research
1.3.1 Objective
The main objective of the study is to determine the optimal farm size in rice
production of Mekong Delta households. The research subjects are rice households
and scientists, policy makers, local authorities, local officials, ... related to the farm
size in rice production Mekong Delta.
1.3.2 Content
The thesis focuses on analyzing cultivation activities with the main crop being
rice because rice is the main product of Mekong Delta households and only analyzes
the model of pure rice cultivation (ie 3 rice crops/year).
The thesis focuses on analyzing the influence of farm size on the efficiency of
rice production activities through various measurement aspects to find the optimal
farm size.
1.3.3 Space

3


The thesis wants to find out about differences in rice production among
provinces in a specific ecological region as a basis for forming subsequent studies

for the remaining 2 regions as well as inter-provincial research of each region.
Therefore, the thesis focuses on three provinces (An Giang, Dong Thap and Can
Tho) because according to some experts, the ecological zoning in rice production in
the Mekong Delta is divided by annual floodplain (An Giang and Dong Thap) and
freshwater alluvium (Can Tho). In addition, these 3 provinces have similar
characteristics in terms of ecology, farm size and rice cultivation practices, which
are provinces in the key rice production areas and have high rice production so the
selection of locations is the survey site, the study will be highly representative for
alluvial and freshwater areas.
1.3.4 Time
Data were collected from 498 rice households in the Mekong Delta during the
three crops (Autumn-Winter crop 2016, the Winter-Spring crop 2017 and Summer
crop 2017). The primary data collection time is from September 2017 to December
2017. The time for analyzing secondary data is from 2010 to 2017. The time for
analyzing data and conducting the thesis is from January 2018 to December 2018.

CHAPTER 2. THEORETICAL FRAMEWORK AND
RESEARCH METHODOLOGY
This chapter presents the theoretical basis of the HQHDSX measures, the
theoretical basis of the influence of the farm size on the HQHDSX is measured based
on different aspects, the theoretical basis of the optimal farm size; Propose an
research framework, research model and analytical methods.
2.1 Theoretical framework
2.1.1 Theoretical framework for measuring the efficiency of production
The efficiency of agricultural production activities in general and rice
production in particular is comprehensively measured through two main aspects:
productivity (land productivity, labor productivity, capital efficiency and total factor
productivity) and production efficiency (including technical efficiency, allocative
efficiency, scale efficiency and economic efficiency). Li et al. (2013) showed that
the efficiency of agricultural production activities is a multi-dimensional concept in

the production process, at least including land productivity, labor productivity,
capital efficiency, technical efficiency and total factor productivity.
According to Coelli et al. (2005), productivity is the output index on the input
index (such as land, labor, and capital), thereby forming land productivity, labor
productivity and capital efficiency. However, Li et al. (2013) argue that each

4


measure of land, labor and capital is only an indicator of single factor productivity,
and thus cannot reflect the whole agricultural production process comprehensively.
Land productivity was researched early on in two ways of measuring in kind
and monetary value. The thesis uses the measure of land productivity by money
(Khan, 1977; Khan, 1979; Mahmood and Nadeem-ul-haque, 1981; Cornia, 1985;
Newel et al., 1997; Heltberg, 1998b) are generalized by total value of output per
farm size (NSDAT). This indicator reflects the efficiency of agricultural land use by
rice households and is the most important goal for many developing countries in
food security.
Similarly, labor productivity can be measured in a variety of ways. Since then,
the research uses the measure of labor productivity (NSLD) by production output on
the number of family workers involved in production of Shafi (1984), Li et al.
(2013), Wickramaarachchi and Weerahewa (2018). Freeman (2008) said that labor
productivity is important in economic analysis and statistics of a country.
Accordingly, capital efficiency is also measured in different ways and this
research uses a measure of the ratio of profit per production cost (Schultz, 1964; Li
et al., 2013) because this is an indicator to evaluate the effectiveness of the return on
investment costs on land.
Going further in measuring the efficiency of production activities through
measuring the production efficiency. According to Farrell (1957), efficiency is the
ability to produce a given level of output at lowest cost. Therefore, economic

efficiency (EE) is a basic goal of the producer and is a measure of the success of the
producer in selecting optimal inputs and outputs. Economic efficiency is the product
of technical and allocative efficiency. Thus, to achieve economic efficiency in
agricultural production in general or in rice cultivation in particular, households need
to achieve both technical efficiency and allocative efficiency (Farrell, 1957;
Dhungana et al., 2004).
Currently, the two most commonly used methods in most researchs are the nonparametric estimation method (DEA) and the parameter estimation method (SFA).
The research used parameter estimation method through stochastic frontier analysis
model (SFA) to estimate the economic efficiency of rice farming households because
of its advantage of being able to separate non-effective parts and noise out of errors
in the estimation model but this estimation method requires determining the shape
of functions and errors. Accordingly, EE is estimated through the stochastic profit
frontier function (Ali and Flinn, 1989; Ali et al., 1994; Rahman, 2003; Nwachukwu
and Onyenweaku, 2007; Thong, 1998; Pham Le Thong, 2011a&b; Pham Le Thong
et al., 2011) have the form:
𝜋 = 𝑓 (𝑃 , 𝑍 , 𝛼 )𝑒

5

(2.1)


Therefore, the economic efficiency of rice production households in the
concept of the stochastic profit frontier function is calculated as follows:
𝐸𝐸 = 𝐸 𝑒 (

)

𝜀

(2.2)

Overall, EE is considered to be a better indicator of the three indicators because
it measures both production techniques and input selection. However, this is still not
the perfect target to measure the efficiency of production activities so it is bound by
the market price.
Total factor productivity (TFP) was defined and formed by Tinberger (1942)
early in empirical research in Germany. However, TFP is widely available and used
by many economists from the definition of Solow (1957), according to Solow, TFP
is a technological level or technological progress through the formula:
𝑌 = 𝐴(𝑡) × 𝐹 (𝐿, 𝐾 ) (2.3)
According to Farrel (1957) the origin of TFP growth was due to changes in
technical efficiency and technological advances (Nishimizu and Page, 1992; Coelli
et al., 2005). Sigit (2004) said that TFP is a measure of productivity of all inputs.
This is a qualitative change (such as skills, management methods, technology). TFP
is understood as growth through technological innovation, the efficiency gained
from improving labor and capital management. According to Li et al. (2013), TFP
which can comprehensively reflect the efficiency of the whole agricultural
production process. Hence, Li et al. (2013), Nkonde et al. (2015) use the production
function of the Cobb-Douglas from to calculate TFP. Following Fan (1991) and
Zhang and Carter (1997), they use the following function form:
𝑆𝐿𝑈𝑂𝑁𝐺 = 𝐴 𝑒

𝐾

𝐿

𝐹𝑆

exp(𝜀) (2.4)

where SLUONG is rice quantity produced by households; K, L, FS represent the
value of capital (all costs of production except imputed family costs), total number of
labor days (hired and family labor), and land inputs (farm size) of households,
respectively; αK, αL and αFS are the output elasticities for capital, labor, and land,
correspondingly; t is time trend term and η is the rate of technoligical progress. Using
natural logarithm, equation (2.4) is estimated as follows:
𝑙𝑛𝑆𝐿𝑈𝑂𝑁𝐺 = (𝑙𝑛𝐴 + ηt) + 𝛼 𝑙𝑛𝐾 + 𝛼 𝑙𝑛𝐿 + 𝛼 𝑙𝑛𝐹𝑆 + 𝜀 (2.5)
Given that this production function is estimated with cross sectional data, the
time trend variable is t=1 and thus the lnA0 + ηt term becomes the constant term. To
get the TFP indicator, the research first compute the returns to scale (RTS)
coefficient, which is the sum of factor output elasticities (𝑅𝑇𝑆 = 𝛼 + 𝛼 + 𝛼 ),
then normalize each factor’s output elasticity and obtain 𝛼′ =
𝛼′

=

and define TFP as:

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, 𝛼′ =

,


𝑇𝐹𝑃 =

𝑆𝐿𝑈𝑂𝑁𝐺
𝐾

𝐿

𝐹𝑆

(2.6)

Based on the theory of TFP, this is an indicator which can comprehensively
reflect the efficiency of the whole agricultural production process because it includes
the use of management techniques with the relevant technology level (Li et al., 2013)
and this is also the index not affected by the price of inputs as well as the price of
output products. Therefore, the thesis uses TFP criteria to determine the optimal farm
size threshold to maximize HQHDSX, at the same time, the thesis still analyzes the
other four criteria as a basis to prove the assertion that “each of these indicators is
not the best measure the efficiency of production activities”.
2.1.2 Theoretical basis of the influence of farm size on the efficiency of
production activities
Wickramaarachchi and Weerahewa (2018) productivity is defined as the ability
of an input unit to produce a given output unit. Agricultural productivity shows the
level of efficiency of households in using a specific input with a certain level of
technology.
The inverse relationship between farm size and the efficiency of production
activities plays an important role in many regions at different times and this
relationship was first discovered in agricultural production in Russia by Chayanov
(1926), then inherited and developed widely in the 1960s and 1970s (Sen, 1962;
Bardhan, 1973). Sen (1962) the small farms in India, which obtaining higher the
efficiency of production activities as they apply more inputs (especially family
labor). Berry and Cline (1979) also demonstrated a relationship similar to Sen in
other developing countries and Deolalikar (1981) argued that this relationship was
only true in traditional agriculture. This relationship became a hotly debated topic

between agricultural economists and development economists (Carter, 1984; Feder,
1985; Benjamin, 1995).
Imperfections in the market of inputs also contribute to the formation of a
strong inverse relationship between farm size and the efficiency of production
activities. First, the analysis of data from fifteen developing countries, Cornia (1985)
shows that systematic output per unit of agricultural land decreases as the farm size
of increase by labor is more abundant and cheaper for small farm. Head of
households' knowledge of land and local climatic conditions accumulated over
generations contributes to an advantage over hiring workers (Rosenzweign and
Wolpin, 1985). The advantage of supervision and knowledge of small farm will
compensate for difficulties in accessing capital and formal insurance in rural markets
(Feder, 1985). An inverse relationship between fram size and the efficiency of
production activities is caused by the imperfections of credit and labor markets when

7


combined with fixed costs of production (Eswaran and Kotwal, 1986). Imperfection
land and insurance motivate smallholders to use more family labor to reduce the
potential adverse effects of price fluctuations (Barrett, 1996). Assuncao and Ghatak
(2003) demonstrated an inverse relationship after controlling households'
heterogeneity of skills. Thapa (2007) also discovered this relationship in Nepal
because it used more labor and cash than large farms. Ansoms et al. (2008) found a
strong inverse relationship between farm size and the efficiency of production
activities Rwanda due to the scarcity of land that forced households to over-exploit
their resources in the case of main household income from agricultural production.
The research also found that increases in non-farm wages and technological
advances will affect the exchange rate, management capacity, presence and level of
market imperfections. It is these factors that will form the inverse relationship
between farm size and HQHDSX (Otsuka, 2013).

However, a number of other studies also explain the existence of inverse
relationships due to the omission of other factors that affect the HQHDSX such as
knowledge and technical understanding as well as socio-economic environmental
issues. In which households must make decisions (Kalirajan, 1990) and based on
previous researches, two of the socio-economic environmental indicators (include
education and income other than agriculture) have been selected to measure the
relationship between farm size and HQHDSX (Bravo – Ureta and Pinheiro, 1997),
differences between households (Assuncao and Ghatak, 2003), land fragmentation
(Wu et al., 2005), differences in soil quality (Benjamin, 1992; Lamb, 2003;
Assuncao and Braido, 2007), soil characteristics and sand content (Barrett et al.,
2010) and the other factors, at the same time omitting the different definitions that
show the HQHDSX. Therefore, Li et al. (2013), Wickramaarach and Weerahewa
(2018) added exogenous variables to control the influence of these factors on the
efficiency of rice production activities of households. However, the level of the
impact of the inverse relationship between farm size and HQHDSX tends to decline
over time (Deininger and Byerlee, 2012; Deininger et al., 2015) due to the
emergence of imperfect labor markets and technological change.
On the contrary, some researches have demonstrated a positive relationship
between farm size and the efficiency of production activities, meaning that large
farm are more effective than small farm. The emergence of a Green Revolution has
increased the role of capital and knowledge, which has led to the emergence of large
farm achieving a higher level of HQHDSX in districts suitable for new technologies
(Deolalikar, 1981). Recent innovations in plant breeding, tillage and information
technology help households easier monitor labor, thus increasing the efficiency of
production activites in traditional agriculture at Eastern Europe and South America
(Helfand and Levine, 2004; Lissitsa and Odening, 2005). And this positive

8

relationship was also discovered in Nigeria due to the high quality inputs used by
large farm of the households (Obasi, 2007), in Japan that having relatively well
factor markets (Kawasaki, 2010) and China due to technological development and
technological transformation (Chen et al., 2011).
Mixed results obtained by Rahman and Rahman (2009) suggest that a positive
relationship between HQHDSX and farm size occurs in advanced technology areas
and the inverse relationship still exists in developing regions. Tamel (2011) in the
US agriculture sector showed that in some areas, farm size and the efficiency of
production activities is positive but in others may have a positive relationship
(Kawasaki, 2010; Ali and Deininger, 2015; Lu et al., 2018) or the negative
relationship (Paul and Githinji, 2017) with farm size depending on the fragmentation
process. Hence, the inverse relationship is a local phenomenon rather than an
indispensable law in production.
The researches not only stop at a simple relationship (negative or positive
relationsgip) but also show a nonlinear relationship (U-shaped or ∩-shaped) between
farm size and HQHDSX. First, Mahmood and Nadeem-ul-haque (1981) have
demonstrated the U-shaped nonlinear relationship between farm size and HQHDSX
when estimating the inputs (farm size, square farm size) with output. Inheriting that
achievement, researchers Byiringiro and Reardon (1996), Heltberg (1998b) have
added the characteristics of soil and region characteristics, followed by Helfand and
Levine (2004) and Ali and Deininger (2015) developed Heltberg's model on the basis
of adding soil characteristics.
However, Dorward (1999), Kimhi (2006), Barrett et al. (2010), Ali and
Deininger (2015), Nkonde et al. (2015), Henderson (2015), Anseeuw et al. (2016),
Wickramaarachchi and Weerahewa (2018) argue that there is an inverted U-shaped
nonlinear relationship between farm size and HQHDSX through different models
from simple (only farm size variables and squared farm size) to the complete model
of information and characteristics of the head of household, land characteristics and
quality, the ability to manage and care for rice fields, ... all show this relationship.
2.1.3 Theoretical backgrounds of optimal farm size

According to economic theory and economics of agricultural production
theory, Debertin (2002) demonstrated the optimal input threshold to maximize the
output through the first derivative calculation method based on specific inputs.
According to Greene (2003), consider finding the x where f(x) is maximized
or minimized. Because f’(x) is the slope of f(x), either optimum must occur where
𝑓 (𝑥) = 0. Otherwise, the function will be increasing or decreasing at x. This result
implies the first-order or necessary condition for an optimum maximum or minimum
is

= 0. Hence, to maximize or minimize a function of several variables, the first-

9


order conditions are

( )

=0

According to microeconomic theory, when the smaller farm, the higher the
average cost will increase, and the larger farm expands, the lower the average cost
will decrease, until a certain farm size (or the maximum farm size) the average cost
will be the minimum and if the optimal farm size is exceeded, the average cost will
increase with increasing farm size, which results in the opposite of the production
function, implies obtain the largest average yield for the optimal farm size.
According to Wickramaarachchi and Weerahewa (2018), the optimal farm size
is the farm size at which the HQHDSX is maximized. Because when farm size is
still small, if farm size continues to expand, the effectiveness will increase and
achieve the highest efficiency at the optimal farm size threshold. At this farm size

threshold, if farm size continues to expand, the efficiency decreases and the optimal
farm size threshold is determined by

.

2.2 Overview of references
2.2.1 The research of the effects of farm size on the efficiency of production
activities
2.2.1.1 Effect of farm size on land productivity
The inverse relationship is discussed and discovered through theory and
experiment on many countries around the world (Mazumdar, 1965; Bharadwaj,
1974; Khan, 1977; Chaddha, 1978; Berry and Cline, 1979; Carter, 1984 ; Cornia,
1985; Feder, 1985; Bhalla and Roy, 1988; Chattopadhyay and Sengupta, 1997;
Heltberg, 1998a&b; Assuncao and Ghatak, 2003; Fan and Chan-Kang, 2005; Barrett
et al., 2010; Chen et al., 2011; Sial et al., 2012; Carletto et al., 2013; Holden and
Fisher, 2013; Ali and Deininger, 2015; Desiere and Jolliffe, 2017) but with a focus
on India (Sen, 1962; Bardhan, 1973; Ghose, 1979; Newell et al., 1997; Assuncao
and Braido, 2007; Gaurav and Mishra, 2015).
However, there are also many studies disagreeing with the above hypothesis
and based on empirical evidence that have provided the opposite opinion, that large
farm households will be more effective than households with small farm (Rao, 1966;
Srivastave et al., 1973; Heltberg, 1998a&b; Khan, 1979; Khan and Maki, 1979; Rao
and Chotigeat, 1981; Kevane, 1996; Akram-Lodhi, 2001; Van Hung and et al., 2007;
Truong Hong Vo Tuan Kiet and Hua Tuan Tai, 2013; Akudugu, 2016).
Thus, farm size can have an impact on land productivity in two dimensions,
showing economies of scale and non-economies of scale. Studies (Mahmood and
Nadeem-ul-haque, 1981; Byiringiro and Reardon, 1996; Heltberg, 1998b; Ali and
Deininger, 2015) have demonstrated a U-shaped nonlinear relationship between
farm size and land productivity. However, Dorward (1999), Barrett et al. (2010), Ali

10


and Deininger (2015), Nkonde et al. (2015), Henderson (2015), Anseeuw et al
(2016), Wickramaarachchi and Weerahewa, 2018 suggest that having an inverted Ushaped nonlinear relationship between farm size and land productivity.
2.2.1.2 The effect of farm size on labor productivity
The efficiency of production activities is measured by labor productivity that
is not as commonly researched as land productivity but has been researched in recent
years and shows a positive relationship between farm size and labor productivity
(Lamb, 2003; Li et al., 2013; Adamopoulos and Restuccia, 2014).
Researchers Byiringiro and Reardon (1996), Nkonde et al. (2015),
Wickramaarachchi and Weerahewa (2018) also found an inverted U-shaped
nonlinear relationship between farm size and labor productivity based on energy
estimation labor productivity with different explanatory variables such as farm size,
square farm size, variables showing the characteristics of the household head, the
characteristics of the land, and differences in the residence area.
2.2.1.3 Effect of farm size on capital efficiency
Although there are very few studies on this relationship, it still shows a clear
relationship like other HQHDSX. First, Li et al. (2013), Wickramaarachchi and
Weerahewa (2018) used capital efficiency measure to measure HQHDSX and show
the positive relationship between farm size and capital efficiency. However, Nkonde
et al. (2015) measured the capital efficiency use through cost efficiency and found
an inverted U-shaped nonlinear relationship between farm size and capital efficiency
in all three cases from single, semi-complete to complete variables.
2.2.1.4 The effect of farm size on economic efficiency
Many researches have demonstrated an inverse relationship between farm size
and economic efficiency (Lau and Yotopoulos, 1971; Tadesse and Krishnamoorthy,
1997; Bagi, 1982; Townsend et al., 1998; Xu and Jeffrey, 1998; Gorton and
Davidova, 2004; Manjunatha et al., 2013). In contrast, Hall and Leveen (1978), Lund
and Hill (1979), Hoque (1988), Kalaitzandonakes et al. (1992), Sharma et al. (1999),

Alvarez and Arias (2004), Rios and Shively (2005), Tipi et al. (2009), Nguyen Huu
Dang (2012) have demonstrated the positive relationship between farm size and
production efficiency.
Researchers not only stop in the linear relationship between farm size and
production efficiency but also research and make judgments about the existence of
nonlinear relationship between farm size and production efficiency. The U-shaped
relationship between farm size and production efficiency is shown through the
varius research of (Helfand and Levine, 2004). In contrast, Hoque (1988), Nguyen
Tien Dung and Le Khuong Ninh (2015), Nguyen Tien Dung (2015) have
demonstrated the inverted U-shaped nonlinear relationship between farm size and

11


production efficiency.
2.2.1.5 The effect of farm size on the total factor productivity
Although the relationship between farm size and TFP is not as deeply
concerned as the relationship between farm size and land productivity. However,
it still shows that there may be a linear relationship (negative or positive
relationship) between TFP and farm size or nonlinear relationship through some
empirical researches. First, Van Zyl et al. (1996), Li et al. (2013), Gautam and
Ahmed (2018) found an inverse relationship between farm size and TFP. In
contrast, other researches have found a positive relationship between farm size and
TFP through experiments in Czech Republic (Hughes, 1998), in Slovakia (Hughes,
2000), in Vietnam (Dinh Bao, 2014) and in Australia (Sheng and Chacellor, 2018).
The research also found nonlinear relationship between farm size and TFP in
two different forms. The U-shaped nonlinear relationship between farm size and
TFP through the research of Nkonde et al. (2015) have proved that the inverted Ushaped nonlinear relationship between farm size and TFP.
2.2.1.6 The effect of farm size on the efficiency of production activities
As just stated, most researcges only use a single measure of HQHDSX, in

which land productivity is commonly used in many researches to explore the
relationship between farm size and HQHDSX. Other ways of measuring HQHDSX
such as labor productivity, capital efficiency, technical efficiency and TFP are
rarely used. In recent years, a comprehensive measurement of the HQHDSX
through various measurement aspects (using 3-5 measurements) was conducted by
Li et al. (2013), Nkonde et al. (2015), Wickramaarachchi and Weerahewa (2018)
have demonstrated a different relationship (linear as negative or positive
relationship, U-shaped or inverted U-shaped nonlinear relationship) between farm
size and HQHDSX depending on the measurement specifically the efficiency of
production activities.
2.2.2 Researches on optimal farm size
Many researches have demonstrated the optimal farm size threshold to
maximize the efficiency of production activities according to one of five different
measurements in the same data set (researches from 3-5 measures representing the
HQHDSX) or different data set (single research a measure of HQHDSX). On the
basis of the first derivative or

based on the estimation results of the model of

factors affecting on HQHDSX (Hoque, 1988; Hassanpour, 2013; Nguyen Tien
Dung, 2015; Nkonde et al., 2015; Wickramaarachchi and Weerahewa, 2018)
2.3 Research methods
2.3.1 Research framework
Land
Productivity

Optimal farm size
by NSDAT

Labor

Productivity

Optimal farm
size by NSLD

12


Source:Research and design

Hình 2.1 Proposes research framework
2.3.2 Data collection
The study selected three provinces in the Mekong Delta with the same
characteristics of the land with large farm rice cultivation of An Giang, Dong Thap
and an average of Can Tho. The study collected randomly 498 rice-producing
households in the Autumn-Winter 2016, Winter-Spring 2017 and Summer 2017
seasons, of which An Giang (225 households), Can Tho (90 households) and Dong
Thap (183 households).
2.3.3 Data analysis
Objective 1: Research using descriptive statistical methods
Objective 2: Research to use 2 ways:
- A two-step estimation method for four ways of measuring the efficiency of
production activities including land productivity, labor productivity, capital
efficiency and TFP.
- An one-step estimation method for economic efficiency measure.
Objective 3: Use necessary conditions and calculation formula of Greene
(2003), Wickramaarachchi và Weerahewa (2018):
𝜕𝐻𝑄𝐻𝐷𝑆𝑋(𝑄𝑀𝐷𝐴𝑇)
𝛽
= 0 => 𝑄𝑀𝐷𝐴𝑇 =

(2.7)
𝜕𝑄𝑀𝐷𝐴𝑇
2𝛽
Objective 4: rely on the achieved results to propose the most effective solutions
2.4 Estimation model of farm size impacts on the efficiency of rice production
activities of Mekong Delta households
The general model measures the impact of farm size on the efficiency of
production activities through various aspects as follows:

13


𝐻𝑄𝐻𝐷𝑆𝑋

= 𝛽 + 𝛽 𝑄𝑀𝐷𝐴𝑇 + 𝛽 𝑄𝑀𝐷𝐴𝑇𝑆𝑄 + 𝛽 𝑄𝑀𝐿𝐷 + 𝛽 𝑁𝑈𝐶𝐻 + 𝛽 𝑇𝐷𝐻𝑉𝐶𝐻
+ 𝛽 𝑇𝑁𝐾𝐻𝐴𝐶 + 𝛽 𝑆𝑂𝑀𝐴𝑁𝐻 + 𝛽 𝐿𝐷𝑇𝐻𝑈𝐸 + 𝛽 𝐿𝐷𝐺𝐷
+ 𝛽 𝐴𝑁𝐺𝐼𝐴𝑁𝐺 + 𝛽 𝐷𝑂𝑁𝐺𝑇𝐻𝐴𝑃 + 𝛽 𝑇𝑉𝑂𝑁 + 𝛽 𝑇𝐻𝐴𝑀𝑁𝐼𝐸𝑁
+ 𝛽 𝐾𝐶𝑅𝑈𝑂𝑁𝐺 + 𝛽 𝑇𝐴𝑃𝐻𝑈𝐴𝑁 + 𝜀

(2.8)

where: HQHDSXk is the efficiency of production activities measured by
different aspects, QMDAT is the farm size of rice cultivation on the largest field (ha),
QMDATSQ is the square of farm size of the household, QMLD is the number of
working-age members of the family involved in rice production (number of
employees), NUCH is the dummy variable representing the gender of the head of
household (= 1 if the head of household is female and = 0 otherwise) , TDHVCH is
the educational level of the head of household (number of classes), TNKHAC is the
household's non-rice income (million VND /year), SOMANH is the number of rice
plots of the household (the number of plots), LDTHUE is the total labor days hired

to work in rice fields (days/ha), LDGD is the total number of family labor days
working on rice fields (days/crop), ANGIANG (= 1 if the household lives in An
Giang and = 0 if in other province), and DONGTHAP (= 1 if the household lives in
Dong Thap and = 0 if in another province), TVON is the total cost of the inputs
(including family labor) (million VND/crop), THAMNIEN is the number of years
of rice farming experience of the head of the household (year), KCRUONG is the
distance from the household to the largest field (km), TAPHUAN (= 1 if the head of
the household participated in training courses in the last 3 years and = 0 if otherwise),
i indicate the number of i rice the households and j showing the number of j crops.

14


CHAPTER 3. OVERVIEW OF RESEARCH AREAS
This chapter presents an overview of the Mekong Delta as well as of the
provinces surveyed mainly regarding farm size.
3.1 Land resources in the Mekong Delta
Land area in the survey area is concentrated on alluvial soil. This soil group
has high fertility and balance, favorable for agricultural production, especially for
rice, coconut, sugarcane, pineapple and fruit trees.
3.2 Current status of rice production
3.2.1 Farm size in the Mekong Delta
* Farm size of agricultural production
Number of agricultural households in the Mekong Delta in general and the
three researched provinces in particular concentrated mainly on the farm size of 0.5
- 2 ha, accounting for 40.36%, followed by the scale of 0.2 - 0.5 ha accounts for
25.13% and the remaining is allocated at other ones.
Table 3.1 Number of households using agricultural land in the Mekong Delta by farm size

Unit: Household

Location
An Giang
Can Tho
Dong Thap
Mekong Delta

< 0,2 ha
37.887
20.942
49.341
509.795

0,2 – 0,5 ha
36.339
26.850
53.713
598.932

0,5 – 2 ha
68.427
43.264
89.269
961.914

> 2 ha
27.395
11.151
24.086
312.455

Total
170.048
102.207
216.409
2.383.335

Nguồn:Tổng điều tra nông thôn, nông nghiệp và thủy sản Việt Nam năm 2016

* Farm size of rice cultivation by locality
6000

ĐBSCL

4000

Đồng Tháp

2000

An Giang

0
2010 2011 2012 2013 2014 2015 2016 2017

Cần Thơ

Source:General Statistics Office 2017

Figure 3.1 Scale of paddy land in the Mekong Delta by location
Farm size of rice cultivation is concentrated in An Giang, followed by Dong

Thap and at least Can Tho. This result forms the number of households surveyed in
the provinces in the study area.
* Farm size of rice cultivation by scale
The total number of rice-growing households in the Mekong Delta accounts
for 19.12% of the total number of rice-growing households nationwide and
concentrates on the scale of 0.5 - 2 ha. The number of rice-growing households in
Dong Thap is higher than the other two provinces and when divided by farming size,

15


the farming scale of the people of 3 provinces of An Giang, Can Tho and Dong Thap
is still similar to households in the Mekong Delta, which means that most people
cultivate on a scale of 0.5 - 2 ha, accounting for about 40%, from 0.2 to 0.5 ha,
accounting for 25% and under 0.5 ha accounts for about 22% and the scale of over
2 ha accounts for about 13%. This implies that people in the surveyed area are
cultivating on a small farm and fragmented so not really bring about optimal the
efficiency of production activities.
60.00
An Giang

40.00

Cần Thơ
Đồng Tháp

20.00

ĐBSCL

0.00
< 0.2 ha

0.2 - 0.5 ha

0.5 - 2 ha

> 2 ha

Source: Vietnam Rural, Agriculture and Fishery Census 2016
Figure 3.2 Structure of households using rice land in the Mekong Delta by scale
3.2.2 Results of rice production in the research area
Đồng Tháp

An Giang

Cần Thơ

6,000.00
4,000.00
2,000.00
0.00
2010

2011

2012

2013

2014

2015

2016

Sơ bộ
2017

Source: General Statistics Office 2017

Figure 3.3 Rice production in the Mekong Delta 2010 - 2017
Rice production in the three provinces of An Giang, Dong Thap and Can Tho
followed the increasing trend over time for the whole period but began to decline
slightly in 2016, of which the highest output of An Giang and Can Tho was the
lowest. Can Tho has the lowest output but the growth rate is quite high when
compared to 2010 and 2017 at about 16%, reaching the highest output of 1,408
million tons in 2015. For An Giang and Dong Thap due to the large output, the
downward trend in the following years is more evident although the output increased
during the period of more than 6% and 14% for each province. To conclude, the
output of the provinces has increased but started to decline slightly in the following
years and slower than the area so the provinces have lower productivity in the
following years.

16


CHAPTER 4. RESEARCH RESULTS AND DISCUSSION
This chapter presents and discusses the results of each study. From there,
proposing solutions for effective use of farm size contributes to improving the

efficiency of production activities, improving incomes and raising the living
standards of households in the Mekong Delta.
4.1 Actual situation of rice production in Mekong Delta households
4.1.1 Land
Table 4.1 Actual situation of farmland
Households (m2/household)
Average
(%)
755,63
3,93
18.401,43
95,82
47,39
0,25
19.204,45
100,00

Soil tyle
Residential
Agricultural
Aquaculture
Tổng cộng

Average (m2/person)
Bình quân
Tỷ lệ (%)
172,52
3,93
4.201,24
95,82

10,82
0,25
4.384,58
100,00

Source: Summary results of self-survey data in 2017

Land is a valuable asset for households because it is an indispensable input in
agricultural production - especially rice - and also a source of inheritance for future
descendants. However, under the impact of urbanization and the inheritance of
children, the farm size has been gradually reduced due to the land being recovered
to build public welfare works, separating households and selling land.
4.1.2 Rice production results of households in the Mekong Delta
* Productivity
Ton/ha
10.00

6.80 6.78
6.16

7.72

8.33
6.68

6.67 7.07

6.14

5.00

Dong Thap
An Giang
Can Tho

0.00

Autumn-Winter Winter-Spring

Summer

Source: Summary results of self-survey data in 2017

Figure 4.1. Yield of seasonal rice manure

In general, there is not much difference in rice yield among provinces. The
higher yielding crop compared to the remaining two crops in the three provinces is
the winter-spring crop because this is the season with favorable weather, climate and
development environment for rice.

17


* Production results
Average profit ratio and net profit ratio of households are quite low and
relatively similar, about 0.5 times and 0.3 times (respectively for autumn-winter and
summer crops), but quite high in winter-spring.
Table 4.2 Rice production results of Mekong Delta households by season
Items

AutumnWinter

Unit

WinterSpring

1. Quantity

Ton
11,40
13,40
Thousand
2. Price
VND/kg
4,80
4,90
3. Revenue
Million VND
55,01
66,18
Million VND
4. Cost (except LDGD)
36,60
37,40
Million
VND
5. Total cost (include LDGD)
40,90
42,00
Million VND

6. Profit except LDGD = (3)-(4)
18,40
28,80
Million
VND
7. Profit include LDGD = (3)-(5)
14,10
24,20
8. Profit ratio = (6)/(4)
Times
0,50
0,77
9. Net profit ratio = (7)/(5)
Times
0,35
0,58
Source: Summary results of self-survey data in 2017

Summer

11,50
4,90
56,35
37,90
42,30
18,40
14,10
0,49
0,33

4.2 Description of the variables in the general model
The dissertation uses a group of explanatory variables to estimate the effects of
these variables on five measures of HQHDSX or there are five models to estimate the
effects of farm size on HQHDSX (including land productivity, labor productivity,
capital efficiency, EE and TFP). Thus, the description of quantitative variables (Table
4.3) and qualitative (Table 4.4) is made before going into the analysis of the estimated
results.
Table 4.3 Quantitative variables in the model (2.8)
Criteria
QMDAT
QMLD
TDHVCH
TNKHAC
SOMANH
LDTHUE
- Autumn-winter
- Winter-spring
- Summer
LDGD
- Autumn-winter
- Winter-spring
- Summer
TVON
- Autumn-winter
- Winter-spring
- Summer
THAMNIEN
KCRUONG

Unit

Ha
Person
Classes
Million VND/year
Plots

Mean
1,71
1,70
5,97
21,33
1,08

Max
17,00
5,00
15,00
100,00
3,00

Min
0,10
1,00
0,00
0,00
1,00

Std. Dev
1,77
0,90

3,51
21,79
0,31

Days/ha

11,80
11,49
11,28

126,88
73,13
71,88

0,08
0,16
0,00

13,68
9,81
8,71

Days/ha

15,05
13,27
12,55

71,67
70,00

70,00

0,31
0,08
0,00

11,82
10,93
10,42

14,15
15,80
15,80
6,00
0,01

3,80
4,05
3,84
10,98
10,77

Million VND/ha

24,04
39,35
24,60
43,16
24,44
40,50

Years
30,07
60,00
Km
4,86
75,00
Source: Summary results of self-survey data in 2017

18


Table 4.4 Quanlitative variables in the model (2.8)
NUCH
Number of household
51
447
498

Criteria
Yes
No
Total

(%)
10,24
89,76
100,00

TAPHUAN
Number of household

297
201
498

(%)
59,64
40,36
100,00

Source: Summary results of self-survey data in 2017

4.3 Optimal farm size of rice production of Mekong Delta households
4.3.1 Description of the variables in the model
Table 4.5 HQHDSX in rice cultivation of people in Mekong Delta
HQHDSX
NSDAT
NSLD
HQDV
EE
TFP

Unit

Autumn
-winter
Mil.VND/ha
32,58
Ton/person
8,70
%

53,79
%
94,45
%
3,44

Mean
Winter- Summer
spring
38,03
33,05
10,28
8,73
77,59
52,98
80,07
72,50
6,09
3,37

Whole
year
103,66
27,71
184,35
45,19
12,79

Autumn
-winter

6,95
10,53
38,23
5,86
0,97

Std. Dev
Winter Summer
-spring
8,22
6,77
13,00
10,55
48,75
36,62
12,88
13,04
1,33
0,95

Source: Summary results of self-survey data in 2017

4.3.2 The influence of the farm size on the efficiency of production activities
4.3.2.1 Autumn-Winter crop
Table 4.6 Influencing factors of farms size to rice HQHDSX of Autumn-Winter crop 2016
Variables
QMDAT
QMDATSQ
QMLD
NUCH

TDHVCH
TNKHAC
SOMANH
LDTHUE
LDGD
ANGIANG
DONGTHAP
TVON
THAMNIEN
KCRUONG

NSDAT
0,2273
-0,0454
0,2766
1,3622
0,0441
0,0156
1,6353
-0,0374
-0,0315
2,0817**
1,5252
0,5029***
-0,0787***
0,0204

NSLD
5,4447***
-0,0530

-2,9247***
-0,1660
-0,1086*
0,0028
1,4088
-0,0101
-0,0052
-0,0463
-0,3427
0,1993***
-0,0387**
0,0048

TAPHUAN
Cons
Number of obs
R-squared
Prob > F

1,0733*
-0,6917*
0,0477
-0,1429
18,4402***
0,8756
1,3619***
0,5273
498
478
498

498
0,1157
0,8371
0,2590
0,0000
0,0000
0,0000
0,0000
Source: Estimated results of self-survey data in 2017

19

HQDV
0,0091
-0,0022*
0,0156
0,0791
0,0033
0,0006
0,0788
-0,0027***
0,0083***
0,1302**
0,0913*
-0,0453***
-0,0036***
0,0007

EE
-0,1637

0,0195**
0,0801
-0,1821
-0,0209
-0,0028
-2,0357**
-0,0176*
-0,0207***
0,2648
0,1879
0,0604***
0,0070*
0,0020

TFP
0,6536***
-0,0350***
0,0193
0,1107
-0,0008
0,0010
0,1717*
-0,0034*
-0,0014
0,2598**
0,2727***
0,0121
-0,0095***
0,0005
0,0566

1,9870***
498
0,5552
0,0000

Whole
year
18,58
33,80
102,96
19,97
2,92


4.3.2.2 Winter-Spring crop
Table 4.7 Factors affecting the farm size to rice HQHDSX of Winter-Spring crop 2017
Variables
QMDAT
QMDATSQ
QMLD
NUCH
TDHVCH
TNKHAC
SOMANH
LDTHUE
LDGD
ANGIANG
DONGTHAP
TVON
THAMNIEN

KCRUONG
TAPHUAN
Cons
Number of obs
R-squared
Prob > F

NSDAT
NSLD
HQDV
EE
0,6560*
6,0002***
0,0334*
-0,0281*
-0,0498*
0,0337
-0,0027*
0,0016
-0,6085
-3,3568*** -0,0244
0,0222
0,7193
-0,0801
0,0482
-0,0343
0,1108
-0,1210*
0,0060
-0,0063*

0,0032
-0,0039
-0,0002
-0,0004
-0,5742
0,3474
-0,0334
0,0601
0,0277
-0,0076
0,0010
-0,0072***
0,0634*
0,0182
0,0161***
-0,0071***
6,3541***
0,4117
0,3430***
-0,1977***
4,2008***
0,1470
0,2137***
-0,1635***
0,2349***
0,0692
-0,0618***
0,0374***
-0,0750**
-0,0464**

-0,0036**
0,0022**
-0,0661***
-0,0066
-0,0025*
0,0026**
-0,2605
-1,2861*** -0,0241
-0,0146
29,4489***
6,0721***
1,9715***
-0,5372***
498
489
498
498
0,1719
0,8555
0,3915
0,0000
0,0000
0,0000
0,0000
Source: Estimated results of self-survey data in 2017

TFP
0,4504***
-0,0231***
-0,0158

0,1849
0,0051
-0,0002
-0,1196
-0,0025
0,0043
0,9682***
0,8484***
0,0114
-0,0160***
-0,0086**
-0,0296
5,0518***
498
0,2763
0,0000

4.3.2.3 Summer crop
Table 4.8 Factors affecting the farm size to rice HQHDSX of Summer crop 2017
Variables
QMDAT
QMDATSQ
QMLD
NUCH
TDHVCH
TNKHAC
SOMANH
LDTHUE
LDGD
ANGIANG

DONGTHAP
TVON
THAMNIEN
KCRUONG
TAPHUAN
Cons
Number of obs
R-squared
Prob > F

NSDAT
NSLD
HQDV
EE
-0,4013
5,1126***
-0,0144
0,0121
0,0024
-0,0033
-0,0002
-0,0000
0,2486
-2,8412***
0,0093
-0,0013
1,9494**
0,1540
0,0860*
-0,0755**

0,0928
-0,0876*
0,0058
-0,0041
-0,0086
0,0016
-0,0006
0,0002
-0,1243
0,2079
-0,0101
0,0190
-0,0092
-0,0230
-0,0015
-0,0008
0,0113
0,0252
0,0110***
-0,0079***
2,8224***
0,1371
0,1490***
-0,0803***
1,7355*
-0,2822
0,0870*
-0,0594**
0,5435***
0,1219**

-0,0392***
0,0219***
-0,0667**
-0,0300*
-0,0027*
0,0020**
-0,0153
-0,0007
-0,0005
0,0000
-0,2308
-1,0920***
-0,0081
-0,0138
19,8454***
3,6866**
1,3470***
-0,1176
498
498
498
498
0,1398
0,8604
0,2508
0,0000
0,0000
0,0000
0,0000
Source: Estimated results of self-survey data in 2017

20

TFP
0,6165***
-0,0308***
0,0287
0,1906**
0,0032
-0,0008
-0,0102
-0,0046
0,0058*
0,2799***
0,2119**
0,0162*
-0,0089***
-0,0013
-0,0514
2,1235***
498
0,5622
0,0000


4.3.2.3 Whole year
Table 4.9 Factors affecting the farm size to rice HQHDSX in the whole year
Variables
QMDAT
QMDATSQ

QMLD
NUCH
TDHVCH
TNKHAC
SOMANH
LDTHUE
LDGD
ANGIANG
DONGTHAP
TVON
THAMNIEN
KCRUONG
TAPHUAN
Cons
Number of obs
R-squared
Prob > F

NSDAT
0,6136
-0,0996
-0,1777
3,9556
0,2515
0,0106
0,7375
-0,0242
0,1069***
10,9699***
7,3019***

0,5009***
-0,2199***
-0,0549
0,7490
62,9592***
498
0,1515
0,0000

NSLD
16,5436***
-0,0222
-9,1353***
0,1861**
-0,3196
0,0002
2,1273
-0,0222
0,0208
0,4228
-0,5430
0,1621**
-0,1137**
-0,0096
-3,0666**
8,1154
498
0,8638
0,0000

HQDV
0,0174
-0,0045
0,0090
0,1821
0,0122
-0,0000
0,0716
-0,0015
0,0057***
0,6099***
0,3824***
-0,0481***
-0,0098***
-0,0028
0,0201
4,5076***
498
0,3049
0,0000

EE
-0,0171
0,0028
0,0089
-0,1147
-0,0099
-0,0008
0,0409
0,0011

-0,0064***
-0,2278***
-0,2002***
0,0214***
0,0054***
0,0018
-0,1922***
-0,3169
498
0,0000

TFP
1,7196***
-0,0889***
0,0274
0,4794
0,0062
0,0000
0,0690
-0,0044
0,0049
1,4695***
1,2979***
0,0242**
-0,0348***
-0,0100
-0,0119
8,3258***
498
0,5088

0,0000

Source: Estimated results of self-survey data in 2017

4.3.3 Optimal farm size
HQHDSX 20.000
15.000

Autumn-Winter

10.000

Winter-Spring

5.000

Summer

0.000
0

2

4

6

8 10 12 14 16 18

Whole year

Farm size (Ha)

Source: Summary results of self-survey data in 2017

Figure 4.2 Optimal farm size in rice cultivation of Mekong Delta households
From the estimation results, the thesis uses the differential method to
determine the optimal farm size threshold for each production crop, implying that
this is the optimal farm size threshold to help households maximize the efficiency
of production activities, because if production exceeds this optimal one, the
household's ability to manage and apply technology is ineffective by (i) It is
difficult to control the working motivation of hired labor, (ii) The capital is
limited and (iii) Low management capacity due to low education level. Hence, to
bring the highest efficiency in rice production, households should invest in a
reasonable farm size for each crop (from 9 ha to 10 ha) and the optimal farm size
threshold for whole year is 9.67 hectares.

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4.4 Solutions to contribute improving the efficiency of rice production
activities of households in the Mekong Delta
4.4.1 The solution uses a reasonable farm size
4.4.1.1 Households with farm size smaller than the optimal farm size
For those households that are cultivating rice on the largest plot of land smaller
than the optimal farm size, these households are largely dependent on whether or
not financially available. Thus, the proposed solution is based on specific division
for each suitable target group.
(i) Households with good conditions and strong financial resources
Households should rent or pledge the land of adjacent farming households to
take advantage of economies of scale. Households can also buy additional land

(accounting for 25.27% of the total farmers' opinions) of neighboring households or
buy land in other areas of the region under the support of the Government from the
Loan policy with preferential interest rates.
Collaborate with neighboring small-scale rice households to expand
production scale with groups, rice cultivation groups or cooperatives.
Households can participate in a large model field to take advantage of the farm
size and government's support policies.
Households and businesses need to link up to establish "large sample fields"
and establish specialized farming areas associated with Viet GAP standards.
(ii) For households with limited financial resources
Rent or mortgage land to neighboring households wishing to expand their
farming.
Boldly transforming industries (especially non-agricultural occupations)
through state support (vocational training, assistance in accessing capital, policies to
attract investment and development of cottage industries and handicrafts) .
Boldly transfer land to neighboring farmers in a suitable form when rice
cultivation is not possible (ie exits to join the labor market).
The government should speed up the transfer of land use rights and voluntary
labor contracts (Li et al., 2013).
4.4.1.2 Households whose farming size is greater than the optimal farm size
Narrow the farming size to take advantage of internal resources as well as
apply economic principles to increase the efficiency of production activities by
dividing rice fields into two fields for household heads and children. Each person
who manages and exploits a field will achieve maximum the efficiency of
production activities.
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