(Luận văn) determinants of water melon production at farm household level in tien giang provice

Research context

Tien Giang's watermelon is a key fruit export to China and Cambodia, yet its production remains unstable In 2007, the region had 3,779 hectares dedicated to watermelon, yielding 70,847 tons, but by 2008, the area decreased to 2,954 hectares with an output of only 55,754 tons The cultivation process is characterized by a lack of concentration and specialization, insufficient market information, and significant price fluctuations, with demand heavily influenced by wholesalers and seasonal changes Producers currently lack data on the economic efficiency and profit contributions of watermelon farming to household incomes This article focuses on the supply side, particularly the economic viability of watermelon cultivation in Tien Giang province.

This research identifies key weaknesses in the watermelon production process that require improvement: a) the low entrepreneurial skills of farmers across all genders and age groups; b) the need for farmers to better adapt to adverse natural conditions, particularly frequent climate changes; and c) the quality of watermelons available for market.

Research problem

It is not yet known what factors lead farmers to gain the highest water melon output

Farmers respond uniquely to various factors affecting their practices This study aims to identify the key factors influencing watermelon production in Tien Giang by utilizing a production function model, the theory of farm household economics, and SWOT analysis.

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This study evaluates the effectiveness of various inputs in watermelon production, including land, labor, and capital, which encompasses both cash and physical resources like fertilizer and seeds Additionally, it emphasizes the importance of market information to ensure that supply meets demand without resulting in surplus or deficit.

Goal and specific objectives of the study

Tien Giang is one of provinces in the core economic region in Western Vietnam

Agriculture remains vital for economic development, and this study aims to enhance agricultural production efficiency while improving the livelihoods of farm households in Tien Giang province, specifically through increased watermelon output.

The specific objectives of the study are to:

1 Identify the potential factors which impact strongly to water melon production process in order to indicate the right ways and approaches to gain higher productivity

2 Estimate economic efficiency of different factors of production used in water melon cultivation

3 Make recommendations and strategic suggestions for government policy and farmer groups to enhance the profitability of water melon production to farmers

Research question

The primary aim of this research is to identify the key factors influencing watermelon production and to assess the economic efficiency of these production factors in watermelon cultivation To achieve this objective, the author poses specific research questions that guide the investigation.

Which factors that impact potentially on Tien Giang's water melon production?

How is economic efficiency of factors that impact to water melon production estimated?

The organization of the thesis

of research and the organization of the thesis

Chapter 2 is literature review This chapter provides: (1) theory about farm household economies, production function and production factors of farm household; (2) empirical studies; and (3) analytic framework of this research

Chapter 3 introduces research methodology including analytical framework (the regression model, variable indication, sign expectation, variable description), data collection and sample distribution and analysis methods

Chapter 4 analyzes watermelon production in Tien Giang province, focusing on how input usage affects yield The author provides a comprehensive overview of the province, highlighting key factors that contribute to watermelon cultivation.

Tien Giang province is characterized by favorable climate and soil conditions that support watermelon production This article explores the competitiveness of watermelon products in the market and provides a comprehensive analysis of watermelon production in Tien Giang It includes a SWOT analysis, insights from farm surveys, and an econometric analysis of how input usage affects watermelon yields.

Chapter 5 presents the conclusion and recommendations for provincial authorities aimed at enhancing farmers' productivity and increasing their benefits.

LITERATURE REVIEW

Theoretical framework

2.1.1 Theory of farm household economies

Peasant economic behavior can be understood through logical deductions based on prior assumptions about household goals and the nature of markets The farm household is viewed as a single decision-making unit that aims to maximize a utility function, where profit maximization aligns with utility maximization in fully competitive markets Variations in economic theories stem from differing assumptions about factor and product markets, rather than household goals Key distinctions between theories often arise from assumptions regarding labor markets and the allocation of household labor time Additionally, household economic behavior is influenced by social relations, which affect how markets operate for different peasants.

The labor force in agriculture primarily consists of farmers, with the concept of a firm easily defined as agricultural farms are typically associated with farm households These households operate farms of varying sizes, with many small farms prevalent in sub-Saharan Africa and parts of South and East Asia, such as Bangladesh, China, the Democratic Republic of the Congo, Egypt, Indonesia, India, and Korea, where most farms are under two hectares In contrast, West European countries often have holdings that span thousands of hectares Additionally, farms can be categorized into various types, including family farms, business farms, and farm enterprises, with specialized farm enterprises being closely tied to the market economy.

Therefore there are still difficulties in making distinctions between farms in term of size of farm resources and nature of production (Boussard 1987) (cited in Tran Tien Khai, 2001 )

Peasants are defined as farm households that primarily rely on family labor for agricultural production while having access to a specific piece of land They operate within a broader economic and political framework that influences their production practices Notably, peasants typically engage partially in input and output markets, which are frequently imperfect or incomplete.

Peasant farm households make up at least 25% of the global population, with a significant concentration in developing countries, where they can constitute up to 70% of the national populace (Bardhan and Udry, 1999; cited in Mariapia Mendola, 2007) Ellis also emphasizes that a substantial portion of the population in these regions consists of peasants.

Hunt (1991) describes peasant farms as dual-function units that serve both production and consumption purposes A portion of their produce is sold to fulfill cash needs and financial responsibilities, while the remainder is consumed by the farmers themselves (Mendola, 2007).

One of the key theories in farm economics is the concept of utility maximization, where farmers make decisions aimed at maximizing their utility Neo-classical economics posits that, given limited resources and technical constraints in production, farms behave in a way that seeks to maximize their utility function According to Ellis (1993), this utility maximization equates to maximizing total income Brossier et al (1997) highlighted the challenge of identifying profit maximization in agriculture through a specific formula (cited in Tran Tien Khai, 2001).

II=P-CV -CF-KA- WA tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg

Where: II is the profit

KA and W A are remuneration of capital and family labor

CV is all variable charges of exterior-bought factors

CF is fixed charges paid to interior

It is difficult to identify the KA and W A; so, farmers maximized the function II +

KA + W A (or P - CV - CF) which is considered as the agricultural revenue or revenue

Economy of scale is a conception come from the neo-classical theory of production

Economies of scale refer to the cost advantages a firm gains through expansion, leading to a decrease in average unit costs as production volume increases This concept highlights the challenges faced by small farms, which struggle to compete with larger farms due to their higher unit costs and reduced competitiveness.

Figure 2.1: The relationship between output and average cost Source: http://en.wikipedia.org/wiki(Economies_of_scale

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As quantity of production increases from Q to Q2, the average cost of each unit decreases from C to C 1•

Ellis (1993) emphasized the significance of indivisible resources in achieving economies of scale in agriculture For instance, the power of a tractor is classified as an indivisible resource, and its effective utilization is contingent upon the land area Consequently, the optimal use of indivisible resources leads to cost economies, which directly influence the output volume that minimizes unit production costs in the short run (cited in Tran Tien Khai, 2001).

Production functions serve as a crucial analytical tool in the neo-classical economic tradition, defining the output generated by a firm, industry, or entire economy based on various input combinations.

Firms transform inputs into outputs during their production processes, utilizing various production factors In a bakery, these inputs consist of the labor force, raw materials like flour and sugar, and capital investments in equipment such as ovens and mixers, all essential for producing goods like bread, cakes, and pastries.

We can divide inputs into the broad categories of labor, materials, and capital

Labor inputs include skilled worked (carpenters, engineers) and unskilled workers (agricultural workers), as well as the entrepreneurial efforts of the firm's managers

Materials such as steel, plastics, electricity, and water are essential for firms as they are transformed into final products Capital encompasses land, buildings, machinery, equipment, and inventories necessary for production.

The following production function describes the relationship between input and output A production function indicates that a firm can obtain the highest output Q from every specified combination of inputs:

It relates the quantity of output (Q) to the quantities of the inputs such as capital (X1), labor (X2), materials (X3) and etc (Robert and Daniel, 2009)

A quadratic production function illustrates the relationship between input levels and output quantity Points below the curve represent technically feasible production levels, while points on the curve indicate the maximum output achievable with the given inputs.

According to Figure 2.2, the production function increases from points A, B, and C, indicating that as more input units are utilized, the output quantity also rises However, at point C, the use of additional input units does not yield extra output; instead, total output starts to decline due to underutilization of inputs.

At point A, increasing additional inputs leads to a rise in output at an accelerating rate, with both marginal physical product (MPP) and average physical product (APP) on the rise The inflection point, known as point X, marks the beginning of diminishing marginal returns From point A to point C, output growth slows down, indicating that additional inputs yield lower increases in output Point B serves as the tangent point between APP and MPP.

B, APP is at a maximum and the marginal curve must be below the average curve

Figure 2.2: Quadratic Production Function Source: http:/ /www.wordiq.com/definition/Production function

The Cobb-Douglas production function is a widely used model that illustrates the relationship between output and inputs Initially proposed by Knut Wicksell, it was later validated through statistical analysis by Charles Cobb and Paul Douglas between 1900 and 1928.

For production, the simplest formula of Cobb-Douglas function 1s (Haughton,

(1) Where: Q is total production, His productive area, Lis labor input a., 1-a are the output elasticity of labor and productive area, respectively

The general productive function is given as follow

Q=AIIXt (2) xi is input variables Formula (2) is transferred into logarit function as follow

In Q = In A + Ia.i In Xi (3) One trouble with formula (3) because it does not allow any Xi equals 0 (ln(O) is undefined)

So, solution is the productive function is changed as follow

In Q =In A+ Ia.iln Xi+ I ~izi

Empirical studies

Tran Tien Khai (200 1) used data of the Project Competitivite de la filiere rizicole dans la region du Mekong, Vietnam including information of rice production from

150 rice farms in four agro-ecological regions during period 1995-1998 Log-linear and Cobb-Douglas models of production and supply function are applied

The production function with log-log is followed:

Ln Q = Ln A+ IaiLnXi + L~iDi and the production function with log-linear is followed:

The equation $ \ln Q = A + \sum_{i} a_i x_i + L_i D_i $ represents the rice productivity of a farm household in a given year, where $ Q $ denotes productivity, $ A $ is the angular coefficient, and $ x_i $ are input variables such as land, labor, and investment costs.

Di is dummy variables which be able to influent to yielding in terms of farm size, agricultural ecology, etc

To estimate the elasticity of rice supply with rice price and agricultural material price, a simple rice supply function is designed as follow:

The equation $ \ln Q = \ln A + \sum_{i} x_i \ln X_i + \sum_{i} D_i $ represents the rice productivity of a farm household in a given year In this equation, $ Q $ denotes rice productivity, $ A $ is the angular coefficient, and $ X_i $ includes variables that affect the rice supply capacity, such as land, labor prices, fertilizer prices, and rice prices Additionally, $ D_i $ represents dummy variables that influence yield based on factors like farm size and agricultural ecology.

Rice land stock and water availability remain the primary constraints to increasing paddy output While investing in fertilizers yields only marginal returns, potash is an exception Additionally, increasing capital investment has minimal impact on paddy output at the current level of cultivation.

In their study "Rice Production," Nguyen Thi Lien, Nguyen Xuan Hai, Pham Hoai Vu, and Trinh Thi Long Huong, similar to the research conducted by Iran Tien Khai, employed the productive function \$\ln Q = \ln A + \sum_{i} a_i \ln X_i + \sum_{i} d_i\$ to analyze the factors influencing rice productivity.

Purano Baneshwor, Kathmandu (2002) used the Cobb-Douglas production function of the following type is estimated:

Y = e'6 Ka Lo-a) U where Y = real GDP, () = constant term (shift factor), L = labor force, K = real capital, U = random error term, and () and a are the parameters to be estimated

This equation assumes constant returns to scale as most empirical growth accounting studies have undertaken A logarithmic transformation of the above equation would be: logY= 8 +a log K + (1- a) log L + U

This paper concludes that capital accumulation is the primary driver of growth in Nepal Economic growth in both developing and developed countries is significantly influenced by factor productivity Additionally, intangible elements such as advancements in education and technology, a supportive economic policy environment, and continuous learning have also played a crucial role in enhancing factor productivity.

In the context of Nepal, accurately estimating the contributions of labor and capital to economic growth is challenging due to a lack of clear information on these variables Consequently, the economic growth that cannot be attributed to labor and capital does not provide a reliable basis for assessing factor productivity gains If a true accounting standard is applied, factor productivity may actually be a negative contributor to Nepal's economic growth.

Jacklin (2008) in "estimates the production, restricted cost, and restricted profit functions using North Dakota agriculture sector data from 1960-2004" also used the

Cobb-Douglas function to represent the production function characterized as:

Where k = 1 K (number of inputs and time 1 T) Converting the inputs and output into logarithms and adding a stochastic error term, the production function can be represented as:

The article discusses the input elasticity represented by variables \$a_1, a_2, \ldots, a_k\$, along with the error term denoted as \$E\$.

Jacklin's thesis employs a quantile regression approach to estimate the Cobb-Douglas production function Unlike ordinary least squares (OLS) regression, which focuses on the mean of the distribution, the quantile regression provides a more nuanced analysis The findings indicate that both traditional OLS and quantile regression methods yield statistically insignificant parameters regarding the relationship between agricultural inputs and aggregate output for North Dakota agriculture during the period from 1960 to 2004.

The Ricardian method, as outlined by Mendelsohn et al (1994), employs a cross-sectional approach to analyze agricultural production This approach is based on the assumption that farmers aim to maximize their income while considering the external conditions affecting their farms The net revenues from farmland (V) serve as an indicator of net productivity, encapsulated in a specific equation.

The Ricardian model analyzes how various exogenous factors, including climate variables, water flow, soil characteristics, and economic conditions, influence net revenues for farmers In this model, the market price of crop $i$ (denoted as $P_i$) and the output of crop $i$ (represented as $Q_i$) are critical, while farmers optimize their purchased inputs (excluding land) to maximize profits based on their farm's specific attributes and prevailing market prices.

The Ricardian approach (Mendelsohn et al., 1994) is the primary method that J

Wang et al., (2009) used in his/her analysis The farmer chooses the crop and inputs for each unit of land that maximizes:

Max rr = IPqiQi (Xi,Li,Ki,IRj,C,W,S)- IPxXi- IPmLi- IPnKi- IPiriRi (5)

Page 15 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg where n is net annual income, P qi is the market price of crop i, Qi is a production function for crop i, Xi is a vector of annual inputs such as seeds, fertilizer, and pesticides for each crop i, Li is a vector of labor (hired and household) for each crop i, Ki is a vector of capital such as tractors and harvesting equipment for each crop i,

The vector $ C $ represents climate variables, while $ IR_i $ denotes the irrigation choices for each crop $ i $ Available water for irrigation is indicated by $ W $, and $ S $ encompasses soil characteristics Additionally, $ P_x $ is a vector of prices for annual inputs, and $ P_m $ represents the prices for various types of labor.

The rental price of capital is denoted as \$P_n\$, while \$P_{ir}\$ represents the annual cost associated with each type of irrigation system Equation (5) builds upon the foundation established in equation (4) The variables \$L_i\$ and \$K_i\$ are crucial factors in determining the physical impact on crop yield and productivity.

Coelli (1996) assessed technical efficiency in agricultural production using the data envelopment analysis (DEA) method The DEA approach offers key benefits, including the elimination of the need for parametric specifications of production technology and the absence of distribution assumptions for inefficiency terms.

Cristina (1998) used a constant returns to scale function of the three primary factors of production such as land, labor, capital to estimate value added in agriculture

The production function serves as a vital tool for development, growth, and macroeconomists, who frequently estimate it by considering both production factors and intermediate inputs Many of these estimations assume constant returns to scale, while others focus on value added as a function of labor and capital Although land may play a minor role in various sectors, it remains a crucial resource in agriculture.

Analytic framework of this research

Conceptual model is constructed by combining factors of production of farm household and some other factors that physical effect to water melon productivity

The author identifies key factors that directly influence watermelon production Figure 2.4, titled "Conceptual Framework," highlights two main relationships: first, the correlation between watermelon yield and input variables, including productive area, labor, chemical fertilizer, pesticides, and seeds; second, the connection between watermelon yield and dummy variables such as market information, local agricultural extension services, and information provided by agricultural extension agents.

In the watermelon production process of Tien Giang province, understanding the relationship between input use variables and dummy variables is crucial By analyzing these relationships, farmers can implement effective strategies to enhance productivity while minimizing costs, ultimately leading to increased profits.

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The conceptual framework illustrated in Figure 2.4 is based on the author's survey conducted in 2010 For the latest updates and full access to the thesis, please contact via email at vbhtj mk gmail.com.

RESEARCH METHODOLOGY

Analytical framework

Based on the production function model and the empirical research findings, the regression model is specified as follows: \$\ln Q = \ln A + \sum_{i=1}^{n} \beta_i \ln X_i + \sum_{j=1}^{m} \gamma_j Z_j\$.

The regression model proposed for this study is expressed as follows: \$ \ln Q = \ln f_{lo} + \beta_1 \ln X_1 + \beta_2 \ln X_2 + \beta_3 \ln X_3 + \beta_4 \ln X_4 + \beta_5 \ln X_5 + \beta_6 l \tilde{X} + \beta_7 D X_7 + \beta_8 X_8 + \beta_9 X_9 + \beta_{10} X_{10} + \epsilon \$, where \$ Q \$ represents the watermelon yield per hectare for the summer-fall crop of 2010.

X 1 is productive area squared of 2010's summer-fall crop

X 2 is land rent cost per hectare of 2010's summer-fall crop

X3 is land preparation cost per hectare of 2010's summer-fall crop )4is labor cost per hectare of 2010's summer-fall crop

X5 is seed cost per hectare of 2010's summer-fall crop

X 6 is fertilizer cost per hectare of 2010's summer-fall crop

X 7 is growing year of producer of 2010's summer-fall crop

X8 is having agri-extension service in location of 2010's summer-fall crop (O=no, 1 =yes)

X9 is having information from agri-extension of 2010's summer-fall crop (O=no, 1 =yes)

X 10 is market information of 2010's summer-fall crop (O=no, l=yes)

The coefficients B0 to B10 represent the impact of various factors on watermelon yield, including productive area, land rent costs, land preparation costs, labor costs, seed costs, fertilizer costs, the growing years of the producer, and market information.

!l is error terms (regression residual) which means there are other factors that influence which effects to water melon yield

This research aims to evaluate the economic efficiency of various inputs, including productive area, land rent, land preparation, seed, chemical fertilizer, and pesticides, on watermelon yield Specifically, it will analyze the impact of a 1% increase in chemical fertilizer on watermelon yield percentage changes Additionally, the study will examine the influence of dummy variables, such as agricultural extension services and market information, on watermelon yield variations.

The study examines the factors influencing watermelon yield per hectare (Q), focusing on several independent variables These include the productive area (X1), land rent cost per hectare (X2), land preparation cost per hectare (X3), labor cost per hectare (X4), seed cost per hectare (X5), fertilizer cost per hectare (X6), the growing year of the producer (X7), access to agricultural extension services in the location (X8), information received from agricultural extension (X9), and market information (X10).

In the initial phase of watermelon cultivation, increasing labor, fertilizer, or productive area can enhance yields However, it's crucial to apply these resources within appropriate limits Excessive labor may lead to higher yields, but it may not justify the associated costs, rendering the investment in labor ineffective.

Excessive use of fertilizer can lead to a decrease in watermelon yield Additionally, the author examined the relationship between productive area and watermelon output, highlighting the concept of economies of scale A small productive area may hinder profitability, while expanding it beyond a manageable level can negatively impact output Therefore, there exists an optimal productive area where yield is maximized, indicating a negative expectation for output when the area exceeds this level.

Q Water melon yield (ton/ha)

AREASQUARED Productive area squared (ha 2 ) -

LAND RENT Land rent cost (Million VND/ha) -

LAND PRE Land preparation cost (Million -

LABOR Labor cost (Million VND/ha) +

SEED Seed cost (Million VND/ha) -

FERTILIZER Fertilizer cost (Million VND/ha) +

EXPERIENCE Growing year of producer (year) +

EXTENSION Having agri-extension service in - location O=No

EXTENINFO Having information from agri- + extension O=No

This research utilizes cross-sectional data on inputs and outputs from watermelon production activities across seven districts in Tien Giang, with data collection taking place in the third quarter of 2010.

The output is the water melon yield of production (Q = Y /ha) Output is measured in tons per hectare

The productive area squared (AreaSquared) is estimated by the cultivated land used for water melon production It is measured in squared hectare

The cost of land rent (LandRent) is expressed in millions of Vietnamese Dong (VND) per hectare Watermelon cultivation faces challenges in previously cultivated soils due to the prevalence of harmful diseases To achieve optimal growth, farmers should utilize new soils or implement intercropping systems, allowing for 1-2 seasons of watermelon cultivation over a span of 2-3 years Consequently, farmers in Tien Giang are compelled to rent quality soil to ensure successful crop production.

The land preparation cost (LandPre) for watermelon production is measured in millions of Vietnamese Dong (VND) per hectare and includes expenses for plastic cover, ash, coir, and irrigation The use of plastic cover enhances watermelon cropping by retaining moisture, controlling weeds, and mitigating certain diseases and pests Prior to applying the plastic cover, ash and coir are mixed into the soil Additionally, the irrigation cost for watermelon is minimal and is incorporated into the overall land preparation cost.

The labor cost (Labor) used in the model included the population work in agriculture (hired and household) It is calculated by total cost of each working day

The household labor cost is expressed in millions of Vietnamese Dong (VND) per hectare and is determined by multiplying the total household working days by the opportunity cost In this study, the author uses the cost of hired labor as a basis for calculating household labor costs For example, if hired labor is compensated with 4 million VND for a duration of 2 months, the author similarly assigns a value of 4 million VND for the household labor over the same period.

The seed cost (Seed) 1s measured m million of Vietnamese Dong (VND) per hectare

Fertilizer costs encompass the total weight of nitrogen, phosphate, potassium, complex fertilizers, and cattle manure utilized during various agricultural stages, including land preparation, seedling support, and fruit support, as well as sideline production activities throughout the study period This variable also accounts for pesticides, which include insecticides, fungicides, herbicides, plant protection drugs, disease prevention drugs, and product stimulation fertilizers The measurement is expressed in millions of Vietnamese Dong (VND) per hectare.

The growing year of producer (Experience) is estimated by year numbers which producer has in their water melon production process It is measured by year number

The having agri-extension service in location (Extension) is measured by dummy variable

The having information from agri-extension is measured by dummy variable as well

Market information is crucial for agricultural production, especially in watermelon farming In Tien Giang, farmers often cultivate watermelons on a large scale without consulting market research, leading to potential oversupply and lower prices during peak seasons like the New Year Holidays Additionally, watermelon prices are affected by demand and supply dynamics in local and nearby provincial markets.

Page 23 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg agricultural prices reflects the market risk faced by agricultural producers It is measured by dummy variable

Agricultural production is significantly influenced by natural conditions, including climate change, floods, unpredictable disasters, and pest or disease invasions Scientific evidence increasingly demonstrates the profound impact of climate change on agriculture, highlighting the need for adaptive strategies to mitigate these effects.

Matthews and Wassmann, 2003; Parry et al., 2004; Tao et al., 2006; etc) Therefore, this factor is omitted in this research.

Data collection and sample distribution

The minimum sample size for this study using the proportional sampling formula in Mason, R.D (1999:292) (cited in Tran Van Long, 2010) where: n = p( 1-p )(Z/E) 2 n = minimum sample size

Z = 1.96 at 95% confidence interval obtained from standard statistical table of normal distribution p = estimated ratio of farm households which plant water melon in Tien Giang (p P%)

(1-p) = q = estimated ratio of farm households which do not plant water melon in Tien Giang (q P%)

Applying the above equation, the minimum needed sample size needed is about 97

A total of 177 respondents were selected for direct interviews, exceeding the minimum required sample size, which ensures a strong representation for this research.

3.2.2 Sample distribution The following table is the sample size is distributed according to water melon output in 2008 of each area across Tien Giang province

Table 3.2: Sample size of each district across Tien Giang province

Water melon yield of each district in 2008 Percentage Sample size

Source: Tien Giang's Rural and Agriculture Development Department

Based on Table 3.2, My Tho City and Go Cong Town each contributed only 2 questionnaires, a number too small to significantly impact the overall results of this research Therefore, the author will add 1 questionnaire to the total for Cai Be and 1 questionnaire to the total for Cho Gao.

The Tien Giang Rural Development and Agriculture Department currently lacks statistics on the number of households involved in watermelon cultivation To address this gap, the author employs a proportional sampling framework to select a research sample based on the watermelon output from each district This approach allows for the calculation of the sampling distribution proportion for each district, facilitating the selection of samples and the collection of relevant information.

14 samples of Tan Phuoc, 53 samples ofCai Be, 30 samples ofCai Lay, 20 samples ofChau Thanh, 33 samples ofCho Gao, 18 samples of Go Cong Tay and 9 samples of Go Cong Dong

3.2.4 Pre-testing of the questionnaires

The questionnaire was developed and pre-tested with approximately 20 experienced watermelon farmers through face-to-face interviews The author dedicated 30 to 45 minutes at each farmer's field to gather information and calculate costs associated with watermelon production The final version of the questionnaire was refined based on their valuable feedback.

The author initially reached out to Mr An, the Director of the Agricultural Seed Center in Tien Giang Province, to present the overarching concept of the research Mr An provided valuable guidance on how to approach and request interviews with respondents, and he also introduced the author to the individuals responsible for each district.

The author presented the overall and specific ideas of the research to the participants, organizing small meetings with approximately 10 respondents each Data collection was conducted through direct interviews over a three-month period from October to December 2010, with each farmer being interviewed face-to-face by the author.

3.2.6 Limitation of data source and collection

Farmers often lack the habit of meticulously recording their crop data, leading to discrepancies when interviewed, as they need time to recall information Consequently, data collected from Cai Be district tends to mirror that from Cai Lay and Go Cong Tay districts For instance, Mr Nguyen Van Be, with a decade of experience in watermelon cultivation across various districts in Tien Giang, noted only minor differences in chemical fertilizer usage and labor costs It is important to highlight that a farmer can typically provide answers to 2 or 3 questionnaires from different districts.

Analysis methods

In order to consider several approaches of water melon's yield will be used in this study:

The descriptive statistics is the first method that the author use in this research to analyze the relationship of each independent variable to dependent variable

The author employed linear regression analysis using SPSS (Statistical Package for the Social Sciences) to identify significant and optimal variables Additionally, structured interviews were conducted with individuals and representatives from specific organizations to gather reliable data on watermelon cultivation through well-designed questionnaires The collected information was then analyzed in alignment with the investigation's objectives.

In addition to the linear regression model, the author employs SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis to evaluate watermelon cultivation This analysis highlights the strengths and weaknesses of the cultivation process, as well as the opportunities and threats it faces in the market.

Base on results of both those approaches, the author will make conclusions and suggestions for my research

This research aims to identify the factors that positively and negatively influence watermelon yield in Tien Giang province by reviewing relevant literature and analyzing data using linear regression models and SWOT analysis The findings are expected to provide significant insights for farmers.

This research aims to propose effective policies for the government to promote watermelon cultivation among farmers, ultimately enhancing their living standards.

ANALYSES OF WATER MELON PRODUCTION IN TIEN

Introduction ofTien Giang province and its water melon production

Tien Giang, an agricultural province in the Mekong River Delta, is a vital part of the South's economic region Located approximately 70 km south of Ho Chi Minh City and 90 km north of Can Tho City, Tien Giang is situated between longitudes 105°50' - 106°45' east and latitudes 10°35' - 10°12' north The province shares borders with Long An and Ho Chi Minh City to the northeast and north, Dong Thap province to the west, and Ben Tre and Vinh Long provinces to the south, while the East Sea lies to the east.

Tien Giang province, located along the northern shore of the Tien River, a tributary of the Mekong River, spans 120 km in length Covering a natural area of 2,481.77 km², Tien Giang represents approximately 6% of the Mekong River Delta, 8.1% of the southern key economic region, and 0.7% of Vietnam's total land area.

Tien Giang features a predominantly flat terrain with neutral alluvial soil, comprising 53% of the province, making it ideal for diverse plant and animal life As of 2009, the population of Tien Giang was approximately 1.67 million, accounting for 9.8% of the Mekong Delta's population, 11.4% of the southern key economic region, and 1.9% of the national population Strategically located, Tien Giang is the second province from Ho Chi Minh City, following Long An, and consists of 10 district-level administrative units.

(8 districts, 1 city, 1 town) and 169 commune-level administrative units, of which,

My Tho city is the second grade city

T'en Giang has equatorial and monsoon tropical climate, so the average temperature islhigh and hot all year Annual average temperature is 27- 27.9°C There are two

Tien Giang experiences two main seasons: a dry season lasting five months from December to April and a rainy season from May to November The region has low average rainfall, ranging from 1,210 to 1,424 mm per year, with precipitation decreasing from north to south and from west to east The average humidity in Tien Giang is between 80% and 85% Additionally, there are two predominant wind directions: north-east during the dry season and south-west during the rainy season, with an average wind speed that varies throughout the year.

I source: http://www tiengiang gov vnlbando/tiengiang.html

14.1.3 Soil condition fbe total natural land of the province is 236,663 hectare, including major land groups as follows: f I Alluvial soil: 53% of the total natural area (125,431 hectare), accounting for large rarts of the districts such as Cai Be, Cai Lay, Chau Thanh, Cho Gao, My Tho city tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg and one part of Go Cong Tay where has the fresh (sweet) water source This is the most favorable soil for agriculture and it is used the whole

+ Salinity soil: 14.6% of the total natural area (34,552 hectare), occupying large parts of Go Cong Dong, Go Cong town, Go Cong Tay and 1 part of Cho Gao

About the characteristic of soil as favorable as alluvial soil; but it is affected by salinity water from the sea during dry season

Acid sulphate soil covers 19.4% of the total natural area, amounting to 45,912 hectares, primarily found in the low-lying regions of Dong Thap Muoi, particularly in the northern parts of Cai Be, Cai Lay, and Tan Phuoc districts.

Mound sandy soil covers 3.1% of the total natural area, amounting to 7,336 hectares, and is primarily found in the districts of Cai Lay, Chau Thanh, and Go Cong Tay, with the highest concentration in Go Cong Dong This type of soil features elevated terrain and a light mechanical composition, making it ideal for residential use and the cultivation of fruit trees and vegetables.

The province predominantly features alluvial soil, comprising 53% of its land, which benefits high-yield rice fields and professional orchards due to abundant freshwater sources Additionally, 19.4% of the area consists of alkaline soil, while 14.6% is classified as saline alluvial soil In recent years, efforts have focused on reclaiming land, expanding production areas, and enhancing crop diversity through the Dong Thap Muoi and Go Cong freshwater development programs, which have successfully increased the productive capacity of the region.

Table 4.1: Land use structure at Tien Giang province Until now, over 90% the total area was used with following objectives:

:soil type Square Structure Square Structure Square i Structure

(hectare) (%) (hectare) (%) (hectare) I (%) :The total square 233.922 100.0 232.609 100.0 236.663 i 100.0 ii Ag;t.i.nllhu-al soil 165.408 70.7 184.883 9.48 181.505 76.69

IL De(lic-ate(l ;o;:oil 10.484 4.48 15.005 6,45 15.887 6.713

Source: http://www.tiengiang.gov.vn/xemtin.asp?idcha5&cap=3&id8

4.1.4 Water melon production in Tien Giang

Watermelon is now a significant cash crop for farmers in various provinces, particularly in the Mekong Delta region, where it serves as an alternative to rice Recent advancements in agricultural practices, including the use of plastic sheets for soil coverage and the application of specialized fertilizers, have contributed to the cultivation of high-yield, widely adapted watermelon varieties.

Rice cultivation in Tien Giang has a long history, but farmers face low incomes of 3-4 million VND per hectare due to an average annual production of 14.2 tons per hectare from three rice crops The yields for each crop are approximately 4.5 tons for the first, 4.2 tons for the second, and 5.5 tons for the third Additionally, farmers encounter significant risks from pests, diseases, and unpredictable weather To improve profitability, recent practices encourage rotating watermelon cultivation with rice, utilizing various combinations such as two rice crops with one watermelon crop or alternating between rice, vegetables, and watermelon Watermelon cultivation can yield an impressive average of 22 tons per hectare per crop.

Watermelon yields can reach between 25 to 30 tons per hectare per crop when proper cultural practices are implemented.

- - - - - - - - - - practices So a farmer can get the average net income is 20 - 25 millions VND/hectare after deducting all expenditures Clearly, income from water melon is higher a lots than income from rice

Nowadays, water melon is planted year around and is planted a lots in following seasons: Christmas, Lunar New Year, after Lunar New Year and summer

Table 4.2: Water melon productive area, water melon output in Tien Giang in 2008

Water melon Productive City/District output (ton) area (ha)

Source: Tien Giang's Rural and Agriculture Development Department

Watermelon is cultivated globally, including in Vietnam, driven by the high demand for fresh fruit and processed products like canned watermelon slices and juice The world production of watermelon has been steadily increasing, with a notable rise from 47 billion tons in 2004 to 93 billion tons.

Page 33 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg billion tons in 1996 Production of other melon gained one third of water melon production

China is the leading producer of watermelons, with a staggering production of 60 billion tons in 2002 Other notable producers include Turkey, Iran, the USA, Egypt, and Mexico Additionally, China dominates the global melon market, accounting for 50% of the world's melon production, followed by Turkey (6.1%), Iran (4.4%), the USA (4.2%), and Spain (3.9%) Despite its significant production, China does not export watermelons or other melons due to the high demand within its domestic market.

Spain is a leading exporter of honeydew and cantaloupe, with over 300 thousand tons exported annually, followed by Mexico and Costa Rica While the USA primarily imports melons, it also exported melons worth 98.1 billion USD in 2004, mainly to Canada (85.2 billion USD) and Japan In Asia, Malaysia emerged as a significant exporter of watermelon, exporting 70 thousand tons in 2003, making it the fifth largest exporter globally, following Spain, Mexico, the USA, and Hungary.

Melon wor1d production source: FAO redr- from USDA H0111culturel &

Analyses of water melon production in Tien Giang province

This chapter discusses the results of the relationship between independent and dependent variables through SWOT analysis and econometric methods The data will be analyzed using SPSS 15.0, focusing on descriptive statistics and a linear regression model.

SWOT ANALYSIS FOR WATERMELON'S CULTIVATION

• Tien Giang has been one of the leading provinces for water melon cultivation in off-seasons for more than 10 years (Southern Fruit Research Institute)

• Farmers in Tien Giang have been applying advanced cultural practices as well as new varieties for higher productivity, quality and profitability of water melon

• Many farmers are very experienced in water melon's cultivation

• A large quantity of marketable water melon fruits could be collected and provided to urgent needs of markets at a particular time

• There were still farmers not fully applying advanced cultural practices transferred from training courses due to problem of understanding of these farmers

Farmers, lacking access to market information from research organizations, tend to cultivate watermelons on a large scale based solely on their investment capacity, without consulting market studies.

This problem could also result in low price of water melon on holidays

The price of watermelon in city markets is significantly affected by the dynamics of supply and demand.

• It should be considered that market information and planning for cultivated area very important to farmers

The demand for watermelons is significant, yet Vietnamese farmers face challenges in competing with both domestic and exotic melons High-quality melons from Thailand and the increasing fruit requirements in Malaysia and China, estimated at 140 kg per person in 2010, highlight the market potential However, the cultivated area for watermelons may decrease due to risks from pests, diseases, and adverse weather conditions such as floods and droughts Despite these challenges, the price stability of watermelons compared to other fruit crops offers potential benefits to farmers Additionally, supplying produce to markets in China, Laos, and Cambodia could lead to better pricing opportunities for watermelons.

4.2.2 Description of water melon production in Tien Giang through farm survey

Table 4.3 presents the minimum, maximum, mean, and standard deviation for each variable based on a total of 177 interviews The minimum represents the smallest value for each variable, while the maximum indicates the largest value.

The mean represents the average value of a variable, while the standard deviation quantifies the dispersion of values around this mean, indicating how spread out the measurements are for each variable.

Table 4.3: Descriptive statistics of yield and input uses variable of water melon production

Unit cost of a water melon ton/ha (million VND) 1.68 6.18 3.06 sts other than Fertilizer and Pesticide (million VND) 34.60 55.62 45.80

Land rent cost (million VND) 5.00 25.00 15.40

Land preparation cost (million VND) 3.73 5.80 4.82

Bed making cost (million VND) 3.00 5.63 4.55

Taking care cost (million VND) 5.00 20.00 8.70

Chemical fertilizer cost (million VND) 1.24 9.69 7.59

Nitrogen fertilizer cost (million VND) 40 3.57 2.70

Phosphate fetilizer cost (million VND) 54 4.04 3.00

Potassium fertilizer cost (million VND) 30 2.39 1.89

Deviation 3.5 5.2 4.88 55 3.83 2.20 33 2.69 40 38 1.68 32 1.02 3.14 48 1.14 129.3 47.2 42 68.2 52 33.3 27 1.78 98 89 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg

Stimulation product cost (million VND) 48 10.57 8.73

Age of producer (year old) 20 59 34

Schooling year of producer (academic year) 0 12 7

Growing year of producer (year) 1 17 6

Source: the author's survey in 2010

Watermelon yields range from a minimum of 12 tons to a maximum of 30 tons per hectare, with an average yield of 22.8 tons per hectare The total cost of cultivation varies, with a minimum of 40.37 million per hectare, a maximum of 78.36 million per hectare, and a median cost of 68.27 million per hectare This indicates that watermelon is a high-yield vegetable that requires careful management.

Among the 177 interviewed farmers, the average age is approximately 35 years, with ages ranging from 20 to 59 years Farmers have an average of 7 years of schooling, with education levels varying from 0 to 12 years Additionally, their experience in farming spans from 1 to 17 years, with an average of about 7 years This data indicates that watermelon cultivation is challenging, as the minimum age of farmers is 20, highlighting the necessity for time to gain experience and knowledge in this field.

Several factors influence watermelon yield, including the productive area, land rent costs, labor expenses, fertilizer costs, land type, and the farmer's age, education, and experience Additionally, access to market information and agricultural extension services plays a crucial role in determining overall productivity.

In 2010's summer-fall crop, almost of farmers gain the high yield According to the above figure 4.3, water melon yield gained mainly from 20 to 25 tons/ha

3 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg

Figure 4.3: The water melon yield of 2010's summer-fall crop Source: The author's survey in 2010

4.2.2.2 Input uses and other factors of water melon production 4.2.2.2.1 Market information

Out of 177 interviewers, 56.49% (100 interviewers) consistently prioritize market information, while 43.51% (77 interviewers) do not Among those who do care, price emerges as the most significant factor in their consideration of market information.

In Tien Giang, located in the Mekong River Delta basin, agriculture plays a crucial role in the economy To enhance agricultural productivity, the region has been continuously improving its agricultural extension services across various districts A survey revealed that 55.93% of respondents reported having access to agricultural extension services, while 44.07% indicated otherwise.

A survey of 78 interviewers revealed a significant lack of agricultural extension services in their locations, which are crucial for providing farmers with essential knowledge on seed selection, fertilizer use, and crop care Conversely, 99 interviewers reported the presence of these services, with 96 of them receiving valuable agricultural information, while only 3 interviewers indicated they had not accessed such information.

Experience plays a crucial role in agricultural development, with 14% of farmers having 7 years of experience Additionally, 12% of farmers possess 6, 5, and 4 years of experience, respectively Meanwhile, 10% have 3 years of experience, and 7% of farmers have 8 and 9 years of experience, respectively Furthermore, 5% of farmers have 2 and 11 years of experience, while 3% have 12 and 13 years of experience, respectively Lastly, 2% of farmers have 1 and 10 years of experience, and only 1% of farmers have 2.5 years of experience.

4.2.2.2.4 Schooling year of farmer (academic year) There are 14% farmers with 5 academic years, 13% farmers with 12 academic years, each 12% farmers with 7, 10 academic years respectively, 11% farmers with

Over the course of 9 academic years, the educational attainment of farmers varies significantly: 9% have completed 4 academic years, 7% have 6 years, 6% have 8 years, and 5% have 11 years Additionally, 4% of farmers have 3 academic years, while 3% each have 0 and 2 academic years, respectively Lastly, 1% of farmers have completed just 1 academic year.

In the total 177 interviewers, their age from 20 to 25 years old is 11%, from 26 to

The age distribution of farmers shows that 25% are 30 years old, 27% are between 31 and 35, 12% are between 36 and 40, 14% are between 41 and 45, 5% are between 46 and 50, and 6% are over 50 The age range of farmers spans from 20 to 59 years, with an average yield varying from 20 to 30 tons per hectare Notably, a 59-year-old farmer achieves a yield of 30 tons per hectare, while a 20-year-old farmer yields 25 tons per hectare, and a 28-year-old farmer produces only 12 tons per hectare.

4.2.2.2.6 Land type to plant water melon

CONCLUSIONS AND RECOMMENDATIONS

Tiêu đề	Determinants of Water Melon Production at Farm Household Level in Tien Giang Province
Tác giả	Luong Thi My Duyen
Người hướng dẫn	Dr. Tran Tien Khai
Trường học	University of Economics Ho Chi Minh City
Chuyên ngành	Development Economics
Thể loại	Thesis
Năm xuất bản	2023
Thành phố	Ho Chi Minh City

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