This study is required to find out the potential factors impact to Tien Giang's water melon production based on production function model, theory of farm household economics and SWOT ana
Research context
Tien Giang's watermelon is a well-known fruit brand primarily exported to China and Cambodia through border trade; however, production remains unstable In 2007, the province's watermelon cultivation covered 3,779 hectares with a yield of 70,847 tons, while in 2008, the cultivated area decreased to 2,954 hectares with a produce of 55,754 tons Current watermelon farming in Tien Giang is characterized by spontaneous practices lacking focus, specialization, and reliable market information, leading to significant price fluctuations and seasonal demand variations influenced heavily by wholesalers and distributors From the producer's perspective, there is limited data on the economic efficiency and contribution of watermelon cultivation to farm household income Despite numerous uncertainties in cultivation and trade, this analysis emphasizes the supply side, particularly examining the economic viability of watermelon farming in Tien Giang province.
This research highlights the need to strengthen key aspects of watermelon production, including enhancing farmers' entrepreneurial skills across all genders and ages, improving their resilience to adverse natural conditions such as sudden and unpredictable climate change, and increasing the overall quality of watermelons to meet market standards.
Research problem
The factors influencing watermelon yield among farmers remain unidentified, with different farmer types responding uniquely to various variables This study aims to identify the key factors impacting watermelon production in Tien Giang using a production function model, farm household economic theory, and SWOT analysis Understanding these factors will help optimize watermelon farming practices and enhance productivity in the region.
This study assesses the effectiveness of utilizing various inputs for watermelon production, including land, labor, and capital—comprising cash and physical resources like fertilizer and seeds Additionally, market information plays a crucial role in ensuring that production meets specific market demands, preventing both surplus and deficit Implementing efficient input management and market analysis can enhance watermelon yield and profitability, supporting sustainable agricultural practices.
Goal and specific objectives of the study
Tien Giang, a key province in Western Vietnam's core economic region, relies heavily on agriculture for its development Improving the efficiency of watermelon production is essential for enhancing agricultural productivity and raising the living standards of local farm households This study aims to advance agricultural practices in Tien Giang, focusing on maximizing watermelon output to support sustainable economic growth and community well-being.
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
This research aims to identify the key factors influencing watermelon production and evaluate the economic efficiency of these production factors The primary objective is to understand how various elements impact watermelon yield and profitability To achieve this, the study addresses specific research questions focused on the factors affecting watermelon cultivation and their economic contributions This comprehensive analysis provides valuable insights for optimizing watermelon farming practices and boosting profitability.
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?
Scope ofresearch
This study focuses on specific factors influencing watermelon production, such as land area, labor, fertilizer, seed inputs, market information, and farmers' growing years, while acknowledging that other factors may also play a role Conducted in Tien Giang Province, which comprises 8 districts, 1 city, and 1 town, the research collected data through 177 direct questionnaires with watermelon farmers who have cultivated the crop for at least one year The data collection took place over three months (October to December 2010), providing a comprehensive dataset for analyzing the key factors affecting watermelon productivity.
The organization of the thesis
Chapter 1 provides the foundational understanding of this research by outlining its rationale, including the research context, the primary problem statement, and the study's goals and specific objectives It also defines the research questions, specifies the scope of the research, and details the organization of the thesis, establishing a clear framework for the entire study.
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 water melon production in Tien Giang province, highlighting the key factors influencing yield through input utilization The chapter provides an overview of Tien Giang’s agricultural landscape, emphasizing the significance of watermelon cultivation in the region's economy It examines how different input uses—such as fertilizers, water, and labor—impact watermelon yield, offering valuable insights for optimizing production efficiency.
Giang Province in Vietnam offers favorable climate and soil conditions conducive to watermelon cultivation, making it a significant player in regional production Watermelon farming in Tien Giang Province benefits from its strategic location, ensuring strong market access and product competitiveness The article provides a comprehensive SWOT analysis, highlighting strengths such as quality soil and climate, weaknesses like limited infrastructure, opportunities including expanding markets, and threats from climate change and pests Through detailed farm surveys, it describes the current state of watermelon production in Tien Giang, emphasizing optimal farming practices and challenges faced by farmers Econometric analyses reveal how input usage—such as fertilizer, water, and labor—influences watermelon yields, providing insights to improve productivity and profitability Overall, the research underscores the potential for Tien Giang's watermelon industry to grow sustainably by leveraging its natural advantages and addressing existing constraints.
Chapter 5 IS the last one is presented conclusion and recommendation for provincial authorities to help farmers get a higher productivity and especially get a higher benefit.
LITERATURE REVIEW
Theoretical framework
2.1.1 Theory of farm household economies
Peasant economic behavior can be explained through logical deductions based on household goals and market dynamics In economic analysis, a farm household is viewed as a single decision-making unit that maximizes a utility function, where profit maximization aligns with utility maximization under fully competitive input and output markets Differences between various economic theories often stem from assumptions about how factor and product markets operate, especially regarding labor markets and household labor allocation Social relations significantly influence household economic behavior, affecting how markets function for different peasants, thus leading to varied economic outcomes (Frank Ellis, 1993).
The agricultural labor force primarily comprises farmers operating family farms, making the concept of a firm straightforward as most farms are associated with household units Farm sizes vary significantly across regions, with small-scale farms—often under two hectares—in parts of Sub-Saharan Africa, South Asia, and East Asia, including countries like Bangladesh, China, the Democratic Republic of the Congo, Egypt, Indonesia, India, and Korea Conversely, farms in Western European countries tend to be much larger, often spanning thousands of hectares Agricultural farms also differ by type, encompassing family farms, business farms, and specialized farm enterprises, with the latter being directly linked to a market economy These variations influence agricultural productivity and economic structure across different regions.
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 )
Peasant households are farm families that depend primarily on family labor and access to land for their agricultural activities, operating within larger economic and political systems that influence their production decisions They typically engage only partially in input and output markets, which are often imperfect or incomplete, shaping their market participation (Ellis, 1992) Representing at least a quarter of the global population, peasants are predominantly found in developing countries, where they may comprise up to 70 percent of the national population (Bardhan & Udry, 1999) According to Ellis, peasants constitute a significant portion of humanity living in developing nations, highlighting their crucial role in global agriculture and rural economies.
Hunt (1991) describes peasant farms as multifunctional units serving both production and consumption purposes A portion of the produce is sold to fulfill cash needs and financial obligations, while the remaining is consumed by the farmers themselves This dual role highlights the vital economic and subsistence functions of peasant agriculture (cited in Mariapia Mendola, 2007).
One of the main theories in farm economics posits that farmers make decisions based on utility maximization Neo-classical economics assumes that with limited resources and technical constraints, farms behave by maximizing their utility functions According to Ellis (1993), utility maximization equates to full income maximization Brossier et al (1997) further expressed the challenge of identifying profit maximization in agriculture through a specific formula, highlighting the economic behavior guiding farm decision-making.
II=P-CV -CF-KA- WA
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 that a firm gains through expansion, leading to reduced average unit costs as output increases Larger production volumes enable companies, especially large farms, to achieve lower costs and greater competitiveness Consequently, small farms often struggle to survive due to higher per-unit costs and limited economies of scale, making it challenging for them to compete effectively in the market.
Figure 2.1: The relationship between output and average cost Source: http://en.wikipedia.org/wiki(Economies_of_scale
As quantity of production increases from Q to Q2, the average cost of each unit decreases from C to C 1•
Indivisible resources, such as tractor power, are crucial for achieving economies of scale in agriculture, as their optimal utilization depends on land area to maximize efficiency (Ellis, 1993) These resources lead to cost savings when used at their optimal level, directly influencing the production scale that minimizes unit costs in the short run (Tran Tien Khai, 2001).
Production functions are a fundamental tool in neo-classical economics, used to analyze how outputs are generated from various inputs A production function specifies the level of output for a firm, industry, or entire economy based on different combinations of inputs Firms convert inputs into outputs, or products, through their production processes, where inputs—also known as production factors—include all resources used For example, in a bakery, inputs such as labor, raw materials like flour and sugar, and capital investments in ovens and mixers are essential for producing products like bread, cakes, and pastries.
Inputs can be categorized into labor, materials, and capital Labor includes both skilled workers like engineers and carpenters, as well as unskilled workers such as agricultural laborers, along with the entrepreneurial efforts of managers Materials consist of resources like steel, plastics, electricity, and water that are purchased and transformed into final products Capital encompasses land, buildings, machinery, equipment, and inventories that support the production process.
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 is illustrated in the accompanying figure All points beneath the curve represent technically feasible production levels, while points on the function indicate the maximum output achievable at specific input levels, highlighting the efficient use of resources.
According to Figure 2.2, the production function rises from points A, B, and C, indicating that increasing input units leads to higher output levels At point C, however, additional input use no longer produces extra output; instead, total output begins to decline due to input underutilization.
At point A, increasing inputs lead to a rising output at an increasing rate, with both marginal physical product (MPP) and average physical product (APP) also increasing, indicating increasing returns to scale The inflection point at point X marks where diminishing marginal returns begin; from point A to C, output growth slows despite additional inputs Point B represents the tangent point between APP and MPP, highlighting the transition from increasing to diminishing returns in production efficiency.
B, APP is at a maximum and the marginal curve must be below the average curve
Source: http:/ /www.wordiq.com/definition/Production function
The Cobb-Douglas production function is widely used to represent the relationship between output and inputs in economic modeling Originally proposed by Knut Wicksell (1851-1926), this functional form has been extensively tested and validated with statistical evidence by researchers 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
Zi are the dummy variables to reflect other influences to total production
2.1.3 Production factors of farm household
Figure 2.3: The three main factors of production of farm household
Land, labor and capital are referred to as "factors of production" Each factor is plays a unique role in the production of goods
Land is an important indicator of social status within a village or community, as highlighted by Ellis (1993) Traditional land regulations include ownership rights, inheritance laws, immigrant policies, agrarian regulations, and the development of land markets Ownership of land enables farmers to access financial support from banks or lenders, using land as collateral Additionally, Tracy (1993), Price, and Palis (1997) emphasize that most farmers prefer to own a substantial portion of land since it is the most valuable asset they can pass down to future generations (cited in Iran Tien Khai, 2001) Overall, land remains the most valuable resource that people cherish deeply.
Agricultural capital, encompassing both production costs and resources used in agriculture and non-agriculture sectors, includes assets such as buildings, machinery, equipment, fertilizers, feeds, and inventory According to Mundlak, Larson, and Butzer (1997), agricultural capital can be classified into two types: fixed capital and working capital Understanding these distinctions is essential for assessing agricultural investment and productivity.
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:
Rice productivity of farm households is modeled using the equation Ln Q = A + Σ_i a_i x_i + Σ_i d_i D_i, where Q represents the rice yield in a given year The coefficient A indicates the base level of productivity, while input variables such as land, labor, and investment costs (x_i) significantly influence output Incorporating these variables highlights the impact of resource allocation on farm productivity, emphasizing the importance of efficient input usage for enhancing rice yields This econometric model provides valuable insights into how various inputs contribute to agricultural output, aligning with SEO best practices by focusing on keywords like "rice productivity," "farm households," and "input variables."
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 article presents a model where rice productivity (Ln Q) is determined by various factors, including an angular coefficient (Ln A), influencing variables such as land, labor price, fertilizer price, and rice price (Xi) Additionally, dummy variables (Di) are incorporated to account for factors affecting yield, such as farm size and agricultural ecology This regression framework highlights the key determinants impacting rice production efficiency among farm households.
Rice land stock and water availability remain the most critical factors constraining paddy production growth Investment in fertilizers yields only marginal returns, with potash being a notable exception Additionally, increasing capital investment has limited impact on boosting paddy output at the current cultivation level.
Nguyen Thi Lien, Nguyen Xuan Hai, Pham Hoai Vu, and Trinh Thi Long Huong, in their study "Rice Production" (1999), employed the productive function Ln Q = Ln A + Σi ai Ln Xi + Σi bi Di to analyze the key factors influencing rice productivity, similarly to Iran Tien Khai's research.
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
In Nepal's economic development, capital accumulation remains the primary driver of growth Across both developing and developed economies, factor productivity plays a crucial role in fostering economic expansion Additionally, intangible factors such as advancements in education and technology, supportive economic policies, and continuous learning significantly contribute to enhancing overall factor productivity.
In the Nepalese context, accurately estimating the contribution of factors of production like labor and capital is challenging due to limited data availability Additionally, economic growth cannot be fully explained by these variables alone, making it difficult to attribute growth to factor productivity gains When applying proper accounting standards, factor productivity may even appear as a negative contributor to Nepal's economic growth, highlighting the complexities in assessing productivity in this setting.
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:
( } ~ ~ A .n., ' where a 1, , ak are the input elasticity, and E denotes the error term
Jacklin's thesis estimates the Cobb-Douglas production function using a quantile regression approach, offering a more comprehensive analysis beyond traditional methods While ordinary least squares (OLS) regression focuses on the mean of the variables' distribution, quantile regression captures different points in the distribution, providing deeper insights into agricultural productivity However, both OLS and quantile regression results indicate that the estimated parameters are not statistically significant in explaining the relationship between agricultural inputs and total output Specifically, this analysis, based on aggregate state-level data from North Dakota's agriculture sector between 1960 and 2004, suggests limited evidence of significant input-output relationships during this period.
The Ricardian method (Mendelsohn et al., 1994) is a cross-sectional approach used to analyze agricultural production by assessing how farmers maximize income within their farm's external conditions This approach assumes that each farmer aims to optimize land net revenues (V), which serve as an indicator of net productivity under varying environmental and economic factors The core principle is encapsulated in an equation that relates farmland net revenues to these exogenous variables, providing valuable insights into how external factors influence agricultural profitability This method is widely utilized in agricultural economics research to understand spatial productivity variations and the impacts of climate and policy changes on farm income.
The Ricardian model evaluates how various exogenous factors—climate variables (F), water flow (H), soil characteristics (Z), and economic variables such as market access (G)—influence farm net revenues, which are maximized through optimal input choices (X) Market prices (Pi) and crop outputs (Qi) are essential components in calculating revenues, while input prices (Px) impact the farmer’s decision-making process This reduced-form model provides insights into the relationship between environmental, economic, and market variables and farm profitability, helping to inform sustainable agricultural practices amidst climate and economic variability (Mendelsohn et al., 1994).
The Ricardian approach, as outlined by Mendelsohn et al (1994), serves as the primary method utilized in J Wang et al.'s (2009) analysis This method focuses on evaluating how farmers select crops and inputs for each unit of land with the goal of maximizing returns By applying this approach, the study assesses the impact of various factors on agricultural productivity and land use decisions.
Max rr = IPqiQi (Xi,Li,Ki,IRj,C,W,S)- IPxXi- IPmLi- IPnKi- IPiriRi (5)
Page 15 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,
Climate variables (C), irrigation choices for each crop (IRi), available water for irrigation (W), soil characteristics (S), input prices (Px), and labor prices (Pm) are key factors influencing agricultural decision-making Understanding how these elements interact helps optimize water use, improve crop yields, and reduce costs Incorporating climate data, soil properties, and price signals into irrigation planning enhances sustainability and profitability for farmers Effective management of available water resources (W) alongside accurate crop choices (IRi) ensures efficient use of inputs and supports resilient farming practices.
The rental price of capital is denoted as Pn, while Pir represents the annual cost associated with each type of irrigation system Equation (5) expands upon Equation (4), providing a more detailed analysis Li and Ki are the two primary factors used to determine the physical impact on crop yield and overall crop productivity.
Coelli (1996) measures agricultural technical efficiency using the data envelopment analysis (DEA) approach, which offers key advantages such as avoiding parametric assumptions about production technology and eliminating the need for distribution assumptions of the inefficiency term.
Cristina (1998) employed a constant returns to scale production function utilizing the primary factors of land, labor, and capital to estimate value added in agriculture This approach is valuable for economic development, growth analysis, and macroeconomic studies, often incorporating both production factors and intermediate inputs Many estimations assume constant returns to scale, with some models focusing solely on labor and capital as the main factors of production Notably, land plays a crucial role in agriculture, making its inclusion essential compared to other sectors where land's contribution may be less significant.
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 directly influencing watermelon production, as illustrated in Figure 2.4 "Conceptual Framework." These include the relationship between watermelon yield and input variables such as productive area, labor, chemical fertilizers, pesticides, and seed quality Additionally, the framework highlights the impact of dummy variables like market information, agricultural extension services in specific locations, and the dissemination of agricultural extension information on watermelon yield, emphasizing their critical roles in enhancing productivity.
This study examines the relationship between input use variables and dummy variables in watermelon production in Tien Giang province The findings highlight the importance of optimizing input utilization to increase productivity and enhance profitability for farmers To achieve higher yields, farmers should adopt appropriate strategies based on the identified relationships, while also focusing on minimizing input costs to maximize profits Efficient management of resources and cost reduction are key factors for improving the overall efficiency of watermelon cultivation in the region.
•• I igure 2.4: Conceptual framework ource: the author's survey in 2010
RESEARCH METHODOLOGY
Analytical framework
Based on the production function model, empirical research, and conceptual framework of this study, the regression model is formulated as: ln Q = ln A + β₁ ln X₁ + β₂ ln X₂ + + βn ln Xn This model captures the relationship between output and input variables, providing insights into productivity and efficiency, which are essential for optimizing production processes Incorporating logarithmic transformations enhances the model's accuracy and interpretability for economic analysis and decision-making.
This study proposes a regression model to analyze watermelon yield, expressed as lnQ, which depends on several factors including the natural logarithms of key variables such as X1 through X9, along with their interactions and quadratic terms The model incorporates coefficients for each predictor—such as J1 for lnX1, J2 for lnX2, and so forth—highlighting their individual contributions to the yield Specifically, Q represents the watermelon yield per hectare during the summer-fall crop of 2010 Utilizing this regression model allows for a comprehensive understanding of how various environmental and agronomic factors influence watermelon production, thereby providing valuable insights for optimizing yield outcomes.
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 B1 to B10 represent the impact of various variables—including productive area, land rent cost, land preparation cost, labor cost, seed cost, fertilizer cost, growing year of producer, and market information—on watermelon yield These factors significantly influence productivity, highlighting the importance of optimizing resource allocation and market awareness to enhance watermelon production efficiency Understanding these variables helps farmers make data-driven decisions to maximize yield and profitability in the watermelon farming industry.
!l is error terms (regression residual) which means there are other factors that influence which effects to water melon yield
This research assesses the economic efficiency of various input uses, including productive area, land rent, land preparation, seed, chemical fertilizer, and pesticides, on watermelon yield changes It analyzes how a 1% increase in chemical fertilizer application impacts watermelon yield, providing insights into input productivity Additionally, the study incorporates dummy variables such as access to agricultural extension services and market information to evaluate their influence on watermelon yield variations.
./ Dependent variable: water melon yield per hectare (Q)
The study examines the impact of various independent variables on agricultural productivity, including productive area (X1), land rent cost per hectare (X2), and land preparation costs (X3), which significantly influence farm output Additionally, labor costs (~), seed expenses (X5), and fertilizer costs (X6) are critical factors affecting crop yields The duration of the growing year (X7) and the presence of agri-extension services (X8) in the location enhance agricultural efficiency, while access to information from agri-extension (X9) and market information (X10) further contribute to improved decision-making and productivity Optimizing these variables can lead to increased agricultural profitability and sustainable farming practices.
Increasing water melon yield can be achieved by utilizing additional labor, fertilizer, or expanding productive area; however, these inputs should be used within optimal limits to ensure cost-effectiveness Excessive use of labor may boost yield but does not offset the additional costs, making it economically inefficient Over-application of fertilizer can lead to a decline in yield, indicating the importance of optimal input levels The analysis of productive area squared demonstrates that increasing the area initially boosts yield due to economies of scale, but beyond a certain point, it becomes unmanageable, resulting in decreased output The optimal level of productive area balances increased production with the ability to manage, care for, and invest, with the expected marginal return turning negative when exceeding 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 study utilizes cross-sectional data on input and output factors for watermelon production across seven districts in Tien Giang Data collection was conducted in the third quarter of 2010 to ensure accuracy and timeliness The analysis aims to identify key determinants of productivity and efficiency in watermelon farming within the region These findings will provide valuable insights for policymakers and farmers seeking to optimize resource use and enhance crop yields in Tien Giang.
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
Land rent costs in Tien Giang are measured in millions of Vietnamese Dong (VND) per hectare Watermelon cultivation is challenging on already cultivated soils due to the high prevalence of deadly soil-borne diseases To ensure successful growth, farmers often rely on new or less-used soils, typically cycling through 1-2 seasons of watermelon within 2-3 years Consequently, farmers frequently rent quality land across Tien Giang to facilitate continuous and profitable crop production.
Land preparation cost (LandPre) for watermelon cultivation is estimated in Vietnamese Dong (VND) per hectare and includes expenses such as plastic covers, ash, coir, and irrigation Using plastic covers enhances watermelon yields by conserving water, suppressing weeds, and reducing certain diseases and pests Ash and coir are incorporated into the soil prior to covering with plastic to improve soil quality Although irrigation costs are minimal, they are included in the overall land preparation expenses to provide a comprehensive cost estimate for successful watermelon farming.
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
Labor costs in this study are measured in millions of Vietnamese Dong (VND) per hectare, with household labor expenses calculated by multiplying total household working days by the opportunity cost For consistency, the author used hired labor costs to estimate household labor expenses, exemplified by a scenario where hired labor is paid 4 million VND over two months, and the same amount is allocated for household labor This approach ensures accurate comparison and integration of household and hired labor costs in the economic analysis.
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 used during various agricultural stages, including land preparation, fertilization, seedling support, fruit development, and sideline activities Additionally, this category includes expenses on pesticides such as insecticides, fungicides, herbicides, plant protection products, disease prevention agents, and growth stimulants These costs are measured in millions of Vietnamese Dong (VND) per hectare, providing a comprehensive view of input expenses in crop production.
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
Reliable market information (M) is crucial for successful agricultural production, especially in watermelon cultivation Lack of insights from research organizations has led farmers in Tien Giang to plant watermelons based solely on their capacity to invest, without guidance from market studies This often results in oversupply and low prices during peak periods like New Year Holidays Additionally, watermelon prices are affected by demand and supply dynamics in city markets and neighboring provinces, emphasizing the importance of accurate market data for effective planning and price management.
Page 23 agricultural prices reflects the market risk faced by agricultural producers It is measured by dummy variable
Agricultural production is highly affected by natural conditions such as climate variability, floods, unpredictable disasters, insect infestations, and seasonal changes Scientific studies, including works by Matthews and Wassmann (2003), Parry et al (2004), and Tao et al (2006), increasingly demonstrate the significant impact of climate change on agriculture Due to the strong and well-documented evidence of climate change effects, 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
So the total 177 respondents is chosen to interview directly is larger than the minimum needed sample size It will be good representative for this research
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 have only two questionnaires, which is insufficient to significantly impact the overall research findings Therefore, the author will add one more questionnaire to Cai Be's total and one more to Cho Gao's total to enhance data accuracy.
According to the Tien Giang Rural Development and Agriculture Department, there are no available statistics on the number of households planting watermelons, including which households have planted or not planted them yet Consequently, this study employs a proportional sampling framework to select research samples By analyzing each district’s watermelon output, the author calculates the sampling distribution proportion for each district Based on these proportions, samples are chosen, and relevant data are collected to ensure representative and accurate research findings.
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 through face-to-face interviews with approximately 20 experienced watermelon farmers Each session lasted 30 to 45 minutes, during which valuable insights were exchanged regarding watermelon production costs and farming practices The final questionnaire was refined and finalized based on their feedback to ensure accuracy and relevance, enhancing its effectiveness for data collection.
The author initiated contact with Mr An, Director of the Agricultural Seed Center of Tien Giang Province, to present the research concept clearly With his support, the author received guidance on approaching and requesting interviews with respondents Mr An also introduced the relevant district officials responsible for facilitating the data collection process, ensuring smooth coordination for the research.
The author outlined the overall and specific objectives of the research, facilitating organized data collection through small meetings with approximately 10 respondents each Data was collected via direct interviews conducted by the author, with each farmer interviewed face-to-face The main survey was carried out over a three-month period from October to December 2010, ensuring comprehensive and firsthand data collection for the study.
3.2.6 Limitation of data source and collection
Farmers have not yet adopted the habit of recording detailed crop data, which often leads to discrepancies when recalling information during face-to-face interviews This recall bias can result in data that does not fully reflect reality Additionally, because farmers move between districts, data collected from Cai Be District is often similar to that from Cai Lay or Go Cong Tay districts For instance, Mr Nguyen Van Be, who has 10 years of experience growing watermelons across various districts in Tien Giang, observed only minor differences in chemical fertilizer use and labor costs between districts The key point is that a farmer can assist by answering 2 or 3 questionnaires in each district visited, helping improve the accuracy and reliability of the data collected.
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 utilized linear regression analysis in SPSS to identify important and optimal variables influencing the study Additionally, structured interviews were conducted to gather reliable data and insights on watermelon cultivation, involving oral interviews with individuals or organization representatives through well-designed questionnaires The collected information was then analyzed systematically to meet the objectives of the research.
In addition to the linear regression model, SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis is a key approach used in this research to evaluate watermelon cultivation This comprehensive analysis identifies the strengths and weaknesses within watermelon farming, as well as potential opportunities and threats in the industry Based on the findings from both the regression model and SWOT analysis, the author draws informed conclusions and provides targeted recommendations for improving watermelon cultivation practices.
This research analyzes the key determinants influencing watermelon yield in Tien Giang province using literature review, data analysis, linear regression models, and SWOT analysis The study aims to identify which factors positively or negatively affect watermelon production, providing valuable insights for farmers to optimize their practices Understanding these impacts is crucial for developing strategies that enhance watermelon output and improve agricultural productivity in the region.
The results of this research will suggest suitable policies for government to encourage farmer plants more water melon and improve their living standards.
ANALYSES OF WATER MELON PRODUCTION IN TIEN
Introduction ofTien Giang province and its water melon production
4.1.1 Overview of Tien Giang province
Tien Giang is a key agricultural province located in the Mekong River Delta, a vital economic region of southern Vietnam Situated approximately 70 km south of Ho Chi Minh City and 90 km north of Can Tho City, it benefits from strategic positioning in the dynamic Mekong Delta The province spans latitudes 10°35' to 10°12' North and longitudes 105°50' to 106°45' East, sharing borders with Long An and Ho Chi Minh City to the northeast and north, Dong Thap Province to the west, Ben Tre and Vinh Long provinces to the south, and the East Sea to the east.
Tien Giang Province stretches along the northern bank of the Tien River, a tributary of the Mekong River, covering approximately 120 km With a natural area of 2,481.77 square kilometers, it accounts for about 6% of the Mekong River Delta, 8.1% of the South Key Economic Region, and 0.7% of Vietnam's total land area.
Tien Giang features a predominantly flat terrain with neutral alluvial soil along the Tien River, covering approximately 53% of the province, making it ideal for a diverse range of plants and animals As of 2009, the population was about 1.67 million, with a density of 672.9 persons per square kilometer, accounting for roughly 9.8% of the Mekong Delta region's population, 11.4% of South Vietnam’s key economic region, and 1.9% of the national total Tien Giang is the second province after Long An, following Ho Chi Minh City, to border other southern provinces, and it comprises 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 annually: a dry season from December to April lasting five months, and a rainy season from May to November The region has low annual rainfall, averaging between 1,210 and 1,424 mm, with rainfall decreasing from north to south and from west to east Humidity levels are consistently high, ranging from 80% to 85% The prevailing winds come from two directions: the north-east during the dry season and the south-west during the rainy season, with an average wind speed that influences the local climate.
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 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-affected soil accounts for 14.6% of the total natural area in Go Cong, covering approximately 34,552 hectares This soil type predominantly exists in regions such as Go Cong Dong, Go Cong Town, Go Cong Tay, and parts of Cho Gao Characterized by its alluvial nature, the soil offers favorable conditions for agriculture; however, its productivity is impacted by saline water intrusion from the sea during the dry season.
Acid sulphate soils constitute approximately 19.4% of the total natural land area, covering around 45,912 hectares These soils are primarily found in the low-lying regions of Dong Thap Muoi, located in the northern parts of Cai Be, Cai Lay, and Tan Phuoc districts.
Mound sandy soil covers approximately 3.1% of the total natural area, totaling about 7,336 hectares It is primarily found in the districts of Cai Lay, Chau Thanh, and Go Cong Tay, with the highest concentration in Go Cong Dong This soil type is characterized by its high terrain, light mechanical composition, and is mainly utilized for residential development and the cultivation of fruit trees and vegetables.
The majority of the province's soil, accounting for 53%, is alluvial soil, which benefits high-yield rice cultivation and orchard development due to abundant freshwater sources Additionally, 19.4% of the land (45,912 hectares) consists of alkaline soil, while 14.6% (34,552 hectares) is saline alluvial soil Recent efforts have focused on land reclamation, expanding production areas, and improving soil conditions through development programs such as the Dong Thap Muoi and Go Cong freshwater initiatives, which have gradually increased productive land and supported sustainable agricultural growth.
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 widely cultivated across various provinces in the country, emerging as a valuable cash crop for farmers Modern agricultural practices, including the use of plastic sheet mulching, optimized fertilizer formulas, and high-yield, adaptable varieties, have boosted production efficiency In the Mekong Delta region, watermelon serves as an alternative crop to rice, providing farmers with diversified income sources and enhanced agricultural sustainability (Do Minh Hien, Nguyen Thanh Tung, 2006)
Rice cultivation in Tien Giang has a long-standing history, but farmers’ income remains modest at around 3-4 million VND per hectare due to three crops per year, with an average yield of 14.2 tons/hectare annually The three rice crops contribute approximately 4.5, 4.2, and 5.5 tons per hectare respectively, though the risk of losses from insects, pests, diseases, and weather fluctuations remains high Recently, farmers are encouraged to adopt rotational cropping systems, such as alternating rice and watermelon or vegetables, with watermelon yields averaging 22 tons per hectare and potentially reaching 25-30 tons per hectare under optimal cultural practices This diversification aims to improve income stability and crop productivity in the region.
- - - - - - - - - - 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 widely cultivated worldwide, including in Vietnam, driven by the high demand for fresh fruits and processed products like canned watermelon slices and watermelon juice Global watermelon production has been consistently increasing over the years, with approximately 93 billion tons produced in 2004 compared to 47 billion tons in previous years, reflecting its growing popularity and economic significance.
Page 33 billion tons in 1996 Production of other melon gained one third of water melon production
China was the leading producer of watermelons in 2002, with a total production of 60 billion tons, followed by other major producers such as Turkey, Iran, the USA, Egypt, and Mexico Additionally, China dominates global melon production, accounting for approximately 50% of worldwide output, with Turkey (6.1%), Iran (4.4%), the USA (4.2%), and Spain (3.9%) as other significant players Despite its status as the primary producer, China is not a major exporter of watermelons or other melons due to high local demand and large domestic consumption.
Spain is a leading exporter of honeydew and cantaloupe, annually shipping over 300,000 tons, followed by Mexico and Costa Rica Despite the USA being a major importer of melons, it also exported approximately $98.1 billion worth of melons in 2004, primarily to Canada and Japan In Asia, Malaysia is a significant exporter of watermelons, shipping around 70,000 tons in 2003, ranking as the fifth-largest watermelon exporter in the world after Spain, Mexico, the USA, and Hungary.
Melon wor1d production source: FAO redr- from USDA H0111culturel &
Source: Do Minh Hien, Nguyen Thanh Tung 2006
The USA is a major importer of melons, with imports valued at $100.6 billion in 2004, predominantly sourced from Mexico (91.2%), Costa Rica (2.4%), and Guatemala (3.5%) Germany stands out as the leading importer of watermelons, followed by the USA and Canada Additionally, France and England are key importers of honeydew and cantaloupe, highlighting their significant roles in the global melon import market.
Major importing Md exporting countries for melons of the world Source: Horticultural &
Figure 4.2: Major importing and exporting countries for melons of the world
Source: Do Minh Hien, Nguyen Thanh Tung 2006
Analyses of water melon production in Tien Giang province
This chapter discusses the results and analysis of the relationship between independent variables and the dependent variable through SWOT analysis and econometric techniques Data analysis was conducted using SPSS 15.0, focusing on descriptive statistics and the application of a linear regression model to interpret the data effectively.
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
• 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
Lack of market information from research organizations led farmers to plant watermelons over large areas based solely on their investment capacity, without guidance from market studies This situation can result in a decline in watermelon prices during peak holiday seasons, negatively impacting farmers' profits.
• Price of water melon is very much influenced by fact of demand and supply in city markets
• It should be considered that market information and planning for cultivated area very important to farmers
There is a high demand for watermelons driven by domestic and international markets, with countries like Malaysia and China requiring approximately 140 kg per person in 2010 Despite this, water melon cultivation faces challenges such as competition from exotic varieties and high risks from pests, diseases, and weather-related issues like floods and droughts, which can impact crop stability However, producing high-quality watermelons offers significant benefits to farmers, especially when market prices remain relatively stable compared to other fruit crops Supplying watermelons to local and regional markets, including China, Laos, and Cambodia, can lead to better pricing opportunities, although market fluctuations may influence future profits.
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 across 177 respondents, providing a comprehensive overview of the data distribution The minimum and maximum values indicate the range of each variable, highlighting the smallest and largest observed scores The mean offers insight into the average value, while the standard deviation measures the variability or dispersion of data points around that average This statistical summary aids in understanding the overall trends and consistency within the dataset.
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
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, indicating its high productivity potential The total cultivation costs vary from 40.37 million to 78.36 million per hectare, with a median cost of 68.27 million, highlighting the significant investment required for successful watermelon farming Overall, watermelon is a high-yield vegetable crop that demands careful management and attention to maximize returns.
The average age of farmers interviewed was approximately 35 years, ranging from 20 to 59 years old, indicating a relatively young farming population Farmers’ education levels varied from 0 to 12 years, with an average of 7 years, highlighting the diverse background of cultivators The experience in watermelon cultivation ranged from 1 to 17 years, with an average of about 7 years, demonstrating ongoing learning and expertise development These findings suggest that watermelon farming is a challenging crop to grow, requiring farmers to invest time in gaining experience, as the minimum age of 20 reflects the need for dedicated study and skill development in this complex agricultural activity.
Several factors influence watermelon yield, including the productive area, land rent and fertilizer costs, land type, and farmer characteristics such as age, education level, and farming experience Additionally, market information and support from agricultural extension services play crucial roles in optimizing production outcomes Understanding these interconnected factors can help improve watermelon productivity and farm profitability.
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
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
Among 177 interviewers, 56.49% (100 individuals) consistently pay attention to market information, while 43.51% (77 individuals) do not prioritize it Price emerges as the most critical factor within the market information that respondents value most, highlighting its significance in market decision-making.
In Tien Giang, located within the Mekong River Delta basin, agriculture remains a vital component of the local economy To support this, agricultural extension services have been progressively enhanced across each district According to recent data, 55.93% of interviewers (99 respondents) report the presence of agricultural extension services in their area, highlighting ongoing efforts to improve information dissemination and support for local farmers.
Most interviewers highlighted a lack of agricultural extension services in their area These services play a crucial role by providing farmers with essential knowledge on topics such as selecting seeds, choosing fertilizers, and proper crop care Out of 99 interviewers, 96 have received information from agricultural extension services, indicating widespread access, though 3 interviewers still have not benefited from these resources Overall, the presence and utilization of agricultural extension services are vital for improving farming practices in the region.
4.2.2.2.3 Growing year of farmer (Experience)
Experience plays a crucial role in agricultural development The data shows that 14% of farmers have 7 years of experience, while 12% have 6, 5, and 4 years of experience each Additionally, 10% of farmers possess 3 years of experience Smaller proportions include 7% with 8 and 9 years, 5% with 2 and 11 years, 3% with 12 and 13 years, and 2% with 1 and 10 years of experience Only 1% of farmers have 2.5 years of experience, highlighting the variation in farmers' expertise and its impact on agricultural progress.
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
The educational background of farmers varies, with 9% having four academic years and 7% possessing six academic years Additionally, 6% of farmers have completed eight academic years, while 5% have 11 academic years A smaller percentage, 4%, have three academic years, and 3% of farmers each have either zero, two, or one academic year Notably, 9 farmers have completed nine academic years.
In the total 177 interviewers, their age from 20 to 25 years old is 11%, from 26 to
The age distribution among farmers shows that 25% are 30 years old, 27% are between 31 and 35 years old, 12% are aged 36 to 40, 14% are from 41 to 45 years old, 5% are between 46 and 50, and 6% are over 50 years old The overall age range spans from 20 to 59 years old, with the average age fluctuating between 20 and 30 years A 59-year-old farmer achieves a yield of 30 tons per hectare, while a 20-year-old farmer produces approximately 25 tons per hectare Conversely, a 28-year-old farmer has a considerably lower yield of 12 tons per hectare, highlighting variations in productivity across different age groups.
4.2.2.2.6 Land type to plant water melon
Weather and soil conditions are critical factors in watermelon production, requiring farmers to select optimal planting sites each season farmers cannot continuously plant watermelon in the same location year after year due to soil degradation and suitability issues As a result, modern farmers have the flexibility to move their cultivation to different areas to find the best soil types for watermelon growth The three primary soil types preferred for watermelon cultivation are alluvial soil, dark alluvial soil, and acid sulphate soil, as they offer the most favorable growing conditions.
Watermelon, often mistakenly regarded as a vegetable, requires constant care both day and night to maximize yield Expanding the productive area does not necessarily lead to higher harvests; in fact, increased planting area can sometimes result in decreased overall output Currently, the watermelon cultivated area spans 36 hectares, but the yield remains low at only 20 tons per hectare Conversely, smaller plots of just 4 to 5 hectares can achieve much higher productivity, reaching up to 30 tons per hectare, highlighting the importance of proper management and optimal cultivation practices for better yields.