Research context
Tien Giang's watermelon is a significant export product to China and Cambodia, yet its production remains unstable In 2007, the region had a watermelon cultivation area of 3,779 hectares, yielding 70,847 tons, but by 2008, the area decreased to 2,954 hectares with an output of only 55,754 tons The current watermelon cultivation in Tien Giang is characterized by a lack of concentration, specialization, and market information, leading to price fluctuations and dependency on wholesalers Additionally, producers lack information on the economic efficiency and profitability of watermelon farming, highlighting a gap in knowledge regarding the supply side and economic viability of watermelon cultivation in the province.
This research identifies key weaknesses in the watermelon production process that require enhancement, including the low entrepreneurial skills of farmers across all genders and age groups, the need for improved resilience against adverse natural conditions, particularly frequent climate change, and the necessity to elevate the quality of watermelons for market competitiveness.
Research problem
The factors influencing watermelon production in Tien Giang remain unclear, as farmers respond differently to various elements affecting their output This study aims to identify the key factors impacting watermelon yields by utilizing a production function model, the theory of farm household economics, and SWOT analysis.
This study evaluates the effectiveness of resources utilized in watermelon production, including land, labor, and capital, which encompasses both cash and physical assets like seeds and fertilizer Additionally, it emphasizes the importance of market information to ensure that supply meets demand, avoiding surplus or deficit situations.
Goal and specific objectives of the study
Tien Giang, a key province in Western Vietnam's economic region, continues to rely heavily on agriculture for its development This study aims to enhance agricultural productivity and uplift the living standards of farm households in Tien Giang by focusing on increasing 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 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?
Scope ofresearch
This research focuses on specific factors influencing watermelon production, such as productive area, labor, fertilizer, seeds, market information, and the growing experience of farmers, while acknowledging that other underlying factors may also play a role Conducted across Tien Giang province, which includes eight districts, one city, and one town, the study gathered data through 177 direct interviews with farmers who have cultivated watermelons for at least one year The data collection took place over three months, from October to December 2010, providing a solid foundation for analysis.
The organization of the thesis
Chapter 1 outlines the foundation of this research by detailing the context, identifying the research problem, and establishing the study's goals and specific objectives It also presents the research questions, defines the scope of the research, and describes 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 impacts yield The author provides a comprehensive overview of the province, highlighting key factors that contribute to successful watermelon cultivation.
Tien Giang province offers favorable climate and soil conditions that enhance watermelon production This article explores the competitive market landscape for watermelons in Tien Giang, providing a comprehensive SWOT analysis It includes detailed insights from farm surveys and examines how various input factors impact watermelon yields through econometric analysis.
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 understood through logical deductions based on prior assumptions regarding household goals and the nature of the market Analyzing the farm household as a single decision-making unit reveals that profit maximization aligns with utility maximization when all input and output markets are competitive The differences in economic theories stem from varying assumptions about factor and product markets rather than household goals Critical distinctions between theories often arise from differing assumptions about labor markets and the allocation of household labor Furthermore, social relations significantly influence how markets operate for different peasants, affecting their economic behavior.
The labor force in agriculture primarily consists of farmers, with the concept of a farm closely tied to household units In many regions, particularly in sub-Saharan Africa and parts of South and East Asia—including countries like Bangladesh, China, and India—farms tend to be quite small, often under two hectares In contrast, Western European countries typically feature much larger holdings, averaging around a thousand hectares Agricultural operations can vary significantly, encompassing family farms, business farms, and specialized farm enterprises that are fully integrated into 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 )
Peasant farm households, primarily composed of family labor and reliant on a specific piece of land, play a significant role in the global population, accounting for at least a quarter of it These households are particularly prevalent in developing countries, where they can represent up to 70% of the national population, as noted by Bardhan and Udry (1999) Despite their substantial presence, peasants engage only partially in imperfect or incomplete input and output markets, influenced by the broader economic and political systems around them (Ellis, 1992; cited in Mariapia Mendola, 2007).
Hunt (1991) describes peasant farms as dual-purpose entities that serve as both production and consumption units 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).
Farm economics is fundamentally rooted in the theory of utility maximization, where farmers make decisions aimed at maximizing their utility given the constraints of limited resources and production factors Neo-classical economics posits that under these conditions, farms behave in a manner that seeks to optimize their utility functions According to Ellis (1993), this concept of utility maximization aligns with the broader goal of maximizing total income Additionally, Brossier et al (1997) highlighted the complexities of profit maximization in agriculture, which can be expressed through specific formulas (cited in Tran Tien Khai, 2001).
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 businesses experience as they expand their production As output increases, various factors contribute to a decrease in the average unit cost for producers 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
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 A prime example is the power of a tractor, which serves as an indivisible resource that must be effectively utilized over a specific land area The optimal use of such resources leads to cost economies, directly influencing the volume of output necessary to minimize unit production costs in the short run (Tran Tien Khai, 2001).
Production functions are essential tools in neo-classical economic analysis, detailing how firms, industries, or entire economies convert various input combinations into outputs In the production process, firms transform inputs—known as production factors—into products or productivity For instance, a bakery utilizes inputs such as labor from its workforce, raw materials like flour and sugar, and capital investments in equipment like ovens and mixers to produce outputs including bread, cakes, and pastries.
Inputs can be categorized into three main types: labor, materials, and capital Labor encompasses both skilled workers, such as carpenters and engineers, and unskilled workers like agricultural laborers, along with the entrepreneurial efforts of management Materials consist of essential goods like steel, plastics, electricity, and water, which are purchased and transformed into final products Capital refers to tangible assets including land, buildings, machinery, equipment, and inventory.
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, with all points beneath the curve being technically feasible Points along the function represent the maximum output achievable for given input levels, highlighting the efficiency of resource utilization in production.
According to Figure 2.2, the production function shows an upward trend at points A, B, and C, indicating that as more input units are utilized, the output quantity increases However, at point C, while additional input units are still being used, the output does not increase; instead, total output starts to decline due to underutilization of inputs.
At point A, the utilization of additional inputs leads to an increasing output, with both the marginal physical product (MPP) and average physical product (APP) rising The inflection point, known as point X, marks the beginning of diminishing marginal returns Between points A and C, output continues to grow but at a decreasing rate as more inputs are added Point B represents the tangency between APP and MPP, highlighting a critical relationship 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 a widely used model that illustrates the relationship between output and inputs in economics 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
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 plays a crucial role in determining a family's social status within a village or community, as highlighted by Ellis (1993) It is governed by traditional regulations, including ownership rights, inheritance rights, immigrant policies, agrarian policies, and the development of land markets Land ownership significantly facilitates access to financing for farmers, as it serves as collateral security Furthermore, as noted by Tracy (1993) and Price and Palis (1997), many farmers aspire to own substantial land, viewing it as a valuable asset to pass down through generations (cited in Iran Tien Khai, 2001) Consequently, land remains one of the most treasured resources that individuals prioritize.
Agricultural capital encompasses the production costs associated with both agricultural and non-agricultural resources It includes essential assets such as buildings, machinery, equipment, fertilizers, feeds, and inventory of unsold products According to Mundlak, Larson, and Butzer (1997), agricultural capital can be categorized into two types: fixed capital and variable capital.
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 (Q) for a farm household in a given year can be modeled by the equation Ln Q = A + Iaixi + L~iDi In this equation, A represents the angular coefficient, while Xi encompasses various input variables, including land, labor, and investment costs incurred by the farm household during that year.
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 + ∑(xiLnXi) + ∑(Di) represents the factors affecting rice productivity for farm households in a given year In this formula, Q denotes the rice yield, A is the angular coefficient, and Xi includes variables influencing rice supply capabilities, such as land, labor costs, fertilizer prices, and rice prices Additionally, Di encompasses dummy variables that impact yield based on factors like farm size and agricultural ecology.
The study found that the primary constraints to increasing paddy output are the availability of rice land and water resources While investing in fertilizers yields limited marginal returns—except for potash—additional capital investment has minimal impact on improving paddy production at the current cultivation levels.
In their study "Rice Production," Nguyen Thi Lien, Nguyen Xuan Hai, Pham Hoai Vu, and Trinh Thi Long Huong applied a productive function similar to that of Iran Tien Khai, represented as Ln Q = Ln A + Ia.iLnXi This equation highlights the relationship between output and various input factors in rice production, contributing valuable insights to agricultural economics.
+ L~iDi to analyze factors which effect to 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's economy Both developing and developed nations experience economic growth largely through factor productivity Additionally, intangible elements such as advancements in education and technology, a conducive economic policy environment, and continuous learning have significantly enhanced factor productivity.
In the context of Nepal, the impact of production factors like labor and capital on economic growth remains unclear due to insufficient data Consequently, the economic growth attributed to these factors cannot be generalized to reflect productivity gains If accurate accounting practices are applied, factor productivity may actually hinder economic growth in Nepal.
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 employs a quantile regression approach to estimate the Cobb-Douglas production function, contrasting it with ordinary least squares (OLS) regression, which focuses on the mean of the distribution The findings indicate that both traditional OLS and quantile regression yield statistically insignificant parameters regarding the relationship between agricultural inputs and aggregate output for North Dakota agriculture from 1960 to 2004, based on aggregate state-level data.
The Ricardian method, as outlined by Mendelsohn et al (1994), is a cross-sectional approach to analyzing agricultural production that posits farmers aim to maximize their income based on the external conditions of their farms This approach highlights that farmland net revenues (V) are indicative 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 framework, farmers aim to maximize their net revenues by selecting purchased inputs, while considering the market price of crops and the specific attributes of their farm This model highlights the interplay between environmental and economic variables in determining agricultural profitability.
The Ricardian approach, as established by Mendelsohn et al (1994), serves as the foundational method utilized by J Wang et al (2009) in their analysis This approach focuses on how farmers select crops and inputs for each unit of land to achieve maximum returns.
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,
The article discusses a model where C represents a vector of climate variables, while IRi denotes the vector of irrigation choices specific to each crop i It also highlights W as the available water for irrigation and S as a vector detailing soil characteristics Additionally, P x refers to the vector of prices for annual inputs, and P m indicates the prices for various types of labor.
The rental price of capital, denoted as Pn, along with the annual cost of each irrigation system, represented by Pir, plays a crucial role in agricultural economics An expanded equation derived from previous analysis highlights the significance of Li and Ki as key factors influencing crop yield and productivity Understanding these relationships is essential for optimizing agricultural output.
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) utilized a constant returns to scale production function, incorporating land, labor, and capital, to estimate value added in agriculture This model serves as a valuable tool for development and macro-economists, who frequently assess both production factors and intermediate inputs While many production function estimations assume constant returns to scale, some focus solely on labor and capital Despite its lesser significance in other sectors, land remains a critical 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 significantly influence watermelon production, as depicted in Figure 2.4, the "conceptual framework." This 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 impact of dummy variables on yield, such as market information, local agricultural extension services, and information provided by agricultural extensions.
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.
•• I igure 2.4: Conceptual framework ource: the author's survey in 2010
RESEARCH METHODOLOGY
Analytical framework
Based on the production function model and the empirical research outlined, the regression model is specified as follows: ln Q = ln A + ∑Lai ln Xi + ∑L ~izi.
The regression model proposed for this study is expressed as lnQ = lnflo + Jl,InX1 + Jl2lnX2 + Jl3lnX3 + Jl4lnX4 + flslnXs + fl6l~ + fl71DX7 + flsXs + P9X9 + fltoXto + J.1, 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 B1 to B10 represent the impact of various factors on watermelon yield, including productive area, land rent costs, land preparation expenses, labor costs, seed costs, fertilizer costs, the producer's growing experience, 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 input factors, including productive area, land rent, land preparation, seed, chemical fertilizer, and pesticides, on watermelon yield fluctuations For example, 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.
./ Dependent variable: water melon yield per hectare (Q)
The study examines various independent variables that influence agricultural productivity, including 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), receipt of information from agricultural extension (X9), and availability of market information (X10).
In the initial phase of watermelon production, the use of additional labor, fertilizer, or productive land can enhance yield; however, these inputs must be applied judiciously Excessive labor may lead to increased yields but can also result in costs that outweigh benefits, making labor use inefficient Similarly, over-fertilization can actually decrease watermelon yields The analysis of productive area reveals that while expanding land can boost output, there is a threshold beyond which management and investment capabilities become strained, leading to diminished returns Ultimately, there exists an optimal level of production area, beyond which the expectation of yield becomes negative, highlighting the importance of balanced resource allocation in achieving profitability.
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 conducted 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
Land rent costs (LandRent) in Tien Giang are measured in millions of Vietnamese Dong (VND) per hectare Watermelon cultivation is challenging in previously cultivated soils due to the presence of harmful diseases Farmers benefit from using new soils or implementing intercropping systems, allowing for 1-2 seasons of watermelon cultivation over 2-3 years Consequently, to ensure successful crop production, farmers must seek quality soil rentals throughout the region.
The land preparation cost (LandPre) for watermelon production is expressed in millions of Vietnamese Dong (VND) per hectare and encompasses various expenses, including plastic cover, ash, coir, and irrigation costs Utilizing plastic cover enhances watermelon cultivation by retaining moisture, controlling weeds, and mitigating certain diseases and pests Prior to applying the plastic cover, ash and coir are incorporated into the soil Additionally, the irrigation cost for watermelon is minimal, and thus, it is included in 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
Household labor costs are assessed in millions of Vietnamese Dong (VND) per hectare, calculated by multiplying the total household working days by the opportunity cost In this study, the author uses the hired labor cost as a basis for determining household labor expenses For example, if hired labor is compensated with 4 million VND for two months, the author similarly estimates the household labor cost at 4 million VND for the same duration.
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, fruit support, and sideline production activities Additionally, this variable incorporates the costs of pesticides, such as insecticides, fungicides, herbicides, and plant protection products, measured 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 adequate market research, leading to oversupply and lower prices during peak seasons, such as the New Year Holidays Additionally, watermelon prices are significantly affected by the dynamics of demand and supply in local city and nearby provincial markets.
Page 23 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 changes, floods, and unpredictable disasters, as well as insect and disease invasions Scientific research has increasingly demonstrated the strong impact of climate change on agriculture, as highlighted in studies by Matthews and Wassmann (2003), Parry et al (2004), and Tao et al (2006) Consequently, this research excludes climate change as a variable.
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 completed questionnaires, a number too small to significantly impact the overall findings of this research To enhance the data, the author will add one questionnaire to the total for Cai Be and one 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 representative research sample By analyzing the watermelon output from each district, the author calculates the sampling distribution proportion for each area, which will guide 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 the input of 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 the valuable feedback received from the farmers.
The author initiated communication with Mr An, the Director of the Agricultural Seed Center in Tien Giang Province, to outline the research objectives Mr An provided valuable guidance on how to approach and interview the respondents, as well as introducing the key contacts in each district to facilitate the process.
The author communicated the overarching and specific concepts of the research to the participants To gather data, small meetings were organized with approximately 10 respondents each, where the author conducted face-to-face interviews This primary survey took place over three months, from October to December 2010, focusing on direct interactions with each farmer.
3.2.6 Limitation of data source and collection
Farmers in the region often do not keep detailed records of their crop activities, making it challenging for the author to gather accurate data during interviews, as they require 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, who has 10 years of experience in watermelon cultivation across various districts in Tien Giang, noted minimal differences in chemical fertilizer usage and labor costs between these areas It is important to highlight that a single farmer can only 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) as the second method to identify significant and optimal variables Additionally, structured interviews were conducted to gather reliable information and data for the linear regression model These oral interviews targeted individuals or representatives from specific organizations, utilizing well-structured questionnaires to obtain valuable insights on watermelon cultivation The collected data was then analyzed in alignment with the investigation's objectives.
In addition to the linear regression model, this research employs SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis to evaluate watermelon cultivation The author assesses the strengths, weaknesses, opportunities, and threats associated with this agricultural practice Based on the findings from both methodologies, the author will draw conclusions and provide recommendations for the study.
This research aims to identify the factors influencing watermelon yield in Tien Giang province by reviewing relevant literature and employing linear regression and SWOT analysis The findings will reveal which determinants positively affect watermelon production and which ones have a negative impact, providing valuable insights for farmers 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 an agricultural province located in the Mekong River Delta, forming part of the key economic region of Southern Vietnam Situated approximately 70 km south of Ho Chi Minh City and 90 km north of Can Tho City, Tien Giang is positioned at coordinates 105°50' - 106°45' east longitude and 10°35' - 10°12' north latitude 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—stretches over 120 km 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 the total land area of Vietnam.
Tien Giang features a predominantly flat terrain with neutral alluvial soil along the Tien River, covering 53% of the province and supporting diverse plant and animal life As of 2009, Tien Giang had a population of 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 distinct seasons: a dry season lasting five months from December to April and a rainy season from May to November The region receives low annual rainfall, averaging between 1,210 to 1,424 mm, which decreases from north to south and from west to east With an average humidity of 80-85%, Tien Giang is characterized by two primary 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 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
Saline soil constitutes 14.6% of the total natural area, covering approximately 34,552 hectares, primarily in Go Cong Dong, Go Cong Town, Go Cong Tay, and parts of Cho Gao While this soil type shares favorable characteristics with alluvial soil, it is significantly impacted by saline water from the sea during the 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 primarily consists of alluvial soil, covering 53% of the area, which benefits high-yield rice fields and orchards Additionally, 19.4% of the land (45,912 hectares) is classified as alkaline soil, while 14.6% (34,552 hectares) consists of saline alluvial soil In recent years, efforts have focused on reclaiming land, expanding production areas, and increasing crop diversity through the Dong Thap Muoi and Go Cong freshwater development programs, leading to significant growth in productive land.
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
: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 increasingly recognized as a lucrative cash crop in various provinces, particularly in the Mekong Delta region, where it serves as an alternative to rice cultivation Farmers are adopting advanced agricultural practices, such as utilizing plastic sheets to cover the soil and implementing optimized fertilizer formulas, alongside cultivating high-yield, widely adapted varieties.
Rice cultivation in Tien Giang has a long history, but farmers earn a low income of 3-4 million VND per hectare due to three annual rice crops, yielding an average of 14.2 tons per hectare The production breakdown includes 4.5 tons for the first crop, 4.2 tons for the second, and 5.5 tons for the third Additionally, farmers face high risks from pests, diseases, and unpredictable weather To enhance profitability, recent practices encourage rotating watermelon with rice, utilizing various combinations such as two rice crops and one watermelon crop or alternating between vegetable and watermelon crops Watermelon cultivation offers significantly higher yields, averaging 22 tons per hectare, and can reach up to 25-30 tons per hectare with proper care.
- - - - - - - - - - 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 Vietnam, driven by high demand for fresh fruit and processed products like canned slices and juice The world production of watermelon has seen significant growth, increasing from 47 billion tons in 2004 to 93 billion tons in recent years.
Page 33 billion tons in 1996 Production of other melon gained one third of water melon production
In 2002, China emerged as the leading producer of watermelons, with a staggering production of 60 billion tons 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, producing over 300,000 tons annually, followed by Mexico and Costa Rica While the USA primarily imports melons, it also exported melons worth $98.1 billion in 2004, mainly to Canada ($85.2 billion) and Japan In Asia, Malaysia emerged as a significant exporter of watermelon, shipping 70,000 tons in 2003, and currently ranks as the fifth largest exporter globally, following Spain, Mexico, the USA, and Hungary.
Melon wor1d production source: FAO redr- from USDA H0111culturel &
Source: Do Minh Hien, Nguyen Thanh Tung 2006
The United States is a significant importer of melons, with Mexico supplying 91.2% of the total imports, amounting to $100.6 billion in 2004 While the U.S ranks high in melon imports, Germany leads in watermelon imports, followed by the U.S and Canada Additionally, France and England are notable importers of honeydew and cantaloupe.
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 of the relationship between independent and dependent variables using SWOT analysis and econometric methods The data will be analyzed through 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
• 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 often cultivate watermelons on a large scale without access to market research, leading to uninformed investment decisions This lack of market insight can contribute to a surplus of watermelons, causing prices to drop, especially during holiday seasons.
• 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
The demand for watermelons is high, but Vietnamese farmers face challenges from both domestic and international competition High-quality melons from Thailand and the increasing fruit requirements in Malaysia and China, estimated at 140 kg per person in 2010, create a competitive market However, the cultivated area for watermelons may decrease due to risks from pests, diseases, and adverse weather conditions like floods and droughts Despite these challenges, the stable pricing of watermelons compared to other fruit crops presents a potential benefit for farmers Additionally, accessing exotic markets in China, Laos, and Cambodia could lead to better pricing opportunities for watermelon producers.
4.2.2 Description of water melon production in Tien Giang through farm survey
Table 4.3 presents key statistical metrics for the 177 participants, including the minimum, maximum, mean, and standard deviation for each variable The minimum represents the lowest value, while the maximum indicates the highest value observed The mean provides the average value, and the standard deviation quantifies the variability or dispersion of the data points around the mean 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
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 significantly, 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-yielding crop, but it requires careful management to achieve optimal results.
Among the 177 interviewed farmers, the average age is approximately 35 years, with ages ranging from 20 to 59 Farmers have an average of 7 years of schooling, with a range from 0 to 12 years, and their experience in farming spans from 1 to 17 years, averaging around 7 years This data indicates that growing watermelons is a challenging endeavor, as the minimum age of farmers is 20, highlighting the necessity for time to gain experience and knowledge in this crop.
Several factors influence watermelon yield, including the productive area, land rental costs, labor expenses, and fertilizer costs Additionally, the type of land, the age and education level of the farmer, their years of experience, and access to market information and agricultural extension services also play significant roles.
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
Out of 177 interviewers, 56.49% (100 interviewers) consistently prioritize market information, while 43.51% (77 interviewers) do not Among those who do consider market information, price emerges as the most significant factor of concern.
Agriculture plays a vital role in Tien Giang's economy, situated in the Mekong River Delta basin To enhance agricultural productivity, the region has been focusing on improving agricultural extension services across its districts A recent survey revealed that 55.93% of respondents reported access to agricultural extension services in their area, while 44.07% indicated they do not have such services available.
A survey of 78 interviewers revealed that the absence of agricultural extension services in their area hinders access to vital farming knowledge These services play a crucial role in educating farmers about essential practices such as seed selection, fertilizer application, and crop maintenance Conversely, among the 99 interviewers who reported having access to agricultural extension services, 96 have successfully received valuable information, while only 3 have not benefited from these resources.
4.2.2.2.3 Growing year of farmer (Experience)
Experience plays a crucial role in agricultural development, with 14% of farmers having seven years of experience Additionally, 12% have six years, while 10% possess three years of experience The distribution continues with 7% of farmers having eight or nine years of experience, and 5% with two or eleven years Furthermore, 3% of farmers have twelve or thirteen years of experience, while 2% have one or ten years Notably, 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
In a study of farmers' educational backgrounds, it was found that 9% of farmers completed 9 academic years, while 7% achieved 6 academic years Additionally, 6% of farmers had 8 academic years, and 5% completed 11 academic years Furthermore, 4% of farmers had 3 academic years, and 3% each had 0 and 2 academic years, respectively Lastly, only 1% of farmers reported having completed 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 reveals that 25% are 30 years old, 27% range from 31 to 35, 12% are between 36 and 40, 14% fall within 41 to 45, 5% are aged 46 to 50, and 6% are over 50 Farmers' ages span from 20 to 59 years, with average yields varying between 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 produces 25 tons, and a 28-year-old farmer yields only 12 tons per hectare.
4.2.2.2.6 Land type to plant water melon
Weather and soil conditions play a vital role in watermelon production, necessitating crop rotation to maintain soil health Farmers today have the flexibility to select optimal soil types for planting watermelons, ensuring better yields The three primary soil types favored for watermelon cultivation are alluvial soil, dark alluvial soil, and acid sulphate soil.
Watermelon, classified as a vegetable, requires constant care around the clock This intensive care is a primary reason why increasing the cultivated area does not lead to higher yields; in fact, yields may even decline Currently, the productive area for watermelon spans 36 hectares, yielding only 20 tons per hectare In contrast, when the productive area is limited to 4 or 5 hectares, the yield can reach up to 30 tons per hectare.