Luận văn thạc sĩ UEH determinants of water melon production at farm household level in tien giang provice

Research context

Tien Giang's watermelon is a key fruit export to China and Cambodia, yet its production remains unstable In 2007, the cultivated area was 3,779 hectares, yielding 70,847 tons, but by 2008, it had decreased to 2,954 hectares with an output of only 55,754 tons The cultivation process is characterized by a lack of concentration and specialization, along with insufficient market information, leading to significant price fluctuations Market demand largely relies on wholesalers and varies seasonally Furthermore, producers lack data on the economic efficiency and profitability of watermelon farming, highlighting the need for a deeper understanding of the supply side and the economic viability of watermelon cultivation in Tien Giang province.

This research identifies key weaknesses in the watermelon production process that require enhancement: a) the low entrepreneurial skills among farmers of all genders and ages; b) the need for improved resilience against adverse natural conditions, particularly sudden and frequent climate changes; and c) the importance of ensuring high-quality watermelons for market competitiveness.

Research problem

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

Farmers respond uniquely to various influences, making it essential to identify the key factors affecting watermelon production in Tien Giang This study employs a production function model, farm household economics theory, and SWOT analysis to uncover these significant impacts.

This study assessed the effectiveness of various inputs in watermelon production, specifically examining resources such as land, labor, and capital, which encompasses both cash and physical assets like fertilizer and seeds Additionally, it emphasized the importance of market information to ensure that supply meets demand, avoiding both surplus and deficit situations.

Goal and specific objectives of the study

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

Agriculture remains vital for economic development, with a primary focus on enhancing the efficiency of agricultural production This study aims to improve the livelihoods of farm households in Tien Giang province by 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

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

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

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

The organization of the thesis

of research and the organization of the thesis

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

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

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

Tien Giang province boasts favorable climate and soil conditions that significantly enhance watermelon production This article explores the competitive market landscape for watermelons in Tien Giang, providing a comprehensive SWOT analysis and insights from farm surveys Additionally, it examines how various input factors influence watermelon yields through econometric analysis, highlighting the province's potential in the agricultural sector.

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 market dynamics in which decisions are made Analyzing the farm household as a unified decision-making entity reveals that profit maximization aligns with utility maximization, provided all input and output markets are competitive The differences in various economic theories stem from distinct assumptions about factor and product markets rather than household objectives Moreover, variations in labor market assumptions and the allocation of household labor time often serve as critical differentiators between theories Ultimately, the economic behavior of households is influenced by social relations, which shape market functionality for different peasant groups.

The labor force in agriculture primarily consists of farmers, with the concept of a farm closely tied to farm households, which vary in size In many regions of sub-Saharan Africa and South and East Asia, including countries like Bangladesh, China, and India, farms are typically very small, often under two hectares Conversely, in West European countries, farms can span thousands of hectares Agricultural operations can be categorized into various types, such as family farms, business farms, and specialized farm enterprises, with the latter being 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 )

Peasants are defined as farm households that primarily depend on family labor for agricultural production while having access to a specific piece of land Their farming practices are influenced by a broader economic and political framework, which can impact their production decisions However, peasants typically engage only partially in input and output markets, which are often characterized by imperfections or incompleteness.

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

Hunt (1991) characterizes peasant farms as integral units of production and consumption, where a portion of their produce is sold to fulfill cash needs and financial obligations, while the remainder is consumed by the farmers themselves (Mendola, 2007).

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

II=P-CV -CF-KA- WA

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 experiences as it expands its 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) highlighted the significance of indivisible resources in achieving economies of scale in agriculture For instance, the power of a tractor, viewed as an indivisible resource, must be effectively utilized over a sufficient land area to enhance efficiency Consequently, these resources lead to cost economies when used at their optimal levels The cost benefits derived from indivisible resources directly influence the output volume that minimizes unit production costs in the short run (Tran Tien Khai, 2001).

Production functions serve as a crucial analytical tool in the neo-classical economic framework, defining how various combinations of inputs yield output for a firm, industry, or entire economy.

In the production process, firms transform inputs into outputs, which can be products or overall productivity Inputs, also known as production factors, encompass all resources utilized during production For instance, a bakery's inputs consist of the labor force, raw materials like flour and sugar, and capital investments in equipment such as ovens and mixers, all essential for producing baked goods like bread, cakes, and pastries.

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

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

Materials such as steel, plastics, electricity, and water are essential for firms as they purchase and convert them into final products Additionally, capital encompasses land, buildings, machinery, equipment, and inventory, all of which are vital for production processes.

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, where all points below the curve are technically feasible Points on the curve represent the maximum output achievable with the given inputs.

As illustrated in Figure 2.2, the production function shows an upward trend from points A, B, and C, indicating that as more input units are utilized, output quantity increases However, at point C, the use of additional input units results in no increase in output; in fact, total output starts to decline due to the underutilization of inputs.

At point A, increasing additional inputs leads to a rise in output at an accelerating rate, with both marginal physical product (MPP) and average physical product (APP) on the rise The inflection point, known as point X, marks the beginning of diminishing marginal returns, as the output growth rate decreases from point A to point C despite continued input increases Point B serves as the tangent between APP and MPP, indicating a critical balance in production efficiency.

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

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

The Cobb-Douglas production function, proposed by Knut Wicksell and empirically validated by Charles Cobb and Paul Douglas between 1900 and 1928, is widely utilized to illustrate the relationship between output and inputs in economic analysis.

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

Empirical studies

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

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

The production function with log-log is followed:

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

The rice productivity (Q) of a farm household in a given year is determined by the equation Ln Q = A + Iaixi + L~iDi In this equation, A represents the angular coefficient, while Xi denotes various input variables, including land, labor, and investment costs associated with the farm household for 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 influencing rice productivity for farm households in a given year In this formula, Q denotes rice productivity, A is the angular coefficient, and Xi includes variables affecting rice supply capacity, such as land, labor costs, fertilizer prices, and rice prices Additionally, Di represents dummy variables that impact yields based on factors like farm size and agricultural ecology.

The study revealed that the primary constraints to increasing paddy output are the availability of rice land and water resources While investing in fertilizers yields minimal returns, with the exception of potash, increasing capital investment has little impact on enhancing paddy production at the current cultivation level.

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, expressed as Ln Q = Ln A + Ia.iLnXi This equation highlights the relationship between output and input factors in rice production, underscoring the significance of productivity analysis in agricultural research.

+ 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 study highlights that capital accumulation is the primary driver of economic growth in Nepal Both developing and developed economies experience growth influenced by factor productivity, which is significantly enhanced by intangible elements such as advancements in education and technology, favorable economic policies, and ongoing learning initiatives.

In the context of Nepal, the impact of production factors like labor and capital on economic growth is difficult to assess due to a lack of clear information Consequently, the economic growth that cannot be attributed to these factors does not provide a reliable basis for evaluating productivity gains If accurate accounting standards were applied, factor productivity could actually be seen as a negative contributor to Nepal's economic growth.

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

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

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

( } ~ ~ 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 relies on the mean of the variable 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 during the period from 1960 to 2004, based on aggregate state-level data.

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

The Ricardian model analyzes the impact of various exogenous factors—such as climate variables, water flow, soil characteristics, and economic conditions—on net revenues in agriculture In this framework, the market price of a crop (Pi) and its output (Qi) are influenced by a vector of purchased inputs (X) that farmers select to optimize their profits By considering these elements, the model provides insights into how external variables shape agricultural profitability.

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

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

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

Page 15 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 the various components influencing agricultural practices, including climate variables represented by vector C, irrigation choices for each crop denoted as IRi, and the available water for irrigation labeled as W Additionally, it highlights the significance of soil characteristics captured in vector S, along with the prices of annual inputs represented by vector P x and the prices for different types of labor indicated by vector P m.

The rental price of capital, denoted as Pn, along with the annual cost of each irrigation system, represented by Pir, are critical components in understanding agricultural economics Building upon Equation (4), Equation (5) provides a more comprehensive analysis Key factors, Li and Ki, play a significant role in determining the physical impact on crop yield and overall productivity.

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

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

The production function serves as a vital tool for development and macroeconomists, who frequently estimate it using both production factors and intermediate inputs While many estimations assume constant returns to scale, some focus on value added as a function of labor and capital Although the role of land may be minimal in certain sectors, it remains a crucial resource in agriculture.

Economists frequently utilize the production function to analyze factors influencing rice productivity, assess technical efficiency in agriculture, and estimate the value added by the primary production factors: land, labor, and capital Based on empirical studies, it is identified that the key factors impacting watermelon production include land, labor, seeds, fertilizers, pesticides, and various dummy variables such as market information and agricultural extension services The author employs the log-log and Cobb-Douglas production functions to (1) illustrate the relationship between watermelon productivity and input variables, (2) analyze the connection between productivity and dummy variables, (3) determine the percentage change in watermelon productivity with a 1% change in each input variable, and (4) evaluate how productivity shifts with a 1% change in each dummy variable.

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 various input variables, including productive area, labor, chemical fertilizer, pesticides, and seeds; second, the impact of dummy variables such as market information, local agricultural extension services, and information provided by agricultural extension agents on watermelon yield.

In Tien Giang province, the relationship between input use variables and dummy variables in watermelon production plays a crucial role in enhancing productivity By analyzing these relationships, farmers can implement effective strategies to increase yields while also focusing on minimizing costs associated with these inputs to maximize their 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 conducted, this study specifies the regression model as follows: ln Q = ln A + ΣLai ln Xi + ΣL~izi.

The regression model proposed for this study is represented as lnQ = lnflo + Jl,InX1 + Jl2lnX2 + Jl3lnX3 + Jl4lnX4 + flslnXs + fl6l~ + fl71DX7 + flsXs + P9X9 + fltoX10 + J.1, where Q denotes 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 access to market information.

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

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

The study examines the factors influencing watermelon yield per hectare (Q), focusing on various independent variables Key determinants include the productive area (X1), land rent cost (X2), land preparation cost (X3), labor cost (X4), seed cost (X5), fertilizer cost (X6), the growing year of the producer (X7), access to agricultural extension services (X8), receipt of information from agricultural extension (X9), and availability of market information (X10) Understanding these variables is essential for optimizing watermelon production and enhancing yield efficiency.

To enhance watermelon yields, it's beneficial to incorporate additional labor, fertilizer, or productive land; however, it's crucial to apply these resources within reasonable limits Excessive labor may lead to increased yields, but it often fails to justify the associated costs, rendering such investments ineffective.

Excessive use of fertilizer can lead to a decrease in watermelon yield, while expanding the productive area can enhance output, reflecting the principle of economies of scale However, if the productive area is too small, profitability becomes challenging Conversely, increasing the productive area beyond a manageable level can result in reduced watermelon output due to inadequate management and investment, indicating that there is an optimal level of production where output is maximized Thus, the relationship between productive area and yield shows a negative expectation beyond this optimal point.

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 analyzes cross-sectional data on inputs and outputs related to watermelon production in seven districts of Tien Giang, with data collection occurring 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 in Tien Giang are measured in millions of Vietnamese Dong (VND) per hectare, and the cultivation of watermelons is challenging in previously farmed soils due to the prevalence of harmful diseases To achieve optimal growth, farmers should utilize new soils or implement intercropping systems that allow for watermelon cultivation every 2-3 years As a result, farmers are compelled to rent high-quality soil in various locations throughout Tien Giang to ensure successful crop production.

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

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

The household labor cost is determined 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 estimating household labor expenses For example, if hired labor is compensated with 4 million VND for two months, the author similarly assigns a value of 4 million VND for the household labor over the same period.

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

Fertilizer costs encompass the total weight of nitrogen, phosphate, potassium, complex fertilizers, and cattle manure utilized during various agricultural stages, including land preparation, seedling support, fruit support, and sideline production activities Additionally, this cost includes expenditures on pesticides, such as insecticides, fungicides, herbicides, plant protection drugs, disease prevention treatments, and growth stimulation fertilizers These costs are 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 without adequate market research, leading to overproduction and subsequently lower prices during peak seasons, such as the New Year holidays Additionally, the prices of watermelons are affected by supply and demand dynamics in local city markets and neighboring provinces.

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 factors, including climate variations, floods, and unpredictable disasters, as well as pest and disease outbreaks Increasing scientific evidence highlights the profound impact of climate change on agriculture, underscoring the need for adaptive strategies to mitigate these effects.

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

Data collection and sample distribution

3.2.1 Sample size 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

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

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

Water melon yield of each district in 2008 Percentage Sample size

Source: Tien Giang's Rural and Agriculture Development Department

According to Table 3.2, My Tho City and Go Cong Town each contributed only two questionnaires, a number too low to significantly impact the overall findings of this research To enhance the data set, 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 growing watermelons, including which households are involved in cultivation and which are not To address this gap, the author employs a proportional sampling framework for the research sample, calculating the sampling distribution proportion based on watermelon output from each district This approach enables the author to select an appropriate sample and gather essential information for the study.

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, crafted and pre-tested through face-to-face interviews with approximately 20 experienced watermelon farmers, aimed to gather valuable insights The author dedicated 30 to 45 minutes at each farmer's field to discuss and calculate costs associated with watermelon production The final version of the questionnaire was developed based on the essential feedback received from these farmers.

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

The author outlined the research's overarching and specific concepts to participants, organizing small meetings with approximately 10 respondents each Over a three-month period from October to December 2010, the author conducted face-to-face interviews with farmers to gather data for the main survey.

3.2.6 Limitation of data source and collection

Farmers in the region often lack the habit of meticulously recording their crop data, which can lead to discrepancies when recalling information during interviews This inconsistency is further compounded by the mobility of farmers, resulting in similar data being reported across different districts, such as Cai Be, Cai Lay, and Go Cong Tay For instance, Mr Nguyen Van Be, with a decade of experience in watermelon cultivation across various districts in Tien Giang, noted only minor differences in chemical fertilizer use and labor costs among them It is important to highlight that a single farmer can provide insights for two or three questionnaires across different districts.

Analysis methods

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

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

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

In addition to the linear regression model, the author employs SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis to evaluate watermelon cultivation This analysis focuses on identifying the strengths and weaknesses of the cultivation process, as well as the opportunities and threats that impact its success.

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

This research aims to identify the factors that positively and negatively influence watermelon yield in Tien Giang province through a comprehensive review of relevant literature and data analysis using linear regression models and SWOT analysis The findings are expected to provide significant insights for farmers, enhancing their understanding of the determinants affecting watermelon production.

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

Tien Giang, an agricultural province in the Mekong River Delta, is part of the key economic region of Southern Vietnam Located approximately 70 km south of Ho Chi Minh City and 90 km north of Can Tho City, Tien Giang is situated between 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, with the East Sea to the east.

Tien Giang province stretches 120 km along the northern bank of the Tien River, a tributary of the Mekong River Covering an area of 2,481.77 km², Tien Giang represents approximately 6% of the Mekong River Delta and 8.1% of the southern key economic region, contributing to 0.7% of Vietnam's total land area.

Tien Giang, characterized by its flat terrain and neutral alluvial soil along the Tien River, covers 53% of the province and supports a diverse range of flora and fauna As of 2009, the population of Tien Giang was approximately 1.67 million, accounting for 9.8% of the Mekong Delta's population, 11.4% of the southern key economic region, and 1.9% of the national population, with a density of 672.9 people per km² Strategically located, Tien Giang is the second province south of 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 from December to April lasting five months, and a rainy season from May to November The region has a low average annual rainfall of 1,210 to 1,424 mm, which decreases from north to south and from west to east The humidity in Tien Giang averages between 80% and 85% Additionally, the predominant wind directions are northeast during the dry season and southwest 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

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

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

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

Mound sandy soil covers 3.1% of the total natural area, equivalent to 7,336 hectares, 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 is characterized by its elevated terrain and light mechanical composition, making it ideal for residential development and the cultivation of fruit trees and vegetables.

The province predominantly features alluvial soil, accounting for 53% of its land, which benefits high-yield rice fields and specialized orchards Additionally, 19.4% of the area consists of alkaline soil, while 14.6% is classified as salinity alluvial soil In recent years, efforts have focused on reclamation and expanding production areas, enhancing crop diversity through the Dong Thap Muoi and Go Cong fresh-water development programs, which have successfully increased the 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 has become a significant cash crop for farmers across various provinces, especially in the Mekong Delta region, where it serves as an alternative to rice Recent advancements in agricultural practices, such as the use of plastic sheets for soil coverage and the application of specialized fertilizers, have contributed to the cultivation of high-yield, widely adapted watermelon varieties.

Rice cultivation in Tien Giang has a long history, yet farmers face low incomes of 3-4 million VND per hectare due to an average annual production of 14.2 tons per hectare from three rice crops (4.5 tons for the first crop, 4.2 tons for the second, and 5.5 tons for the third) The risks of crop loss remain significant due to pests, diseases, and unpredictable weather To improve profitability, recent practices encourage the rotational cultivation of watermelon alongside rice, with various combinations such as two rice crops and one watermelon crop, or alternating between rice and vegetables Watermelon yields an impressive average of 22 tons per hectare per crop, enhancing farmers' income potential.

Water melon yield even can reach from 25 to 30 tons/hectare/crop if good cultural

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

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

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

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

Source: Tien Giang's Rural and Agriculture Development Department

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

Page 33 billion tons in 1996 Production of other melon gained one third of water melon production

China dominates global watermelon production, yielding 60 billion tons in 2002, while other significant producers include Turkey, Iran, the USA, Egypt, and Mexico Additionally, China accounts for 50% of the world's melon production, with Turkey (6.1%), Iran (4.4%), the USA (4.2%), and Spain (3.9%) following behind Despite its leading production, China does not export watermelons or other melons due to the high domestic demand in its 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, with Canada and Japan being the main recipients In Asia, Malaysia exported 70,000 tons of watermelon in 2003, making it the fifth largest exporter globally, after Spain, Mexico, the USA, and Hungary.

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

Figure 4.1: Melon world production Source: Do Minh Hien, Nguyen Thanh Tung 2006

The USA is a significant importer of melons, with 91.2% of its melon imports in 2004 coming from Mexico, totaling $100.6 billion, while Costa Rica and Guatemala contributed 2.4% and 3.5%, respectively Germany leads in watermelon imports, followed by the USA and Canada, with France and England also importing 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

According to the Southern Fruit Research Institute (SOFRI), the demand for watermelon in both domestic and exotic markets has significantly increased in recent years, with an estimated requirement of 140 kg per person in 2010 and projected growth in the future This has led to encouragement for farmers in various provinces to cultivate watermelon in rice fields Long An province stands out as a major producer in the Mekong Delta, yielding 75,000 tons from 4,500 hectares in 2006 An Giang produced 26,560 tons from 1,469 hectares in 2004, increasing to 32,875 tons from 1,558 hectares in 2005 Additionally, Tra Vinh plans to produce 138,000 tons of watermelon from 4,600 hectares in the coming years (SOFRI, 2006).

Giang province produced 55,754 tons water melon from 2,954 hectares and it is consumed mainly in Ho Chi Minh city and exported to Cambodia and a small region of China by bordering.

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 using SPSS 15.0, focusing on descriptive statistics and a linear regression model.

SWOT ANALYSIS FOR WATERMELON'S CULTIVATION

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

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

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

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

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

Farmers, lacking access to market information from research organizations, tended to cultivate watermelons on as large a scale as their investment allowed, often without consulting market studies.

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

• 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 significant, but Vietnamese farmers face challenges from both domestic and exotic competitors High-quality melons from Thailand and the growing fruit requirements in Malaysia and China, estimated at about 140 kg per person in 2010, intensify this competition Additionally, the cultivated area for watermelons may decrease due to risks associated with pests, diseases, and adverse weather conditions such as floods and droughts Despite these challenges, the stable pricing of watermelons compared to other fruit crops presents potential benefits for farmers However, the supply to markets remains uncertain, as prices for watermelons may not meet expectations, particularly in lucrative markets like China, Laos, and Cambodia.

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

Table 4.3 presents the statistical analysis of 177 interviewed participants, detailing the minimum, maximum, mean, and standard deviation for each variable The minimum represents the lowest value observed, while the maximum indicates the highest value recorded for each variable.

The mean represents the average value of a variable, while the standard deviation quantifies the dispersion of data points around this average, indicating how much the values of each variable vary.

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 and a maximum of 78.36 million per hectare, resulting in a median cost of 68.27 million per hectare This indicates that while watermelon is a high-yield vegetable, it requires careful management and attention to achieve optimal results.

Among the 177 interviewed farmers, the average age is approximately 35 years, with ages ranging from 20 to 59 The farmers have an average of 7 years of education, with schooling years varying from 0 to 12 Additionally, their experience in farming spans from 1 to 17 years, also averaging around 7 years These statistics indicate that watermelon cultivation is challenging, as the minimum age of farmers is 20, highlighting the necessity for time to gain practical experience.

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

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

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

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

Out of 177 interviewers, 56.49% (100 interviewers) prioritize market information, while 43.51% (77 interviewers) do not Among those who do focus on market data, price emerges as the most significant factor of concern.

Agriculture plays a crucial role in Tien Giang's economy, situated in the Mekong River Delta basin To enhance agricultural productivity, the region has been continuously improving its agricultural extension services across various districts According to recent data, 55.93% of respondents confirmed the availability of agricultural extension services in their area, while 44.07% indicated otherwise.

A survey of 78 interviewers revealed a significant lack of agricultural extension services in certain locations, which play a crucial role in providing farmers with essential knowledge, including seed selection, fertilizer choices, and crop care Conversely, 99 interviewers reported the presence of these services, with 96 individuals successfully receiving valuable agricultural information, while only 3 interviewers indicated they had not accessed such resources.

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 12% of farmers having four years, 12% with five years, and 7% each for eight and nine years of experience Furthermore, 5% of farmers have two and eleven years of experience, respectively, and 3% have twelve and thirteen years Lastly, 2% of farmers have one and ten years of experience, while only 1% 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 recent analysis of farmers' educational backgrounds, it was found that 9% of farmers have completed 9 academic years, while 7% possess 4 academic years Additionally, 6% of farmers have 6 academic years, and 5% have 8 academic years Furthermore, 4% of farmers have completed 11 academic years, and 3% each have 3 or 0 academic years, respectively Lastly, only 1% of farmers have achieved 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% are between 31 and 35, 12% are between 36 and 40, 14% are between 41 and 45, 5% are between 46 and 50, and 6% are over 50 years old The farmers' ages range from 20 to 59 years, with an average yield 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 yields 25 tons per hectare, and a 28-year-old farmer produces only 12 tons per hectare.

4.2.2.2.6 Land type to plant water melon

Weather and soil conditions play a vital role in watermelon production, making it essential for farmers to select the optimal planting area each season Instead of using the same land year after year, farmers today have the flexibility to choose the best soil types for their watermelon crops The three primary soil types favored for watermelon cultivation include alluvial soil, dark alluvial soil, and acid sulphate soil.

CONCLUSIONS AND RECOMMENDATIONS

Conclusions

Recent findings indicate that increasing inputs does not enhance watermelon productivity, and access to agricultural extension services and information does not lead to significant changes in productivity levels either.

Farmers often use excessive inputs, so it's essential for them to reassess their usage to minimize costs and increase profits Additionally, provincial authorities should enhance the quality of information provided by agricultural extension services and organize campaigns to encourage farmers to engage with these services and utilize the valuable information they offer.

Currently, soil quality has not significantly impacted watermelon yields, as producers actively seek the best soil types for cultivation However, without effective methods to prevent soil diseases and degradation, watermelon yields may decline in the future.

Proper care and improved cultivation practices are essential for watermelon production, as relying solely on increased inputs like fertilizers does not guarantee higher yields Specifically, in acid sulphate soils, it is crucial to apply more phosphate (P2O5) during the land preparation phase Comparative data indicates that the optimal amount of phosphate fertilizer for acid sulphate soil ranges from 74 to 81 kg P2O5 per hectare, highlighting the importance of tailored fertilization strategies for different soil types.

To optimize watermelon yield, farmers should focus on selecting productive areas that align with their growing year capabilities Recent regression analysis indicates that watermelon production is nearing its maximum potential, highlighting the need for improvements in cultivation practices to enhance productivity further.

Page 63 i practices and the production techniques to a higher level, in which improve seed quality, find out new seeds and use suitable fertilizer level are crucial methods

Producers believe that watermelon production yields higher income compared to rice, leading them to focus solely on maximizing their yields without considering critical factors such as market demand, supply, and competition This oversight can result in market surpluses and decreased prices, ultimately reducing farmers' profits To improve their financial outcomes, farmers should aim to minimize costs and strategically reduce the quantity of watermelons they supply to the market to drive up prices Additionally, provincial authorities must enhance market information dissemination, forecast potential market trends, and educate farmers on how to access this information, enabling them to time their market supply effectively.

Currently, the watermelon varietals in Tien Giang are limited, primarily consisting of Super Roan Chau and Phu Dong, largely due to farmers' growing conditions There is a lack of focus on the market demand for diverse varieties Additionally, other watermelon types are cultivated in smaller areas because they are more prone to diseases and yield less.

Watermelon cultivation is a stable and profitable venture, allowing producers to enhance their livelihoods With a short growth cycle of just 60 days, farmers can supply the market promptly, and watermelon's market price often surpasses that of rice Many farmers have years of experience that help them achieve higher yields while minimizing growing costs, ultimately leading to increased profits As a result, most farmers have seen significant improvements in their quality of life through watermelon farming.

Recommendations

Farmers often face losses due to low crop prices, making it essential for them to optimize their watermelon production for higher yields while minimizing costs To achieve greater profits, they must utilize resources efficiently, including the appropriate use of chemical fertilizers and pesticides, as well as enhancing cultivation techniques Additionally, by reducing watermelon production, farmers can create scarcity, allowing them to command higher prices in the market, ultimately leading to increased profitability.

To address the challenges faced by watermelon growers, it is essential for various organizations to provide support in developing effective cultivation plans and business investments This assistance will help ensure an adequate supply of high-quality watermelons to meet market demands, ultimately enabling farmers to achieve better profits from their production Local authorities should focus on facilitating watermelon development through targeted support initiatives.

Provincial authorities of Tien Giang have considerations "water melon" as potentially fruit for poor farmers gradually to become the wealthy Firstly, they

Page 65 should orgamze R&D department (Research & Development department) to research all characteristics of family Cucuibitaceae clearly and understand clearly one by one due to profitably sustainable development of water melon Then they should have many plans, many actually supporting programmes to introduce water melon to all farmers Cultivated technical support courses on cultural practices of water melon are organized regularly for all farmers Therefore inexperienced farmers can study knowledge from agricultural engineers and professional growers though training courses and field workshops Objectives of these training courses are to provide basis knowledge of water melon for fresh farmers, to update advanced cultural practices for experienced farmers, especially to help them transfer experience and learn each other In addition to, provincial authorities should have suitable policy to encourage and to support growers and other members such as wholesalers, distributors, retailers

Provincial authorities should analyze the production capabilities of neighboring provinces like Long An, Tra Vinh, and Binh Thuan, while also investigating foreign markets in China, Thailand, and Spain It's essential to forecast the demand for watermelons in both domestic and international markets, particularly during peak seasons such as Christmas, New Year, and Lunar New Year Additionally, researching and predicting market prices will enable farmers to produce the required quantity at competitive prices Implementing strategies to mitigate the risks of overproduction and price drops is crucial for sustaining the agricultural economy.

Provincial authorities must prioritize agricultural extension services at the district level and enhance their quality by investing in skilled human resources By improving the flow of information from agricultural extension programs, farmers can achieve greater efficiency and productivity in their operations.

The National Environment and Resources Department, along with Tien Giang province's authorities, must implement effective land use policies to ensure sustainable watermelon cultivation, as traditional soils are losing productivity Currently, farmers are relocating within the province to rent land, but this practice leads to land degradation To combat this issue, it is essential to adopt a land structuring cycle of two years for watermelon farming, encouraging or mandating farmers to follow these guidelines Failure to do so may exacerbate pest and disease problems, affecting not only older soils but also newly cultivated areas due to the continuous planting of watermelons in the same soil.

To enhance watermelon cultivation, it is essential to study and introduce a variety of new seed types to farmers Market acceptance is crucial, with a focus on round and oblong shaped watermelons, including green striped skin and red flesh varieties, as well as those with yellow flesh Additionally, high-quality seedless varieties should be prioritized By diversifying their watermelon offerings, farmers can attract a broader clientele.

LIMITATION

The production function illustrates the non-monetary relationship between physical input variables and watermelon yield, focusing solely on input use without incorporating prices or costs It is essential to avoid monetary variables in this analysis; however, the author faced challenges in isolating the effects of costs, such as land rent, land preparation, and labor, from the research model.

Farmers often lack the habit of meticulously recording details about their crops, which led the author to invest significant time in conducting surveys This reliance on farmers' recollections may result in discrepancies between the collected data and actual farming practices.

Results of this research are just suitable in short-run The author has not had enough data to prove those results in long-run

Rita Butzer, Yair Mundlak, Donald F Larson 1997 The determinants of agricultural production: A cross - country analysis Page 7

Tran Van Long 2010 Determinants of self-medication of adults in Ca Mau city

Do Minh Hi en, Nguyen Thanh Tung 2006 Analysis of water melon value chain in

Long An Province Southern Fruit Research Institute (SOFRI)

IFAD 2010 Calculating the Sample Size from http://www.ifad.org/gender/tools/hfs/anthropomettry/ant 3.htm

Jinxia Wang, Robert Mendelsohn, Arief Diar, Jikun Huang, Scott Rozelle, Lijuan

Zhang 2009 The impact of climate change on China's agriculture

Nguyen Khac Minh, Giang Thanh Long 2009 Efficiency Estimates for the

Agricultural Production in Vietnam: A comparison of Parametric and Non- parametric Approaches Development Economics and Public Policy, Vol 10, No2 Page 7- 8

Robert S Pindyck, Daniel L Rubinfeld 2009 Chapter 6: Production In seventh edition, Microeconomics Page 195 - 197 United State of America: Pearson Education, Inc., Upper Saddle River, New Jersey, 07458

Frank Ellis 1993 Chapter 3: Elements of peasant political economy In second edition Page 45 - 65 Peasant economics: Farm households and Agrarian development

Coelli 1996 A Guide to DEAP Version 2.1: A Data Envelopment Analysis

(Computer) Program Center for Economic Productivity Analysis (CEPA) Working Paper No.8/96 University of New England, Armidale

Cristina Echevarria 1998 A three - factor agricultural production function: The case of Canada International Economic Journal, Vol12 Page 11- 12

Tien Giang Rural Development and Agriculture Department 2010 Statistics and

Information about Squares, Productivity of water melon in 2007, 2008, 2009 in Tien Giang

Randomness refers to the absence of a predictable pattern or order in events, symbols, or sequences While individual random events are unpredictable, their outcomes can be predicted over repeated trials when a known probability distribution is applied For instance, while rolling two dice yields unpredictable results, the likelihood of rolling a sum of 7 is statistically higher than rolling a 4.**Mathematical and Statistical Applications**In mathematics and statistics, randomness is formalized through concepts like random variables and random processes, which help in calculating probabilities and understanding complex systems Techniques such as Monte Carlo methods leverage randomness for simulations in various scientific fields, emphasizing its significance in computational science.**Historical Context**Historically, randomness has been associated with fate and chance, evident in ancient practices like dice throwing The formal study of randomness began with early Chinese and Greek philosophers, evolving significantly during the 16th century with Italian mathematicians The 20th century saw a shift in perspective, recognizing the utility of randomness in algorithm design and computational efficiency.**Scientific Relevance**Randomness plays a crucial role across scientific disciplines, including chaos theory, cryptography, and quantum mechanics In biology, it contributes to evolutionary processes through random genetic mutations, which are then filtered by natural selection, underscoring the interplay between chance and determinism in the development of life.

2010 at 08:31 http://www wordip.com/ definition/Production function http://en.wikipedia.org/wiki/Cobb%E2%80%93Douglas Dominique Haughton, Jonathan Haughton Sarah Bales, Truong Thi Kim Chuyen,

Nguyen Nguyet Nga, Hoang Van Kinh 1999 Chapter 14: Rice Production

Page 222 - 232 Vietnamese household- Econometrics analysis

Tran Tien Khai 2001 Rice production and supply at farm level in the Mekong

The River Delta region of Vietnam was the focus of a scientific report presented at the National Scientific Conference for Agricultural Study, specifically within the Agricultural Economics and Policies section This conference, organized by the Ministry of Agriculture and Rural Development, took place in Hanoi in November 2002, highlighting significant developments and research findings from the years 1995 to 1998.

Purano Baneshwor, Kathmandu 2002 Nepal: Country Study Report (Global

Research Project) Page 2 - 3 & Page 43 Institute for Integrated Development Studies (liDS)

Mariapia Mendola 2007 Farm Household Production Theories: A Review of

"Institutional" and "Behavioral" Responses Page 2 - 3

Jacklin Beatriz Marroquin 2008 Examination of North Dakota's production, cost and profit function: a quantile regression approach Page 17-20

Robert Eastwood, Michael Lipton, Andrew Newell 2004 Farm Size University of

Vietnam- Netherlands Programme For M.A in Development Economics

DETERMINANTS OF WATER MELON PRODUCTION FUNCTION OF 2010'S SUMMER- FALL CROP IN TIEN GIANG PROVINCE

Survey place: Date: / / 2010 District: Tester: Commune: Respondents: Hamlet: Gender:

I am a student of Vietnam - Netherlands Programme, in my research plan, I come back to Tien Giang to find out the determinants of water melon production process

This article explores the various factors influencing watermelon production at the household level It aims to identify both the positive and negative elements that impact watermelon yield, with the goal of enhancing overall productivity for each household By understanding these factors, we can implement strategies to improve watermelon cultivation and increase harvests.

Participation in this discussion is entirely voluntary, with households chosen at random The information collected today is solely for research purposes We kindly ask you to answer a few questions below, and we greatly appreciate your family's cooperation.

PART 2: QUESTIONNAIRE Question 1: How many tons of water melon per hectare do you gain in 2010's summer-fall crop?

Unit: tons/ha 2010's summer-fall crop

Question 2: Would you tell me how many hectares you planted water melon in 2010's summer-fall crop?

Unit: ha 2010's summer-fall crop

Question 3: Would you tell me how much your land rent cost per hectare is?

Land rent cost (million VND)

Unit: million/ha 2010's summer-fall crop

Question 4: Would you tell me how much your land preparation cost per hectare is?

Unit: million/ha 2010's summer-fall crop Land preparation cost (million VND)

+ Agricultural materials + Irrigation fuel cost (electronic, petrol or oil) i

Question 5: Would you tell me how much your seed cost per hectare is?

Unit: million/ha 2010's summer-fall crop

Seed cost +Seed amount (seed a.i) +Seed amount (million VND a.i)

Question 6: Would you tell me how much your labor cost per hectare is?

Unit: million/ha 2010's summer-fall crop

Labor cost (million VND) + Bed making cost + Sowing cost + Pruning cost + Harvesting cost + Transport cost +Taking care cost

Question 7: Would you tell me how much your fertilizer cost per hectare is?

Table 1: The fertilizer cost per hectare

Unit: million/ha 2010's summer-fall crop Fertilizer cost (million VND)

+Nitrogen fertilizer cost + Phosphate fertilizer cost + Potassium fertilizer cost + Pesticide fertilizer cost + Insecticide fertilizer cost + Fungicide fertilizer cost

+ Herbicide fertilizer cost + Water melon stimulation fertilizer cost

Table 2: The chemical fertilizer amount per hectare

Unit: kg/ha 2010's summer-fall crop Chemical fertilizer amount

+ Nitrogen amount (N) +Phosphate amount (P 2 0 5) +Potassium amount (K 2 0)

Question 8: Would you tell me which soil type you choose to plant water melon in 2010's summer-fall crop?

2010's summer-fall crop + Alluvial soil

+ Dark alluvial soil + Acid sulphate soil

Question 9: Would you tell me the total cost that you used for 1 hectare is how much?

Unit: million/ha 2010's summer-fall crop

Question 10: How old is producer/farmer?

Question 11: How many schooling years is producer in? (academic year)

Question 12: How many growing year is producer in? (experience)

Question 13: Is there any agri-extension service in your location?

Question 14: Do you receive any information related to water melon from agri- extension?

Question 15: According to you, is the market information important to your water melon production process? If yes, what information that you care the most?

Yes: Information that you care the most Reasons

Question 16: How did your living standard change since you planted water melon?

I sincerely thank his/her cooperation

Table 1: Correlations among water melon yield and input uses variables u -eott"'• c - ~

- - th ,FotalziO' -omlon lleWt -~oorr -~t ,_,,.,.t ->Wt ,., -~ ""'~VNll ~1 ODG 001 488 00! O:i'O C I h~~~~~-~,db~, ~ -;~~.~~:,== ~ ~.w¥.4 -~~~ ~;~~"J -~:~-~ ~+ -~:~g, J -~+ -~J -~~~r+ -~~!£~+ -:~~~ ~f -~:~~.-+ -:~~~ ~~ ~+ -~+ -~-c ~ ~.- ( - ~~- : ~~ :~~- : ã : : : :~ .113!1 ~: ã : : : : -::i hcm~~~~-; -i~;;~~~~ -r 7 !~~:~ -~.:~,t -~.:~~~ ~:rl -7,,.~,.+ -~.:~~-~ 2 '"~,f -~,*~~ ~~~ -7,.;~~~ ~-~:Hr 7 o~ i -~+ -~-~:Hr -7 ,w~ -~t -7 ,*,f -7,~, -~

117 117 117 ., ~ -~ -11111ã '""" z11ãã 21gãã uzã Mil 1 mãã u1•• -~ã "' '" h~;;'~-~~~~ ~~~-~~~~~ r -~-~~ ~~~ ~-~~ -~-~ -7,ãm~ ~Ơ.4 -d.,~ + -~ ~ ~ -~ • ~.+ -~.:~~~~ -Ơ~ ~ ~ ~-~~ ~~ -7.~:~ ~+ -~+ -~1 ~

~~~~~ ~~~-~~~~~,~ ~ -7,~ -T,~~~+ -~J -:~;:~-~ ~+ -~4 -~ • ~ + -~4 -~:~~:+ -~4 -_*::~ ~-~~:4 -,"~'+ -~.T.,~,t -,:~~,.-~ ~7+ ~:~*r~ ~-~ -~ h~~-. ~"-to~o;F, ~;;~~ r 7.,~~.~ ~ ~ + -~~~"-~. -*,.~ r -~.OM~ ~,~ ~ ~'~"+ -".M~O~ ~+ -"-~~ã:~ -T.~ ~.M~.~ ~+ -.T.:~~ ~.;~~-~ -i.~~' t qt -~.; -~ 81!1-~~ sn 00! 1911 m ooa c h~~~ ~~~~. ~~~~~~=,== ~ -7,~ -7,=~ ~~~,.d. -~~ -~~~.+ -~4 -~ :~".+ -~-~g,.M -~.:~ ~.:~~4 -~:~ ~4 -~~~;+ -~.:~ ~~~u-f -~.:~-+ -~.~~"~. -" 1 ~ ~~~~. -~~~~~~.M; ~ ~:8:4 -~:~ ~-~~,4 -~-~:~ ~-~:~.-+ -7.-.~~~d -~ ~~;+ -~,:~:4 -~+ -~-~~~4 -~:~ ~-~~.~ ~-:~~~+ -.f :~ ~~~"-~ ~-~~~+ -~-~~~~ ~-: -~ h~~~.-~,~. -~';_~M; r 7.~ -T,~ ~-'*"-d -T.~ -T,_,~,t -~~ ~+ -~.,ffi ~ -7 ~~ ~-~~~~ -f.~ ~.:~~~ ,_,f.r~ f,'"~ ~~ i.u~rf -~ • ~.-~ ~; -~

VNll Slg.~l 071 ooa 081 001 031 MI 071 000 000 IIOC 0 ;.-12-t*i~ -:~: 117 • : -~~-ã : : : ã:~;:ã tn 1~ ã ã : ã ã ~ t- _.;;;;;;;;-~,;;;;-~ -i-'-"-= + ~,;f ~.r -~: ;;"~ -,JlF.! f -,J;.;'"-. + -l; £".,f -_,.;;;, r -,; :;;"~ -.¥~;;, r -~::;,~~ -.£~~Hr -,;.:irr ~.ii::.~ ,:iil:~~ -~;;r,-,t -~,:;;::rl r ::: ::;;~~ ~,i;.~rl -7 1 r -::i

VN:IJ S~g.~, 021 m 1 t-~~~ .~~ -::i'i;;;;~ . -t -~~~Hr 7 ,*w~ ~~~ã"r~ -f,.:~ ~~~~~ ~~ "-:~~~ -T,,~Fr ~-'~F~ ~.#:~~ ~-~-:~ ~-~~Hr -,~~;~ -j,~~ ~-ffi,Hrl -::i.,~-9 -~rl -7.~1 r -::f

IMJJ lllg.~J 047 ue: GH an ,12 "" OOl In 091 - c ~ "'"'"'~"'':"""'""'"'-,. -~~1=_=_."~-:~"'.=. 1 ~ • !#:~H -~: *"ã+ -.lli:~-~ ~.:*' + -*+ -~:+ ~- lll O+ -*~ ~ :;J 4 -.* ~ _,'":;,j. -." ~ -: :il";=l ~~=~ ~ *'ã"'+ -i::ou'*"~ -~ffi=lr -i.; -~

~~~~~ -l'i;;;;.,-~ + -.¥~~ ~~~~d -~-~::+ ~~ -i~ ~-~::+ ~~~~"Hr ~.!#~;~ ~ ~~4 -*:~-r -l~~-+ -.!#:Hr - m rl -l.:fr.-~ ~'+ ~mrl -:.~ •• Hr ~~c ~

Table 2: The relationship between water melon yield and land type

Land type N 1 alluvial soils 91 22.69 dark alluvial soils 16 22.71 acid sulphate soils 70 23.10

The article presents the means for groups divided into homogeneous subsets, utilizing a harmonic mean sample size of 34.178 It highlights that the group sizes are unequal, and therefore, the harmonic mean of these sizes is applied However, it is important to note that the Type I error levels are not guaranteed in this context.

Table 3: The relationship between water melon yield and having information from agri-extension

Having information Std Error from agri-extension N Mean Std Deviation Mean

Yield (ton/ha) no 81 23.16 3.450 383 yes 96 22.59 3.615 369

Eaualitv of Variances !-test for Eaualitv of Means

95% Confidence Interval of the Mean Std Error Difference

F Sia I df Sia 12-tailedl Difference Difference Lower Upper

Yield (ton/ha) t:qual variances

Table 4: The relationship between water melon yield and having agri-extension service in location

Having agri-extension Std Error service in location N Mean Std Deviation Mean

Yield (ton/ha) no 78 23.19 3.435 389 yes 99 22.58 3.618 364

Eaualitv of Variances !-test for Eaualitv of Means

95% Confidence Interval of the Mean Std Error Difference

F Sio t df Sio (2-tailedl Difference Difference Lower Upper Yield (ton/ha) Equal variances

Table 5: The relationship between water melon yield and having market information

Having market Std Error information N Mean Std Deviation Mean

Yield (ton/ha) no 77 22.86 3.597 410 yes 100 22.84 3.516 352

Eaualitv of Variances t-test for Eaualitv of Means

95% Confidence Interval of the Mean Std Error Difference

F Sig t df Sia 12-tailedl Difference Difference Lower Upper

Yield (ton/ha) Equal variances

Table 6: The relationship between the total cost and land type

Total cost/ha (million VND)

Squares df Mean Square F Sia

Total cost/ha (million VND)

Land type N alluvial soils 91 acid sulphate soils 70 dark alluvial soils 16 Sig

The means for groups within homogeneous subsets are presented, utilizing a Harmonic Mean with a sample size of 34.178 Given that the group sizes are unequal, the harmonic mean of these sizes is applied; however, it is important to note that Type I error levels cannot be guaranteed.

Table 7: The relationship between the total cost and having information from agri-extension

Having information Std Error from agri-extension N Mean Std Deviation Mean

Equality of Variances !-test for Enualitv of Means

95% Confidence Interval of the Mean Std Error Difference

F SiCl I df Siq (2-tailedl Difference Difference Lower Upper Total cosVha Equal variances

Table 8: The relationship between the total cost and having agri-extension service in location

Having agri-extension Std Error service in location N Mean Std Deviation Mean

Equality of Variances !-test for Equality of Means

F Sig t df Sig (2-tailed) Difference Difference

Total cosl/ha Equal variances

Table 9: The relationship between the total cost and having market information

Having market Std Error information N Mean Std Deviation Mean

Equality of Variances t-test for Equality of Means

F Sig t df Sig (2-tailed) Difference Difference Total cosl/ha Equal variances

Table 10: The relationship between the taking care cost and land type

Taking care cost (million VND)

Squares df Mean Square F SiQ

95% Confidence Interval of the Difference Lower Upper -1.06782 1.85693 -1.10743 1.89654

95% Confidence Interval of the Difference Lower Upper -.41563 2.49914 -.35326 2.43677

Taking care cost (million VND) Duncan a.b

Landty_l)e N 1 alluvial soils 91 8.4371 acid sulphate soils 70 8.7770 dark alluvial soils 16 9.9580

The article discusses the display of means for groups categorized into homogeneous subsets, highlighting that the harmonic mean sample size is 34.178 It notes that the group sizes are unequal, necessitating the use of the harmonic mean of the group sizes, while also indicating that Type I error levels cannot be guaranteed.

Table 11: The relationship between taking care cost and having information from agri-extension

Having information Std Error from agri-extension N Mean Std Deviation Mean

Equality of Variances !-test for Equality of Means

95% Confidence Interval of the Mean Std Error Difference

F Siq I df Sig (2-tailedl Difference Difference Lower Upper Taking care cost Equal variances

Table 12: The relationship between taking care cost and having agri-extension service in location

Having agri-extension Std Error service in location N Mean Deviation Mean

Eaualitv of Variances !-test for Equality of Means

F Sig t df Si!l (2-tailed) Difference Difference Taking care cost Equal variances

Table 13: The relationship between taking care cost and having market information

Having market Std Error information N Mean Std Deviation Mean

Equality of Variances t-test for Equality of Means

F Sig t df Sig (2-tailed) Difference Difference Taking care cost Equal variances

95% Confidence Interval of the Difference Lower Upper -.48818 1.39184 -.51937 1.42303

95% Confidence Interval of the Difference Lower Upper -1.01827 86915 -1.01988 87076

Table 14: The relationship between chemical fertilizer amount and land type

Squares df Mean Square F Sig

Land type N 1 2 alluvial soils 91 782.2802 dark alluvial soils 16 788.2500 acid sulphate soils 70 863.9571

The article presents the means for groups categorized into homogeneous subsets, indicating a sample size of 34.178 based on the harmonic mean It notes that the group sizes are unequal, which necessitates the use of the harmonic mean of these sizes However, it is important to highlight that the Type I error levels cannot be guaranteed in this context.

Table 15: The relationship between phosphate fertilizer amount and land type

Land type N 1 alluvial soils 91 296.4176 dark alluvial soils 16 303.6250 acid sulphate soils 70

The article discusses the use of harmonic means for analyzing groups within homogeneous subsets, highlighting that the sample size calculated is approximately 34.178 It notes that the sizes of the groups are unequal, necessitating the use of the harmonic mean of these sizes However, it is important to acknowledge that the Type I error levels cannot be assured in this context.

Table 16: The relationship between chemical fertilizer amount and having information from agri-extension

Having information Std Error from agri-extension N Mean Std Deviation Mean

Chemical fertilizer no 81 818.0679 137.47129 15.27459 amount (kg a.i) yes 96 812.6354 122.80227 12.53345

Equalitv of Variances t-tast for Equality of Means

F Sig t df Sig (2-tailed) Difference Difference Lower Upper Chemical fertilizer Equal variances

Table 17: The relationship between chemical fertilizer amount and having agri-extension service in location

Having agri-extension Std Error service in location N Mean Std Deviation Mean

Chemical fertilizer no 78 815.7628 135.97435 15.39606 amount (kg a.i) yes 99 814.6162 124.62558 12.52534

Equalitv of Variances !-test for Equalitv of Means

95% Confidence Interval of the Mean Std Error Difference

F Sig t df Sig (2-tailed) Difference Difference Lower Upper Chemical fertilizer Equal variances

Table 18: The relationship between chemical fertilizer amount and having market information

Having market Std Std Error information N Mean Deviation Mean

Chemical fertilizer no 77 831.2078 116.67632 13.29650 amount (kg a.i) yes 100 802.7350 137.65420 13.76542

Equality of Variances !-test for Equalitv of Means

95% Confidence Interval of the Mean Std Error Difference

F Sig t df Sig (2-tailed) Difference Difference Lower Upper c ;nemicel fertilizer Equal var1ances

Table 19: The relationship between pesticide cost and land type

ANOVA Pesticide cost (million VND)

Pesticide cost (million VND) Duncan a,b

Land type N 1 2 dark alluvial soils 16 14.3833 alluvial soils 91 14.4406 acid sulphate soils 70 15.5539

The article discusses the display of means for groups categorized into homogeneous subsets, utilizing a harmonic mean sample size of 34.178 It highlights that the group sizes are unequal and emphasizes the importance of the harmonic mean of these sizes However, it also notes that Type I error levels are not guaranteed in this context.

Table 20: The relationship between pesticide cost and having information from agri-extension

Having information Std Error from agri-extension N Mean Std Deviation Mean

Levene's Test for Equality of Variances t-test for Equality of Means

95% Confidence Interval of the Mean Std Error Difference

F Sig t df Sig (2-tailed) Difference Difference Lower Upper Pesticide cost Equal variances