INTRODUCTION
Background information
Vietnam's total natural land area is approximately 33.12 million hectares, with agricultural and forestry land making up about 82.4% and 54.64% of the total land area, respectively (MONRE, 2016) The agricultural sector is vital to Vietnam's economy, contributing 18.14% to the gross domestic product in 2016 (WB, 2016) Despite its importance, the agricultural production's share of the economy has been declining, dropping from 22.7% in 2000 to 18.14% in 2016 (WB).
The agricultural sector in Vietnam plays a crucial role in the national strategy for food security and supports industrial development (MARD, 2009) By 2010, it employed 48% of the workforce (JICA, 2013) Despite consistent growth over the past two decades, the sector's contribution has decreased relative to the faster growth of industry and services.
Table 1 1 Land statistics of Vietnam
Land types Area (1000 ha) Ratio (%)
Source: Ministry of Natural Resource and Environment, 2016
Agricultural exports have shown consistent growth over the years, contributing significantly to a positive trade balance Key export products include rice, rubber, coffee, cashew nuts, as well as fishery and forestry products Notably, the total export value soared to $25 billion in 2011, marking a doubling of the export value compared to 2007 (JICA, 2013).
Despite significant achievements in poverty reduction, socio-economic development, and food security in Vietnam, agriculture faces numerous challenges Key issues include unstable development and low competitiveness in the global market, driven by small-scale and fragmented production that results in high costs Additionally, concerns over food safety and low production efficiency are rising Support services and industries related to agricultural development remain underdeveloped, with most exports consisting of raw or minimally processed products, leading to lower added value and quality compared to other countries Notably, cropping makes up over 50% of agricultural activities, with rice production remaining the most crucial crop.
Table 1 2 Land use structure in Northern mountainous region of Vietnam
Land types Area (1000 ha) Ratio (%)
Source: Ministry of Natural Resource and Environment, 2016
The Northern mountainous region of Vietnam boasts significant forestry production, encompassing approximately 2.1 million hectares, which represents 71.4% of the region's total agricultural land Agricultural production accounts for around 28% of the land use in the area As outlined in the Ministry of Agriculture and Rural Development's plan for 2011-2020, the NMR aims to prioritize forestry development alongside the cultivation of advantageous industrial crops, including tea, Arabica coffee, maize, lychee, and soybean.
In Vietnam, rice cultivation dominates the agricultural landscape, occupying 59.2% of the total annual cropping land area, according to MONRE (2016) Over the decade from 2007 to 2016, the total area dedicated to rice farming consistently grew, reaching about 8 million hectares by 2016 (GSO, 2018) This substantial rice production area significantly surpasses that of other crops, including maize, peanuts, soybeans, and cotton.
Figure 1 1 Planted area of major crops in Vietnam (1000 ha)
Source: General Statistic Office of Vietnam, 2018
The northern upland area of Vietnam encompasses approximately 2.1 million hectares of agricultural land, with 77% designated for annual crops Rice is a significant crop in this region, representing 35.4% of the total cultivated land Additionally, perennial crops such as tea, fruits, and Arabica coffee make up around 23% of the agricultural land Notably, over 90% of the local population relies on agriculture for their livelihoods.
Between 2007 and 2016, approximately 11 million people in rural areas relied on agricultural activities, such as cropping, animal husbandry, and forest economics, for their primary income Rice production plays a crucial role in these households, contributing about 25% to their income While not primarily aimed at commercial sales or exports, rice self-sufficiency is vital for food security, especially in regions with challenging transportation systems due to hilly and complex topography (Bac et al., 2013).
Table 1 3 Structure land use of NMR
Land type Area (1000 ha) Ratio (%)
Source: Ministry of Natural Resource and Environment, 2016
Over the past decade, Vietnam has experienced significant trends in the production of major perennial plants The area dedicated to rubber cultivation has expanded considerably, while the planted areas for coffee and pepper have seen moderate increases In contrast, the tea planted area has remained relatively stable throughout the years.
Figure 1 2 Planted perennial area of Vietnam Source: General Statistic Office of Vietnam, 2018
Vietnam is amongst few nations in the world that have advantages of natural and climatic conditions for tea production (SOMO, 2007) Tea production is taking place in
39 out of 64 provinces all over the country with total 130 thousand ha NMR has the largest tea production area in comparison with other four regions of Vietnam, with about
93 thousand ha accounting for 72% of total planted tea area of Vietnam
Figure 1 3 Planted tea distribution in Vietnam Source: General Statistic Office of Vietnam, 2011
Cashew nut Rubber Coffee Tea Pepper
Red river deltaNorthern mountainous areaNorth central and coastal areaCentral highland
Similarly, the region also provide the highest tea production quantity of Vietnam, accounting for 66% of total produced tea quantity
Figure 1 4 Proportion of tea production among regions in Vietnam
Source: General Statistic Office of Vietnam, 2011
Tea production is vital to Vietnam's cultural heritage and economic landscape, with a history of tea cultivation and consumption that spans approximately 3,000 years In 2012, the tea industry contributed $224.8 million to the nation's export value, with over 146,800 tons of tea products shipped abroad This sector engages around 400,000 households in production and related activities, supporting their income and livelihoods, and it provides employment for approximately 1.5 million individuals.
Red river delta Northern mountainous area
North central and coastal area Central highland
Figure 1 5 Variability of tea yield in Vietnam Source: General Statistic Office of Vietnam, 2014
Production efficiency, risk and VietGAP adoption in Vietnam
Production involves converting inputs like land, labor, capital, and fertilizer into outputs such as goods and services, applicable not only in agriculture but across various sectors The performance of production is typically reflected in the differences in inputs and outputs, with the primary goals in agricultural production including profit maximization, cost reduction, and maximizing output, which can differ by time and firm Key concepts related to this process include technical efficiency, productive efficiency, and economic efficiency.
Production efficiency consists of two key components: technical efficiency and allocative efficiency Technical efficiency refers to a farmer's capability to minimize waste during production, aiming to maximize output from given inputs or to achieve a specific output while minimizing input usage The goal of assessing technical efficiency is to identify strategies for enhancing output or reducing input usage within the available resources.
Red river delta Northern mountainous area North central and coastal area Central highland
Vietnam's tea yield conditions are influenced by the optimal combination of inputs and outputs, which are assessed without considering market prices Technical efficiency, a key measure in this context, can be broken down into three components: scale efficiency, which reflects productivity gains from optimal firm size; congestion, where increasing certain inputs may lead to reduced output; and pure technical efficiency, as defined by Farrell in 1957.
Economic efficiency focuses on maximizing output without increasing conventional input usage, with existing technologies often proving more cost-effective than new ones when current practices are inefficient (Shapiro, 1977) It can be divided into two main types: technical efficiency, which assesses a farmer's capacity to achieve maximum output using available technologies, and allocative efficiency, which evaluates the optimal application of inputs based on their prices (Farrell, 1957; Shapiro, 1977) Technical efficiency (TE) ranges from 0 to 1, where a TE of 1 indicates that a firm operates at full technical efficiency, demonstrating the potential for maximum productivity.
Production efficiency is crucial for enhancing agricultural output, with numerous studies highlighting its importance (Thiam, 2001) In Vietnam, the agricultural sector plays a vital role in driving economic growth, ensuring food security, promoting social stability, and reducing poverty As a result, both the government and researchers are keenly focused on improving efficiency within this sector However, existing research on the production efficiency of crops like rice, tea, and vegetables indicates that Vietnamese farmers often operate below optimal efficiency levels (Hong et al., 2015; Bac et al., 2013; Tran, 2008; Vu, 2005) This presents a significant opportunity for farmers to lower costs through enhanced efficiency, especially in light of limited land resources Additionally, adopting new technologies necessitates greater capital investment and time, while the fragmented and small-scale nature of agricultural production in Vietnam poses further challenges to implementing advanced techniques.
1.2.2 Linkage between agricultural risk and efficiency
Production is the process of transforming inputs like land, labor, and capital into goods and services, applicable across various sectors, including agriculture It is closely tied to the natural environment and is subject to uncertainties and risks that farmers cannot control, such as weather and market fluctuations Effective risk management is crucial for farmers, incorporating strategies like on-farm decisions, structural changes, market instruments, government support, and income diversification A standard risk management analysis involves three steps: identifying risk sources and variability, selecting optimal management tools, and designing supportive government policies Alternatively, a holistic approach considers the complex interrelationships among these elements, moving beyond a linear analysis.
Risk analysis in agricultural production has garnered significant attention from researchers, resulting in a wealth of literature on the subject Agricultural production faces numerous sources of risks and uncertainties, highlighting the importance of understanding these challenges for effective management.
Agricultural production in Vietnam is significantly influenced by various risky factors, which are not evenly distributed among farmers due to the complex and changing natural and climatic conditions (Riwthong et al., 2017) These risks can be categorized into five main sources: production, marketing, financial, legal and environmental, and human resource risks (USDA, 1997) Research by Tiedemann (2013) highlights the relationship between production risk and efficiency, revealing that output variability in both German organic and conventional farming is primarily driven by production risk Given the adverse effects of these risks on farmers' production output, it is crucial for them to identify and effectively manage these risks (Drollette, 2009).
1.2.3 The situation of VietGAP adoption
VietGAP, inspired by the GlobalGAP standards, was developed in Vietnam to address the challenges of conventional agriculture, particularly the negative impacts of excessive pesticide and fertilizer use on health and the environment With growing consumer concerns over food safety, Good Agricultural Practices (GAPs) have been promoted, encompassing guidelines for all production phases, from field selection to post-harvest In response, the Vietnamese government established VietGAP, aligned with Hazard Analysis Critical Control Points (HACCP) and AseanGAP principles, through the decree No 379/QD-BNN-KHCN issued by the Ministry of Agriculture and Rural Development on January 28, 2008 VietGAP aims to minimize risks in the production of safe fruits and vegetables while providing economic benefits to farmers, such as increased revenue, reduced costs, improved market access, and enhanced resilience against poor agricultural practices.
Despite the practical benefits of VietGAP adoption, the number of certified farmers in Vietnam remains low This limited adoption is primarily due to several barriers, including the lower popularity of VietGAP compared to other standards like GlobalGAP in the market.
The lack of international recognition for the domestic VietGAP standard discourages farmers and producers from investing in certification, as there are no incentives to do so Additionally, the infrastructure requirements for adopting VietGAP pose challenges for many Vietnamese farmers, who typically manage small land areas averaging 0.25 hectares Furthermore, the high costs associated with obtaining and maintaining VietGAP certification deter new producers from applying and discourage existing farmers from renewing their certificates (Nabeshima, 2015).
Problem statement
Vietnam's agriculture has seen significant growth over the past two decades, yet the country remains a developing nation with a low average income While the agricultural sector's contribution to GDP is declining due to the rapid expansion of industry and services, it continues to play a crucial role in the economy Approximately 65.5% of the population resides in rural areas, where agriculture serves as the primary income source for many households Despite a remarkable reduction in the national poverty rate from 58.1% in 1993 to 13.5% in 2014, challenges persist, particularly for the rural poor who heavily depend on agricultural production Ethnic minorities face even higher poverty rates, with La Hu at 84.9% and H’Mong at 82.9% Additionally, the northern mountainous region has the highest poverty rate in the country Consequently, the Vietnamese government prioritizes agricultural development, rural growth, and support for farmers.
Over the past two decades, Vietnam's agriculture has seen significant growth, primarily driven by the expansion of planting land; however, this horizontal growth is nearing its limits due to the scarcity of undeveloped agricultural land and the country's high population density Research indicates that Vietnamese farmers lack efficiency in various cropping activities, including rice, tea, and vegetables, highlighting the need to enhance production efficiency and optimize land use as crucial factors for future growth The Vietnamese government has recognized improving sector efficiency as a priority goal, with initiatives like the tea production development plan established in 1999 aimed at boosting production, increasing export turnover, and providing employment for farmers reliant on tea cultivation This policy seeks to alleviate poverty in upland tea-producing areas, which often face challenges such as small-scale farming and limited off-farm income opportunities, while also implementing measures to develop the tea value chain and improve market access for rural farmers.
1990, and “the Enterprise Law” which was enacted in 1999 and revised in 2005
Agricultural activities are heavily influenced by natural conditions and environmental factors, leading to significant variability in production outcomes due to inherent risks These risks stem from unpredictable biological, climatic, and price fluctuations that are beyond the control of producers, a situation evident in Vietnam's agricultural sector Additionally, the country's agriculture is characterized by small-scale, scattered production and low technology adoption, resulting in lower productivity and product quality, which diminishes market competitiveness To enhance agricultural production and increase farmers' income, it is crucial to implement advanced technology and effectively manage risk factors.
VietGAP standards were introduced as a crucial response to growing food safety concerns among consumers in Vietnam and abroad Established by the Ministry of Agriculture and Rural Development (MARD) in 2008, these Vietnamese Good Agricultural Practices align with GlobalGAP, ASEAN Good Agricultural Practices, and Hazard Analysis and Critical Control Points Additionally, VietGAP promotes eco-friendly production by maximizing the use of organic components in farming and protection methods.
Initially designed for vegetable production in Vietnam, the VietGAP standard package was expanded to include fruit and tea production in 2009 The implementation of VietGAP standards aims to enhance farmers' economic value and minimize production risks by offering premium prices, improved market access, and reduced average production costs However, the adoption of VietGAP has not met expectations, with varying perspectives among farmers regarding its economic benefits While numerous studies have examined production efficiency, there is a lack of research specifically addressing the production efficiency of crops under VietGAP and its effects on household income.
Research objective
The study aims to analyze the production efficiency of tea and rice farmers in northern Vietnam, focusing on the significance of VietGAP tea production for farmers' livelihoods and the risks they encounter Specifically, it seeks to explore the factors influencing production inefficiency among rice and tea farmers, as well as to examine the economic aspects of adopting VietGAP practices, farmers' perceptions of risk sources, and their management strategies in response to these challenges.
To achieve the overall objective, three main research questions need to be investigated:
1 Do tea and rice farmers operate at fully efficient levels or is there any potential for improving farmer’s production efficiency? And which factors have effects on improving production efficiencies of farmers?
2 How does VietGAP tea production affect household’s income in the study area? And what are determinants for shifting from conventional to VietGAP tea production?
3 What is source of risks facing by farmers and how do they respond to those risk sources?
Organization and structure of the dissertation
The study is structured around two primary objectives and is divided into seven chapters Chapters 2 and 3 focus on Objective 1, while Chapters 4, 5, and 6 address Objective 2 Chapter 1, titled "Introduction," provides an overview of the agricultural sector, highlights major crops and perennial plants, and outlines the problem statement and study objectives A detailed analysis of the current production levels and profit efficiency of rice and tea farmers is presented in Chapter 4.
Chapters 2 and 3 will discuss the determinants of enhancing technical and profit efficiency for farmers Chapter 4 will analyze the factors influencing farmers' decisions to adopt VietGAP tea production The significance of VietGAP tea production on household income will be covered in Chapter 5 Chapter 6 will detail farmers' risk perceptions and their management responses Finally, Chapter 7 will present the main findings of the research along with policy implications.
The structure of the dissertation is presented as figure 1.6 below
Figure 1 6 Overall structure of the dissertation
Objective 1 To analyze productive efficiency of tea and rice farmers
Chapter 2 Analysis of technical efficiency of rice farmers and its determinants
Chapter 3 Analysis of profit efficiency of tea farmers and its determinants
Objective 2 To determine the economics of adoption, risk sources and farmers’ risk management response
Chapter 4 Factors affecting farmers’ decision to adopt VietGAP production
Chapter 5 Assessing impacts of VietGAP production on farmers’ income
Chapter 6 Farmer’s perception of risk sources and their management response
Chapter 7 Conclusion and policy implication
Selection of study area
Northern Vietnam, encompassing the midland and northern mountainous regions, spans an area of 95,222 km² and is home to approximately 11.98 million people from various ethnic minority groups This region includes 14 provinces in the northwest and northeast, characterized by mountainous terrain, and is primarily driven by agricultural and forestry economies due to its favorable natural and climatic conditions Key crops in the area include rice, maize, tea, rubber, and Arabica coffee The study focused on Thai Nguyen province, which has a population of 1,227.4 thousand and an average density of 384 persons per square kilometer Thai Nguyen is divided into nine administrative units, including seven districts, one city, and one town Tea and rice farming are vital for rural households, with rice primarily for self-sufficiency and tea cultivated for commercial purposes The research involved a field survey conducted in two phases, collecting rice production data from 120 farmers and tea production data through interviews with 116 VietGAP and 210 conventional tea farmers Observations lacking complete information were excluded from the analysis, and a pretest survey refined the questionnaire for clarity and time management Experienced staff conducted the survey, ensuring comprehensive data collection, with rice data utilized in Chapter 2 and tea production data in subsequent chapters.
Figure 1 7 Map of study area
PRODUCTIVE EFFICIENCY OF RICE FARMERS AND ITS
Introduction
Rice is a vital staple for the Vietnamese population, significantly contributing to household income in rural areas and playing a crucial role in agricultural growth that aids poverty reduction Despite these advancements, challenges persist, with a nationwide poverty rate of 13.5% in 2014, particularly high among ethnic minority groups, which face a poverty rate of 25.4% Income disparity remains pronounced between urban and rural regions, as well as between delta and mountainous areas The northern upland region, encompassing approximately 1.6 million hectares of annual cropping land, dedicates 579 thousand hectares to rice cultivation, making it the fourth largest rice-producing area in Vietnam Here, agricultural activities, especially rice production, account for about 25% of household income, underscoring its importance in rural and mountainous communities Additionally, rice self-sufficiency enhances food security in upland areas, where transportation remains challenging due to complex topography.
While numerous studies have examined the productive efficiency of agricultural crops in Vietnam (Nguyen et al., 2003; Linh, 2008), research focusing specifically on the technical efficiency of rice production remains limited Most of these studies are concentrated in the primary rice-producing regions, namely the Mekong River Delta and the Red River Delta Additionally, some research has attempted to assess the technical efficiency of rice production on a national scale, operating under the significant assumption that regional differences across Vietnam are minimal (Khai and Yabe).
This study aims to analyze technical efficiency and assess the effects of different fertilizers on rice production in Vietnam The findings will provide valuable insights into the overall landscape of rice cultivation in the country.
Methodology
Technical efficiency (TE) is a crucial metric for production firms, assessing how effectively resources like land, labor, capital, and materials are utilized Researchers focus on measuring TE to evaluate the productivity levels of farmers in agricultural production, particularly addressing whether rice farmers can enhance their productivity under existing conditions TE, along with allocative efficiency (AE), constitutes the two key components of economic efficiency (EE).
2.2.1.1 Economic, technical and allocative efficiency
Production involves converting inputs like land, labor, capital, and fertilizer into outputs such as goods and services, applicable across various sectors, including agriculture The performance of production is reflected in the varying inputs and outputs, with agricultural production aiming for objectives like profit maximization, cost reduction, or maximizing output, which can differ by time and firm Key concepts in this context include technical efficiency, productive efficiency, and economic efficiency.
Production efficiency consists of two key components: technical efficiency and allocative efficiency Technical efficiency refers to a firm's ability to minimize waste during production, maximizing output from given inputs or minimizing inputs for a set output The goal of assessing technical efficiency is to identify ways to enhance output or reduce inputs under existing conditions In contrast, allocative efficiency focuses on the optimal combination of inputs and outputs based on market prices Measuring technical efficiency involves evaluating input and output quantities without considering their prices Additionally, technical efficiency can be broken down into three subcomponents: scale efficiency, which assesses productivity gains from optimal firm size; congestion, where an increase in certain inputs may lead to decreased output; and pure technical efficiency, as defined by Farrell in 1957.
Economic efficiency focuses on maximizing output without increasing the use of conventional inputs, often making the use of existing technologies more cost-effective than adopting new ones, especially when current practices are inefficient (Shapiro, 1977) It can be divided into two categories: technical efficiency, which assesses a farmer's capability to achieve maximum output using available technologies, and allocative efficiency, which evaluates the optimal application of inputs relative to their prices (Farrell, 1957; Shapiro, 1977) Technical efficiency (TE) ranges from 0 to 1, with a TE of 1 indicating that a firm operates at full technical efficiency, achieving the highest possible output.
In microeconomic theory, a production function defines the output of a firm based on various input combinations, focusing on the maximum output achievable with a specific set of inputs It can also be described as the minimum input requirements necessary to produce a given output using available technologies Economists utilize production functions to analyze technical and allocative efficiency, as outputs falling below the production frontier indicate inefficiency in the firm's operations.
Two primary methods are used to estimate a firm's technical efficiency: parametric and non-parametric approaches The parametric method, known as Stochastic Frontier Analysis (SFA), relies on a predefined functional relationship between inputs and outputs, utilizing statistical techniques to estimate parameters In contrast, the non-parametric method, referred to as Data Envelopment Analysis (DEA), constructs a linear piecewise function based solely on empirical data without assuming any specific relationship Comprehensive reviews of these methodologies have been conducted by Kalirajan and Shand (1999) and Bravo-Utera and Pinheiro.
The optimal method for assessing efficiency in agriculture remains ambiguous, as demonstrated by empirical analyses comparing Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) (Sharma et al 1999) While the efficiency scores obtained from each approach may differ quantitatively, the ordinal rankings of farms are largely consistent across both methods Ultimately, the selection of the appropriate methodology depends on the study's objectives, the characteristics of the farms involved, and the underlying assumptions about the data generation process.
Data Envelopment Analysis (DEA) is a mathematical programming technique designed for evaluating multiple-input and multiple-output production technologies Developed by Charnes et al in 1978, DEA employs linear programming to create a non-parametric piecewise frontier that assesses the efficiency of each data point against this frontier The method relies on the clear distinction between inputs and outputs, with each data point representing a decision-making unit (DMU) or producer The primary goal of each DMU is to maximize outputs while utilizing inputs as efficiently as possible (Zheng et al., 2004).
The Stochastic Frontier Approach (SFA) utilizes econometric methods based on a deterministic parameter frontier, as proposed by Aigner et al (1977) in their stochastic frontier production function model This model, expressed as lnqi = xiβ + νi – ui, includes output (qi), input logarithms (xi), unknown parameters (β), statistical noise (νi), and technical inefficiency (ui) SFA effectively distinguishes between noise and inefficiency, but is best suited for scenarios with a single output and multiple inputs In contrast, the Data Envelopment Analysis (DEA) method is non-stochastic and non-parametric, failing to differentiate between noise and inefficiency, yet it excels in handling multiple inputs and outputs The production frontier model for calculating technical efficiency is limited to single output production, and the choice of estimation techniques varies depending on whether the data is cross-sectional or panel.
This study utilizes Stochastic Frontier Analysis (SFA) to estimate technical efficiency in rice production in Vietnam, as SFA effectively distinguishes between noise and technical inefficiency The analysis focuses on rice yield as the dependent variable, acknowledging that each farmer operates under similar harvest regularities due to small rice areas per household and uniform technological and natural conditions The SFA is suitable for production systems with one output and multiple inputs, which in this case includes seed, fertilizers, labor, pesticides, and hired machinery The widely used Cobb-Douglas production function is employed, and taking the logarithm of both sides results in the specified model.
The Cobb-Douglas production function can be expressed by following equation ln yi = βo + Σ βj ln (xij) + εi (2.1)
The rice yield (yi) of the i-th farm is influenced by various inputs (xij) used by the farmer, including seed, nitrogen, phosphorus, potassium, pesticide, labor days, and hired machinery, represented by parameters (βj) that need to be estimated The model incorporates an intercept (βo) and an error term (εi), which consists of two components: Vi, a random variable error related to uncontrollable factors like measurement errors and natural disasters, and Ui, a non-negative random variable linked to farm-specific factors affecting the technical efficiency of rice farmers Vi is assumed to be independently and identically distributed and independent of Ui, while Ui follows a truncated-normal distribution with a mean (à) and variance (δ²).
Ui can also have other distributions, FRONTIER 4.1c computer program used in the study can only harmonize with above assumption The term ài is defined as follows ài = δo + δ1Z1j + δ2Z2j + δ3Z3j + δ4Z4j + δ5Z5j + δ6Z6j + ωi (2.2)
The inefficiency effects in agricultural productivity can be estimated using the two-stage estimation technique in FRONTIER 4.1c In this model, δo represents the intercept term, while δj denotes the parameters for the independent variables Key factors influencing efficiency include Z1j, which measures farmers' experience in years; Z2j, representing their education level in years; Z3j, indicating household size in persons; Z4j, reflecting the number of land plots; and Z5j, which distinguishes between regions, with a value of 1 for the Northeast and 0 for the Northwest Additionally, Z6j assesses credit access for farmers.
Farmers are classified as having borrowed credit loans from financial agencies if the value equals 1, and 0 otherwise, with ωi representing an unobservable error term The maximum likelihood estimates (MLEs) for all parameters related to the stochastic frontier production and inefficiency model were simultaneously estimated using the FRONTIER 4.1c software developed by Coelli.
1996) This program also presented the coefficients of variance parameters σ 2 = σ 2 v + σ 2 u (2.3) γ = σ 2 u/ σ 2 v (2.4)
The γ parameter, or gamma, indicates the proportion of inefficiency in the total residual variance, ranging from zero to one A gamma value of zero signifies that all variations in rice yield are attributed to noise, while a value of one indicates that all variations result from technical inefficiency (Coelli and Battese, 1996).
A study was conducted in Thai Nguyen province, located in the northern upland region of Vietnam, utilizing a multistage sampling technique to select 120 farmers from 18 villages across two districts The selection process considered factors such as geographical location, rice production areas, and family status Data was gathered through interviews with farmers using a detailed questionnaire that covered household characteristics, farm size, and information on rice yield, varieties, fertilizer use, credit, and agricultural extension services Additionally, secondary data was sourced from the General Statistic Office of Vietnam and community reports during the 2011 field survey.
Results and discussion
Table 2 1 Descriptive statistic of variables in the model
Variable Unit Mean St.d Min Max
Note: * 1 unit = 1000m 2 ; **vnd: monetary unit of Vietnam
Source: Author’s data was surveyed in 2011 (n0)
Table 2.1 presents summary statistics of production function variables and farm-specific characteristics that influence technical inefficiency The average rice yield among sampled farmers was 427 kg per unit, with an average seed application of 6.91 kg per unit Farmers utilized various types of fertilizers, including nitrogen, phosphorus, and potassium, while none reported the use of organic fertilizers Additionally, chemical pesticides were commonly used in the study area to mitigate damage from pests and insects.
Annual variations in crop production are influenced by specific pest statuses, while rice production remains largely manual due to small-scale, scattered, and sloping land, resulting in low mechanization levels Machinery is primarily utilized for land preparation In the analysis of technical efficiency, descriptive statistics of output and input variables are presented alongside determinants of technical inefficiency, as shown in Table 2.1 The average age of household heads is approximately 44 years, with 30% having completed primary school and 59.17% having attended secondary school On average, each household employs about 4.78 laborers, with numbers ranging from 1 to more.
The study examines 11 laborers across an average of 5.55 land plots per household It incorporates two key dummy variables: one representing the area, which assesses the varying natural conditions in the Northwest and Northeast regions, and another focused on credit access, designed to evaluate the impact of credit utilization on rice yield improvements in the study area.
2.3.2 Estimation of stochastic frontier production function
The MLEs of stochastic frontier production function is shown in Table 2.2 below Gamma values (γ) of model are 0.863 and its value is statistically significant at 1% level
The study reveals that 86.3% of the variation in rice yield is attributed to technical inefficiency in production, confirming the suitability of the stochastic frontier function model The presence of technical inefficiency was assessed using the Likelihood Ratio (LR) test, where the null hypothesis (Ho) posits that the gamma value equals zero, indicating that yield variations are due to random noise In contrast, the alternative hypothesis (H1) suggests that the gamma value differs from zero, supporting the model's adequacy The LR test follows a mixed Chi-square distribution, and in this study, the LR value of 35.86 exceeds the critical Chi-square value of 20.09, leading to the rejection of the null hypothesis Consequently, it indicates that farmers at the study site are not fully technically efficient.
Table 2 2 Estimated parameters of stochastic frontier production function
Note: *, **, *** are significant level at 10%, 5%, 1% respectively; χ 2 (1%, 8) = 20.09
Source: author’s surveyed data in 2011 (n0)
The analysis revealed positive relationships between rice yield and inputs such as seed, nitrogen, phosphorous, potassium, labor, and hired machinery Notably, the coefficients for nitrogen, potassium, and machinery usage were statistically significant at the 1% and 5% levels, suggesting that rice farmers can enhance yields by increasing nitrogen and potassium application, as well as employing hired machinery for improved land preparation Conversely, the coefficients for seed, phosphorous, and labor were not statistically significant, while the pesticide variable showed a negative coefficient, indicating that excessive pesticide use may adversely affect rice growth, although this finding was not statistically significant.
2.3.3 Input elasticity and its responsiveness to rice yield
The elasticity of various inputs is assessed to quantify their impact on rice yield, utilizing the principles of the Cobb-Douglas production function and natural logarithms The parameters derived from this function serve as elastic coefficients, reflecting the significance of each input on output in percentage terms The coefficients of the Cobb-Douglas production function are detailed in Table 2.2.
The study indicates that among the sampled farmers, nitrogen input has the highest impact on rice yield, with an elasticity of 0.031, meaning a 1% increase in nitrogen leads to a 0.031% rise in yield Potassium input follows with an elasticity of 0.011, while machinery input has a lower elasticity of 0.003 Additionally, other factors such as seed, phosphorus, pesticide costs, and working days show varying relationships with rice yield, but their coefficients lack statistical significance This may suggest that farmers may be overusing these inputs or have reached optimal levels of application.
2.3.4 Frequency distribution of technical efficiency
Frequency distribution of technical efficiency in Northern upland area is presented in Table 2.3 Average technical efficiency scores (TE) of rice production were
Farmers have the potential to enhance their technical efficiency by an average of 12% based on the current inputs and technology, as indicated by a technical efficiency (TE) score of 88 percent This finding is lower than the results reported by Hassan and Ahmad (2005) but slightly higher than those of Shehu et al (2007) and Nguyen et al (2003).
The study reveals a significant variation in technical efficiency among farmers, ranging from 45% to 98% The most efficient farmers achieved an impressive average of 98%, while the least efficient operated at only 45% Notably, approximately 80% of farmers in the area attained a technical efficiency exceeding 80%, demonstrating that over three-fourths of farmers are functioning at a relatively efficient level.
Table 2 3 Frequency distribution of technical efficiency
Efficiency level No of farmer Percentage (%)
Source: author’s surveyed data in 2011 (n0)
Farmers can significantly reduce their production costs by an average of 9 percent if they reach the optimal technical efficiency level of 98 percent This potential cost saving is calculated using the formula: Cost saving = 1 – TEmean/TEmax.
2.3.5 Analysis of determinants of technical inefficiency
The study identified key factors influencing technical inefficiency in rice production, alongside its technical efficiency analysis The Maximum Likelihood Estimates (MLEs) presented in Table 2.4 highlight that experience, education level, household size, and regional variables exhibit negative relationships with technical inefficiency, indicating a positive correlation with technical efficiency Notably, both education level and household size significantly impact technical inefficiency, with p-values of 10% and 5%, respectively This suggests that enhancing the education of household heads and increasing labor availability can effectively reduce inefficiency, aligning with findings from Bravo-Ureta and Pinheiro.
(1997) This might be because more educated farmers are more likely to access better-
Table 2 4 Determinants affecting technical inefficiency
Note: *, **, *** are significant level at 10%, 5%, 1% respectively
A survey conducted in 2011 revealed that larger household sizes positively impact timely rice cultivation, aligning with findings from Shehu et al (2007) This is particularly relevant in areas with increased rice cultivation due to rotations and multiple growing seasons Additionally, the study found a statistically significant negative relationship between region and inefficiency, suggesting that the favorable natural conditions in the Northeast, such as rain and soil fertility, contribute to better outcomes compared to the Northwest Conversely, the number of land plots showed a statistically significant positive correlation with inefficiency, indicating that more plots require additional working days and costs, thereby reducing technical efficiency This aligns with research by Hung (2007) and Rahman et al (2009) on land fragmentation in Vietnam and Bangladesh Although credit typically enhances technical efficiency, in this study area, it had the opposite effect, as many farmers used loans for non-agricultural purposes or mismanaged inputs like fertilizers and pesticides, negatively impacting rice production This finding supports Battese and Broca (1997) but contrasts with Idiong (2007), who assessed the technical efficiency of rice farmers in Nigeria.
2.3.6 Estimation of potential rice yield
As defined in method section, technical efficiency (TE) is ratio of actual yield (observable yield) to frontier yield (potential yield) So it could be expressed by equation below
TE = Actual yield/Potential yield (2.5)
From above concept, we could estimate potential yield for each rice farmer by the following equation:
Potential rice yield = Actual rice yield/TE (2.6)
In case all farmers could reach the potential rice yield in given set of inputs and technology, average rice yield could be reached 480.98 kg per unit
This research evaluates the technical efficiency of rice farmers in northern upland Vietnam, revealing an average technical efficiency score of 88%, indicating room for improvement through better production practices and decision-making The study highlights that nitrogen, potassium, and machinery significantly enhance rice yield, while an imbalance in fertilizer application among farmers contributes to inefficiency Additionally, factors such as the educational level of the household head and family size positively influence technical efficiency, whereas an increasing number of land plots negatively impacts it.
To enhance the technical efficiency of rice production, it is essential for agricultural extension services to encourage farmers to use fertilizers more effectively, ensuring a balanced application of different types Additionally, investing in public education is crucial, as a higher educational level of household heads positively influences efficiency, benefiting not just rice production but the agricultural sector as a whole in the long term Finally, public intervention should focus on mitigating the adverse effects of small-scale production and land fragmentation in agriculture.
Conclusions and recommendations
Tea is a vital component of Vietnamese culture and economy, with a history spanning over 3,000 years It serves as a significant cash crop for farmers in northern Vietnam, contributing to the livelihood of approximately 400,000 households and generating around 1.5 million jobs in the industry In 2012, Vietnam exported 146,700 tons of tea, valued at USD 224.6 million Despite the steady increase in tea consumption and exports since the 1990s, the industry faces challenges, particularly due to conventional farming practices that rely heavily on pesticides and chemical fertilizers, posing risks to health and the environment In response to growing consumer concerns about food safety, Vietnam has shifted towards VietGAP tea production, which emphasizes eco-friendly practices and reduces chemical residues, marking a transition towards organic farming.
Analyzing and comparing various tea production practices is crucial for understanding farmers' decision-making processes (Tran, 2009) These practices significantly influence production efficiency, making it essential to examine the efficiency of VietGAP tea farmers (Nguyen et al., 2015; Tran, 2008) Additionally, the impact of Good Agricultural Practices (GAP) evaluations varies by country and crop.
PROFIT EFFICIENCY OF TEA FARMERS AND ITS
Introduction
Tea is a vital component of Vietnam's culture and economy, with a history that spans over 3,000 years It serves as an essential cash crop for farmers in northern Vietnam, contributing significantly to employment and livelihoods, with around 400,000 households involved in tea production and approximately 1.5 million jobs linked to the industry In 2012, Vietnam exported about 146,700 tons of tea products, valued at USD 224.6 million Despite the steady increase in tea consumption and exports since the 1990s, challenges persist, particularly due to conventional farming practices and reliance on pesticides, which pose risks to human health and the environment As domestic consumers grow increasingly concerned about food safety, there has been a shift towards VietGAP tea production, which emphasizes eco-friendly practices and the reduction of chemical residues, marking a transition towards organic farming methods in the Vietnamese tea industry.
Analyzing various tea production practices is crucial for understanding farmers' decision-making (Tran, 2009) Different farming methods can lead to varying production efficiencies, making it essential to study the efficiency of VietGAP tea farmers (Nguyen et al., 2015; Tran, 2008) The impact of Good Agricultural Practices (GAP) varies by country and crop, with some studies indicating a positive effect on technical efficiency (Taraka et al., 2011; Ha, 2014b), while others report that GAP adopters do not benefit from premium pricing (Calvin et al., 2004; Subervie and Vagneron, 2012; Pongvinyoo et al., 2015) Conversely, some research highlights positive outcomes in price, yield, or income (Kariuki et al., 2012; Islam et al., 2012) In Vietnam, there is limited information on the economic efficiency of VietGAP tea production due to its relatively recent implementation Tran (2008) assessed the economic efficiency of organic tea farmers in Thai Nguyen province, while Saigenji (2011) explored the impact of contract farming on production efficiency and household income in northern Vietnam Hong and Yabe (2015) examined profit efficiency without focusing on specific tea production practices To fill this research gap, we compared the profit efficiency of VietGAP and conventional tea farmers using propensity score matching to address selection bias.
Methodology and data collection
3.2.1 Measurement of production and profit efficiency
Over the past two decades, empirical studies on agricultural production efficiency have primarily focused on two categories: one assessing productive efficiency based on input demand price responses, and the other examining production inefficiency without considering these price responses (Arnade and Trueblood 2002) The profit function estimation technique was introduced to address both production inefficiency and price response (Kumbhakar 1996) Production inefficiency is typically analyzed through three components: technical, allocative, and scale inefficiency A production unit is deemed technically inefficient if its output falls below the maximum feasible level, known as the frontier output Additionally, allocative inefficiency occurs when inputs are not utilized in optimal proportions relative to observed input prices and output levels Finally, a firm lacks scale efficiency if it fails to equate product price with marginal cost in the profit maximization framework (Kumbhakar et al.).
Econometrics integrates multiple measurements into a cohesive system, enhancing efficiency through simultaneous estimation within a profit function framework (Ali and Flinn 1989; Kumbhakar et al 1989; Wang et al 1996) A frontier production function is commonly utilized to assess efficiency and its components (Battese and Coelli 1995), but this approach may be inadequate when farmers encounter varying prices and differing factor endowments (Ali and Flinn 1989) Consequently, the stochastic profit function is employed to determine firm-specific efficiency (Kumbhakar et al 1989; Ali and Flinn 1989; Wang et al 1996) This profit function approach incorporates technical, allocative, and scale inefficiencies into the profit relationship, with production decision errors resulting in lower profits Two main frontier methods for measuring production efficiency are the econometric and mathematical programming approaches (Lovell 1993) The stochastic frontier model consists of a symmetric component that captures random variations and measurement errors, and a one-sided component that reflects inefficiency relative to the stochastic frontier (Aigner et al 1977) The stochastic frontier approach (SFA) effectively distinguishes noise effects from technical inefficiency, with the stochastic profit function defined as πi = f(Pi, Zi) exp(ξi), where π represents normalized profit for the i-th farm, and ξi is an error term comprising two components (Ali and Flinn, 1989) The profit efficiency (PE) of the i-th farm within this framework is also defined.
PE = E[exp(-ài) | ξi], (3.3) where E is an expectation operator that can be estimated by obtaining the expressions for the conditional expectation ài upon the observed value of ξi (0 ≤ PE ≤ 1)
Over the past decade, various approaches to impact evaluation and econometric methods have been utilized (Khandker et al 2010), with the selection of the most appropriate method often debated in empirical economic analysis (Wang et al 2014) For instance, treatment effects can be assessed through regression model coefficients (Imben 2004), while other studies have employed dummy variables to indicate whether farmers cultivated specific crops or adopted improved technologies (Walker et al 2004) Ideally, impact evaluation yields the most accurate results when comparing the same farmers before and after adoption, minimizing original differences that could lead to biased outcomes This necessitates conducting baseline surveys to gather data on potential adopters prior to adoption Although this is feasible in trial research with small samples, it poses significant challenges at a regional scale Furthermore, the literature often relies on non-randomized observational studies instead of randomized trials for data collection (Becker and Ichino).
In 2002, it was observed that participants in agricultural practices often differ significantly from nonparticipants, even without treatment High-skilled individuals, for instance, tend to have a greater likelihood of achieving specific goals (Caliendo and Kopeinig, 2008) This indicates that inherent differences among farmers can significantly influence their production decisions and overall performance Therefore, when comparing the profit efficiency of tea cultivation practices, it is crucial to control for these differences to avoid biased results.
Tea producers have various options for selecting production inputs and marketing their products, which results in price variations influenced by location and product quality It can be assumed that these producers allocate inputs optimally by aligning their ratios with marginal productivity In the economic analysis of profit efficiency, a farm is considered to maximize profit under conditions of perfectly competitive markets and established output technology Profit efficiency refers to a production unit's capacity to achieve the highest possible profit given the prevailing prices and fixed factors Conversely, profit inefficiency occurs when a producer fails to operate on the profit frontier, leading to potential profit losses.
The profit efficiency of a tea farmer, as defined in the research, refers to the profit realized from operating on the profit frontier while accounting for variable input prices and quasi-fixed input quantities Specifically, a farm's profit is calculated by subtracting total variable costs from total revenue.
The Cobb-Douglas production function can be expressed in logarithmic form as ln νn/p = α0 + Σαi ln Pi/p + Σαq ln zq + νi – ài, where νn/p represents the normalized profit frontier, Pi/p indicates the normalized input prices, and zq refers to the quantities of quasi-fixed inputs, with αi and αq being unknown parameters In this context, Pi signifies the price of the i-th input utilized by each tea farm, normalized by the tea price (p) of the farm, and encompasses costs such as chemical fertilizers (converted to NPK), organic compounds, pesticide expenses, labor, and other associated costs.
In the context of a tea farm, zq represents the amount of fixed inputs utilized, such as the farm's size in hectares Additionally, νi accounts for statistical noise, while ài reflects the impact of profit inefficiency The parameter α is an unknown variable that requires estimation.
Maximum likelihood estimation is employed to estimate unknown parameters, with the likelihood function defined by variance parameters, σ² (σ²v + σ²u) and γ = σ²u/σ²v (Battese and Coelli, 1995) Using specific statistical software, the profit efficiency levels of selected tea farms were predicted A regression model was then applied to identify the factors influencing the profit efficiency of tea farmers.
The profit efficiency level (PE) of the i-th tea farmer is influenced by various socioeconomic and farm characteristics, represented by the equation PE = βo + ΣβjZj + ω Key variables include gender, formal education, family labor, farming experience, access to irrigation and credit, the ratio of tea income, cooperative membership, and the status of farming machinery Additionally, the error term (ω) accounts for factors not included in the model.
In randomized experiments, the mean effect of treatment on the treated group is typically estimated by comparing the mean outcome values of treatment and control groups However, this method is not suitable for VietGAP tea farmers due to their non-random selection Therefore, we utilized propensity score matching (PSM) to assess the impact of VietGAP application on tea farmers, employing cross-sectional data for a more accurate evaluation.
The Propensity Score Matching (PSM) method is utilized to align individuals with comparable characteristics across two distinct groups, aiming to establish an experimental condition through random selection of participants and non-participants This two-step mathematical approach, as outlined by Becker and Ichino (2002), begins by estimating a farmer's propensity score using logit or probit models.
The model used in this study is represented by the equation Y(1,0) = β0 + β1X1 + β2X2 + … + βnXn, where Y indicates the dependent variable distinguishing between VietGAP and conventional farmers, and β represents the estimated coefficients while Xn includes the covariates The selection of covariates is informed by economic theory and previous research, as emphasized by Sianesi (2004) and Smith and Todd (2005), with the understanding that omitting key variables can lead to significant bias in results (Dehejia and Wahba, 1999) Following the methodology of Noltze et al (2012), this study includes covariates such as household size, formal education of the household head, credit access, and extension access, while also introducing irrigation and machinery use as indicators of mechanization in tea production (Tran 2008) Additionally, factors like gender, farming experience, and farm size, which can influence the adoption of agricultural innovations, are included as per Kersting and Wollni (2012) To further assess the significance of tea income in the region, the variable "ratio of tea income" was also incorporated into the model.
Then, the propensity score was estimated using the following equation:
In the second step, farmers with similar propensity scores between the groups were matched to estimate the average treatment effect for the treated (ATT), denoted as
ATT = E(Y1-Y0)|x, D=1) = E(Y1| x, D=1) - E(Y0| x, D=1), (3.8) where D is an indicator equal to one if the farmer applies VietGAP, and zero is otherwise,
In evaluating the impact of VietGAP adoption, Y1 represents the outcome for adopters while Y0 denotes the outcome for non-adopters, with x as a vector of control variables Single nearest neighbor matching (NNM) is employed to align similar observations, ensuring that treatment and control groups exhibit statistically identical variable means Propensity score matching (PSM) relies on the assumptions of conditional independence and common support; however, unobservable variables may influence both VietGAP adoption and its outcomes, potentially introducing hidden biases These biases could result in an overestimation of treatment effects if adopters are inherently more likely to meet VietGAP standards, or an underestimation if negative unobserved selection is present Therefore, it is crucial to conduct a balancing test to ensure that significant differences are not systematically present after conditioning on the propensity score Effective matching should achieve a substantially lower standardized bias and yield statistically insignificant likelihood ratio tests concerning the joint significance of all regressors, thereby validating the matching quality.
After matching, it is essential to ensure common support by visually inspecting the density distributions of propensity scores between treatment and control groups Alternatively, the Kolmogorov-Smirnov nonparametric test can be employed for comparison If significant differences are observed in the density distribution's maxima and minima, it is advisable to exclude cases that fall outside the support of the other distribution.
Results and discussion
3.3.1 Socio-economic characteristics of tea farmers
The study employed descriptive statistics to analyze the current status of tea farms in the area As shown in Table 3.2, key variables and distinct characteristics of the farms are detailed, providing insight into their descriptive features.
Table 3 2 Descriptive statistics of tea production practices
Variables All samples VietGAP CON Diff t-stat
Organic (kg/ha) 2025.3 2557.3 1724.3 832.9 *** 3.8233 Pescost (liter/ha) 175.1 157.2 185.2 -28.04 * -2.0963 Labor (man-day) 1088.5 1236.3 1004.8 231.6 *** 3.8239 Ocost (K.vnd) 25,566.9 24,839.7 25,978.4 -1138.7 -0.8610 Input variables of profit function
Variables Mean Std.dev Min Max
In a 2016 survey conducted by the author, data was collected from 116 VietGAP farms and 210 conventional farms, revealing significant differences in agricultural practices The findings indicate a noteworthy distinction in performance metrics, with statistical significance levels marked at 1%, 5%, and 10% The monetary unit used in the analysis is K.vnd, equivalent to thousands of dong, with an exchange rate of approximately 1 USD to 21 K.vnd.
A comparison of VietGAP and conventional tea farmers was conducted using t-statistics, revealing a strong correlation between "age" and "experience." Ultimately, only the "experience" variable was retained in the regression analysis The findings showed that the average tea yield is approximately 8,151 kg per hectare, with VietGAP tea farmers achieving significantly higher yields than their conventional counterparts at a 5% significance level.
VietGAP tea farmers incur higher production costs compared to conventional farmers, primarily due to their increased use of organic fertilizers, which they recognize as beneficial for sustainability While VietGAP farmers enjoy lower pesticide costs, variability in expenses can arise from factors such as farm size and pest prevalence Technical training on pesticide use within the VietGAP program positively influences farmers' attitudes towards chemical use in tea cultivation Additionally, VietGAP farms demand more labor, with an average of 1,100 labor-days per hectare annually, especially during the labor-intensive harvesting season Despite these differences, the average size of tea farms remains consistent at approximately 0.35 hectares per farmer, showing no significant disparity between VietGAP and conventional practices.
Table 3.3 presents comparative statistics for the key variables utilized in the regression analysis The difference between the two groups is calculated by subtracting the mean of conventional tea farmers from the mean of VietGAP tea farmers Additionally, the t-statistic value reflects the significance level of the observed differences.
Table 3 3 Comparative statistics of model variables
Variables All samples VietGAP CON Diff t-stat
The study reveals significant differences between VietGAP adopters and non-adopters, with statistical significance at the 1%, 5%, and 10% levels, denoted by ***, **, and * respectively The difference (Diff.) is calculated as the mean of VietGAP adopters minus the mean of non-adopters, highlighting the impact of VietGAP practices on farm performance This analysis is based on data collected from a survey conducted in 2016, which included 116 VietGAP farms and 210 conventional farms.
Most of the tea farmers (85%) have a basic education at a secondary or high school level
A limited number of tea farmers possess higher education, highlighting a lack of interest in tea production among educated youth, which may hinder technology adoption and access to premium tea markets Additionally, the findings reveal that farmers have an average of 22 years of experience in tea cultivation and generate approximately 62% of their total income from tea, indicating that tea production is a dominant economic activity in the region.
Farmers adhering to VietGAP standards have significantly more family labor available compared to conventional tea farmers, with a statistically significant difference at the 1% level While VietGAP farms are not categorized as intensive agriculture, they require more labor, particularly during the harvesting period The stringent requirements for VietGAP certified tea necessitate greater investment in the production system, including irrigation to ensure water quality Certified organizations regularly verify water sources and other input factors on tea farms, and only those meeting all regulated standards receive the VietGAP trademark Consequently, the presence of an effective irrigation system is a key factor in adopting these new practices, leading to a higher level of irrigation in VietGAP tea farms compared to their conventional counterparts Additionally, there is a significant difference in tea income ratios between the two farming groups.
Farmers practicing VietGAP earn significantly higher incomes from tea production compared to their conventional counterparts, indicating that conventional farmers rely less on tea for their income This disparity is further supported by the higher investment in machinery and cooperative membership among VietGAP farmers However, no significant differences were found between the two groups regarding gender, education, farming experience, or access to credit.
3.3.2 Estimated result of profit frontier function
Economic efficiency is crucial for both farmers and policymakers, with a dual method employed to evaluate the profit efficiency of tea farmers The analysis utilized a dummy variable, “adop,” to compare VietGAP and conventional farms, revealing that tea farmers engaged in the VietGAP program exhibit significantly higher profit efficiency This increased efficiency may stem from superior farm and farmer characteristics rather than solely the VietGAP program itself Further analysis will be conducted to address potential bias in selection Additionally, the positive correlation between farm size and profit efficiency indicates that larger tea producers achieve greater profitability compared to those with smaller plots, aligning with findings from previous studies by Ali and Byerlee (1991), Kolawole (2006), Abdulai and Huffman (2000), and Tran and Yamagida (2015).
Table 3 4 Estimation result of profit efficiency among tea farmers
Variables Coefficient Std.dev z-stat p>|z|
Note: *** , ** , and * denote significance at the 1%, 5%, and 10% levels, respectively
Source: author’s surveyed data in 2016 (n26)
VietGAP tea production emphasizes the reduced use of chemical components and pesticides, focusing instead on organic fertilizers and biological pest control methods This approach ensures that VietGAP tea products are free from pesticide and chemical residues, certified by authorized agencies However, rising costs of production inputs, particularly chemical and organic fertilizers, negatively impact profit efficiency, as supported by previous studies The strong dependence on fertilizer inputs is evident, while labor costs significantly influence profit efficiency; as labor prices rise, profit margins for tea farmers decline Conversely, higher expenses related to hired irrigation, machinery, and processing positively affect profit efficiency, indicating that farmers who utilize machinery in production and post-harvest stages can achieve greater profitability.
3.3.3 Factors explaining the profit efficiency of tea farmers
Understanding the factors influencing the profit efficiency of tea farmers is crucial for policy development Using a Tobit model, we analyzed various features of farms and farmers to identify these factors, with profit efficiency scores as the dependent variable Notably, access to an irrigation system significantly enhances profit efficiency, as regular irrigation mitigates yield loss, particularly during dry seasons and for high-yield tea varieties Additionally, the ratio of tea income, or "Ritea," positively impacts profit efficiency, indicating that farmers who derive more income from tea production are more invested in their farms This reliance on tea income as a primary family resource drives farmers to dedicate more time and attention to their tea operations, resulting in superior performance compared to their peers These findings align with previous research by Hong and Yabe (2015), Ali and Flinn (1989), and Wang et al (1996).
Farmers who rely more on off-farm income tend to operate less efficiently, according to research from 2003 However, participation in production cooperatives or groups significantly enhances profit efficiency for tea farmers Engaging in these cooperatives allows farmers greater access to new information, technical training, and opportunities for experience exchange and information sharing (Hong and Yabe, 2015).
Table 3 5 Factors affecting profit efficiency of tea farmers
Education junior level -0.024 0.021 -1.12 0.262 senior level -0.023 0.023 -1.01 0.313 higher level -0.008 0.029 -0.29 0.773
Note: *** , ** , and * denote significance at the 1%, 5%, and 10% levels, respectively
Source: author’s surveyed data in 2016 (n26)
Joining a production group is increasingly appealing to tea farmers as it helps reduce costs associated with machinery, processing, packaging, and trademark registration Technical support from extension services enhances profit efficiency by providing valuable information on disease prevention and new farming techniques Factors such as gender, education level, household size, credit access, machinery, and experience do not significantly impact profit efficiency Although higher education typically correlates with increased efficiency, most local farmers possess only basic education, limiting its relevance to farming practices This aligns with findings by Collie et al (2002), indicating that education level does not greatly influence efficiency Additionally, access to credit and machinery shows similar effects on profit efficiency across different farm types, though the negative association with credit access is not statistically significant, as few farmers in the area utilize credit from agencies.
3.3.4 Distribution of profit efficiency and average treatment effect
Frequency distribution of the profit efficiency of both farmer groups are presented in table 3.6 Most farmers (72%) operate with profit efficiency scores ranging from 0.70 to 0.89
Table 3 6 Frequency distribution of profit efficiency (PE)
Source: author’s surveyed data in 2016
The average profit efficiency score of tea farmers is approximately 74%, with a notable variation ranging from 29% to 94% Notably, VietGAP tea farmers demonstrate a higher mean profit efficiency of 76.4% compared to 73.4% for conventional tea farmers This indicates a significant opportunity to enhance profit efficiency among tea farmers by up to 26%.
3.3.5 Propensity score for VietGAP tea adoption
Conclusions and recommendations
Tea production in northern Vietnam is primarily characterized by small-scale farming, with an average tea farmland size of 0.35 hectares This sector is crucial for household income, serving as a significant financial resource for families in the region A study utilizing a stochastic profit frontier function examined the profit efficiency of tea production, employing a propensity score matching approach to address potential self-selection biases The findings revealed that tea farmers are not achieving optimal profit efficiency, with safe tea producers having the potential to enhance their efficiency by approximately 24%, while conventional tea farmers could see a 27% increase Additionally, the results indicated that transitioning to safe tea production practices can lead to higher profit efficiency for tea farmers.
The study suggests several policy implications to enhance the profit efficiency of tea farms, notably through the development of irrigation systems and improving the operational efficiency of cooperatives A larger production scale is crucial for encouraging farmers to adopt VietGAP standards, as it enables the utilization of machinery and other production inputs Therefore, public policies should focus on promoting eco-friendly production practices by supporting innovations that mitigate the challenges associated with small production scales Additionally, providing suitable labor-saving machinery and improving extension services could serve as effective incentives for conversion in the study area.