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ISSN : 1225-8504(Print)
ISSN : 2287-8165(Online)
Journal of the Korean Society of International Agriculture Vol.34 No.2 pp.110-121
DOI : https://doi.org/10.12719/KSIA.2022.34.2.110

# A Study on the Farmers’ Participation in Local Farmer’s Organization: The Case of the Banana Farmer’s Production Groups in Savannakhet Province, Lao PDR

Phoudthavong Sengsouriya*, Phonesavard Sibounnavong**, Jonghwa Kim*†
*Department of Agricultural and Resource Economics, Kangwon National University, South Korea
**Department of Postharvest Technology and Product Promotion, Savannakhet University, Lao PDR
Corresponding author (Phone) +82-33-250-8664 (E-mail) kjonghwa@kangwon.ac.kr
January 9, 2022 May 22, 2022 May 23, 2022

## Abstract

To assist farmers in shifting from subsistence to commercial agriculture, the government of Laos has promoted farmer organizations throughout the country. The farmer production groups have gradually transformed and emerged into strong cooperatives or associations. The purpose of this study was to examine the factors affecting farmers’ participation in FPGs. Data were collected from primary and secondary sources. A total of 140 respondents participated in a field survey in three kumb ban (village groups) in the Xepon District. Descriptive statistics and binary logistics regression models were used to analyze the data. The results show that the education level of household head, farmland size, access to credit, and perceived trust for farmer production group membership are significantly different from those who are non-members of the groups. However, non-members of FPGs face more constrain in accessing the market, as compared to the members of FPGs. The empirical results from the Binary Logit Model indicate that education level of headed household, farmland size, access to credit, perceived trust, and constraint to market are factors that significantly determine farmers’ participation in FPGs. This study contributes to the empirical literature by providing a better understanding of farmer participation in the groups. The government of Laos should consider and promote access to credit, farmer learning, and market integration to improve farmers' participation in FPGs.

# 지역 농가조직에서의 농업인 참여에 대한 연구: 라오스 사완나켓 지역의 바나나 농가 생산조직을 중심으로

푸다봉 센구리야*, 퍼언사밧 시분아봉**, 김종화*†
*강원대학교 농업자원경제학전공
**라오스 사완나켓대학교 수확 후 기술 및 제품 홍보학과

## INTRODUCTION

In Laos, the majority of the poor population still depends directly or indirectly on agriculture for survival. Most of them live in remote areas and are small-scale farmers. Simultaneously, building farmers' production capacities along with improving their access to market has become an essential element of strategy to promote rural development and poverty eradication. Thus, the Lao National Development Priorities and Goals emphasize the objective of eliminating Laos’ status as a least developed country by the end of 2020 through sustainable and equitable development. It also aims to eradicate poverty through environmental protection and sustainable use of natural resources, increased participation of all stakeholders in the development process, continued implementation of the New Economic Mechanism, as well as achievement of permanent food security along with stimulating the development of farmer groups, associations, and cooperative in the country. These formulations play crucial roles in community agricultural production and commerce. Meanwhile, the policies promoted the diversification of smallholder farming, thereby increasing the production volume and strengthening the linkage between farmer organizations and local traders, agro-processors, and agribusiness companies (MAF, 2010). Consequently, the Government of Laos and the Ministry of Agriculture and Forestry focused on promoting the Farmer Organization, which is an essential factor in promoting the medium-and long-term development of the agriculture sector in Laos. Therefore, in 2010, the Prime Minister’s Decree on Groups and Cooperatives was issued. The Ministry of Agriculture and Forestry released the new Agriculture Development Strategy 2011-2020 to ensure a gradual transition from self-subsistence agriculture to commercial smallholder farmer production including innovative technologies along with the creation of voluntary farmer organizations.

Farmer participation is a crucial factor for sustainable agriculture in rural areas. It includes the issues the national and local levels (Subedi, 2008) such as lacking participation, no partnerships, developments, or programs (Aref, 2011). Low participation can be the reason for the failure of many developmental attempts in developing countries, which is a concern of the World Bank (World Bank, 1993). Consequently, a lack of participation in the implementation of an agricultural policy can lead to an unsuccessful agricultural development project. Smallholder farmers can increase their incomes through commercial agriculture (Onphanhdala et al., 2016). The numerous poor across the world are rural residents who rely on agricultural practices for their daily livelihoods. Raising smallholders’ income is crucial to help reduce poverty, and it is commonly known that increased commercialization among smallholders leads to higher production, specialization, and higher incomes (Barrett, 2008). However, the movement from subsistence agriculture is crucial for increasing smallholder farmers’ incomes, especially in the case of the Lao PDR. Such a shift requires establishing a farmer production group (FPGs) that can cultivate the production capacity of farmers. FPGs ensure input supply, information, and market access to their members. These aim to support and create opportunities for Laotian farmers to become entrepreneurs. Second, it requires linking small-scale farmers to agricultural value chains. A common form of vertical integration is contract farming. The buyer enters into an agreement with the farmers for the production and supply of agricultural products at a predetermined price.

Banana has been selected as one of the target crops to attain as driven tools for rural development, especially in remote highland areas. It is also essential market-oriented produce and a significant source of income for many smallholder farmers. Banana production is seemingly one of the main cash crops that people in this area cultivate, besides rice, cassava, and industry tree (making incense sticks). However, these areas are primarily mountainous, so it is difficult for them to cultivate rice before planting highland rice in the form of slash and burn practices. But nowadays, they shift to another crop. So, rice growing is only growing for consumption in their family. Banana is also the government’s tool to help poor people in remote area step out of poverty.

Most studies in the field of farmer organizations have focused only on cooperative memberships (Arayesh, 2011;Esayas, 2017;Gijselinckx & Bussels, 2014;Hagos et al., 2018;Luu, 2020;Mojo et al., 2017;Nugusse et al., 2013;Zheng et al., 2012). In the case of Laos, which is still in the early stages of shifting and strengthening existing farmers’ groups, turn to agriculture cooperatives. Previous studies on farmer organizations have not detailed the FPGs. A large number of farmer organization investigation studies in Laos are conducted using a qualitative approach, and there are few studies undertaken using the quantitative analysis approach. Moreover, limited research has been conducted at the microeconomic level, emphasizing farming households’ participation in farming organizations. Therefore, the findings of this study may contribute to the literature on farmer organization, especially in FPGs, which can be relevant for agencies and policymakers to understand a new dimension of the FPGs perspective in Laos. Furthermore, understanding the determinants that influence participation in farmer production groups is necessary for development in this field. Hence, the main objective of this study are as follows. To examine the factors affecting banana farmers’ participation in FPGs and propose suggestions to improve farmers’ participation in the groups. The remainder of this paper is organized as follows: Section 2 presents the reviews of existing literature, and Section 3 describes the materials and methods. Section 4 explains the results and discussion. Finally, Section 5 concluded the study.

## LITERATURE REVIEW

In the last two decades, modern farmer organizations such as cooperatives and associations in Laos, have been developed, promoted and operated as prioritized issues. However, their legal status has not been defined, and there are few empirical studies on Lao farmers’ organizations; there is little and limited research and data available (DRDC, 2018). Additionally, the promotion of farmer production groups by the government has not provided sustained benefits to the members, while farmer-to-farmer extension and farm-to-market linkages have been limited to small scales. Private sector service provision models have struggled to establish and sustain staff and services without external support (ADB, 2018). These issues may lead to slow progress and an unsuccessful implementation. Another reason for the transformation of farmer groups into formal cooperatives or the slow and limited progress of associations is the new form of power leading to a loss of control by government agencies (Castella & Bouahom, 2014).

The establishment and strengthening of Farmer Organizations is considered one of the foremost challenges for the agricultural and rural development in Laos as the agriculture contribution to GDP is approximately 15-20% annually. Currently, Laos has almost 4,420 farmer groups with 1,582 plantation groups, 1,480 feeding groups, 50 administration groups, and 41 processing groups (Rasabandith S., 2020). However, most of them are weak in organization. They are unstable with a small number of members with limited access to credit and market (DRDC, 2018). The establishment of FPGs is a crucial determinant of improving prices and other issues of negotiation as well as better access to government policy. Establishing FPGs is challenging due to the constraints of generally low levels of educational attainment, know-how, and experience of farmers in organizing and operating farmer organizations. However, FPGs can be an essential tool to support farmers in terms of providing technical training and extension services, credit accessibility, farmer knowledge, and market integration (Onphanhdala et al., 2016). Furthermore, the establishment and sustainability of farmer organizations are often conditioned on external support, for example, from NGOs, government agencies, or private businesses. Moreover, farmer organizations incur transaction costs related to internal governance and incentive problems (Fischer & Qaim, 2012). Smallholder farmers can overcome market failures and maintain themselves in the right position in the market by organizing themselves into groups or producer organizations (Gyau et al., 2016). Farmer organizations can also offer a platform for capacity building, information exchange, and innovation in rural settings. Participation in farmer or collective action groups may require updated information on the activities of unions and operations. Hence, farmers have a better opportunity to obtain reliable information related to agricultural production and management from a group member than farmers who are far away (Hagos et al., 2018). Farmer organizations, cooperatives, and similar forms of collective action are avenues for reducing high transaction costs (Markelova et al., 2009). This is consistent with the previous studies which highlighted that the role of cooperatives is to help farmers reduce the transaction costs associated with production, distribution, marketing, and risk reduction (Nugusse et al., 2013). Roy & Thorat (2008) showed that in India, grape marketing cooperatives reduced transaction costs and contributed to a better bargaining position for smallholders vis-à-vis foreign traders. However, these examples cannot be generalized. There are also cases where collective action did not improve the farmers’ situation, and where groups dissolved after a disappointing experience (Poulton et al., 2010). This is consistent with the findings of Castella & Bouahom (2014), who revealed that most of the groups dissolved after the project ended.

Recent empirical studies have emphasized the following variables as the main determinants of farmers’ participation in farmer organizations: farmers’ sex, age, education level of household head, farm size, off-farm income, credit access, social capital, perceived trust, land tenure status, and market access (Karli et al., 2006;Arayesh, 2011;Agbonlahor et al., 2012;Zheng et al., 2012;Nugusse et al., 2013;Gijselinckx & Bussels, 2014;Gyau et al., 2016;Mojo et al., 2017;Luu, 2020).

The sex of farmers is one of the factors influencing farmers’ participation decisions in collective action, because group activities can be time-consuming, thereby lowering the incentive for women to participate (Weinberger & Jütting, 2001). The human capital of farmers represented by the education level of household heads, their age, agricultural knowledge, and experience, may affect participation decisions in collective action because of imperfect markets (Luu, 2020). The educational level of a farmer is positively correlated with participation decisions because of the assumed link between education and knowledge. According to Gyau et al (2016), education is likely to have a positive influence on participation because well-educated farmers are more likely to have the skills and networks necessary to initiate and manage an association. Farmers with a higher level of education are more likely to have better information about the environment in which the cooperative operates and are more likely to participate in general assemblies. Moreover, while a higher educational level can make them feel more confident, they are more likely to pursue positions on boards or committees. Therefore, a lower level of education has been significantly associated with lower proactive member participation in governance (Cechin et al., 2013). Young heads of households are more likely to acquire new knowledge and learn new techniques than are others (Gyau et al., 2016;Mojo et al., 2017;Weinberger & Jütting, 2001).

Household size was used to measure labor availability. A farmer with a large household can easily participate in an agricultural project while delegating other important activities to another household member, and vice versa. Simultaneously, each active labor among the household members could be the source of information or beneficiaries of the agriculture project. Hence, the likelihood of coming into contact with an agriculture project increases with an increase in household size (Etwire et al., 2013).

Farmland size, off-farm income, and credit accessibility are the financial resources for farm operations. People join collective action groups to acquire financial support (Nugusse et al., 2013). Farmland size is the total land available to farmers for agricultural production. Additionally, other findings indicate that farmland possession affects farmers’ interest in becoming members (Broussard, 2012). The lack of farmland is one of the reasons for people's mobility from rural to urban areas (Todaro & Smith, 2011). Farmers who own their land are more likely to nurture it and can more easily qualify for agricultural credit to upgrade their operations (ADB, 2018). Access to credit is expected to positively influence the decision to participate in an agricultural project. Access to financial institutions has been shown to influence rural people to become members of collective societies (Nugusse et al., 2013). Social capital is known as the membership in farmers’ associations and the number of relatives that farmers can rely on for critical support, and it is the number of traders that farmers know in and outside the village. Farmers’ social capital can affect the probability of collective action in many ways, particularly in information exchange, market access, labor exchange, and capital access, as well as in coping with risks in production and the market (Luu, 2020).

Trust and positive attitudes toward collective action are necessary (Luu, 2020). Trust/mistrust of leadership is the crucial determinant for joining a farmer organization (Yi et al., 2017). Previous studies report that individuals are willing to take action toward a group’s shared target whenever they feel that other group members might also take such an action (Fehr & Gachter, 2000). The perception of trust from collective action is one of the key variables used by (Gyau et al., 2016). Group cohesion may be defined as “the strength of members’ desire to remain in a group and their commitment to it” (Hansen, 2002). It also emphasized that group cohesion is an outcome of members’ positive feelings toward each other and the entire group. Furthermore, cohesion is an individual’s sense of belonging to a particular group and feelings of morale associated with membership in the group (Bollen & Hoyle, 1990). Farmland tenure is a descriptor that differentiates self-owned land from a property rented from another landholding in the village. Farmers are more likely to manage self-owned land more favorably than rented land (Kassie et al., 2013), because increased land tenure security has been found to increase investment incentives (Besley, 1995).

Market access can influence farmers’ decision-making for participating in FPGs. Market access is directly associated with the transaction costs incurred when households participate in input and output marketing activities. Transaction costs are barriers to market participation by smallholder rice farmers and are responsible for significant market failures in developing countries (Kassie et al., 2013;Pretty et al., 2011). Furthermore, recent studies have pointed out that farmers' accessibility to input and output markets likely affects transaction costs, and subsequently, the likelihood of collective action (Luu, 2020). In such situations, collective action is likely to improve smallholder market access (Rao & Qaim, 2011).

## MATERIAL AND METHOD

### Brief description of the study area

Savannakhet province is located in the southern region of the Lao PDR, approximately 470 km from Vientiane. Its geographic coordinates are between 16° and 17°N and between 105° and 106°E (Figure 1). The province has a total land area of 21,774 km2 (DAFO, 2020). In the last decade, under the national program on the stabilization of shifting cultivation, which was carried out by government agencies (PAFO and DAFO) in collaboration with international organizations (SIDA, FOMACOP, CIDSE, OXFAM, and Action North Sud), the area of shifting cultivation has significantly reduced (Bui Tan et al., 2015). The Xepon District is a mountainous district bordering Vietnam. There are 14 Kumb Ban (village groups), comprising 88 villages and 7,859 households. The total population of the district is 45,993, of which 23,068 are women. Out of the population comprising the ethnic minority groups, 68.64% are Lao Theung and 31.36% are Lao Loum (ADB, 2014). Rice, cassava, and banana are the main crops grown in Xepon District. Crop farming, livestock rearing, and small-scale commercial and economic activities in this area. Banana is an alternative crop for the poor in this area. Approximately 15,750 tons of banana was produced in 315 hectares of planting area in 2020 (DAFO, 2020). Banana farmers sell their products directly or through collectors to retailers in their village. A very small number of farmers or family members sell retail stores to urban consumers without intermediaries. Most retailers are from Pakse province and Vietnamese retailers.

### Sampling procedures and data collection

We used primary data collected from farmers in the study area. Stratified sampling was applied to select kumb ban(village groups), villages, and households. In the first stage, three kumb ban were purposively selected from the existing products. In the second stage, three villages (one village per Kumb ban) were chosen randomly. Finally, a probability proportionate-to-size sampling technique was applied to determine the required number of households to conduct the survey interviews in the village to obtain the sample of 140 households. The sample obtained was divided into two groups: those who were members of FPGs (89 respondents) and non-members of FPGs (51 respondents). Semi-structured questionnaires were designed, pre-tested, and administered to banana farmers in the Xepon District of Savannakhet Province. A questionnaire was developed to collect data on banana farmers and banana farm characteristics by conducting face-to-face interviews.

### Analytical Techniques

The analysis was performed using descriptive statistics and a binary logistic regression model. First, we used an independent sample t-test and a chi-square test to compare the differences in explanatory variables between the two categories. Then, we estimated the parameters and marginal effect using a logistic regression model to analyze the factors influencing the decision to participate in the FPGs. Variables included in the model were checked for multicollinearity by considering the variance inflation factor (Nahayo et al., 2017). The goodness-of-fit of the model was tested using the Hosmer-Lemeshow Goodness of fit test (Nahayo et al., 2017). STATA version 14.0 was used to analyze the quantitative data.

### Logistic regression model

The choice of farmers’ participation in the FPGs was analyzed using a logistic regression modelling framework. A binary choice model is appropriate for the analysis given that participation in the FPGs fits into a dichotomous choice (i.e., whether a farmer participates or does not participate in the FPGs), essentially taking on values of zero and one (Cox, 1958;Adenuga, A.H.et al., 2012). The dependent variable is coded 1 if the farmer is a member of an FPGs, and 0 otherwise, contingent upon a set of explanatory variables hypothesized to influence members of the FPGs. This study applies a binary logistic model to analyze the factors affecting the probability of participating in farmer production groups. There is no assumption about how the predictor variables are distributed. Therefore, logistic regression is considered relatively flexible, easy to use, and yields results that are easy to interpret. The model also had no assumptions of linearity or heteroscedasticity. The binary logistic regression model used in this study has been applied in similar studies by (Suvedi et al., 2017;Nahayo et al., 2017;Jack et al., 2020;Okeyo et al., 2020;Tuan & Lee, 2021)

$P i = F ( Z i ) = 1 / 1 + e − ( α + β i X )$
(1)

where Pi is the probability of participating in FPGs, Xi represents the ith explanatory variable, α and βi are the parameters to be estimated, and e is the base of the natural logarithm. The equation can be written in terms of odds ratios and the log of odds as:

$P i 1 − P i = e i Z$
(2)

1−Pi is the probability of households not participating FPGs. Taking the natural log of the equation gives;

$l n ( P i 1 − P i ) = Z i = α + β 1 X 1 + β 2 X 2 + … + β n X n + U i$
(3)

where Ui is the error term, randomly distributed, β1, β2βn are the parameters to be estimated, and X1, X2Xn are the explanatory variables. Based on Equation 3, we specify our model as

$Z i = β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 X 4 + β 5 X 5 + β 6 X 6 + β 7 X 7 + β 8 X 8 + β 9 X 9 + β 10 X 10 + β 11 X 11 + β 12 X 12 + β 13 X 13 + Ui$
(4)

where Zi represents the participation of FPGs (dependent variable); X1 to X13 represent sex (dummy), age (years), educational level (years), household size (number of household members), active labor force (number of active laborers), farmland size (hectares), access to credit (dummy), off-farm income (dummy), farming experience (years), social capital (number of traders), perceived trust (dummy), land tenure (dummy), and constraint to market (distance in km). At the same time, β0 to β13 are the parameters to be estimated, and U1 is the error term.

## RESULTS AND CONSIDERATION

### Characteristics of respondents and variables summary

The variables selected for the study are summarized in Table 1. The majority of the sampled households participated in FPGs (63%). Previous studies (Awotide et al., 2014;Ghimire et al., 2015;Mariano et al., 2012;Okeyo et al., 2020) have shown that sex affects adoption decisions. Therefore, we draw an a priori expectation that sex can positively or negatively affect participation in FPGs. Larger number of male farmers (64%) than female farmers constituted the respondents. The mean age was 46-yearold, implying that farming was mainly dominated by the elderly. Based on previous findings, we hypothesized that age could have either a positive or negative effect on participation in FPGs. The average duration of schooling was 4.9 years. Education was hypothesized to affect participation since it positively enhances awareness. Based on several recent studies, the remaining variables, including household size, active labor force, farmland size, access to credit, off-farm income, social capital, perceived trust, and land tenure, have also been hypothesized to have positive effects on participation decisions (Gijselinckx & Bussels, 2014;Gyau et al., 2016;Luu, 2020;Mojo et al., 2017;Nugusse et al., 2013). However, the variable of constraint to market was hypothesized to have negative effects on the participation of FPGs.

### Comparison between membership and non-membership characteristics

This section discusses the data on the demographic and socioeconomic characteristics of households, collected using survey questionnaires. Interviewed households were classified into category (FPGs members) and comparison (FPGs non-members) groups. The findings in Table 2 reveal that the education level of the headed household was literate. The mean years of schooling were found to be 3.176 and 6.022 for non-membership and membership, respectively, which was significant at 1% level. The average farmland size of memberships had a larger size averaging 2.16 hectares, compared to 1.72 hectares for nonmemberships of FPGs. The variations in farmland size were significant at 1% level of significance. Regarding access to credit, 75% of FPGs memberships had access to credit compared with 53% of the FPGs non-memberships. Access to credit enables farmers to purchase farm input. Furthermore, in terms of perceived trust in the group’s collective action, 91% of FPGs membership trusted in the group operation and action of the whole group compared to 56% of the FPGs non-memberships. The findings also revealed that the constraint to market for farmers with membership and without membership was significantly different. The non-members of FPGs faced a more significant constraint to market than those with group membership.

### Determinants of farmers’ decisions to participate in the FPGs

The logistic regression analysis results, showing the parameter estimates and marginal effects, are presented in Table 3. The likelihood ratio statistic suggested that the model was significant (p < 0.01). Additionally, in terms of the variance inflation factor values, there was no collinearity among the independent variables of the estimated model (Nahayo et al., 2017). Table 3 presents an inferential goodness-of-fit test, the Hosmer-Lemeshow test. Hosmer- Lemeshow test statistics 5.44 was insignificant (p>.05), suggesting that the model fits the data well.

Farmland size positively and significantly influenced farmers’ decisions to participate in FPGs (P<0.05). Additionally, in terms of land acquisition, the odds ratio showed that farmers who inherited or bought the land were 3.137 times more likely to participate in the FPGs. The marginal effect result revealed that a one-hectare increase in the size of the land size allocated to banana production would increase FPGs participation by 12.4%. This implies that the greater the proportion of land inherited or purchased, the higher the significant participation. Moreover, farmland size can be considered as a proxy for the level of commercialization. Therefore, farmers who cultivate an additional hectare of land tend to shift away from subsistence production and are more likely to participate in the agriculture project to access inputs, technology, and market ( Etwire et al., 2013). This result is contrary to a previous study, which found that households with large farmlands (above average) do not want to participate in collective action societies (Nugusse et al., 2013). The argument might differ from the study's result because the condition and characteristics of the farmer group in the previous research are different from this study's case. Most of the banana farmers group in the study site are still young. Some of the more prominent landowners expect to benefit from the program in terms of input support, such as banana shoots (in the early stage of the program). Furthermore, continuous acquisition of extensive farmland and production throughout the year led them to earn benefits for their household, as well as obtain technical support from local extension services programs. Therefore, they prefer to keep their membership.

Perceived trust had a positive and significant influence on farmers’ decisions to participate in FPGs (P<0.01). The odds ratio shows that the farmers who trust in group actions are about 15.96 times more likely to participate in an FPGs. The marginal effect result revealed that farmer who trust in group’s action will increase FPGs participation by 30.1%. This was probably due to group members' trust in their - committee. They are village leaders trusted by people. The group leaders will also be a person devoted to developing their homeland and bringing them out of poverty. However, sometimes, they trust their neighbors who have joined the group as they see them acquire benefit from participation. Similarly, Castella & Bouahom (2014) reported that local leadership plays a crucial role in connecting group members to villages and district institutions. A strong local group leader is also a key person in building a cohesive community, leading to members' trust in the groups' activities. However, recent studies have revealed that there is no significant relationship between perceived trust in collective action and the possibility of participating in the farmer organization (Luu, 2020).

Constraint to market is negatively and significantly associated with a household head’s decision to participate in FPGs at p < 0.01. The negative β- coefficient of -0.66, associated with a unit of one-kilometer increase in the distance to the agricultural input/output market, can decrease the probability of participating in FPGs action by 48.4%. The marginal effect result revealed that an increase in the distance to the agricultural input/output market will decrease the probability of participating in FPGs by 7.1 %. This indicates that farmers who live near the market are more likely to join FPGs than those live further away. This is because of access to better information access on the dynamics of market forces. Furthermore, a farmer's accessibility to input and output markets likely affects transaction costs, and subsequently, the likelihood of FPGs action, as supported by Luu (2020), who revealed similar findings.

## 적 요

라오스 정부는 농업인들이 생계농에서 상업농으로 전환할 수 있도록 돕기 위하여 전국에서 농업인 조직을 장려하고 있으 며, 이에 따라 농업인 생산조직이 출현되었고 점차 강한 형태 의 협동조합이나 연합체로 전환되고 있음.

본 논문의 목적은 농업인 생산단체(FPG)에서의 농업인 참 여가 영향을 미치는 요인을 탐구하는 것에 있음.

이를 위하여 1차 및 2차 데이터를 활용하였으며, 제폰 지역 의 3 쿰반(마을그룹)에서 현장조사를 통하여 140명의 응답 데 이터를 수집하였고, 이 데이터를 기술통계(t 검정)과 이항로짓 회귀모델을 사용하여 분석하였음.

먼저 평균에 대한 t 검정 결과, 농업인 생산단체 회원과 비 회원은 세대주 교육수준, 농지규모, 신용접근도, 인지된 신뢰도, 시장에 대한 제약 변수들에서 유의한 차이가 있는 것으로 나 타났고, 그 중 시장에 대한 제약 변수에서는 FPG 비회원이 회원보다 평균값이 높았음.

또한 이항로짓모형 결과에서는 세대주의 교육수준, 농지 규 모, 신용 접근성, 인지된 신뢰, 시장에 대한 제약 변수들이 농 업인의 FPG 참여를 결정하는 유의한 요인인 것으로 나타났음.

본 연구는 생산단체에서 농업인 참여에 대한 이해도를 높이 고, 라오스 정부가 FPG에 농업인 참여를 향상시킬 수 있도록 신용접근도, 농업인 교육, 시장 통합을 고려하고 촉진해야 한 다는 점을 실증적 연구로서 기여하였음.

## ACKNOWLEDGMENTS

The authors thank the Korean Council for University Education (KCUE) which provided the Higher Education for ASEAN Talents (HEAT) scholarship for pursuing a Ph.D. in Economics at the Department of Agricultural and Resource Economics, Kangwon National University, South Korea.

## Figure

Map of the study area, Source: Inthavong et al.(2017).

## Table

Respondent characteristics (all cases), Source: Surveyed 2019.

Comparisons of the means of explanatory variables between groups, Source: Survey 2019.

Parameter estimates and marginal effects of the explanatory variables from the binary logit regression model, Source: Surveyed 2019.

## Reference

1. Abdoulaye, T. , Abass, A. , Maziya-Dixon, B. , Tarawali, G. , Okechukwu, R. , Rusike, J. , Alene, A. , Manyong, V. , Ayedun, B. 2014. Awareness and adoption of improved cassava varieties and processing technologies in Nigeria. J. Dev. Agri. Eco. 6:67- 75.
2. ADB.2014. Sustainable Natural Resources Management and Productivity Enhancement Project Banana, Sweet Potato, and Peanut Value Chain Development. Ministry of Agriculture and Forestry. p.219.
3. ADB.2018. Agriculture, Natural Resources, and Rural Development Sector Assessment, Strategy, and Road Map. Asian Development Bank: Manila, Philippines. p.42.
4. Adenuga, A.H. , Omotesho, K.F. , Olatinwo, K.B. , Muhammad- Lawal, A. , Fatoba, I. 2012. Determinants of Fertilizer Usage in Dry Season Amaranthus Vegetable Production in Kwara State, Nigeria. Afri. J. 12:126-134.
5. Agbonlahor, M.U. , Enilolobo, O.S. , Sodiaya, C.I. , Akerele, O. , Oke, J.T. 2012. Accelerating rural growth through collective action: groups’ activities and determinants of participation in southwestern Nigeria. J. Rur. Soc. Sci. 27:114-136.
6. Arayesh, B. 2011. Identifying the factors affecting the participation of agricultural cooperatives’ members. Am. J. Agri. Bio. Sci. 6:560-566.
7. Aref, F. 2011. Barriers to community capacity building for tourism development in communities in Shiraz, Iran. J. Sus. Tou. 19:347-359.
8. Awotide, B.A. , Abdoulaye, T. , Alene, A. , Manyong, V.M. 2014. Assessing the extent and determinants of adoption of improved cassava varieties in south-western Nigeria. J. Dev. Agri. Eco. 6:376-385.
9. Barrett, C.B. 2008. Smallholder market participation: Concepts and evidence from eastern and southern Africa. Food Pol. 33:299-317.
10. Besley, T. 1995. Property rights and investment incentives: Theory and evidence from Ghana. J. Poli. Eco. 103:903-937.
11. Bollen, K.A. , Hoyle, R.H. 1990. Perceived Cohesion: A Conceptual and Empirical Examination. Soci. For. 69:479-504.
12. Broussard, N.H. 2012. Food aid and adult nutrition in rural Ethio pia. Agri. Eco. 43:45-59.
13. Bui Tan, Y. , Jerome, V. , Anousith, K. , Grant, A. , Mienmany, S. , Silivong, P. , Khamphamy, K. , Alice, F. , Leocadio, S. 2015. Situation Analysis and Needs Assessment Report for Pailom village and Savannakhet province – Laos. CGIAR Research Program on Climate Change, Copenhagen, Denmark, Agriculture and Food Security (CCAFS). p.44.
14. Castella, J.C. , Bouahom, B. 2014. Farmer cooperatives are the missing link to meet market demands in Laos. Dev. Prac. 24:185-198.
15. Cechin, A. , Bijman, J. , Pascucci, S. , Zylbersztajn, D. , Omta, O. 2013. Drivers of pro-active member participation in agricultural cooperatives: Evidence from Brazil. An. Pub. Coop. Eco. 84:443-468.
16. Cox, D.R. 1958. The Regression Analysis of Binary Sequences. J. Roy. Stat. Soci. 20:215-232.
17. DAFO.2020. Agriculture, forestry and rural Development annual report 2020 and Direction 2021. Department of Agriculture and Forestry: Savannakhet, Lao PDR. p.41.
18. DRDC.2018. Final Report on 2018 KAPEX Joint Research: Lao PDR. Department of Rural Development and Cooperatives: Vientiane, Lao PDR. p.103.
19. Esayas, B. 2017. Determinants of Women’ s Participation in Agricultural Cooperatives Activities: The Case of Sodo Zuria Woreda, Wolaita. J. Cul. Soci. Dev. 27:27-35.
20. Etwire, P.M. , Dogbe, W. , Wiredu, A.N. , Martey, E. , Etwire, E. , Robert, K. , Wahaga, E. 2013. Factors Influencing Farmer’ s Participation in Agricultural Projects: The case of the Agricultural Value Chain Mentorship Project in the Northern Region of Ghana. J. Eco. Sus. Dev. 4:36-44.
21. Fehr, B.E. , Gachter, S. 2000. Cooperation and Punishment in Public Goods Experiments. Am. Eco. Asso. 90:980-994.
22. Ghimire, R. , Huang, W.C. , Shrestha, R.B. 2015. Factors Affecting Adoption of Improved Rice Varieties among Rural Farm Households in Central Nepal. Rice Sci. 22:35-43.
23. Gijselinckx, C. , Bussels, M. 2014. Farmers’ cooperatives in Europe: Social and historical determinants of cooperative membership in agriculture. Ann. Pub. Coop. Eco. 85:509-530.
24. Gyau, A. , Mbugua, M. , Oduol, J. 2016. Determinants of participation and intensity of participation in collective action: Evidence from smallholder avocado farmers in Kenya. J. Cha. Net. Sci. 16:147-156.
25. Hagos, K. , Tesfaye, L. , Girmay, T. 2018. Determinants of smallholder farmers participation in seed producing cooperatives in Southern Zone of Tigray, Ethiopia. J. Agri. Ext. Rur. Dev. 10:75-83.
26. Hansen, M. 2002. The impact of trust on cooperative membership retention, performance, and satisfaction: an exploratory study. Int. Food Agri. Manag. Re. 5:41-59.
27. Inthavong, N. , Nonaka, D. , Kounnavong, S. , Iwagami, M. , Phommala, S. , Kobayashi, J. , Hongvanthong, B. , Pongvongsa, T. , Brey, P.T. , Kano, S. 2017. Individual and household factors associated with incidences of village malaria in Xepon district, Savannakhet province, Lao PDR. Tropi. Medi. Heal. 45:1-11.
28. Jack, C. , Adenuga, A.H. , Ashfield, A. , Wallace, M. 2020. Investigating the Drivers of Farmers’ Engagement in a Participatory Extension Programme: The Case of Northern Ireland Business Development Groups. Sustainability. 12:1-15.
29. Karli, B. , Bilgiç, A. , Çelik, Y. 2006. Factors affecting farmers’ decision to enter agricultural cooperatives using random utility model in the South Eastern anatolian region of Turkey. J. Agri. Rur. Dev. Trop. Subtro. 107:115-127.
30. Kassie, M. , Jaleta, M. , Shiferaw, B. , Mmbando, F. , Mekuria, M. 2013. Adoption of interrelated sustainable agricultural practices in smallholder systems: Evidence from rural Tanzania. Techno. Forecas. Soci. Cha. 80:525-540.
31. Khonje, M. , Manda, J. , Alene, A.D. , Kassie, M. 2015. Analysis of Adoption and Impacts of Improved Maize Varieties in Eastern Zambia. World Development. 66. pp.695-706.
32. Luu, T.D. 2020. A Multinomial Logit Model Analysis of Farmers’ Participation in Agricultural Cooperatives: Evidence from Vietnam. App. Eco. J. 27:1-22.
33. MAF.2010. Strategy for Agricultural Development 2011 to 2020 Sector Framework, Vision and Goals. Ministry of Agriculture and Forestry: Vientiane, Lao PDR. p.67.
34. Mariano, M.J. , Villano, R. , Fleming, E. 2012. Factors influencing farmers’ adoption of modern rice technologies and good management practices in the Philippines. Agri. Sys. 110:41-53.
35. Markelova, H. , Meinzen-Dick, R. , Hellin, J. , Dohrn, S. 2009. Collective action for smallholder market access. Fo. Poli. 34:1- 7.
36. Mojo, D. , Fischer, C. , Degefa, T. 2017. The determinants and economic impacts of membership in coffee farmer cooperatives: recent evidence from rural Ethiopia. J. Rur. Stu. 50:84- 94.
37. Nahayo, A. , Omondi, M.O. , Zhang, X. , Hui, L.I. , Qing, L. , Pan, G.X. , Joseph, S. 2017. Factors influencing farmers’ participation in crop intensification program in Rwanda. J. Integ. Agri. 16:1406-1416.
38. Nugusse, W.Z. , Van Huylenbroeck, G. , Buysse, J. 2013. Determinants of rural people to join cooperatives in Northern Ethiopia. Int. J. Soci. Eco. 40:1094-1107.
39. Okeyo, S.O. , Ndirangu, S.N. , Isaboke, H.N. , Njeru, L.K. , Omenda, J.A. 2020. Analysis of the determinants of farmer participation in sorghum farming among small-scale farmers in Siaya County, Kenya. Sci. Afri. 10 e00559:1-10.
40. Oladejo, J.A. , Olawuyi, S.O. 2011. Analysis of Women Participation in Agricultural Production in Egbedore Local Government Area of Osun State, Nigeria Analysis of Women Participation in Agricultural Production in Egbedore Local Government Area of Osun State, Nigeria. Int. J. Agri. Eco.Rur. Dev. 4:1-12.
41. Onphanhdala, P. , Philavong, V. , Phomvixay, P. 2016. Promoting Farmer Production Group and Written Contract Farming as Key Success Factors: Evidence from NRI Nam Bak Subproject. North Rural Infrastructure Development Sector Project (NRI): Vientiane, Lao PDR. p.26.
42. Poulton, C. , Dorward, A. , Kydd, J. 2010. The Future of Small Farms: New Directions for Services, Institutions, and Intermediation. World Development. 38. pp.1413-1428.
43. Pretty, J. , Toulmin, C. , Williams, S. 2011. Sustainable intensification in African agriculture. Int. J. Agri. Sus. 9:5-24.
44. Rao, E.J.O. , Qaim, M. 2011. Supermarkets, Farm Household Income, and Poverty: Insights from Kenya. World Development. 39. pp.784-796.
45. Rasabandith, S. 2020. Experiencing exchange about groups and cooperatives development in 18 province, during 22-23 December, the Department of Rural Development and Cooperatives (DRDC), Ministry of Agriculture and Forestry (MAF): Vientiane, Lao PDR. p.19.
46. Roy, D. , Thorat, A. 2008. Success in High Value Horticultural Export Markets for the Small Farmers: The Case of Mahagrapes in India. World Development. 36. pp.1874-1890.
47. Subedi, R. 2008. Women Farmers’ Participation in Agriculture Training : in Kavre District of Nepal. Larenstein University of Applied Sciences: Wageningen, The Netherlands. p.85.
48. Suvedi, M. , Ghimire, R. , Kaplowitz, M. 2017. Farmers’ participation in extension programs and technology adoption in rural Nepal: a logistic regression analysis. J. Agri. Edu. Ext. 23:351- 371.
49. Todaro, M. , Smith, S. 2011. Economic Development, 11th Edition. Pearson Education. Inc. Boston, USA. p.829.
50. Tuan, C.M. , Lee, S.H. 2021. Factors Affecting Organic Fertilizer Adoption in Rice Production in Vietnam. J. Korean Soc. Int. Agric. 33:130-138.
51. Weinberger, K. , Jütting, J.P. 2001. Women’s participation in local organizations: Conditions and constraints. Wor. Dev. 29:1391-1404.
52. Wongnaa, C.A. , Awunyo-Vitor, D. , Bakang, J.E.A.2018. Factors affecting Adoption of Maize Production Technologies: A study in Ghana. J. Agri. Sci. 13:81-99.
53. World Bank.1993. World Development Report Investing in Health. New York, USA. Oxford University Press. New York, USA. p.348.
54. Yi, H. , Guma Allen, P. , Lee, S. 2017. Operating Cases of Farmer Organizations in the Rice Industry of Korea and Its Implication for Uganda. J. Korean Soc. Int. Agric. 29:217-225.
55. Zheng, S. , Wang, Z. , Awokuse, T.O. 2012. Determinants of producers’ participation in agricultural cooperatives: Evidence from Northern China. App. Eco. Persp. Pol. 34:167-186.