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This study explores the integration of smallholder farmers into the export market in Vietnam, an emerging economy. By introducing a prospective framework, we seek to provide insight into factors that influence this integration process.

This study examines the expected growth and entry of Vietnamese smallholder farmers into high-value export markets. We collected information from 200 independent farmers as well as from five local extension workers, who provided information on 50 farmers.

The study reveals that the adoption of new business models is more influential than the variables traditionally included in models of export-market integration in predicting expected growth and entry into high-value export markets. In addition, the results highlight divergent views between farmers and extension workers regarding the role of collectors, with farmers perceiving collectors as potential partners, while extension workers see them as impediments to growth.

The prospective model presented in this study highlights the importance of policy interventions aimed at promoting new business models and addressing infrastructure and capital constraints for the sustainable transformation of agricultural sectors in emerging markets.

This is one of the first articles to apply a prospective approach to export-market integration and demonstrate its efficacy through an empirical study. The suggested prospective approach could facilitate the design of policies aimed at export-market integration within the context of dynamic, emerging markets.

Despite the abundance of small-scale farms in the USA and their importance for both rural economic development and food availability, the extensive research on small business management and entrepreneurship has mostly neglected the agricultural context, leaving many of these farms' business challenges unexplored. The authors focus on informing a specific decision faced by small farm managers: selling directly to consumers (i.e. farmer's markets) versus selling through aggregators. By collecting historical data and a series of interviews with industry experts, the authors employ simulation methodology to offer a framework that advises how small-scale farmers can allocate their product across these two channels to increase revenue in a given season. The results, which are relevant for operations management, small business management and entrepreneurship literature, can help small-scale farmers improve their performance and compete against their larger counterparts.

The authors rely on historical and interview data from key industry players (an aggregator and a small farm manager) to design a simulation analysis that determines which factors influence season-long farm revenue performance under varying strategies of channel allocation and commodity production.

The model suggests that farm managers should plan to evenly split their production between the two distribution channels, but if an even split is not possible, they should plan to keep a larger percentage in the nonaggregator (farmers' market/direct) channel. Further, the authors find that farmers can benefit significantly from a strong aggregator channel customer base, which suggests that farmers should promote and advertise the aggregator channel even if they only use it for a limited amount of their product.

The authors integrate small business management and operations management literature to study a widely understudied context and present practical implications for the performance of small-scale farms.

The global organic market is expanding, and India is in an advantageous position with the highest number of organic producers worldwide. Although many articles have been published on the value chain of organic products from India, no significant studies were found related to the value chain analysis of organic pineapple. This study aims to know the various aspects of the organic pineapple value chain, i.e. network structure, value addition at various stages of chain actors, value chain upgradation and governance structure.

The study is explorative in nature, and primary data from various actors involved in the chain is collected and analyzed. Primary data through a structured schedule and interviews are collected from farmers and traders. A multistage sampling plan has been adopted. A sample of 75 farmers was randomly selected from the study area. For traders, snowball sampling is used due to the nonavailability of the sampling frame. A total of 10 commission agents, 10 wholesalers and 20 retailers were thus selected for the study. For objectives 1 and 4, descriptive statistics are used. For objective 2, a modified formula described by (Murthy et al., 2007) is used to calculate farmer’s net price and marketing margin. For objective 3, Garrett’s ranking technique is used to identify various constraints in upgrading the organic pineapple value chain in Assam.

This study shows that the value chain of organic pineapple is in the initial stage and proper value addition is required to have a complete regulated value chain. Six marketing channel is identified, and products are sold through farmer producer company only in case of export and trade with distant buyers. The marketing efficiency for channels II and III is 1.69 and 0.99, respectively. The degree of value addition for channel II in the hands of the commission agent, wholesaler and retailer is 11.65%, 4.56% and 12.60%, respectively. In the various constraints in upgrading the value chain, farmers rank “policy support” as a major constraint. In the governance structure, trade with distant traders and exports is done formally and through written contracts.

The study performs value chain analysis of organic pineapple in Cachar district of Assam, India for the year January 2022–January 2023. Future studies are encouraged related to various aspects of the supply chain and value chain of organic pineapple from various northeastern states of India and other states.

The study will help policymakers and key actors to know the existing chain and frame a well-coordinated and regulated value chain.

This study is one of the first study to explore the value chain of organic pineapple of Cachar district of Assam, India. Implementation of these findings can help various actors to strengthen the existing value chain.

In Uganda, smallholder farmers produce about 70% of the food but receive very low prices on the sales channels they use. To improve farmers' livelihoods, other innovative sales channels such as farmers' markets (FMs) have to be explored. Therefore, the study aimed to determine the potential of establishing farmers' markets in Uganda, focusing on Kampala district.

A qualitative methodology was used to understand farmers' perceptions and the factors that could influence the success of the farmers' markets. In addition, the potential of FMs has been analyzed through the components of the Localized Agri-Food System (LAFS).

The respondents had a positive perception of the farmers' markets, and farmers have an urgent need for other sales channels that could be profitable for them. Factors that could influence the success of the FMs were highlighted, including infrastructure in the marketplace, transportation and taxation by the government.

The study had a limited sample size of the farmers, and it was carried out in 3 divisions of Kampala district, an urban area; thus, the findings cannot be generalized to fit the other regions of Uganda.

The LAFS showed that the foundation for establishing FMs is available; however, it has to be strengthened through the coordination of different stakeholders that work with the farmers.

The study aims to analyze the impact of market participation on small paddy farmers' income and consumption expenditure. The study also estimates various determinants affecting the market participation of smallholders. Further, the study computes the efficiency of different paddy marketing channels and identifies the determinants that impact the marketing channel selection of paddy growers in Eastern India.

The study used the propensity score matching (PSM) approach to measure the impact of market participation on farm income and per capita consumption. Further, the study employed Acharya and Aggarwal's composite index approach to estimate the marketing efficiency of various paddy marketing channels. Further, a multinomial logit model was used to determine the marketing channel selection constraints.

The outcomes indicate that market participation positively impacts farm income and consumption expenditure. Education, membership in farmers' organizations, price information and distance to the marketplace significantly affect farmers' market participation. The results show that the producer–retailer marketing channel is the most efficient compared to others. However, most paddy farmers sell paddy to farmgate collectors due to a lack of market information, vehicle ownership, storage system, and inability to take the risk of venturing out of the farmgate into markets.

The study uses primary data and captures only farmers' perspectives to measure the impact of market participation, marketing channel efficiency and determinants for market channel selection. The other stakeholder's perceptions can be included in future studies.

Rarely does any study identifies the efficiency of different marketing channels for paddy farmers in India and includes cognitive factors like risk perception and trust in buyers as constraints for market channel selection.

This study examines the drivers of marketing channel participation amongst smallholder rice farmers in the northern region of Ghana.

Using a sample of 397 smallholder rice farmers drawn from a multistage sampling procedure, the study employed multivariate probit (MVP) model in the empirical estimations. In this context, the model assumes that a rice farmer should at least participate in one market channel.

The study reveals that the wholesaler market channel is the most commonly used channel among farmers, followed by the aggregator channel. However, the processor market channel is the least patronized one in the study area. The results also show statistically significant correlation coefficients in four out of the six possible combinations, implying that market channel participation among smallholder farmers is not mutually exclusive. Rice market channel participation is positively and significantly influenced by age, gender, household headship, access to credit, extension service, irrigation, improved seed and access to price information.

This study is limited in extending the analysis to include the impact of market channels on some outcome measures. This is due to data limitation.

The findings of this study add to the growing literature on smallholder market channel participation in Ghana.

Farmers' risk attitudes and risk perceptions play an essential role in shaping risk management strategies to address risks and uncertainties. Contract farming is considered as one of the feasible approaches to tackle farmers' concerns. However, risk perspectives under various categories have not been included in studies on farmers' preferences for contract farming in the literature, especially in Vietnam. This study aims to determine factors affecting farmers' choices of different contract farming practices.

The explanatory factor analysis (EFA) and multinomial logit model (MNL) were applied to explore the impacts of risk perspectives on farmers' preferences for contract farming. Data have been collected from 211 rice farmers in An Giang Province, “the rice bowl” of the Mekong Delta, Vietnam.

The study found that farm size, cooperatives, extension, market access and trust have significantly impacted on contract participation while a delay payment was a barrier for farmers' motivation to opt for the contract. Farmers' contract choices were also influenced by their risk attitudes and perceptions under different risk dimensions. The financial, policy and human risk-averse behavior predisposed farmers to single out the full contract while the policy and human risk-loving and production, market and finance risk-averse respondents were in favor of the marketing contract. Moreover, the findings indicated that the more farmers concerned about risk of weather and market, the more choices for the full contract, whereas the risk perceptions of weather and policy encouraged farmers to use the limited contract. By contrast, farmers who perceived the impacts of risk of diseases/pests and human were likely to adopt the marketing contract.

This study just focuses on collecting data from farmers’ perspective. Future studies involving stakeholders such as enterprises and policy makers are strongly recommended so as to design suitable contracts and enforce contract schemes effectively in Vietnam.

The findings also contribute to the literature on different types of contracts and the multidimensional aspect of risk for rice production in Vietnam.

This study analyzes the impact of COVID-19 on agricultural production in South India by evaluating the influence of market channels and socioeconomic conditions on the production decisions of farmers during two key cropping seasons. We base our analysis on primary data from 200 marginal, small and medium farmers, primarily focusing on the key seasonal crops, namely paddy and black gram.

We studied the downstream supply chains of paddy and black gram crops in the district of Villupuram, situated in the South Indian state of Tamil Nadu. Using a Bi-Probit model, we analyzed the production decisions of marginal, small and medium farmers engaged in paddy and black gram cultivation. Various factors are considered, including farmers’ socioeconomic characteristics, gender, market channels accessed and the coping strategies employed.

After the easing of lockdown measures in June 2020, our research revealed substantial disruptions in agricultural production during the critical Kharif and Rabi seasons. Most farmers refrained from returning to their fields during the Kharif season; those who did produced millet as the main crop. Factors such as choice of market channels in previous seasons, economic status, access to all-weather roads, labor availability, gender and coping strategies played an important role in the return to production in the subsequent Kharif and Rabi seasons.

Our data revealed several interesting threads related to price volatility, irrigation and access to markets and their impact on food security. The role of intermediaries and market channels in providing liquidity emerges as an important aspect of farmers' choice of markets. The pandemic impacted all these factors, but a detailed analysis was beyond the scope of this study.

We also find that resilience to economic shocks varies not only by economic status but also by gender and social groups. Farmers with female members are more likely to be resilient, and marginal and small farmers primarily belong to social groups that are economically less developed.

This study contributes to the literature on factors influencing farmer choice and decision-making and provides nuances to discussions by analyzing crop-specific supply chains, highlighting the critical role of socioeconomic factors. It also highlights the role of demographics and infrastructural factors like access to all-weather roads and access to markets that influence farmers’ production decisions.

The aim of this study was to determine the impact of oral communication in improving the marketing and financial management skills of sweet potato farmers.

The study used a quasi-experimental design with a pre- and post-test approach. The sample was 540 sweet potato farmers that were randomly assigned to training (n = 270) and no-training groups (n = 270) with analysis of covariance (ANCOVA) as the method of data analysis. While the training group received oral training sessions for three farming seasons beginning in 2019, 2020 and 2021, the no-training group did not receive any intervention.

Before the training sessions, all the sweet potato farmers scored low on marketing skills like advertising, sales promotion and sales forecasting. Both groups also scored low on financial management skills like budgeting, investments, saving and controlling expenditures. Their annual income level was also low and both groups did not significantly differ. However, after the training and during the follow-up evaluation, the participants in the training group reported a significant improvement in their marketing skills and financial management skills. There was also an improvement in their income level from $238 (N109,480) at baseline to $523 (N240,580) after the training and $782 (N359,720) after the follow-up evaluation. On the other hand, the no-training group reported a staggered fluctuation in their income of $241 (N110,860) at baseline, $371(N170,660) during post-training evaluation and $214 (N98,440) at follow-up assessment.

The first limitation is that the study examined only one crop. There is a need to pay attention to farmers of other crops for better understanding. Another limitation of the study is that the researchers examined only oral communication. There is a need to compare more than one training to understand which is more effective. Finally, the current study did not consider the moderating effect of other factors like the source of labour and expenses.

This study has shown that oral communication is an effective tool for promoting the acquisition of marketing and financial management skills and enhancing agribusiness.

The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

The price of vegetables is not considered as the government sets the base price for the vegetables.

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.