Volume 20, Issue 2

Recent academic research presents a large volume of studies on the organic market from the perspective of consumers’ motivation and purchasing preferences. However, these studies adopt a competitive or cooperative approach, but overlook the institutional complexity of emerging markets. This study aims to investigate the organic food production chain of emerging countries from the perspective of coopetition, taking into account the influence of formal institution agents. We focused our analysis on the chain of the organic food products, with the organic ice-cream producer as the key node of the chain. It is a single-case study conducted through in loco interviews with participants in the organic food products’ chain, along with secondary data. The results show the influence of formal institution agents and non-financial incentives as primary drivers of entrepreneurial strategic decisions. We contributed to the coopetition literature by demonstrating the influence of formal institution agents on value creation and value capture in the organic chain.

An important particularity of the dairy chain is that many times the main interest of the dairy industry relies on milk components, the so-called milk solids. Paying for milk solids content is a way of trying to create incentives for farms to invest in improving the solids content. However, little is known about the effects of this type of payment on the dairy supply chain. The paper proposes a microeconomic model to analyze the effects of paying for milk solids content on the performance of farms, dairy processors and consumer welfare. Based on the model, we find that this mechanism improves the yield of milk in producing dairy products and benefits farms, processor and consumers simultaneously. Extensions demonstrate the robustness of results and provide a generalized model and conditions for which these results are valid.

The organized Exchange spot (cash) cheese market is a private industry institution that has historically performed a primary price discovery function in the U.S. dairy industry. In addition to affecting cheese prices in contract cheese market, the Exchange spot cheese prices have influenced prices paid for milk at the farm level that are set within the system of Federal Milk Marketing Orders (FMMOs). The effects that the Exchange spot cheese market has on FMMOs milk pricing attract increased attention in light of the recent concerns about increasing milk price volatility and its effect on the dairy farm profitability. While the design of milk pricing within FMMOs has evolved over the last three decades, the effects of the Exchange spot cheese prices on FMMOs milk pricing have intensified. The research analyzes the conduct and performance of the Exchange spot cheese market during three FMMOs milk pricing regimes (Minnesota-Wisconsin price regime, Basic Formula Price and modern Multiple Component Pricing regime), with a particular focus on the Exchange spot cheese price behavior.

This paper reviews rice procurement operations of Government of India from the standpoints of cost of procurement as well as effectiveness in supporting farmers’ incomes. The two channels in use for procuring rice till 2015, were custom milling of rice and levy. In the first, the government bought paddy directly from farmers at the minimum support price (MSP) and got it milled from private millers; while in the second, it purchased rice from private millers at a pre-announced levy price thus providing indirect price support to farmers. Secondary data reveal that levy, despite implying lower cost of procurement was discriminated against till about a decade back and eventually abolished in 2015 in favor of custom milling, better trusted to provide minimum price support. We analyze data from auctions of paddy from a year when levy was still important to investigate its impact on farmers’ revenues. We use semi-nonparametric estimates of millers’ values to simulate farmers’ expected revenues and find these to be rather close to the MSP; a closer analysis shows that bidder competition is critical to this result. Finally, we use our estimates to quantify the impact of change in levy price on farmers’ revenues and use this to discuss ways to revive the levy channel.

Many studies show that participation in contract farming has positive impacts on farm productivity and incomes. Most of the literature, however, does not take into account that contracts vary in their specifications, making empirical evidence scarce on the diverse impacts of different types of contracts. In this study, we investigate the driving forces of participation in marketing and production contracts, relative to spot markets. We also study the extent to which different contract types add additional benefits to smallholder farmers, using recent survey data of 389 cashew farmers in Ghana. To account for selection bias arising from observed and unobserved factors, we apply a multinomial endogenous switching regression method and implement a counterfactual analysis. The empirical results demonstrate that farmers who participate in production contracts obtain significantly higher cashew yields, cashew net revenues, and are more food secure compared to spot market farmers. We also find substantial heterogeneity in the impact of marketing and production contracts across scale of operation. Small sized farms that participate in production contracts tend to benefit the most. Marketing contracts, however, do not appear to benefit cashew farmers.

The research aims of this study are bi-fold: to study factors influencing the uptake of contract farming (CF) and to compare the predicting power of the artificial neural network model (ANN) and the Multinomial Logit Model (MNL) on predicting CF participation in the Mekong Delta, Vietnam. ANN and MNL were employed to analyze on the basis of the transaction cost theory. To validate the ANN, a 10-fold cross-validation procedure was applied to avoid model overfitting. The sensitivity analysis of ANN was used to elicit the magnitude of the correlation between predictors. Multicollinearity was examined with all VIFs lower than two. Among predictors, the most influential roles of the cooperatives and the extension agents/services in supporting CF participation are reported. Also, farmers who conduct frequent access to the market incline to participate in CF. Risk perceptions and preferences are dissimilar across domains, which are also mainly interpreted that risk-averse farmers tend to opt for CF as an effective solution to risks perceived. Thus, heterogeneous approaches should be tailored to promote CF. The findings suggest that MNL outperforms ANN in terms of accuracy percentage and mean absolute error (MAE). However, this result should not be generalized base on the constraint of the data threshold as articulated in the study. The sensitivity analysis of ANN and the estimation results of the MNL relatively agreed on the importance of model predictors. This study is the first to investigate the impacts of the domain-specific risk perceptions and attitudes on CF and also contribute to the debate over the performance between the conventional econometric models versus machine learning techniques.

An empirical specification of the conjectural variations model, which conforms to microeconomic theory, is estimated for the US catfish industry. We find the existence of market power exerted by US catfish processors. Processors force the price paid to catfish growers down by 52.88 cent per pound of live catfish, which costs US catfish growers about $300 million a year. US catfish growers can deter the negative effects of processors’ market power by increasing their farm supply flexibility. Further studies are needed to address the dynamics and competitive game strategies in the US catfish industry.