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PurposeThe objective is to have a better understanding of the impacts of the COVID-19 pandemic on food supply chain in Wuhan.Design/methodology/approachThrough a simplified flow, the authors qualitatively analyze the impacts of the COVID-19 pandemic on food supply chain. Data was gathered through a telephone survey of food suppliers in Wuhan.FindingsThe prevention measures of the COVID-19 pandemic had negative impacts on food supply chain in Wuhan. About 83.1% of food suppliers experienced a decrease in revenues. This is influenced by factors including food category on sale, purchase channel of food, food supplier's household registration and the number of the COVID-19 patients in the located community.Research limitations/implicationsDue to the limitation of available data, there is a lack of quantitative analysis on the impact on food supply chain. The sample size of food suppliers is limited.Practical implicationsThis study identifies the challenges in the food supply chain resulting from the control measures implemented during the COVID-19 pandemic in Wuhan and provides a reference for the design of control measures in other regions.Originality/valueThis study supplements the literature regarding the impact of public health emergencies such as the COVID-19 pandemic on food supply chain, especially food suppliers' revenues.

Farmers in Pakistan continue to produce maize under various types of risks and adopt several strategies to manage those risks. This study is the first attempt to investigate the factors affecting the concurrent adoption of off-farm income diversification and agricultural credit which the farmers use to manage the risk to maize production. We apply bivariate and multinomial probit approaches to the primary data collected from four districts of Punjab Province in Pakistan. The results show that strong correlations exist between the off-farm diversification and agricultural credit which indicates that the use of one risk management strategy leads to another. The findings demonstrate that education, livestock number, maize farming experience, perceptions of biological risks and risk-averse nature of the growers significantly encourage the adoption of diversification as a risk management tool while farm size inversely affects the adoption of diversification. Similarly, in the adoption equation of credit, maize farming experience, farm size, perceptions of price and biological risks and risk attitude of farmers significantly enhance the chances of adopting agricultural credit to manage farm risks. These findings are important for the relevant stakeholders who seek to offer carefully designed risk minimizing options to the maize farmers.

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Purpose – Whether there exists an inverse relationship (IR) between farm size and its efficiency remains a hotly debated question among agricultural economists. In most studies to date, farm efficiency is measured by land productivity. Thus, the IR actually measures the relationship between farm size and land productivity. The purpose of this paper is to examine and understand the IR from a novel angle by using multiple definitions of farm efficiency indicators like labor productivity, profit ratio, total factor productivity (TFP) and technical efficiency (TE). Design/methodology/approach – By using the farm-level panel data from Hubei province in China from 1999 to 2003, this paper employs the two-way fixed effect model of panel data and the stochastic frontier analysis of Battese and Coelli model to investigate the relationship between farm size and its production efficiency derived from the multiple definitions of production efficiency indicators including land productivity, labor productivity, profit ratio, TFP and TE. Findings – The study confirmed the IR between land productivity and farm size, as in many formal studies. However, the relationship between farm size and other agricultural efficiency indicators may be positive, negative or uncorrelated at, depending on how the farm efficiency is defined. Therefore, the paper concluded that the relationship between farm size and its production efficiency is mixed. This paper provides economic explanations for the IR through the comprehensive study using the expansion of agricultural efficiency indicators. Practical implications – Because different agricultural efficiency indicators have different policy implications for China's future agricultural and land policy, the findings have tremendous policy implications, particularly in terms of the current debate on large or small farm development strategy, the also so-called “go big or small” agricultural strategy. In this sense, the Chinese household responsibility system has played a critical role in its agriculture and will continue to play a critical role in terms of social security and social equality. Any reform to this system should proceed with caution. Originality/value – While most existing studies only try to explain the IR from the perspective of land productivity, this paper attempts to propose a novel angle to examine the IR by using multiple definitions of agricultural efficiency and hopes to find some new conclusions.

Crop production in developing and emerging countries is increasingly dependent on the usage of farm machinery. However, it remains unclear whether low-productive and high-productive farmers benefit equally from farm machinery use. To address the research gap, this study examines the potential heterogeneous effects of farm machinery use on maize yields, using an unconditional quantile regression model and survey data from China. We employ a control function approach to address the selection bias issue associated with farm machinery use. The empirical results show that the use of farm machinery significant increases maize yields for all the selected quantiles (except for the 80th quantile); the low-productive farmers tend to benefit more from farm machinery use relative to their high-productive counterparts; and farm machinery use reduces the inequality and variability of maize yields.

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This study examines the determinants of adoption of organic soil amendments (OSAs) such as organic fertiliser and farmyard manure and its impact on crop yields and net returns, using household survey data of 558 wheat farmers in China. We employ an endogenous switching regression model to account for selection bias stemming from both observable and unobservable factors. The empirical results show that household size, dependency ratio, machine ownership and non-paid labour are main factors that determine farmers' decision to adopt OSA, and the OSA adoption has a positive and statistically significant impact on wheat yields and net returns. In particular, the treatment effects of OSA adoption are to increase wheat yields and net returns by appropriately 22 and 24 per cent, respectively. Moreover, disaggregated analysis by farm size reveals that large-scale households tend to obtain higher wheat yields and net returns than their small-scale counterparts.