World agricultural convergence.

The goal of this article is to answer two questions: Has world agricultural convergence occurred? If not, how can this goal be fulfilled? This article introduces a model averaging method to consider both a parametric and a semi-parametric SFA model to better estimate technical efficiency. Then, three types of convergence tests are employed to check if world agricultural catch-up occurs and to determine the degree of convergence across different groups of countries. The empirical results on a balanced panel of 126 countries from 1970-2014 show that world agricultural convergence has not occurred. This article then investigates the situations in different groups of countries and discusses how to use international trade, irrigation system, and structural transformation to improve agricultural efficiency and to diminish the efficiency gap among different countries in the future.

The spatiotemporal pattern of surface ozone and its impact on agricultural productivity in China.

The slowing of agricultural productivity growth globally over the past two decades has brought a new urgency to detect its drivers and potential solutions. We show that air pollution, particularly surface ozone (O3), is strongly associated with declining agricultural total factor productivity (TFP) in China. We employ machine learning algorithms to generate estimates of high-resolution surface O3 concentrations from 2002 to 2019. Results indicate that China's O3 pollution has intensified over this 18-year period. We coupled these O3 estimates with a statistical model to show that rising O3 pollution during nonwinter seasons has reduced agricultural TFP by 18% over the 2002-2015 period. Agricultural TFP is projected to increase by 60% if surface O3 concentrations were reduced to meet the WHO air quality standards. This productivity gain has the potential to counter expected productivity losses from 2°C warming.

The Zoonotic diseases, agricultural production, and impact channels: Evidence from China.

The outbreak and wide spread of COVID-19 poses a new threat to global food security. This paper aims to address two important policy related issues, that is which agricultural subsector suffers more under zoonotic diseases and how do zoonotic diseases affect these subsectors. Using provincial panel data of 24 main farm commodities in China from 2002 to 2017, this paper identifies the impacts of zoonotic diseases and projects the potential disruption of COVID-19 to agricultural output in China under three scenarios. The main findings are as follows. First, zoonotic diseases have adverse impacts on almost all the farm commodities, while livestock on average suffers more than crops. Second, zoonotic diseases affect these subsectors mainly through the channel of adverse shocks on total factor productivity (TFP). Third, while a few subsectors can find a way to offset part of the TFP loss by applying more input, most subsectors suffer from both input reduction and TFP loss. Fourth, the spread of COVID-19 is projected to lower the growth rates of China's crop and livestock sector by 1.1%-2.3% and 1.3%-2.6%, with TFP loss by 1.1%-2.0% and 1.4%-2.7%, respectively, in 2020. This paper then discusses several policy implications for mitigating the negative impacts of COVID-19 on agricultural production in China and elsewhere.