This paper presents a rapid assessment of current and likely future impacts of the COVID-19 outbreak on rural economies given their socio-economic characteristics. Drawing principally on current evidence for the UK, as well as lessons from the 2001 Foot and Mouth Disease outbreak and the 2007/8 financial crises, it outlines the likely key demand and supply effects, paying attention to the situation for agriculture as well as discussing the implications for rural communities. A distinction is made between the effects on businesses offering goods and services for out-of-home as opposed to in-home consumption. Gendered dimensions are also noted as likely business and household strategies for coping and adaptation. The paper concludes with a brief mapping of a research agenda for studying the longer-term effects of COVID-19 on rural economies.

• Adverse weather conditions were analysed using a range of adverse weather indices. • Future UK climate is expected to remain favourable for wheat production. • Wetter winter and spring could, however, increase the risk of waterlogging. • Use of global climate model ensembles to quantify prediction uncertainty is advised.

SUMMARY: The present paper presents a meta-analysis of the economic and agronomic performance of genetically modified (GM) crops worldwide. Bayesian, classical and non-parametric approaches were used to evaluate the performance of GM cropsv.their conventional counterparts. The two main GM crop traits (herbicide tolerant (HT) and insect resistant (Bt)) and three of the main GM crops produced worldwide (Bt cotton, HT soybean and Bt maize) were analysed in terms of yield, production cost and gross margin. The scope of the analysis covers developing and developed countries, six world regions, and all countries combined. Results from the statistical analyses indicate that GM crops perform better than their conventional counterparts in agronomic and economic (gross margin) terms. Regarding countries’ level of development, GM crops tend to perform better in developing countries than in developed countries, with Bt cotton being the most profitable crop grown.

An approach to incorporate spatial dependence into stochastic frontier analysis is developed and applied to a sample of 215 dairy farms in England and Wales. A number of alternative specifications for the spatial weight matrix are used to analyse the effect of these on the estimation of spatial dependence. Estimation is conducted using a Bayesian approach and results indicate that spatial dependence is present when explaining technical inefficiency.