This paper analyses the impacts on global agricultural markets of the demand shock caused by the COVID-19 pandemic and the first wave of lockdown measures imposed by the governments in the first semester of 2020 to contain it. Specifically, we perform a scenario-based analysis on the IMF economic growth forecasts for 2020 and 2021 using a global multi-commodity agricultural market model. According to our results, the sharp decline in economic growth causes a decrease in international meat prices by 7–18% in 2020 and dairy products by 4–7% compared to a business as usual situation. Following the slowdown of the economy, biofuel prices fall strongly in 2020, followed by their main feedstocks, maize and oilseeds. Although the income losses and local supply chain disruptions associated with the pandemic undoubtedly has led to an increase in food insecurity in many developing countries, global food consumption is largely unaffected due to the inelastic demand of most agricultural commodities and the short duration of the shock. From an environmental viewpoint, the COVID-19 impacts point to a modest reduction of direct greenhouse gases from agriculture of about 1% or 50 million tonnes of carbon dioxide equivalents in 2020 and 2021.

This article reviews major impacts of climate change on agriculture and forestry.

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This paper presents the utilization of surface fluxes and relative evapotranspiration derived from satellites for crop yield prediction using a dedicated crop growth simulation algorithm, the Environmental Analysis and Remote Sensing (EARS) Crop Growth Simulation algorithm (EARS-CGS). The objective was to test the EARS-CGS algorithm independent of ground data for crop yield prediction at national level in Europe. The algorithm is based on existing crop yield models but has been modified to assimilate satellite derived global solar radiation and actual evaporation information. The algorithm simulates crop biomass. A statistical method is utilized to relate crop biomass to crop yield and to correct for regional differences in yields that are not the result of radiation or water limitation. Six years of Meteosat data were processed to predict winter wheat and spring barley yields for Spain and the UK. The predicted yields were compared to the national reported yields and to forecasts of the European Statistical Office (EUROSTAT) and the Monitoring Agriculture by Remote Sensing-Crop Growth Monitoring System (MARS-CGMS). To evaluate the timeliness of the predictions the reported yields were compared to yield predictions made at different stages of the growing season. The results presented in this paper demonstrate that crop yields predicted from meteorological satellites can be applied to provide timely and reliable crop yield forecasts.