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Most scenarios from Integrated Assessment Models (IAMs) that project greenhouse gas emissions include the use of bioenergy as a means to reduce CO2 emissions or even to achieving negative emissions (together with CCS). The potential amount of CO2 that can be removed from the atmosphere depends, among others, on the yields of bioenergy crops, the land available to grow these crops and the efficiency with which CO2 produced by combustion is captured. While bioenergy crop yields can be simulated by models, estimates of the spatial distribution of bioenergy yields under current technology based on a large number of observations are currently lacking. In this study, a random forest algorithm is used to upscale a bioenergy yield dataset of 3,963 observations covering Miscanthus, switchgrass, eucalypt, poplar and willow using climatic and soil conditions as explanatory variables. The results are global yield maps of five important lignocellulosic bioenergy crops under current technology, climate and atmospheric CO2 conditions at a 0.5° × 0.5° spatial resolution. We also provide a combined “best bioenergy crop” yield map by selecting the one of the five crop types with the highest yield in each of the grid cell, eucalypt and Miscanthus in most cases. The global median yield of the best crop is 16.3 t DM ha-1 yr-1. High yields mainly occur in the Amazon region and Southeast Asia. We further compare our empirically derived maps with yield maps used in three IAMs and find that the median yields in our maps are > 50 % higher than those in the IAM maps. Our estimates of gridded bioenergy crop yields can be used to provide bioenergy yields for IAMs, to evaluate land surface models, or to identify the most suitable lands for future bioenergy crop plantations. [ABSTRACT FROM AUTHOR]

This study explores the effects of agricultural trade liberalisation and concomitant changes in agricultural areas and livestock production on greenhouse gas emissions using the coupled LEITAP–IMAGE modelling system. The results indicate that liberalisation leads to an increase in total greenhouse gas emissions by about 6% compared to the reference scenario value in 2015. The increase in CO2 emissions are caused by vegetation clearance due to a rapid expansion of agricultural area; mainly in South America and Southeast Asia. Increased methane emissions in the case of full liberalisation are caused by less intensive cattle farming in regions such as South America and Southeast Asia. This pattern is observed up to 2050. Total global production of milk, dairy and beef do not change with full liberalisation, but production shifts were observed from North America and Europe to South America and Southeast Asia. Results are less pronounced in variants where trade liberalisation is only implemented partially. Remarkably, our study shows in the trade barrier removal scenario larger numbers of dairy cows in Australia and New Zealand (ANZ) then with full liberalisation scenario or a variant in which only milk quota are abolished. This illustrates that different types of liberalisation need to be analysed regionally and per commodity before general conclusions on the impact of trade liberalisation can be drawn. Our study contributes new information on greenhouse gas emissions to a vast number of trade liberalisation studies that focus on economic impacts. The combined economic-environmental impacts need to be assessed in detail before general conclusions on trade liberalisation can be given. [Copyright &y& Elsevier]