planting dates, inputs of fertilizer for high input systems, periods of harvest), but more detailed studies suggest that this gain could be higher, for example if including switch among existing cultivars (Leclère _et al._ , 2013; Osborne _et al._ , 2013). While this would place our results on the pessimistic range, including the impact of changes in water resource availability and droughts would go in the opposite direction: while we don’t capture them, literature based on observation has reported frequent underestimation of gridded simulation methods to predict crop response to extreme events (Lobell _et al._ , 2011; Lobell _et al._ , 2014). Similarly, additional negative impacts not accounted for include: rising tropospheric ozone concentration, and potentially higher occurrence of pests and diseases, or increased competition with weeds (Porter _et al._ , 2014). Furthermore, impacts of climate change on livestock are considered here through the feed availability channel only, whereas many other channels could also influence the future of the sector, in particular the role of heat and water stress on the productivity and mortality rate of animals, and the risk of increased spread of vector‐borne diseases (Havlík _et al._ , 2015).

For all the reasons above, the biophysical impacts of climate change are subject to a relatively high uncertainty, although some large patterns remain observed. As an illustration, Leclère _et al._ (2014) try to disentangle in their modeling analysis the signal‐to‐noise ratio associated with the biophysical shocks coming from the climate change uncertainty. They find negative impacts on crop yield for North America (excl. Canada), Brazil, Sub‐Saharan Africa and most of Eastern and South Asia. However, only for Brazil, the signal appears higher than the noise and the negative impact considered as robust across scenarios. The robustness of economic response in models is for this reason limited, and is additionally affected by their own intrinsic parameterization uncertainty, as evidenced by the diversity of economic model specification and parameterization.

Several comparison exercises looked at how different economic models respond to similar sets of climate scenarios to understand where the largest sources of uncertainty lie in economic impact assessments. In the framework of the ISI‐MIP project, Nelson _et al._ (2014) compared the economic responses of 9 global economic models applied to agricultural issues, including GLOBIOM. They showed that models tend to increase the spread of biophysical input shocks when coming to variables such as final yield, area and production responses, but that consumption responses were much less dispersed than the input. Models attributed differently the adjustments to the shock across the different adaptation channels in the model, management, expansion, trade and demand. Most computable general equilibrium models (CGEs) showed less reaction to the climate shock (average below +25% after equilibrium at horizon 2050 for a RCP8.5 without CO2 effects). Partial equilibrium models (with the exception of GCAM) and the CGE MAGNET showed average responsiveness in prices above +25% for the same shock, with the strongest price sensitivity for MAGPIE (Nelson _et al._ ), Fig S5). In response to price changes, adjustments differed across models, AIM, FARM, GCAM, and MAGNET adjusting mainly through area change, MAGPIE mainly through endogenous yield change, and ENVISAGE, GLOBIOM, GTEM relying almost equally on the two effects. Contribution from trade to absorption of the shock was also very different across models (Nelson _et al._ ), Fig S13 and S14): five models predicted limited trade adjustments (all CGEs except MAGNET, as well as MAGPIE), two models moderate trade adjustments (MAGNET, IMPACT) and two models large trade adjustments (GLOBIOM, GCAM). In the case of GLOBIOM, it is noteworthy that the trade setting for the comparison exercise is the same as considered for this paper for the integrated trade representation of the SSP5 scenario.

The final impact on food consumption in Nelson _et al._ (2014) was estimated to be ‐3% for RCP8.5 by 2050, under SSP2. The climate scenarios used were considered without CO2 effects but included a large 39