| Popis: |
Today’s food system is more global than ever. In addition to food, also the key inputs to food production such as fertilisers, machinery and pesticides are traded among countries. Shocks and disturbances in the trade flows of agricultural inputs, caused by e.g., conflict, can potentially be devastating to the food production and yields even for otherwise self-sufficient countries. However, the impact of these agricultural input shocks on crop yields has not yet been assessed globally. In this study, we modelled the effects of agricultural input shocks using global spatial data on crop yields, fertilisers, machinery and pesticides using a random forest machine learning algorithm. We show that shocks in fertilisers cause the most drastic yield losses. Areas with the highest crop yields suffer the most from all agricultural input shocks, while low-yielding areas are seldom affected. Yield losses in these high-yielding ‘breadbasket’ areas of the world would be detrimental to global food security. For example, global maize production could decrease up to 50%, and global wheat production up to 30% if agricultural input availability were to drop by 75%. Western Europe and the US are among the areas most affected by shocks in agricultural inputs. Our study provides important information in high spatial definition to be used in future discussions on food security and resilience. |
