1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Sciences, Beijing 100101, China.
2National Meteorological Center, Beijing 100081, China.
3EWHALE Lab, Department of Biology and Wildlife, Institute of Arctic Biology, University of Alaska Fairbanks (UAF), 419 Irving I, P.O. Box 757000, Fairbanks AK 99775, USA.
4State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, China
China’s sugar production and consumption continues to increase. This process is already ongoing for over 15 years and over 90% of the sugar production comes from sugarcane (Saccharum officinarum). Most of the sugarcane is planted in the south (e.g. the Chinese provinces of Yunnan, Guangxi, Guangdong and Hainan) and it represents there a major economic crop in these landscapes. As found virtually worldwide, climate change is generally expected to influence such suitable planting areas. Here we started a first empirical assessment how climate change would influence the spatial distribution of those current and future suitable planting areas of this strategic crop in China. We employed an ensemble machine learning algorithm (Random Forest; bagging) and increasingly used and robust species distribution models (SDMs). These are based on our compiled and best publicly available crop data sampled from the Chinese sugarcane industry map. They were linked with bioclimate variables from the Worldclim database. This powerful concept allowed us to project sugarcane’s current and future (2070) suitable distributions based on the climate niche. Our results were extrapolated to three Global Circulation Models (GCMs; BCC-CSM1-1, CNRM-CM5 and MIROC-ESM) under three representative concentration pathways (RCPs of 2.6, 4.5 and 8.5). The evaluations of these models indicated that our results had a powerful performance (AUC=0.97, TSS=0.96) for robust inference. Bioclimatic variables related to temperature were the most important predictors for sugarcane planting. All models showed similar increasing spatial trends in suitable distribution area and just a few original suitable areas would be lost. Our finding puts emphasize on new growing areas, their soil and management. It is the first to provide the necessary background in the future to safely cultivate sugarcane in climate-suitable areas and to obtain more sugar production for farmers and the industry; it is of large and strategic importance for food security and national autonomy of this central commodity.