Document Type : Research Paper
Authors
1
Key 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.
2
National Meteorological Center, Beijing 100081, China.
3
EWHALE 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.
4
State 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
Abstract
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.
Keywords
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