Scientia Agricultura Sinica ›› 2023, Vol. 56 ›› Issue (18): 3530-3542.doi: 10.3864/j.issn.0578-1752.2023.18.004


Influence of Future Climate Change on the Climate Suitability of Potato Cultivation in China

ZHANG ZhiLiang1,2,3(), HE ZhiHao1,2, RU XiaoYa1,2, JIANG TengCong1,2, HE YingBin3, FENG Hao2,4, YU Qiang4,5, HE JianQiang1,2,5()   

  1. 1 Northwest A&F University/Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Yangling 712100, Shaanxi
    2 Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, Shaanxi
    3 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081
    4 Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resource/State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Yangling 712100, Shaanxi
    5 Key Laboratory of Eco-Environment and Meteorology, Qinling Mountains and Loess Plateau, Shaanxi Meteorological Bureau, Xi’an 710016
  • Received:2022-08-31 Accepted:2022-12-05 Online:2023-09-16 Published:2023-09-21
  • Contact: HE JianQiang


Objective】As the fourth staple food crop in China, potato suitability evaluation is of great significance to ensure national food security. Based on climate data, this study constructed an integrated species distribution model to predict the climate suitable area of potato in China in the future, and provided an important scientific reference for optimizing potato planting in China.【Method】In this study, the future climate data derived from six different global climate models (GCMs) were used to drive an ensemble of five different species distribution models (SDMs) to simulate the temporal and spatial distribution characteristics of climate suitable areas of potato cultivation in China in the historical (1970-2000) and four future (2021-2040, 2041-2060, 2061-2080, and 2081-2100) periods under four greenhouse gas emission scenarios (ssp126, ssp245, ssp370, and ssp585). 【Result】 (1) The precipitation in the wettest month, the highest temperature in the warmest month, and the average temperature in the coldest quarter were the main meteorological factors that affected the climate suitability of potato in China, with their contribution rates of 54.7%, 21.4% and 18.1%, respectively. (2) In four scenarios of greenhouse gas emission, the prediction results of various suitable areas were basically the same, showing the similar trends that the areas of suitable and low suitable would become larger, while the area of high suitable would become smaller. Only in Hainan, Tibet, Xinjiang and some other regions, the climate was not suitable for potato planting. The suitable potato planting areas (including both suitable and high suitable) exceed 50% in all cases. (3) In the future, the low suitable and suitable areas for potato planting will increase greatly, while the high suitable areas will decrease. The order of areas of different suitable grades would remain: suitable areas>low suitable areas>high suitable areas. (4) With the increase of greenhouse gas emission level, the high suitable area in China would be greatly reduced. For spatial distribution, the high suitable areas were mainly in Northeast China, Gansu, western Xinjiang, and some parts of southwest China. From the perspective of time, the future climate change would greatly affect the northwest of Shaanxi, the middle and lower reaches of the Yangtze River, the central and western Inner Mongolia and other regions. The climate suitability of potato planting would obviously decrease. 【Conclusion】In this study, the integrated species distribution models were constructed to predict the temporal and spatial distribution characteristics of potato climate suitable areas in the future. Northeast, Gansu, Southwest and other regions of China could be the main potato planting areas, while Xinjiang and other regions could be the main development areas. The rest regions should be given priority to the development of other staple crops and cash crops according to local conditions.

Key words: potato, climate change, global climate model, species distribution model, climate suitability

Fig. 1

Distributions of potato planting districts in China Map content approval number: JINGSHENZI (2023) G1636. The same as below The dots of ‘Existence’ refer to the actual potato planting sites, where data were from the National Meteorological Information Center of China Meteorological Administration (; the dots of ‘Non-existence’ refer to the sites where there was no potato cultivation"

Table 1

SDM model inputs meteorological environmental factors"

Whether to use for modeling
bio1 年平均温度 Annual mean temperature 否 No
bio2 平均日变化范围(月平均(最高温度-最低温度))
Mean diurnal range (mean of monthly (max temp - min temp))
是 Yes
bio3 等温性(BIO2/BIO7) Isothermality (BIO2/BIO7) (×100) 是 Yes
bio4 温度季节性(标准偏差×100) Temperature seasonality (standard deviation ×100) 否 No
bio5 最暖月份的最高温度 Max temperature of warmest month 是 Yes
bio6 最冷月份的最低温度 Min temperature of coldest month 否 No
bio7 温度年变化范围(BIO5-BIO6) Temperature Annual Range (BIO5-BIO6) 否 No
bio8 最潮湿地区的平均温度 Mean temperature of wettest quarter 否 No
bio9 最干燥季度的平均温度 Mean temperature of driest quarter 否 No
bio10 最温暖季度的平均温度 Mean temperature of warmest quarter 否 No
bio11 最冷季度的平均温度 Mean temperature of coldest quarter 是 Yes
bio12 年降水量 Annual precipitation 否 No mm
bio13 最湿月份的降水量 Precipitation of wettest month 是 Yes mm
bio14 最干旱月份的降水量 Precipitation of driest month 否 No mm
bio15 降水季节性(变异系数) Precipitation seasonality (coefficient of variation) 是 Yes
bio16 最潮湿地区的降水量 Precipitation of wettest quarter 否 No mm
bio17 最干燥季度的降水量 Precipitation of driest quarter 否 No mm
bio18 最热季度的降水量 Precipitation of warmest quarter 否 No mm
bio19 最冷季度的降水量 Precipitation of coldest quarter 否 No mm

Fig. 2

Correlation diagram of the input environment factors in the species distribution models (SDMs)"

Table 2

Characteristics of species distribution model used in this study"

Selection of statistical characteristics of random variables is the most suitable criterion for objective conditions
Recursive binary segmentation is used to eliminate the interaction between predictors, and a large set of small regression trees is established to represent the nonlinear correlation between responses and their predictors
Adaptive spline regression method for processing high-dimensional data
Map data from low-dimensional vector to high-dimensional vector, and maximize the distance between positive set and negative set in high-dimensional space
Bagging algorithm is introduced into Cart decision tree for multiple random sampling, and then a single decision tree classifier is trained to complete the construction of the integrated model

Fig. 3

Distribution of climate suitability of potato cultivation in the historical period of 1970-2000 in China"

Fig. 4

Spatial and temporal distribution characteristics of potential climate-suitable areas for potato cultivation in China under four different greenhouse gas emission scenarios (ssp126, ssp245, ssp370, and ssp585) in four future periods (2021-2040, 2041-2060, 2061-2080, and 2081-2100) under future climate change"

Table 3

Summary of the climate suitable areas of different grades for potato cultivation at different greenhouse gas emission scenarios in different periods under future climate change in China (×104 km2)"

1970-2000 262 407 296
ssp126 2021-2040 270 479 216
2041-2060 275 501 187
2061-2080 287 500 177
2081-2100 283 501 179
ssp245 2021-2040 275 476 214
2041-2060 283 504 177
2061-2080 301 507 156
2081-2100 320 497 147
ssp370 2021-2040 272 473 219
2041-2060 285 503 176
2061-2080 314 510 141
2081-2100 345 511 108
ssp585 2021-2040 273 484 207
2041-2060 294 509 161
2061-2080 339 509 116
2081-2100 388 507 69

Fig. 5

Characteristics spatial and temporal distributions of high-climate-suitable areas for potato cultivation under four different greenhouse gas emission scenarios (SSP126, SSP245, SSP370, and SSP585) in China Green areas were the high-suitable areas for potato planting in the period of 2081-2100, while red areas were the reduced high-suitable areas in the future compared with the baseline period of 1970-2000"

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