Planting suitability of China's main grain crops under future climate change

被引:13
作者
Lv, Tong [1 ,2 ,3 ,4 ]
Peng, Shouzhang [1 ,2 ,3 ,6 ]
Liu, Bo [4 ]
Liu, Yunuo [4 ]
Ding, Yongxia [5 ]
机构
[1] Northwest A&F Univ, Coll Soil & Water Conservat Sci & Engn, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling 712100, Peoples R China
[2] Chinese Acad Sci, Inst Soil & Water Conservat, Yangling 712100, Shaanxi, Peoples R China
[3] Minist Water Resources, Yangling 712100, Shaanxi, Peoples R China
[4] Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Peoples R China
[5] Baoji Univ Arts & Sci, Coll Geog & Environm, Shaanxi Key Lab Disasters Monitoring Mech Simulat, Baoji 721013, Peoples R China
[6] Inst Soil & Water Conservat, 26 Xinong Rd, Yangling 712100, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
High yield; Stable yield; Planting suitability; Climate change; China; YIELD; TEMPERATURE; WHEAT; PRECIPITATION; IMPACTS; REGIONS; RICE; MANAGEMENT; STABILITY; DYNAMICS;
D O I
10.1016/j.fcr.2023.109112
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
How to plan crop planting under global warming is a key issue in the context of an emerging global food crisis. Although previous studies have utilized crop potential distribution for crop planting strategies, they overlooked the high and stable crop-yielding areas within the potential distribution zone, which hinders the optimal utili-zation of these areas. Taking China as a case, this study proposed a high and stable yield index based on crop potential yield using a hybrid model at site scale (i.e., establishing the relationship between the observed crop yield and the outputs of process-based LPJ-GUESS model as well as climate variables using the random forest method) and assessed planting suitability of China's main grain crops (i.e., maize, wheat, and rice) under future climate change using the index. According to the results, (1) the determination coefficients between observed and modeled yield in the hybrid models were 0.71, 0.49, and 0.66 for maize, wheat, and rice, respectively, suggesting that the hybrid model had an acceptable performance. Moreover, the hybrid models had much better performance than the LPJ-GUESS model in crop yield simulation at site scale. (2) Compared with the 2001-2020, future average potential yield of three crops in the actual cultivated land would decline in 2081-2100, where the declined areas for maize, wheat, and rice would account for 83.8-89.2 %, 68.2-70.2 %, and 74.2-80.9 % of cultivated land, respectively. (3) High yield and stable yield areas of each crop do not overlap completely spatially, indicating that establishment of the high and stable yield index for crop planting suitability mea-surement is necessary. Compared with the 2001-2020, the optimal suitability areas of each crop will decrease under future climate change, implying that future climate change will reduce and shift the high and stable yield area of each crop. (4) Spatial overlay between the actual distribution and the optimal suitability area of each crop demonstrates that the optimal suitability area of each crop has not and will not be occupied completely by actual crop planting, suggesting a large available area for the adjustment of future crop planting area. This work could facilitate spatial optimization of crop planting to adapt to future climate change in China.
引用
收藏
页数:11
相关论文
共 55 条
[21]   The central trend in crop yields under climate change in China: A systematic review [J].
Liu, Yuan ;
Li, Ning ;
Zhang, Zhengtao ;
Huang, Chengfang ;
Chen, Xi ;
Wang, Fang .
SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 704
[22]   Plausible changes in wheat-growing periods and grain yield in China triggered by future climate change under multiple scenarios and periods [J].
Liu, Yujie ;
Chen, Qiaomin ;
Chen, Jie ;
Pan, Tao ;
Ge, Quansheng .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2021, 147 (741) :4371-4387
[23]   The effects of past climate change on the northern limits of maize planting in Northeast China [J].
Liu, Zhijuan ;
Yang, Xiaoguang ;
Chen, Fu ;
Wang, Enli .
CLIMATIC CHANGE, 2013, 117 (04) :891-902
[24]  
Lobell DB, 2011, SCIENCE, V333, P616, DOI [10.1126/science.1204531, 10.1126/science.1206376]
[25]   Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources [J].
Luo, Yuchuan ;
Zhang, Zhao ;
Li, Ziyue ;
Chen, Yi ;
Zhang, Liangliang ;
Cao, Juan ;
Tao, Fulu .
ENVIRONMENTAL RESEARCH LETTERS, 2020, 15 (07)
[26]   Climate change impacts on regional rice production in China [J].
Lv, Zunfu ;
Zhu, Yan ;
Liu, Xiaojun ;
Ye, Hongbao ;
Tian, Yongchao ;
Li, Feifei .
CLIMATIC CHANGE, 2018, 147 (3-4) :523-537
[27]   Socio-ecological factors determine crop performance in agricultural systems [J].
Nkurunziza, Libere ;
Watson, Christine A. ;
Oborn, Ingrid ;
Smith, Henrik G. ;
Bergkvist, Goran ;
Bengtsson, Jan .
SCIENTIFIC REPORTS, 2020, 10 (01)
[28]   Soil carbon management in large-scale Earth system modelling: implications for crop yields and nitrogen leaching [J].
Olin, S. ;
Lindeskog, M. ;
Pugh, T. A. M. ;
Schurgers, G. ;
Warlind, D. ;
Mishurov, M. ;
Zaehle, S. ;
Stocker, B. D. ;
Smith, B. ;
Arneth, A. .
EARTH SYSTEM DYNAMICS, 2015, 6 (02) :745-768
[29]   Modelling the response of yields and tissue C : N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe [J].
Olin, S. ;
Schurgers, G. ;
Lindeskog, M. ;
Warlind, D. ;
Smith, B. ;
Bodin, P. ;
Holmer, J. ;
Arneth, A. .
BIOGEOSCIENCES, 2015, 12 (08) :2489-2515
[30]   Machine learning for large-scale crop yield forecasting [J].
Paudel, Dilli ;
Boogaard, Hendrik ;
de Wit, Allard ;
Janssen, Sander ;
Osinga, Sjoukje ;
Pylianidis, Christos ;
Athanasiadis, Ioannis N. .
AGRICULTURAL SYSTEMS, 2021, 187