未来气候变化对中国马铃薯种植气候适宜性的影响

doi:10.3864/j.issn.0578-1752.2023.18.004

中国农业科学 ›› 2023, Vol. 56 ›› Issue (18): 3530-3542.doi: 10.3864/j.issn.0578-1752.2023.18.004

• 耕作栽培·生理生化·农业信息技术 • 上一篇下一篇

未来气候变化对中国马铃薯种植气候适宜性的影响

张志良¹^,²^,³(), 和志豪¹^,², 茹晓雅¹^,², 蒋腾聪¹^,², 何英彬³, 冯浩²^,⁴, 于强⁴^,⁵, 何建强¹^,²^,⁵()

¹ 西北农林科技大学/旱区农业水土工程教育部重点实验室，陕西杨凌 712100
² 西北农林科技大学中国旱区节水农业研究院，陕西杨凌 712100
³ 中国农业科学院农业资源与农业区划研究所，北京 100081
⁴ 中国科学院水利部水土保持研究所/黄土高原土壤侵蚀与旱地农业国家重点实验室，陕西杨凌 712100
⁵ 陕西省气象局秦岭和黄土高原生态环境气象重点实验室，西安 710016

收稿日期:2022-08-31 接受日期:2022-12-05 出版日期:2023-09-16 发布日期:2023-09-21
通信作者:
何建强，E-mail：jianqiang_he@nwafu.edu.cn
联系方式: 张志良，E-mail：zhiliang_zhang@nwafu.edu.cn。
基金资助:
国家自然科学基金项目(52079115); 陕西省重点研发计划重点产业创新链（群）-农业领域项目(2019ZDLNY07-03); 西北农林科技大学人才专项资金(千人计划项目); 高等学校学科创新引智计划（111计划）(B12007); 中国农业科学院农业资源与农业区划研究所创新工程(2021-2015)

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 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 Published:2023-09-16 Online:2023-09-21

摘要/Abstract

摘要：

【目的】马铃薯作为中国第四大主粮作物，其适宜性评价对保障国家粮食安全具有重要意义。本研究基于气候数据，构建集成物种分布模型预测中国未来时期马铃薯气候适宜区，为优化中国马铃薯种植提供重要科学参考。【方法】利用6种全球气候模式（global climate models，GCMs）未来气候数据驱动5种物种分布模型（species distribution models，SDMs），集成模拟预测未来4种温室气体排放情景（ssp126、ssp245、ssp370、ssp585）下，中国历史上（1970—2000年）和4个未来时期（2021—2040、2041—2060、2061—2080、2081—2100年）的马铃薯气候适宜区时空分布特征。【结果】（1）最湿月份的降水量、最暖月份的最高温度，以及最冷季度的平均温度是影响中国马铃薯气候适宜度的主要气象因子，对模拟结果的贡献率分别为54.7%、21.4%和18.1%。（2）4种温室气体排放情景下对于各适宜等级区域的预测结果变化基本一致，都呈现适宜区、低适宜区面积变大而高适宜区面积变小的趋势，仅在海南、西藏、新疆等地局部存在种植气候不适宜区。马铃薯适宜种植区（适宜区和高适宜区）的面积在各种情况下均超过50%。（3）在未来各时期马铃薯种植低适宜区和适宜区面积将大幅增加，而高适宜区面积则呈下降趋势，各适宜等级区域面积总体依旧保持：适宜区>低适宜区>高适宜区。（4）随着温室气体排放等级的提高，中国马铃薯高适宜区将大幅减小。从空间分布上看，中国马铃薯种植高适宜区主要以东北地区、甘肃地区、新疆西部，以及西南部分区域为主；从时间顺序上看，陕西北部、长江中下游区域、内蒙古中西部等区域受未来气候变化影响较大，马铃薯气候适宜度减小趋势明显。【结论】利用构建的集成物种分布模型预测了未来时期中国马铃薯气候适宜区时空分布特征。根据模型模拟结果，建议东北、甘肃、西南等地区可以作为未来马铃薯的主要种植区域，新疆等地区可以作为主要发展区域，其他地区应按照当地情况优先发展其他粮食和经济作物。

关键词: 马铃薯, 气候变化, 全球气候模式, 物种分布模型, 气候适宜性

Abstract:

【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

张志良, 和志豪, 茹晓雅, 蒋腾聪, 何英彬, 冯浩, 于强, 何建强. 未来气候变化对中国马铃薯种植气候适宜性的影响[J]. 中国农业科学, 2023, 56(18): 3530-3542.

ZHANG ZhiLiang, HE ZhiHao, RU XiaoYa, JIANG TengCong, HE YingBin, FENG Hao, YU Qiang, HE JianQiang. Influence of Future Climate Change on the Climate Suitability of Potato Cultivation in China[J]. Scientia Agricultura Sinica, 2023, 56(18): 3530-3542.

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[43]	KRAMER-SCHADT S, NIEDBALLA J, PILGRIM J D, SCHRÖDER B, LINDENBORN J, REINFELDER V, STILLFRIED M, HECKMANN I, SCHARF A K, AUGERI D M, CHEYNE S M, HEARN A J, ROSS J, MACDONALD D W, MATHAI J, EATON J, MARSHALL A J, SEMIADI G, RUSTAM R, BERNARD H, ALFRED R, SAMEJIMA H, DUCKWORTH J W, BREITENMOSER- WUERSTEN C, BELANT J L, HOFER H, WILTING A. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions, 2013, 19(11): 1366-1379. doi: 10.1111/ddi.12096
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变量 Variable	定义 Definition	是否用于建模 Whether to use for modeling	单位 Unit
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

模型 Model	特征 Characteristics	参考文献 References
最大熵 Maxent	选择随机变量统计特性最符合客观情况的准则 Selection of statistical characteristics of random variables is the most suitable criterion for objective conditions	[15]
增强回归树 BRT	使用递归二元分割来消除预测因子之间的相互作用，建立一个小回归树的大集合来表示响应及其预测因子之间的非线性关联 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	[16]
多元自适应回归样条 MARS	自适应处理高维数据的样条回归方法 Adaptive spline regression method for processing high-dimensional data	[17]
支持向量机 SVM	将数据从低维向量映射到高维向量，在高维空间中使正集和负集之间的距离最大化 Map data from low-dimensional vector to high-dimensional vector, and maximize the distance between positive set and negative set in high-dimensional space	[18]
随机森林 RF	在Cart决策树中引入Bagging算法进行多次有放回随机抽样，然后训练得到单个决策树分类器，完成对集成模型的构建 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	[19]

情景 Scenario	时期 Period	低适宜区 Low-suitable	适宜区 Suitable	高适宜区 High-suitable
历史时期 Historical	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

未来气候变化对中国马铃薯种植气候适宜性的影响

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

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