干旱区生态环境知识资源中心(Arid): Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks

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DOI	10.1177/0309133320956631
	Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks
	Liang, Boyi; Liu, Hongyan; Quine, Timothy A.; Chen, Xiaoqiu; Hallett, Paul D.; Cressey, Elizabeth L.; Zhu, Xinrong; Cao, Jing; Yang, Shunhua; Wu, Lu; Hartley, Iain P.
通讯作者	Liu, HY
来源期刊	PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT
ISSN	0309-1333
EISSN	1477-0296
英文摘要	The area of karst terrain in China covers 3.63x10(6)km(2), with more than 40% in the southwestern region over the Guizhou Plateau. Karst comprises exposed carbonate bedrock over approximately 1.30x10(6)km(2)of this area, which suffers from soil degradation and poor crop yield. This paper aims to gain a better understanding of the environmental controls on crop yield in order to enable more sustainable use of natural resources for food production and development. More precisely, four kinds of artificial neural network were used to analyse and simulate the spatial patterns of crop yield for seven crop species grown in Guizhou Province, exploring the relationships with meteorological, soil, irrigation and fertilization factors. The results of spatial classification showed that most regions of high-level crop yield per area and total crop yield are located in the central-north area of Guizhou. Moreover, the three artificial neural networks used to simulate the spatial patterns of crop yield all demonstrated a good correlation coefficient between simulated and true yield. However, the Back Propagation network had the best performance based on both accuracy and runtime. Among the 13 influencing factors investigated, temperature (16.4%), radiation (15.3%), soil moisture (13.5%), fertilization of N (13.5%) and P (12.4%) had the largest contribution to crop yield spatial distribution. These results suggest that neural networks have potential application in identifying environmental controls on crop yield and in modelling spatial patterns of crop yield, which could enable local stakeholders to realize sustainable development and crop production goals.
英文关键词	Karst critical zone crop yield artificial neural network crop model Guizhou
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000571808600001
WOS关键词	KARST ROCKY DESERTIFICATION ; CRITICAL ZONE OBSERVATORIES ; CLIMATE-CHANGE ; SOIL-MOISTURE ; FOOD SECURITY ; GREEN PROGRAM ; LAND-USE ; VEGETATION ; CHINA ; MODEL
WOS类目	Geography, Physical ; Geosciences, Multidisciplinary
WOS研究方向	Physical Geography ; Geology
来源机构	北京大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/328265
作者单位	[Liang, Boyi; Liu, Hongyan; Chen, Xiaoqiu; Zhu, Xinrong; Cao, Jing; Wu, Lu] Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China; [Liang, Boyi; Quine, Timothy A.; Cressey, Elizabeth L.; Hartley, Iain P.] Univ Exeter, Coll Life & Environm Sci, Amory Bldg,Rennes Dr, Exeter, Devon, England; [Hallett, Paul D.; Yang, Shunhua] Univ Aberdeen, Inst Biol & Environm Sci, Aberdeen, Scotland; [Yang, Shunhua] Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing, Peoples R China; [Yang, Shunhua] Univ, Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liang, Boyi,Liu, Hongyan,Quine, Timothy A.,et al. Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks[J]. 北京大学.
APA	Liang, Boyi.,Liu, Hongyan.,Quine, Timothy A..,Chen, Xiaoqiu.,Hallett, Paul D..,...&Hartley, Iain P..
MLA	Liang, Boyi,et al."Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks".PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT