Spatial_temporal_dynamics_revised manuscript
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Spatial_temporal_dynamics_revised manuscript
1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscriptchen’, Bù Luand, shailaja Fennell-. Feng Wang’.4Dan Meng4, Yaolin Liu3, Limin Jiao3, Jing Wang35a. School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei Province 430079, China6b. State Key Laboratory of Soil and Sustainable Agriculture, Chinese Academy of Sciences. Nanjing. 2 Spatial_temporal_dynamics_revised manuscript10008.7China8c. Centre of Development Studies. University of Cambridge. Cambridge. CB3 9DT. UK9d. Department of Land Economy. University of Cambridge.Spatial_temporal_dynamics_revised manuscript
Cambridge. CB3 9EP. UKlOAbstract 0 Understanding the spatial-temporal dynamics of grain production and the influencing 11 factors at the county level1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscriptent of society. This 13study aims to evaluate China’s grain yield (GY) from 2000 to 2014 and investigate the potential 14driving factors (PDFs) that affect the spatial-temporal dynamics of GY, including land, labor force, 15capital, and macro-background. Specifically, the locational Gini coefficient Spatial_temporal_dynamics_revised manuscript and exploratory spatial Ibdata analysis (ESDA) were used to characterize the spatial patterns of GY and its correlations with 17BfsJ Spatial regressiSpatial_temporal_dynamics_revised manuscript
on models (SRMs) were employed to investigate the spatial dependence of GY 18on each PDF in 2000, 2005, 2010 and 2014. Results reveal that China's gra1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscripton of counties in the northeastern agricultural regions with high grain yield 21 has increased, while the number of low-yielding counties has increased in other agricultural regions.22This finding highlights the increasing trend of spatial polarization in grain production. The23significant bivariate Spatial_temporal_dynamics_revised manuscript Moran’s I (p<0.05) further revealed a global spatial spillover effect in the spatial 24correlation of GY and four PDFs. The spatial correlations coulSpatial_temporal_dynamics_revised manuscript
d be categorized into four types: high 25GY and high PDFs, high GY and low PDFs, low GY and high PDFs, and low GY and low PDFs. 2GSRMS were capable of1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscriptmics nationwide. The 28exploration of the spatial relationships between GY and PDFs provide a reference for formulating 29scientific and reasonable agricultural policies.30Keywords: food security, grain yield, potential driving forces, ESDA, county311 Introduction32 Food security, which is a pivotal Spatial_temporal_dynamics_revised manuscript issue in the establishment of human civilization and 33development in the 21st century, is among the major issues threatening the sustainable developSpatial_temporal_dynamics_revised manuscript
ment 34of human society (Arora, 2018). In 2018, a total of 113 million people in 53 countries suffered from 35severe food insecurity during the world’1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscript have 37facilitated the reduction in the proportion of hungry people (United Nations Publications, 2018). 38Nevertheless, global food security continues to face major challenges because of climate change 39(Lobell et al.. 2011; Ostfeld et al., 2013; Wheeler and Von Braun, 2013), urbanization (Lunett Spatial_temporal_dynamics_revised manuscripta et al., 402010; Rozelle and Veeck, 2016), reduced arable land (Godfray et al., 2010), and water scarcity (Lu et41al., 2016). The Sustainable DevelopSpatial_temporal_dynamics_revised manuscript
ment Goals Report (2018) indicated that one out of nine people42ÍI1 the uor.d t.Kkụ > i:idlnoi.rShed. Moreover, 1/4 of the world’s children suffer fro1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscriptany factors. Ensuring grain security can be considered from 45four aspects: grain production, grain consumption (Gilligan and Hoddinott, 2007), grain storage (Jian 46and Jayas, 2012), and grain trade (Headey, 2011). kmong them, grain production is fundamental in Uachieving the sustainable developmen Spatial_temporal_dynamics_revised manuscriptt goal of “zero hunger” by providing adequate grain, including 43 wheat, rice, and corn (Neumann et al., 2010). Therefore, research on the spatial-temSpatial_temporal_dynamics_revised manuscript
poral patterns 49and potential influencing mechanisms of grain yield (GY) is importance.50 GY depends on various potential driving factors (PDFs), suc1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscripting, 2008), and urbanization development ( Shi et al., 2013; Li et al., 2017). The 53strength of the dependence may vary spatially and temporally (Chen et al., 2018). The majority of54previous studies have focused on a single factor, rather than factors from various perspectives (e.g., 55input-outpu Spatial_temporal_dynamics_revised manuscriptt, supply-demand), thereby hindering the comprehensive understanding of the spatial-56temporal dynamics of GY. The emergence of multi-source and heterSpatial_temporal_dynamics_revised manuscript
ogeneous data sets and gradual57improvement of quantitative research on geographical and spatial phenomena provides substantial58opportunity for inves1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscription that CŨjmfIt:'qualitative and quantitative approaches should be conducted on the comprehensive mechanism of61|nultiple factors affecting grain yield, which are essential in promoting an efficient and clean63 The spatial scale is also a major concern in the research on grain production. Previous Spatial_temporal_dynamics_revised manuscriptstudies 64have focused on the national (Pellegrini and Fernandez. 2018; Wang et al., 2018), regional (Bandara 65and Cai, 2014), provincial (Chai et alSpatial_temporal_dynamics_revised manuscript
., 2019; Liu and Chen, 2007), and municipal (Brown and 66Waldron, 2013) levels. Few studies with the province as a study area have revealed that GY an1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscriptch is not conducive for interregional comparison. Because 69of the scale effect caused by the modifiable areal unit problem (MAUP) (Fotheringham et al., 2012), 70the results may differ across scales. Moreover, China has a vast territory and evident differences in 71^atural and social conditions amon Spatial_temporal_dynamics_revised manuscriptg regions, which could result in differences in grain production 72^cross regions. Accordingly, a superior research level should be adopted to reflectSpatial_temporal_dynamics_revised manuscript
further the internal 73differences in the spatial-temporal variations of grain production.74 China is the largest developing country in the world wit1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscriptincreasing for over ten years since 2000 because of the adjustment of 77agricultural policies and popularization of agricultural mechanization (Wang et al., 2018). 78Hf*vertheless, China’s grain security over the past 20 years has been influenced considerably by the 79|effects of climate change (Roz Spatial_temporal_dynamics_revised manuscriptelle and Veeck, 2016), rural labor outflow (Taylor et al., 2003), rapid 8(-urbanization (Deng et al.. 2015), and other factors (e.g., desertification,Spatial_temporal_dynamics_revised manuscript
salinization, heavy metal 81|pollution, and unsustainable land-use patterns). To date, the spatial patterns of China’s grain 82production do not matc1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscriptster have moved toward84Northeast China (Wang et al., 2018). Such spatial agglomeration trends may promote grain 85production but it also brings hidden dangers to China’s food security. Fortunately, the improvement 86of the agricultural technology level has provided an opportunity to increase GY (Ha Spatial_temporal_dynamics_revised manuscriptzell, 2009; Fan et 87aL, 2012). For example, improvements in the mechanization level and the promotion of bio-88pesticides, bio-fertilizers, and otherSpatial_temporal_dynamics_revised manuscript
technologies have greatly promoted the grain yield by improving 89|gricultural production efficiency. Nevertheless, how will social or economic proce1 Spatial-temporal dynamics of grain yield and the potential driving factors2at the county level in China3Jiawei Pan’. Yiyun Chen^*, Yan zhang’, Min c Spatial_temporal_dynamics_revised manuscriptl spillover) of GY on PDFs and the underlying mechanisms need 92further investigation.93 This study aims to:(l) explore the spatial-temporal dynamics of GY in China; (2) investigate 94the spatial correlation and its heterogeneity between GY and PDFs using global and local bivariate 95Moran's I. and Spatial_temporal_dynamics_revised manuscript(3) reveal the spatial dependence of GY on PDFs using spatial regression (e.g., spatial 96error model (SEM)). Note that the entire research process waSpatial_temporal_dynamics_revised manuscript
s conducted at the county level, thus 97enabling the development of new insights into the spatial dependence and heterogeneity of GY.9s2 Materials andGọi ngay
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