Temporal evolution of maize yield spatial heterogeneity in northeast China: shift of dominant factors from human management to climate change

被引:0
作者
Lu, Chenxi [1 ,2 ]
Leng, Guoyong [1 ]
Yu, Linfei [1 ]
Qiu, Jiali [1 ]
Peng, Jian [3 ,4 ]
机构
[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] UFZ Helmholtz Ctr Environm Res, Dept Remote Sensing, Permoserstr 15, D-04318 Leipzig, Germany
[4] Univ Leipzig, Remote Sensing Ctr Earth Syst Res RSC4Earth, D-04103 Leipzig, Germany
基金
中国国家自然科学基金;
关键词
Maize yield; Management; Climate change; Spatial heterogeneity; Northeast China; SPRING MAIZE; FERTILIZER USE; HEAT-STRESS; CROP YIELDS; TEMPERATURE; NITROGEN; DROUGHT; MODEL; GAPS; PRECIPITATION;
D O I
10.1016/j.jclepro.2025.145957
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Northeast China (NEC) is a major agricultural region in China but exhibits large spatial heterogeneity in crop yield. To date, it remains unclear how multiple climate and management factors have affected the spatial heterogeneity of crop yields and whether the dominant factors have changed over time. Here, we synthesize multi-source datasets and develop geographic detector and regression models to evaluate the effects of 19 climate and management variables on the spatial heterogeneity of maize yield in NEC during 1984-2013. We found that the spatial heterogeneity of maize yield (measured by the coefficient of variation) has exhibited an upward tendency of 0.74 %center dot y-1 during 1984-2013, with a significant change point detected around the year 2000. Specifically, an increasing trend of 0.47 %center dot y-1 is observed for 1984-2000, while a downward trend of-0.68 %center dot y-1 is revealed for 2001-2013. At the annual scale, fertilization and low temperature are the dominant factors affecting the spatial heterogeneity of maize yield in 1984-2000, whereas precipitation-related factors play leading roles in 2001-2013. At the decadal scale, fertilization and low-temperature control the upward trend in the spatial heterogeneity of maize yield during 1984-2000, while wind is the dominant factor in the downward trend from 2001 to 2013. Our findings highlight that the dominant factors controlling the spatial heterogeneity of maize yield have temporally shifted from management to climate factors, which underscore the importance of prioritizing resources to address rising climate risks by improving irrigation, drainage systems and crop varieties.
引用
收藏
页数:13
相关论文
共 99 条
[51]   Tracking the impact of typhoons on maize growth and recovery using Sentinel-1 and Sentinel-2 data: A case study of Northeast China [J].
Mu, Yongling ;
Chen, Shengbo ;
Cao, Yijing ;
Zhu, Bingxue ;
Li, Anzhen ;
Cui, Liang ;
Dai, Rui ;
Zeng, Qinghong .
AGRICULTURAL AND FOREST METEOROLOGY, 2024, 359
[52]   The Role of Soil pH in Plant Nutrition and Soil Remediation [J].
Neina, Dora .
APPLIED AND ENVIRONMENTAL SOIL SCIENCE, 2019, 2019
[53]   The yield gap of global grain production: A spatial analysis [J].
Neumann, Kathleen ;
Verburg, Peter H. ;
Stehfest, Elke ;
Mueller, Christoph .
AGRICULTURAL SYSTEMS, 2010, 103 (05) :316-326
[54]   A spatial-temporal continuous dataset of the transpiration to evapotranspiration ratio in China from 1981-2015 [J].
Niu, Zhongen ;
He, Honglin ;
Zhu, Gaofeng ;
Ren, Xiaoli ;
Zhang, Li ;
Zhang, Kun .
SCIENTIFIC DATA, 2020, 7 (01)
[55]   Effects of nitrogen and irrigation on water use of maize crops [J].
Ogola, JBO ;
Wheeler, TR ;
Harris, PM .
FIELD CROPS RESEARCH, 2002, 78 (2-3) :105-117
[56]   Spatial-temporal dynamics of grain yield and the potential driving factors at the county level in China [J].
Pan, Jiawei ;
Chen, Yiyun ;
Zhang, Yan ;
Chen, Min ;
Fennell, Shailaja ;
Luan, Bo ;
Wang, Feng ;
Meng, Dan ;
Liu, Yaolin ;
Jiao, Limin ;
Wang, Jing .
JOURNAL OF CLEANER PRODUCTION, 2020, 255
[57]   Spatial and Temporal Variations in Fertilizer Use Across Prefecture-level Cities in China from 2000 to 2015 [J].
Pan X.-D. ;
Li P. ;
Feng Z.-Z. ;
Duan C.-Q. .
Huanjing Kexue/Environmental Science, 2019, 40 (10) :4733-4742
[58]   Benefits of Seasonal Climate Prediction and Satellite Data for Forecasting US Maize Yield [J].
Peng, Bin ;
Guan, Kaiyu ;
Pan, Ming ;
Li, Yan .
GEOPHYSICAL RESEARCH LETTERS, 2018, 45 (18) :9662-9671
[59]   HRLT: a high-resolution (1 d, 1 km) and long-term (1961-2019) gridded dataset for surface temperature and precipitation across China [J].
Qin, Rongzhu ;
Zhao, Zeyu ;
Xu, Jia ;
Ye, Jian-Sheng ;
Li, Feng-Min ;
Zhang, Feng .
EARTH SYSTEM SCIENCE DATA, 2022, 14 (11) :4793-4810
[60]   Climate variation explains a third of global crop yield variability [J].
Ray, Deepak K. ;
Gerber, James S. ;
MacDonald, Graham K. ;
West, Paul C. .
NATURE COMMUNICATIONS, 2015, 6