Impacts of climate change on agrometeorological indices at winter wheat overwintering stage in northern China during 2021-2050

2018 ◽  
Vol 38 (15) ◽  
pp. 5576-5588 ◽  
Author(s):  
Zhixin Hao ◽  
Xiu Geng ◽  
Fang Wang ◽  
Jingyun Zheng
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Northern China ◽  
Impacts Of Climate Change

scholarly journals Climate Change Impacts on Winter Wheat Yield in Northern China

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiu Geng ◽  
Fang Wang ◽  
Wei Ren ◽  
Zhixin Hao
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Negative Impact ◽  
Climatic Factors ◽  
Wheat Yield ◽  
Growing Season ◽  
Northern China ◽  
Series Data ◽  
Winter Wheat Yield ◽  
Impacts Of Climate Change

Exploring the impacts of climate change on agriculture is one of important topics with respect to climate change. We quantitatively examined the impacts of climate change on winter wheat yield in Northern China using the Cobb–Douglas production function. Utilizing time-series data of agricultural production and meteorological observations from 1981 to 2016, the impacts of climatic factors on wheat production were assessed. It was found that the contribution of climatic factors to winter wheat yield per unit area (WYPA) was 0.762–1.921% in absolute terms. Growing season average temperature (GSAT) had a negative impact on WYPA for the period of 1981–2016. A 1% increase in GSAT could lead to a loss of 0.109% of WYPA when the other factors were constant. While growing season precipitation (GSP) had a positive impact on WYPA, as a 1% increase in GSP could result in 0.186% increase in WYPA, other factors kept constant. Then, the impacts on WYPA for the period 2021–2050 under two different emissions scenarios RCP4.5 and RCP8.5 were forecasted. For the whole study area, GSAT is projected to increase 1.37°C under RCP4.5 and 1.54°C under RCP8.5 for the period 2021–2050, which will lower the average WYPA by 1.75% and 1.97%, respectively. GSP is tended to increase by 17.31% under RCP4.5 and 22.22% under RCP8.5 and will give a rise of 3.22% and 4.13% in WYPA. The comprehensive effect of GSAT and GSP will increase WYPA by 1.47% under RCP4.5 and 2.16% under RCP8.5.

Assessing impacts of climate change on habitat suitability of Coregonus ussuriensis and other coldwater fishes in northern China

2018 ◽  
Vol 26 (33) ◽  
pp. 34058-34066
Author(s):  
Baoxiu Xing ◽  
He Chen ◽  
Qingfeng Chen ◽  
Yan Zhang ◽  
Zifang Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Habitat Suitability ◽  
Northern China ◽  
Impacts Of Climate Change

scholarly journals Impacts of climate change on water requirements of winter wheat over 59 years in the Huang-Huai-Hai Plain

2016 ◽  
Vol 11 (No. 1) ◽  
pp. 11-19 ◽  
Author(s):  
H. Huang ◽  
Y. Han ◽  
J. Song ◽  
Z. Zhang ◽  
H. Xiao
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Water Requirements ◽  
Impacts Of Climate Change

Modelling the impacts of climate change and crop management on phenological trends of spring and winter wheat in China

2018 ◽  
Vol 248 ◽  
pp. 518-526 ◽  
Author(s):  
Yujie Liu ◽  
Qiaomin Chen ◽  
Quansheng Ge ◽  
Junhu Dai ◽  
Ya Qin ◽  
...  
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Crop Management ◽  
Impacts Of Climate Change

scholarly journals A framework for quantifying the impacts of climate change and human activities on hydrological drought in a semiarid basin of Northern China

2019 ◽  
Vol 33 (7) ◽  
pp. 1075-1088 ◽  
Author(s):  
Shanhu Jiang ◽  
Menghao Wang ◽  
Liliang Ren ◽  
Chong‐Yu Xu ◽  
Fei Yuan ◽  
...  
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Northern China ◽  
Hydrological Drought ◽  
Impacts Of Climate Change

Impacts of climate change on winter wheat water requirement in Haihe River Basin

2014 ◽  
Vol 21 (5) ◽  
pp. 677-697 ◽  
Author(s):  
Zhe Yuan ◽  
Denghua Yan ◽  
Zhiyong Yang ◽  
Jun Yin ◽  
Patrick Breach ◽  
...  
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
River Basin ◽  
Water Requirement ◽  
Haihe River Basin ◽  
Haihe River ◽  
Impacts Of Climate Change

scholarly journals Direct Impacts of Climate Change and Indirect Impacts of Non-Climate Change on Land Surface Phenology Variation across Northern China

2018 ◽  
Vol 7 (11) ◽  
pp. 451 ◽  
Author(s):  
Zhaohui Luo ◽  
Qingmei Song ◽  
Tao Wang ◽  
Huanmu Zeng ◽  
Tao He ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Northern China ◽  
Ecosystem Dynamics ◽  
Land Surface Phenology ◽  
Mean Values ◽  
Combined Impact ◽  
Impacts Of Climate Change

Land surface phenology (LSP) is a sensitive indicator of climate change. Understanding the variation in LSP under various impacts can improve our knowledge on ecosystem dynamics and biosphere-atmosphere interactions. Over recent decades, LSP derived from remote sensing data and climate change-related variation of LSP have been widely reported at the regional and global scales. However, the smoothing methods of the vegetation index (i.e., NDVI) are diverse, and discrepancies among methods may result in different results. Additionally, LSP is affected by climate change and non-climate change simultaneously. However, few studies have focused on the isolated impacts of climate change and the impacts of non-climate change on LSP variation. In this study, four methods were applied to reconstruct the MODIS enhanced vegetation index (EVI) dataset to choose the best smoothing result to estimate LSP. Subsequently, the variation in the start of season (SOS) and end of season (EOS) under isolated impacts of climate change were analyzed. Furthermore, the indirect effects of isolated impacts of non-climate change were conducted based on the differences between the combined impact (the impacts of both climate change and non-climate change) and isolated impacts of climate change. Our results indicated that the Savitzky-Golay method is the best method of the four for smoothing EVI in Northern China. Additionally, SOS displayed an advanced trend under the impacts of both climate change and non-climate change (hereafter called the combined impact), isolated impacts of climate change, and isolated impacts of non-climate change, with mean values of −0.26, −0.07, and −0.17 days per year, respectively. Moreover, the trend of SOS continued after 2000, but the magnitudes of changes in SOS after 2000 were lower than those that were estimated over the last two decades of the twentieth century (previous studies). EOS showed a delayed trend under the combined impact and isolated impacts of non-climate change, with mean values of 0.41 and 0.43 days per year, respectively. However, EOS advanced with a mean value of −0.16 days per year under the isolated impacts of climate change. Furthermore, the absolute mean values of SOS and EOS trends under the isolated impacts of non-climate change were larger than that of the isolated impacts of climate change, indicating that the effect of non-climate change on LSP variation was larger than that of climate change. With regard to the relative contribution of climatic factors to the variation in SOS and EOS, the proportion of solar radiation was the largest for both SOS and EOS, followed by precipitation and temperature.

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