Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China

Pei, Wei; Dong, Liu; Li, Tianxiao; Cheng, Kun; Cui, Song

doi:10.1007/s00704-017-2182-x

Cited by 46 publications

(40 citation statements)

References 35 publications

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“…Additionally, for an index (negative index), the smaller the index value was, the more efficient the scheme was. This index value was normalized as follows [29]: For an index (positive index), the larger the index value was, the more efficient the irrigation scheme was. This index value was normalized by the following formula [29]:…”

Section: Irrigation Scheme Decision-making Modelmentioning

confidence: 99%

“…This index value was normalized as follows [29]: For an index (positive index), the larger the index value was, the more efficient the irrigation scheme was. This index value was normalized by the following formula [29]:…”

Section: Irrigation Scheme Decision-making Modelmentioning

confidence: 99%

“…, y(3, n 3 )) is the n-dimensional unit projection vector. Furthermore, according to projection pursuit theory, the obtained projection eigenvalue point Z(i) should satisfy a certain distribution characteristic [29,30,39]. In detail, the distribution of local projection points within a given distance should be as concentrated as possible, and, meanwhile, the overall distribution of all projection points should be as scattered as possible.…”

Section: Irrigation Scheme Decision-making Modelmentioning

confidence: 99%

“…However, for some problems, the study object is not the original index, but a variable converted from the original index; if the sequence of original index values is used to obtain the entropy value, it may bring redundant or inaccurate information and reduce the validity of the entropy weight method. Similarly, for the projection pursuit model, which is also a powerful means to determine index weight [29,30], most of the relevant studies directly use the sequence of original index values to construct the projection eigenvalue. It may be not in accordance with the projection regulation for a specific research problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

Jiang

Jin

et al. 2019

Entropy

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To provide a scientific reference for formulating an effective soybean irrigation schedule in the Huaibei Plain, potted water deficit experiments with nine alternative irrigation schemes during the 2015 and 2016 seasons were conducted. An irrigation scheme decision-making index system was established from the aspects of crop water consumption, crop growth process and crop water use efficiency. Moreover, a grey entropy weight method and a grey relation–projection pursuit model were proposed to calculate the weight of each decision-making index. Then, nine alternative schemes were sorted according to the comprehensive grey relation degree of each scheme in the two seasons. The results showed that, when using the entropy weight method or projection pursuit model to determine index weight, it was more direct and effective to obtain the corresponding entropy value or projection eigenvalue according to the sequence of the actual study object. The decision-making results from the perspective of actual soybean growth responses at each stage for various irrigation schemes were mostly consistent in 2015 and 2016. Specifically, for an integrated target of lower water consumption and stable biomass yields, the scheme with moderate-deficit irrigation at the soybean branching stage or seedling stage and adequate irrigation at the flowering-podding and seed filling stages is relatively optimal.

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Section: Irrigation Scheme Decision-making Modelmentioning

confidence: 99%

Section: Irrigation Scheme Decision-making Modelmentioning

confidence: 99%

Section: Irrigation Scheme Decision-making Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

Jiang

Jin

et al. 2019

Entropy

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show abstract

“…Zhang et al (2019) assessed the resilience to landslides in Shenzhen, one of the biggest cities in China. Pei et al (2018) suggested a declining trend of drought risk in Heilongjiang Province in Northeast China. Sun et al (2017) provided projections of future changes in population exposure to droughts for the Haihe River Basin, with a decrease of 30% and an increase of 75% in the 1.5°C and 2.0°C global warming scenarios, respectively.…”

Section: Introductionmentioning

confidence: 99%

Changes of Population, Built-up Land, and Cropland Exposure to Natural Hazards in China from 1995 to 2015

Chen

Xie

2019

Int J Disaster Risk Sci

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By using the latest China population grid and land-use data, we assess the changing exposure of China's population and land uses to the hazards of storm surges, droughts, earthquakes, floods, and landslides from 1995 to 2015. We found that the single-hazard areas and the multihazard areas covered 43% and 26% of China's territory, respectively. Population grew faster in the hazard-prone areas than in the non-hazard areas. Built-up area expanded more rapidly in the areas prone to earthquakes and landslides. Cropland changed rapidly in many hazard-prone areas. The hazard-prone areas affected by floods featured the highest cropland loss rates, while the areas prone to earthquakes and landslides featured the highest cropland growth rates. We detected areas with significant exposure changes by using hot spot analysis. It was found that population and built-up land in the Pearl River Basin were increasingly exposed to storm surges, floods, and landslides. The Haihe River Basin and Huaihe River Basin also showed a consistent increase of population and built-up land exposure to droughts and earthquakes. These findings can provide a foundation for the design and implementation of protection and adaptation strategies to improve the resilience of Chinese society to natural hazards. Keywords China Á Exposure to natural hazards Á Population change Á Built-up land Á Cropland & Xiaocong Xu

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Spatiotemporal characteristics of droughts and floods in northeastern China and their impacts on agriculture

Zhou

et al. 2018

Stoch Environ Res Risk Assess

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Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China

Cited by 46 publications

References 35 publications

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

Decision-Making of Irrigation Scheme for Soybeans in the Huaibei Plain Based on Grey Entropy Weight and Grey Relation–Projection Pursuit

Changes of Population, Built-up Land, and Cropland Exposure to Natural Hazards in China from 1995 to 2015

Spatiotemporal characteristics of droughts and floods in northeastern China and their impacts on agriculture

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