A System Analysis on Steppe Sustainability and Its Driving Forces—A Case Study in China

Zhao, Xia; Gao, Qian; Yue, Yaojie; Duan, Lian; Pan, Shun

doi:10.3390/su10010233

Cited by 8 publications

(6 citation statements)

References 80 publications

(123 reference statements)

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“…The NDVI in the Qilian Mountains is closely related to precipitation, but negatively correlated with precipitation in oasis area. Zhao et al analyzed the relationship between NDVI and climate in the grassland region of northern China from 2000 to 2010 [5]. The results show that precipitation is the strongest positive driving force, followed by average temperature.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal changes of NDVI and correlation with meteorological factors in northern china from 1985-2015

2019

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The NDVI in northern china is vulnerable and is greatly affected by meteorological factors. In this study, the correlation between changes of NDVI and the major meteorological factors (temperature, precipitation) in northern China in the past 30 years with Trend analysis and partial correlation analysis methods. The results show that: (1) In the recent 30 years, the annual average NDVI in northern China shows an upward trend of volatility, with an annual growth rate of 0.0015, and the overall vegetation coverage increases. (2) In the context of global warming, the average annual temperature in the research area increased significantly, with an average annual growth rate of 0.0353, while the average annual growth rate of precipitation was not significant, with an average annual growth rate of 0.1591. (3) NDVI has a higher correlation with air temperature in high altitude alpine and plateau areas, and a higher correlation with precipitation in grassland and desert grassland areas.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal changes of NDVI and correlation with meteorological factors in northern china from 1985-2015

2019

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“…For example, we did not completely separate the respective effects of hail diameter and hail density on cotton damages. To address this issue, analytical methods, such as natural log transformation of the observed data, stepwise regression to build a more comprehensive predictive model, and the Copula model [58], may provide insights. In summary, further analysis of the impacts of hail-induced crop damages on yield and fiber quality via these analytical methods will deepen our understanding of the vulnerability of cotton subjected to hail hazard.…”

Section: Discussionmentioning

confidence: 99%

Vulnerability of cotton subjected to hail damage

Yue

Zhou²,

Zhu

et al. 2019

PLoS ONE

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This paper establishes the quantitative relationships between hail fall parameters and crop damages by examining the impacts of 49 hail hazard scenarios on cotton in the bud stage and boll stage. This study utilizes simulated cotton hail hazard to analyze the following data: hail size, hail fall density, and crop damages (i.e., defoliation rate, branch breaking rate, and the fruit falling rate). The results are as follows: 1) cotton vulnerability increased via an increase in crop damages as the hail hazard magnitude increased; 2) crop damages exhibit significant logistic relationships with hail diameter and hail fall density, and the fit was better at the bud stage than at the boll stage; 3) cotton is more vulnerable to hail hazard at the bud stage than at the boll stage, and the bud stage is a critical period for cotton hail disaster prevention and mitigation; and 4) damages to cotton plant at the bud stage and boll stage were less sensitive to hail size from hail fall density. Thus, we suggest that hail diameter can be used as the priority indicator to predict hail-induced crop damages. These results provide a sound basis for developing a comprehensive evaluation of hail damage in cotton, which is crucial for promoting sustainable cotton production. We recommend that the impacts of hail-induced crop damages on yield and fiber quality need to be addressed further in future studies.

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“…Compared to linear models, the copula model can be applied to analyze nonlinear correlation between variables with the following advantages [52][53][54][55]: (i) The copula model captures abnormal information by visually displaying the tail features of the variable distribution, (ii) the copula model is suitable for variables obeying any type of distribution, and (iii) the copula model is powerful for analyzing the nonlinear correlation between variables. Recently, the copula model was applied with satisfactory results in geoscience, hydrology, finance, and other fields [56][57][58][59][60][61][62]. Therefore, the copula model was introduced to analyze the effect factors on the heterogeneity of dominant air pollutants in this study, and this analysis process is described below.…”

Section: Effect Factors Analysis Methodsmentioning

confidence: 99%

Statistical Analysis of Spatiotemporal Heterogeneity of the Distribution of Air Quality and Dominant Air Pollutants and the Effect Factors in Qingdao Urban Zones

Zhao

Gao

Sun³

et al. 2018

Atmosphere

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Air pollution has impacted people's lives in urban China, and the analysis of the distribution and driving factors behind air quality has become a current research focus. In this study, the temporal heterogeneity of air quality (AQ) and the dominant air pollutants across the four seasons were analyzed based on the Kruskal-Wallis rank-sum test method. Then, the spatial heterogeneity of AQ and the dominant air pollutants across four sites were analyzed based on the Wilcoxon signed-rank test method. Finally, the copula model was introduced to analyze the effect of relative factors on dominant air pollutants. The results show that AQ and dominant air pollutants present significant spatiotemporal heterogeneity in the study area. AQ is worst in winter and best in summer. PM 10 , O 3 , and PM 2.5 are the dominant air pollutants in spring, summer, and winter, respectively. The average concentration of dominant air pollutants presents significant and diverse daily peaks and troughs across the four sites. The main driving factors are pollutants such as SO 2 , NO 2 , and CO, so pollutant emission reduction is the key to improving air quality. Corresponding pollution control measures should account for this heterogeneity in terms of AQ and the dominant air pollutants among different urban zones.

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A System Analysis on Steppe Sustainability and Its Driving Forces—A Case Study in China

Cited by 8 publications

References 80 publications

Spatiotemporal changes of NDVI and correlation with meteorological factors in northern china from 1985-2015

Spatiotemporal changes of NDVI and correlation with meteorological factors in northern china from 1985-2015

Vulnerability of cotton subjected to hail damage

Statistical Analysis of Spatiotemporal Heterogeneity of the Distribution of Air Quality and Dominant Air Pollutants and the Effect Factors in Qingdao Urban Zones

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