Risk assessment of drought disaster in summer maize cultivated areas of the Huang-Huai-Hai plain, eastern China

Hu, Zhenghua; Wu, Zhurong; Zhang, Yixuan; Qi, Liqun; Islam, Abu Reza Md. Towfiqul; Pan, Congcong

doi:10.1007/s10661-021-09224-6

Cited by 12 publications

(7 citation statements)

References 34 publications

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“…Bal et al selected the temperature and precipitation data of 1636 meteorological stations in India, and calculated the yield loss of six rain-fed crops such as cotton and peanut based on the DSI index [46]. Hu et al considered daily temperature and precipitation data from 1960 to 2015 to calculate the drought index for summer maize fertility and calculated the maize yield reduction based on the relationship between climatic yield and time trend yield [47]. In this study, the Jensen model (phase multiplication model) was used to solve the above problem and it had a good prediction of yield and could faithfully reflect the functional relationship between stage water consumption and final yield.…”

Section: Advantages Of the Drought Risk Assessment Methods Proposed I...mentioning

confidence: 99%

A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss

Liu

Pan

et al. 2023

Sustainability

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China is one of the regions with the most frequent drought disasters and serious social and economic losses. Agricultural drought loss is one the most serious natural disasters. Due to climate change, the regional agricultural drought risk assessment has always been the focus of the academic circle. This study takes Zunyi City as an example, which is the most typical City of karst landform development. The monthly precipitation data set of ground meteorological observation stations in Zunyi City from 1956 to 2020 was selected, and the drought characteristic variables were extracted by the coupled use of the precipitation anomaly percentage (Pa) index and the theory of runs. The Copula function was applied to establish the joint distribution model of characteristic variables, obtaining the drought frequency and drought return periods. Combined with the Jensen model, the agricultural drought loss rate under different drought return periods in the target year (2020) was calculated and evaluated. The results showed that the Gumbel-Hougaard copula function was suitable for the joint distribution of drought joint variables in Zunyi City. From 1956 to 2020, fewer droughts occurred in Zhengan and Wuchuan, and the most droughts took place in Fenggang, Meitan, and Yuqing. The average drought duration in each county was about 1.5 months, and the average drought severity was about 0.35 in spatial distribution. Crop loss rate caused by drought increased and the affected area expanded with the increase of drought return periods (5, 10, 20, 50, and 100 years) in temporal distribution. Meanwhile, the drought disaster was most drastic in the eastern region, followed by the south, north, west, and central area. The results were highly consistent with the historical drought in Zunyi City, which verified the validity of the model. This study could provide scientific knowledge for drought resistance and reasonable mitigation programing for the security of the regional agricultural production and the sustainability of social and economic development.

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Section: Advantages Of the Drought Risk Assessment Methods Proposed I...mentioning

confidence: 99%

A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss

Liu

Pan

et al. 2023

Sustainability

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“…The relative meteorological yield shows that the relative variability of yield fluctuation deviating from the trend, that is, the amplitude of yield fluctuation, is not affected by time and space, and is comparable. However, when the value is negative, it indicates that the meteorological conditions are unfavorable to the overall crop production, and the crop yield reduction, that is, the yield reduction rate 32 .…”

Section: Methodsmentioning

confidence: 99%

Comprehensive climatic suitability evaluation of peanut in Huang-Huai-Hai region under the background of climate change

Wei

Yang

et al. 2022

Sci Rep

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The climate changes influence the growing suitability of peanut, an important oil crop. Climatic suitability evaluation in the Huang-Huai-Hai region, the main peanut producing region of China, which can optimize peanut planting structure and provide basis for increasing output. In this study, the temperature, precipitation, sunshine and comprehensive suitability models were established by using the climatic suitability function in different growth periods of peanut. In this study, the climate suitability function of peanut in different growth periods was used to establish the temperature, precipitation, sunshine and comprehensive suitability model. Combined with the meteorological data after Anusplin interpolation, the spatial distribution and chronological change of peanut climate suitability were analyzed. The results show that with climate change, the overall climate becomes warmer and drier and the temperature and precipitation suitability increase, but the sunshine suitability decreases. Based on the comprehensive suitability model, the suitability evaluation results are divided into four levels: the most suitable, suitable, sub-suitable and unsuitable. Among them, the most suitable peanut planting areas in the Huang-Huai-Hai region are concentrated in the west of the Haihe River Basin and the Huaihe River Basin. The data from the next 30 years show that both the most suitable and suitable areas have been expanded. Through the verification of yield correlation analysis and spatial distribution of disaster frequency, it can be seen that the evaluation results have high accuracy, which can be used to guide and optimize peanut production practices.

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“…The correlation coefficient is a statistical indicator that describes the closeness of the correlation between things or data variables [41]. For the correlation study of bivariate data, the Pearson correlation coefficient is usually used, and its calculation formula is shown in formula (1).…”

Section: Image Feature Correlation Analysismentioning

confidence: 99%

“…Agricultural drought has become one of the main challenges facing global agricultural production and crop safety [1], [2]. As a multi-dimensional stress, drought can cause changes in crop phenotype, physiology, biochemistry, and molecular level.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging

Yan

2022

IEEE Access

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Drought has become one of the main challenges facing global agricultural production and crop safety. Drought stress will lead to the termination of crop photosynthesis, which will seriously affect the growth and development of crops. We aimed to study a method for identificaton of the drought stress in tomato seedlings using chlorophyll fluorescence imaging. In this study, chlorophyll fluorescence parameters and there corresponding chlorophyll fluorescence images of 4 different drought stress levels were collected. Then three feature optimization algorithms which were Successive Projections Algorithm, Iteratively Retains Informative Variables and Variable Iterative Space Shrinkage Approac were used to choose important parameters. A total of five common parameters were obtained, and the corresponding chlorophyll fluorescence images of the five common parameters were selected. And two types of image features were used to study and analyze drought stress classes: histogram features and texture features. The Pearson correlations of the features were calculated and the high correlated features were input into three models, which were Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and k-Nearest Neighbor (KNN), to identify drought stress classes. The recognition accuracy rate of LDA, SVM and KNN were 86.8%, 87.1% and 76.5% respectively. Our experiment results showed that the five common fluorescence parameters and there corresponding image features could be used to evaluate the drought stress classes of tomato seedlings, and had a good evaluation effect. This research provideed a new method for monitoring drought stress classes and had considerable prospects for non-destructive diagnosis of plant drought stress. INDEX TERMSChlorophyll fluorescence imaging, drought stress, feature extraction, machine learning, tomato seedlings. YAN LONG received the B.S. degree in electronic and information engineering and the M.S. degree in communication engineering from Shandong University, China, in 2005 and 2008, respectively, and the Ph.D. degree in agricultural electrification and automation from Northwest A&F University, China, in 2015. Since 2018, she has been an Associate Professor with the Mechanical and Electronic Engineering Department, Northwest A&F University. Her research interests include digital agriculture and agricultural information engineering, agricultural electronics, and automation technology. MINJUAN MA received the B.S. degree in electronic information engineering from Northwest A&F University, China, in 2019, where she is currently pursuing the M.S. degree in agricultural electrification and automation. Her research interests include digital agriculture and agricultural information engineering.

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Risk assessment of drought disaster in summer maize cultivated areas of the Huang-Huai-Hai plain, eastern China

Cited by 12 publications

References 34 publications

A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss

A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss

Comprehensive climatic suitability evaluation of peanut in Huang-Huai-Hai region under the background of climate change

Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging

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