Identifying influence patterns of regional agricultural drought vulnerability using a two-phased grey rough combined model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huifang Sun ◽  
Liping Fang ◽  
Yaoguo Dang ◽  
Wenxin Mao
Keyword(s):  
Influencing Factors ◽  
Temporal Dynamics ◽  
Decision Rules ◽  
Agricultural Drought ◽  
Drought Vulnerability ◽  
Entropy Weight ◽  
Combined Model ◽  
Content Type ◽  
Base Points ◽  
Double Base

PurposeA core challenge of assessing regional agricultural drought vulnerability (RADV) is to reveal what vulnerability factors, under which kinds of synergistic combinations and at what strengths, will lead to higher vulnerability: namely, the influence patterns of RADV.Design/methodology/approachA two-phased grey rough combined model is proposed to identify influence patterns of RADV from a new perspective of learning and mining historical cases. The grey entropy weight clustering with double base points is proposed to assess degrees of RADV. The simplest decision rules that reflect the complex synergistic relationships between RADV and its influencing factors are extracted using the rough set approach.FindingsThe results exemplified by China's Henan Province in the years 2008–2016 show higher degrees of RADV in the north and west regions of the province, in comparison with the south and east. In the patterns with higher RADV, the higher proportion of agricultural population appears in all decision rules as a core feature. A smaller quantity of water resources per unit of cultivated land area and a lower adaptive capacity, involving levels of irrigation technology and economic development, present a significant synergistic influence relationship that distinguishes the features of higher vulnerability from those of the lower.Originality/valueThe proposed grey rough combined model not only evaluates temporal dynamics and spatial differences of RADV but also extracts the decision rules between RADV and its influencing factors. The identified influence patterns inspire managerial implications for preventing and reducing agricultural drought through its historical evolution and formation mechanism.

scholarly journals Assessment on Agricultural Drought Vulnerability and Spatial Heterogeneity Study in China

2021 ◽  
Vol 18 (9) ◽  
pp. 4449
Author(s):  
Hongpeng Guo ◽  
Jia Chen ◽  
Chulin Pan
Keyword(s):  
Influencing Factors ◽  
Agricultural Drought ◽  
Contribution Rate ◽  
Drought Vulnerability ◽  
Entropy Weight ◽  
Scoring Method ◽  
Resilience Index ◽  
Entropy Weight Method ◽  
Forest Coverage ◽  
Northwest Region

Reducing drought vulnerability is a basis to achieve sustainable development in agriculture. The study focuses on agricultural drought vulnerability in China by selecting 12 indicators from two aspects: drought sensitivity and resilience to drought. In this study, the degree of agricultural drought vulnerability in China has been evaluated by entropy weight method and weighted comprehensive scoring method. The influencing factors have also been analyzed by a contribution model. The results show that: (1) From 1978 to 2018, agricultural drought vulnerability showed a decreasing trend in China with more less vulnerable to mildly vulnerable cities, and less highly vulnerable cities. At the same time, there is a trend where highly vulnerable cities have been converted to mildly vulnerable cities, whereas mildly vulnerable cities have been converted to less vulnerable cities. (2) This paper analyzes the influencing factors of agricultural drought vulnerability by dividing China into six geographic regions. It reveals that the contribution rate of resilience index is over 50% in the central, southern, and eastern parts of China, where agricultural drought vulnerability is relatively low. However, the contribution rate of sensitivity is 75% in the Southwest and Northwest region, where the agricultural drought vulnerability is relatively high. Among influencing factors, the multiple-crop index, the proportion of the rural population and the forest coverage rate have higher contribution rate. This study carries reference significance for understanding the vulnerability of agricultural drought in China and it provides measures for drought prevention and mitigation.

Application of Combination Evaluation for Academic Title Evaluation

2011 ◽  
Vol 50-51 ◽  
pp. 756-760
Author(s):  
Bao Feng Li ◽  
Jing Guo Qu ◽  
Pu Yu Hao
Keyword(s):  
Associate Professor ◽  
Topsis Method ◽  
Entropy Weight ◽  
Base Points ◽  
Subjective Weight ◽  
Evaluation Models ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Double Base

In this paper, using the relevant data of 34 teaching staffs who participate in the academic title evaluation of associate professor in 2010, firstly it introduces the entropy weight method, Topsis method with subjective weight, Topsis method with objective weight and double base points method with subjective weight to evaluate and sort the performance of 34 teaching staffs. Secondly, two combination evaluation models are constructed to do the same work and the conclusions are more science and rational.

Application of combined model with DGM(1,1) and linear regression in grain yield prediction

2018 ◽  
Vol 8 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Bingjun Li ◽  
Weiming Yang ◽  
Xiaolu Li
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Grain Yield ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Entropy Weight ◽  
Combined Model ◽  
Content Type ◽  
Combination Model

Purpose The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations. Design/methodology/approach Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values. Findings The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction. Practical implications The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed. Originality/value This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

scholarly journals Identifying Influencing Factors of Regional Agricultural Drought Vulnerability Based on PSR-TGRC Method

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dongxing Zhang ◽  
Wenkai Cao ◽  
Bing Qi
Keyword(s):  
Influencing Factors ◽  
Agricultural Drought ◽  
Analysis Method ◽  
Drought Vulnerability ◽  
Relative Variation ◽  
Factors Affecting ◽  
Relational Analysis ◽  
Theoretical Support ◽  
Number Of Factors ◽  
Evaluation Problem

Regional agricultural drought vulnerability (RADV) is a complex nonlinear problem caused by the interaction of multiple factors, and an objective and systematic method is proposed by this paper to identify its influencing factors, which plays an important role in preventing and regulating the risks of regional agricultural drought. Firstly, to provide a reference for the evaluation problem in selecting the number of factors, the influencing factors affecting RADV are revealed by using the method of phase space reconstruction (PSR). Secondly, to rank the importance of influencing factors, a grey trend relational analysis (TGRA) method is proposed, considering the dynamic development relationship between the RADV index and the influencing factors and integrating the absolute and relative variation of sequences in each corresponding period. Finally, to reduce the collinearity between the influencing factors, a grey trend relational clustering (TGRC) analysis method is proposed. According to the above steps, the process of identifying factors based on PSR-TGRC method is formed. Taking Henan Province as an example, 14 main influencing factors and their effects on RADV are identified from all 42 factors, and the identification results which are consistent with the actual drought relief work show the rationality and practicality of PSR-TGRC method and provide theoretical support for formulating strategies of regional agricultural disaster prevention and mitigation.

Data-driven structure selection for the grey NGMC(1,N) model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dang Luo ◽  
Decai Sun
Keyword(s):  
Correction Term ◽  
Geometric Interpretation ◽  
Selection Method ◽  
Agricultural Drought ◽  
Grey Model ◽  
Model Parameters ◽  
Drought Vulnerability ◽  
Content Type ◽  
Structure Selection ◽  
Lasso Method

PurposeWith the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.Design/methodology/approachThe linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.FindingsBy adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.Practical implicationsThis paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.Originality/valueBased on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.

A Material Selection Method Based on Double Base Points

2014 ◽  
Vol 707 ◽  
pp. 509-513
Author(s):  
Shi Gang Chao
Keyword(s):  
Material Selection ◽  
Selection Method ◽  
Distance Measures ◽  
Entropy Weight ◽  
Base Points ◽  
Decision Making Problem ◽  
New Material ◽  
Double Base ◽  
Multiple Attributes Decision Making ◽  
Selection Of

The selection of the most desirable material contains many evaluation attributes, and thus leads to hard be solved. The material selection is actually a multiple attributes decision making problem, which has been studied by many authors. The aim of this study is to propose a new material selection method, which is an improved double base points method through defining the entropy weight and used the relative approach degree to measure the distance measures. The method may avoid the influence of subjective factors through the entropy weight, is very suitable for material selection problem. The applied example proves that the method is both effective and exercisable.

Platform ecological circle for cold chain logistics enterprises: the value co-creation analysis

2020 ◽  
Vol 120 (4) ◽  
pp. 675-691 ◽  
Author(s):  
Benhong Peng ◽  
Yuanyuan Wang ◽  
Sardar Zahid ◽  
Guo Wei ◽  
Ehsan Elahi
Keyword(s):  
Influencing Factors ◽  
Resource Sharing ◽  
Structural Equation ◽  
Dynamic Capabilities ◽  
Positive Influence ◽  
Cold Chain ◽  
Enterprise Performance ◽  
Logistics Industry ◽  
Content Type ◽  
Logistics Enterprises

Purpose The purpose of this paper is to propose a framework of value co-creation in platform ecological circle for cold chain logistics enterprises to guide the transformation and development of cold chain logistics industry. Design/methodology/approach This paper establishes a conceptual framework for the research on the platform ecological circle in cold chain logistics, utilizes a structural equation model to investigate the influencing factors of the value co-creation of the platform ecological circle in the cold chain logistics enterprises and elaborates the internal relations between different influencing factors regarding the value co-creation and enterprises’ performance. Findings Results show that resource sharing in logistics platform ecological circle can stimulate the interaction among enterprises and this produces a positive influence on their dynamic capabilities, which, in turn, affects the they to work together to plan, implement and solve problems, so as to achieve the goal of improving enterprise performance. Practical implications The shared resources and value co-creation activities in the platform ecological circle are very important for the transformation and development of cold chain logistics enterprises. Therefore, enterprises should promote value co-creation through realizing resource sharing and creating a win-win cooperation mechanism. Originality/value This paper targets at incorporating the resource sharing in platform ecological circle for cold chain logistics enterprises, explores from an empirical perspective the role of the resource sharing in cold chain logistics enterprises in enhancing the dynamic capabilities of enterprises, thereby encouraging the value co-creation behavior, and ultimately boosts enterprise performance and stimulates business development.

scholarly journals Investigating the Spatio-Temporal Variation of Soil Moisture and Agricultural Drought towards Supporting Water Resources Management in the Red River Basin of Vietnam

2021 ◽  
Vol 13 (9) ◽  
pp. 4926
Author(s):  
Nguyen Duc Luong ◽  
Nguyen Hoang Hiep ◽  
Thi Hieu Bui
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Temporal Dynamics ◽  
Regional Scale ◽  
Dry Season ◽  
Red River ◽  
Agricultural Drought ◽  
Severe Drought ◽  
Red River Basin ◽  
Spatio Temporal

The increasing serious droughts recently might have significant impacts on socioeconomic development in the Red River basin (RRB). This study applied the variable infiltration capacity (VIC) model to investigate spatio-temporal dynamics of soil moisture in the northeast, northwest, and Red River Delta (RRD) regions of the RRB part belongs to territory of Vietnam. The soil moisture dataset simulated for 10 years (2005–2014) was utilized to establish the soil moisture anomaly percentage index (SMAPI) for assessing intensity of agricultural drought. Soil moisture appeared to co-vary with precipitation, air temperature, evapotranspiration, and various features of land cover, topography, and soil type in three regions of the RRB. SMAPI analysis revealed that more areas in the northeast experienced severe droughts compared to those in other regions, especially in the dry season and transitional months. Meanwhile, the northwest mainly suffered from mild drought and a slightly wet condition during the dry season. Different from that, the RRD mainly had moderately to very wet conditions throughout the year. The areas of both agricultural and forested lands associated with severe drought in the dry season were larger than those in the wet season. Generally, VIC-based soil moisture approach offered a feasible solution for improving soil moisture and agricultural drought monitoring capabilities at the regional scale.

The role of agency in project initiation decisions

2014 ◽  
Vol 7 (3) ◽  
pp. 518-535 ◽  
Author(s):  
Mark Mullaly
Keyword(s):  
Decision Making ◽  
Decision Rules ◽  
Personal Characteristics ◽  
Decision Makers ◽  
Organizational Dynamics ◽  
Substantive Theory ◽  
Content Type ◽  
The Subject ◽  
Board Level

Purpose – The purpose of this paper is to explore the role of decision rules and agency in supporting project initiation decisions, and the influences of agency on decision-making effectiveness. Design/methodology/approach – The study this paper is based upon used grounded theory methodology, and sought to understand the influences of individual decision makers on project initiation decisions within organizations. Data collection involved 28 participants who were involved in project initiation decisions within their organizations, who discussed the process of project initiation in their organization and their role within that process. Findings – The study demonstrates that the overall effectiveness of project initiation decisions is a product of agency, process effectiveness or rule effectiveness. The employment of agency can have a direct influence on decision-making effectiveness, it can compensate for organizational inadequacies of a process or political nature, and it can be constrained in the evidence of formal and effective organizational practices. Research limitations/implications – While agency was recognized by all participants, there are clearly circumstances where actors perceive the ability to exercise agency to be externally constrained. The study is exploratory, contributing to the development of substantive theory. Theory testing as well as a more in-depth investigation of the underlying drivers of agency would be valuable. Practical implications – The study provides executives and individuals supporting the initiation of projects with insights on how to effectively influence the effectiveness of project initiation decisions, and the degree to which personal characteristics influence organizational dynamics. Originality/value – Most discussions of agency has been framed the subject as an executive- or board-level phenomenon. The current study demonstrates that agency is in fact being perceived and operationalized at all levels. Those demonstrating agency in the majority of instances in this study do so in exercising stewardship behaviours. This has important implications for how agency is perceived by executives, and by how agency is exercised by actors at all levels of the organization.

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