Bingjun Li, Shuhua Zhang, Wenyan Li and Yifan Zhang
Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the…
Abstract
Purpose
Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.
Design/methodology/approach
Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.
Findings
The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.
Originality/value
By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.
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The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data…
Abstract
Purpose
The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA), based on the previous study of grey relational decision-making model, and it considers the advantages of the decision-making schemes and the subjective preferences of decision makers.
Design/methodology/approach
First of all, through AHP, the preference of each index is analyzed and the index weight is determined. Second, the DEA model is adopted to obtain the index weight from the perspective of the most beneficial to each scheme and objectively reflect the advantages of different schemes. Then, assign the comprehensive weights to each index of the grey relational decision-making model of three-parameter interval grey number, and calculate the grey relation degree of each scheme to rank the schemes.
Findings
The effectiveness of the model is proved by an example of carrier aircraft selection.
Practical implications
The applicability of this model is analyzed by taking carrier aircraft selection as an example. In fact, this model can also be widely used in agriculture, industry, economy, society and other fields.
Originality/value
In this paper, the combination of AHP and DEA is used to determine the index weight. Based on which, the grey relation degree under the three-parameter interval grey number is calculated. It intended the application space of the grey relational decision-making model.
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Bingjun Li, Weiming Yang and Xiaolu Li
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.
Abstract
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.
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Sandang Guo, Yaqian Jing and Bingjun Li
The purpose of this paper is to make multivariable gray model to be available for the application on interval gray number sequences directly, the matrix form of interval…
Abstract
Purpose
The purpose of this paper is to make multivariable gray model to be available for the application on interval gray number sequences directly, the matrix form of interval multivariable gray model (IMGM(1,m,k) model) is constructed to simulate and forecast original interval gray number sequences in this paper.
Design/methodology/approach
Firstly, the interval gray number is regarded as a three-dimensional column vector, and the parameters of multivariable gray model are expressed in matrix form. Based on the dynamic gray action and optimized background value, the interval multivariable gray model is constructed. Finally, two examples and comparisons are carried out to verify the effectiveness of IMGM(1,m,k) model.
Findings
The model is applied to simulate and predict expert value, foreign direct investment, automobile sales and steel output, respectively. The results show that the proposed model has better simulation and prediction performance than another two models.
Practical implications
Due to the uncertainty information and continuous changing of reality, the interval gray numbers are used to characterize full information of original data. And the IMGM(1,m,k) model not only considers the characteristics of parameters changing with time but also takes into account information on lower, middle and upper bounds of interval gray numbers simultaneously to make better suitable for practical application.
Originality/value
The main contribution of this paper is to propose a new interval multivariable gray model, which considers the interaction between the lower, middle and upper bounds of interval numbers and need not to transform interval gray number sequences into real sequences. According to combining different characteristics of each bound of interval gray numbers, the matrix form of interval multivariable gray model is established to simulate and forecast interval gray numbers. In addition, the model introduces dynamic gray action to reflect the changes of parameters over time. Instead of white equation of classic MGM(1,m), the difference equation is directly used to solve the simulated and predicted values.
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The purpose of this paper is to comprehensively and accurately analyze the supply and demand structural balance of grain in the context of China's agricultural supply-side reform…
Abstract
Purpose
The purpose of this paper is to comprehensively and accurately analyze the supply and demand structural balance of grain in the context of China's agricultural supply-side reform. By subdividing and forecasting the supply and demand, it is beneficial for targeted production in the case of clear demand and supply trends of main grain varieties.
Design/methodology/approach
This paper forecasted and analyzed the demand of main grain varieties by the grey interval forecast, and based on the grey incidence analysis of more influence factors, forecasted the grain production with GM (1,N) model.
Findings
The results show that the demand and yield will keep sustainable growth in the next three years, while there is still a big gap between the supply and demand of the main grain varieties in China and the soybean's production is far behind the growing demand.
Practical implications
This paper can make full use of the information to provide the evidence for government to formulate policies and put forward some correlative suggestions for growers.
Originality/value
In this paper, the grey model technology is applied to the structural reform of grain supply side, and different models are used to predict the structural balance of supply and demand of different kinds of grain. The study of grain supply and demand structural balance in China is vital to ensure grain security in the context of agricultural supply-side reform.
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The purpose of this study to provide a reference basis for effectively managing the risk of agrometeorological disasters in Henan Province, speeding up the establishment of a…
Abstract
Purpose
The purpose of this study to provide a reference basis for effectively managing the risk of agrometeorological disasters in Henan Province, speeding up the establishment of a scientific and reasonable system of agrometeorological disasters prevention and reduction and guaranteeing grain security.
Design/methodology/approach
Firstly, according to the statistical data of areas covered by natural disaster, areas affected by natural disaster, sown area of grain crops and output of grain crops from 1979 to 2018 in Henan Province, China. We have constructed an agrometeorological disaster risk assessment system for Henan province, China, which is composed of indicators such as rate covered by natural disaster, rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability. The variation characteristics of agrometeorological disasters in Henan Province and their effects on agricultural production are analyzed. Secondly, the grey relational analysis method is used to analyze the relation degree between the main agrometeorological disaster factors and the output of grain crops of Henan Province. Based on the grey BP neural network, the rate covered by various natural disaster and the rate affected by various natural disaster are simulated and predicted.
Findings
The results show that: (1) the freeze injury in the study period has a greater contingency, the intensity of the disaster is also greater, followed by floods. Droughts, windstorm and hail are Henan Province normal disasters. (2) According to the degree of disaster vulnerability, the ability to resist agricultural disasters in Henan Province is weak. (3) During the study period, drought and flood are the key agrometeorological disasters affecting the grain output of Henan Province, China.
Practical implications
The systematic analysis and evaluation of agrometeorological disasters are conducive to the sustainable development of agriculture, and at the same time, it can provide appropriate and effective measures for the assessment and reduction of economic losses and risks.
Originality/value
By calculating and analyzing the rate covered by natural disaster, the rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability of crops in Henan Province of China and using grey BP neural network simulation projections for the rate covered by various natural disaster and the rate affected by various natural disaster, the risk assessment system of agrometeorological disasters in Henan is constructed, which provides a scientific basis for systematic analysis and evaluation of agrometeorological disasters.
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Jing Ye, Bingjun Li and Fang Liu
This paper aims to find an effective and standardized function transformation method to apply in both high-growth original data sequences and low-growth original data sequences…
Abstract
Purpose
This paper aims to find an effective and standardized function transformation method to apply in both high-growth original data sequences and low-growth original data sequences, which can improve the accuracy of model prediction in GM(1, 1) forecast.
Design/methodology/approach
In GM(1, 1) forecast, many original data sequences need to meet the quasi-exponential characteristic by methods of function transformation. However, many methods of function transformation have complex transformation processes or narrow application range. On the basis of the research results of Ye and Li, the paper presents a standardized approach based on to original data sequences and designs four situations of the standardized approach. By using high-growth and low-growth original data sequences as the objects, respectively, the paper verifies the effectiveness of the proposed method and compares the forecasting effects of GM(1, 1) based on function transformation with the original GM(1, 1).
Findings
Most of the results show that function transformations can improve the accuracy of the conventional GM(1, 1) forecast, and transform is a powerful tool to effectively process original data sequence of GM(1, 1) modeling.
Practical implications
GM(1, 1) forecast have been widely used in many fields such as agriculture, economy, meteorology, and geology. The proposed method in this paper can effectively apply to prediction of high-growth original data sequences and low-growth original data sequences, to some extent, enrich and deepen application of GM(1, 1) forecast.
Originality/value
The paper succeeds in providing a standardized approach based on and designs four intensity levels for different data sequences based on the standardized approach.
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Bingjun Li, Chunhua He, Liping Hu and Yanhua Li
The purpose of this paper is to realize dynamical grey incidence order of influencing factors of grain production in Henan province using grey systems theory.
Abstract
Purpose
The purpose of this paper is to realize dynamical grey incidence order of influencing factors of grain production in Henan province using grey systems theory.
Design/methodology/approach
Starting from choosing influence factors on grain production and dividing the 30 years (from 1979 to 2009 year) of grain production in Henan province into three periods, the authors calculate grey incidence degree between grain yield and every influencing factor by grey incidence analysis method, respectively, then obtain the grey incidence order of influencing factors in every period. Also based on the three grey incidence orders from different periods, the authors find a changeable tendency of influencing factors on grain production and key influencing factors on grain production in different periods. Finally, to keep Henan province grain production stable and sustainable, several policy suggestions are given.
Findings
The results are convincing: it is effective and powerful to analyze dynamically influencing factors of grain production using grey systems theory, and it is urgent to strengthen agricultural science and technology input, and pay close attention to the influence of dosage of pesticide and fertilizers on grain production.
Practical implications
Grey incidence analysis and findings exposed in the paper can be used by agricultural firms to optimize grain production plans, and by government to formulate reasonable agricultural production policies.
Originality/value
The paper succeeds in getting dynamical grey incidence order of influencing factors of grain production in Henan province using grey systems theory.
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Liu Sifeng, Zhao Liang, Dang Yaoguo and Li Bingjun
A new model called the G‐C‐D model, which is used to measure the technological advance, is built in this paper. The progress in non‐technical elements in Solow's “remaining value”…
Abstract
A new model called the G‐C‐D model, which is used to measure the technological advance, is built in this paper. The progress in non‐technical elements in Solow's “remaining value” is removed by using the idea, method and modeling technique of grey system theory. So, the difficult technical problem in the measurement of technological advance has been solved to a certain extent. The periodic G‐C‐D model of Henan Province is built in four different periods and the contribution rate of periodic technological advance of Henan Province is measured.
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Sifeng Liu, Yi Lin, Yaoguo Dang and Bingjun Li
In this paper, first a new model, the G‐C‐D model, which is used to measure the technological advance, is built. The progress with non‐technical elements in Solow's “remaining…
Abstract
In this paper, first a new model, the G‐C‐D model, which is used to measure the technological advance, is built. The progress with non‐technical elements in Solow's “remaining value” is removed by using the idea, method and modeling technique of grey system theory. So the difficult technical problem in measurement of technological advance has been solved to a certain extent. Secondly, another new model, the G‐E model, which combines the Grey model with the econometrics model, is built. Using the principle of grey incidence to analyse and cluster system factors, adopting the GM(1,1) simulated values of system's variables to build the econometrics model and confirming the predicted values with grey models, some difficult techniques in econometrics model building have been solved. Thirdly, the periodic G‐C‐D model of Henan Province is built in four different periods and the contribution rate of the periodic technological advance of Henan Province is measured. Lastly, the technical change and the relation between the technical change and the funds for science and technology of Henan Province are analysed with the grey production function (the G‐C‐D) and the grey‐econometrics combined model (the G‐E), and some useful outcome for policy‐making body are obtained.