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Analysis of agricultural economic development and optimisation measures under the strategy of rural revitalisation

Publicado en línea: 14 Nov 2022
Volumen & Edición: AHEAD OF PRINT
Páginas: -
Recibido: 02 May 2022
Aceptado: 30 Jun 2022
Detalles de la revista
License
Formato
Revista
eISSN
2444-8656
Primera edición
01 Jan 2016
Calendario de la edición
2 veces al año
Idiomas
Inglés
Introduction

Agricultural economy [1] is the foundation of the national economy and plays an important role in the development of the national economy, which cannot be replaced by other industries. Looking at the current situation of my country's agricultural development, it has the disadvantages of starting late with a relatively weak development level. The shortcomings of agricultural modernisation development directly drag down the overall process of my country's ‘four modernisations’ [2], Against the background of such a development environment, in order to make up for the insufficiency of agricultural development, the 19th National Congress of the Communist Party of China proposed a rural revitalisation strategy [3], adhering to the priority development of agriculture and rural areas [4]. The general requirements for effective governance and a prosperous life [5], establish and improve the urban-rural integrated development system [6] and policy system [7] and accelerate the modernisation of agriculture and rural areas. In order to develop agriculture, the optimisation of the industrial structure must be at the forefront. The speed of agricultural development directly determines the speed of the development of the secondary and tertiary industries. Therefore, it is even more important to optimise the industrial structure of agricultural economic development [8]. All points of entry to study its ways to promote rural revitalisation should be analysed.

Many experts and scholars in my country have made outstanding contributions to the research on promoting agricultural economic development and optimisation measures, and their professional research results have provided strong support for the development of modern agriculture. Some scholars believe that the optimisation of the agricultural industrial structure is the key to agricultural development in the new era [9], which not only plays a vital role in increasing farmers’ income but also meets the market's demand for agricultural products to a certain extent and realises the integration of the agricultural industry and market. Double coordination between [10]. Some scholars believe that the development of the agricultural industry cannot be limited to the current one [11], and the goal should be long term, realise dynamic monitoring [12], constantly adjust the influencing factors in the research process and even provide agricultural development through policy innovation. Other scholars proposed that the adjustment of the agricultural industry should be carried out in different levels and regions [13], organically combining the industrial structure, production structure, variety structure and other aspects [14] to find an excellent and feasible plan for each level, which promotes agricultural economic development [15].

On the basis of summarising and studying related theories, this paper deeply discusses the research results of the predecessors, starting from the significance of agricultural economic development, and analyses the advantages of optimising the agricultural industrial structure for agricultural development under the strategy of rural revitalisation [16]. According to relevant research, the agricultural industrial structure optimisation measurement index system was constructed, and according to the 22 specific indicators in the ‘Rural Revitalization Strategic Plan (2018–2022)’, the cointegration test was carried out through Eviws7.0 [17] and Granger causality test [18], using the analytic hierarchy process [19] and entropy method [20] to compare and analyse data, to measure the degree of rural revitalisation by the optimisation of the agricultural industrial structure [21], to analyse agricultural economic development of the optimisation strategy [22], and to strive to contribute a little to the overall layout of rural revitalisation.

The significance of experimental equipment for agricultural economic development

Both social and economic development and natural development have certain laws to follow, and the forms of agricultural production and rural economic development are influenced by them to form a certain development model, that is, agricultural economy. The development of agricultural economy directly determines the income of farmers. If farmers want to get rich, they must develop agriculture and improve economic benefits. The proposal of the rural revitalisation strategy provides better policy support for the development of agriculture and rural economy. This theoretical background still needs further in-depth discussions by scholars to make up for the shortcomings in the field of agricultural science and technology in my country.

Theoretical significance

How to achieve the clear goals and requirements put forward by rural revitalisation? What are the ways? How should the results be evaluated? These are the issues that need to be discussed at present. Although many experts and scholars have achieved some results through their own study and efforts, these academic achievements are still far from enough on the road of rural revitalisation. The author discusses the agricultural economic development and optimisation measures under the rural revitalisation strategy and strives to find more abundant development paths for rural revitalisation, sort out new ideas, and further optimise the agricultural and rural industrial structure and improve the solution through rationalised data calculation and analysis. ability to deal with practical problems.

Practical significance

Rural revitalisation is the most favourable strategy proposed by the party and the state for rural development in the new era [23]. To realise the area of rural revitalisation, it is necessary to manage all aspects and achieve the goal through multiple channels. Judging from the current situation of agricultural development in my country, there are many agricultural practitioners and a wide range of areas, which can meet the demand for labour output to a certain extent. However, engaging in agricultural production also has the disadvantages of unstable returns, large risk fluctuations and long production cycles. Therefore, my country's agricultural development is at a disadvantage. With the continuous development of the social economy, the needs of agricultural economic development are also increasing day by day, and the inconsistency between the agricultural industrial structure and the requirements of agricultural development goals has become a major obstacle to development. Because the optimisation and reform of agricultural economic development is carried out in parallel, it is of practical significance to implement rural revitalisation and improve the level of agricultural economic development.

Calculation of the promotional degree of rural revitalisation by the optimisation of the agricultural industrial structure
Model building and data collection and processing

Since the time series in reality are mostly non-stationary, if the regression analysis is carried out directly, there will be a ‘pseudo-regression’ phenomenon, which will affect the research results. For cointegration analysis [24], the stationarity of the time series [25] needs to be checked, which proceeds as follows:

Fig. 1

Eng1e-Granger cointegration analysis flow chart

Model building

The cointegration test commonly used Engle-Granger cointegration test or Johansen cointegration test. The EG cointegration test is generally suitable for cointegration between two variables [26], and the Johansen cointegration test is generally used for cointegration between multiple variables. This paper is a test between multiple groups of two variables; so, the EG cointegration test is used [27]. This paper establishes a cointegration regression model to test the long-term equilibrium relationship between agricultural industrial structure optimisation and rural revitalisation [28]. This long-term equilibrium relationship is maintained by continuously adjusting short-term fluctuations; so, an error correction model (ECM) needs to be introduced [29] to be additional to that. Lnyt=b0+b1lnxt+εt {\rm{Ln}}{{\rm{y}}_{\rm{t}}} = {{\rm{b}}_0} + {{\rm{b}}_1}\ln {{\rm{x}}_{\rm{t}}} + {\varepsilon _{\rm{t}}}

Among them is the explanatory variable lnxt, where xt represents the selected indicators related to the measurement of agricultural industrial structure optimisation, and Lnyt is the explained variable, where yt represents the selected indicators related to rural revitalisation, and ɛt is the amount of random error.

The ECM is as follows: ΔLnyt=β0+β1Δlnxtλecm+εt \Delta {\rm{Ln}}{{\rm{y}}_{\rm{t}}} = {\beta _0} + {\beta _1}\Delta \ln {{\rm{x}}_{\rm{t}}} - {\lambda _{{\rm{ecm}}}} + {\varepsilon _{\rm{t}}} where ecm represents the error correction term, β0 is a constant term and, β1, λ is a parameter term, which ɛ1 is the random error amount.

Data selection and interpretation

The data are mainly from the 2001–2017 ‘A Statistical Yearbook’ and ‘A Government Work Report,’ and the official data are released by the city's bureau of statistics. This article selects reviews with 7.0 as the analysis tool for EG cointegration analysis, regression analysis and Granger causality test [30]. For the convenience of inspection, the data of each indicator are expressed as follows: the annual growth rate of agricultural output value (ZC), the comprehensive grain production capacity (LS), the proportion of rural employees engaged in non-agricultural industries (JS) and the agricultural labour productivity (LD), the growth rate of agricultural added value (XJ), the income ratio of urban and rural residents (CJ), the per capita net operating income of rural households (NJ), the Engel coefficient (NG) of rural residents and the proportion of rural residents’ expenditure on education, culture and entertainment (JY). According to the corresponding indicators in Figure 2, this paper will analyse the annual growth rate of agricultural output value and comprehensive grain production capacity (denoted as L4-1), the proportion of non-agricultural labour force and agricultural labour productivity (L4) among rural employees, the ratio of the growth rate of agricultural added value to the income of urban and rural residents (L4-3), the per capita net operating income of rural households and the Engel's coefficient of rural residents (L4-4) and the ratio of per capita net operating income of rural households to rural residents. The proportion of education, culture and entertainment expenditure (L4-5) is analysed in five groups of time series data. In order to ensure the accuracy and scientificity of the research and prevent the variables and numerical values from changing drastically, the logarithm of each group of data is used as a variable for research. LNZC, LNLS, LNJS, LNLD, LNXJ, LNCJ, LNNJ, LNNG, LNJY (logarithmic processing will not affect the nature of the data) are recorded, and the ADF test is performed. It can be seen that the labour force engaged in non-agricultural industries among the rural employees has gradually declined, and the number was the smallest in 2016, and the change in the labour generation rate after 2003 was small. The per capita net operating income of rural households and the Engel's coefficient of rural residents gradually decreased and reached their peaks in 2010. From Figure 4(a), it can be seen that the per capita operating net income of rural households has gradually increased, and the proportion of labour force has decreased. It shows that the living standard of rural residents has been improved.

Fig. 2

The proportion of non-agricultural labour force and agricultural labour productivity data among rural employees in a city (1981–2021)

Fig. 3

Data on per capita net operating income of rural households and Engel's coefficient of rural residents in a city (2001–2021)

Fig. 4

Per capita operating net income of rural households and Engel's coefficient of rural residents in a city (2001–2021). (a) Per capita operating net income of rural households. (b) Engel's coefficient of rural residents

Stationarity test of each group of variable data

The principle of the ADF test method is that the null hypothesis is that the sequence has a unit root, that is, non-stationary. When testing time series data, under the conditions of a given confidence level (1%, 5%, 10%), when the obtained statistic is significantly less than the critical statistical value of confidence, it means that the null hypothesis is rejected, that is, it is stationary; otherwise, it is non-stationary. The ADF test model is as follows: Model(1)Δyt=δyt1+j=1pλjΔytj+μt {\rm{Model}}{\kern 1pt} {(1)}\quad \Delta {y_t} = \delta {y_{t - 1}} + \sum\limits_{j = 1}^p {\lambda _j}\Delta {y_{t - j}} + {\mu _t} Model(2)Δyt=α+δyt1+j=1pλjΔytj+μt {\rm{Model}}{\kern 1pt} {(2)}\quad \Delta {y_t} = \alpha + \delta {y_{t - 1}} + \sum\limits_{j = 1}^p {\lambda _j}\Delta {y_{t - j}} + {\mu _t} Model(3)Δyt=α+βt+δyt1+j=1pλjΔytj+μt {\rm{Model}}{\kern 1pt} {(3)}\quad \Delta {y_t} = \alpha + \beta t + \delta {y_{t - 1}} + \sum\limits_{j = 1}^p {\lambda _j}\Delta {y_{t - j}} + {\mu _t} t is a time variable, indicating a certain trend of the series over time. First start the test from model 3, then model 2 and finally model 1. The test can be stopped until the test result rejects the null hypothesis. Otherwise, the test will continue until model 1. The time series is considered stationary if any of the model detection results reject the null hypothesis.

The results of the data test for each group of variables:

Fig. 5

(a) ADF test results. (b) Numerical changes of 10% and 5%. (c) Change curve when P-value is 0.05

Taking LNZC as an example, when the sequence LNZC has a constant term, a time trend term and a lag order, the ADF value is greater than the critical value at the 5% significance level, and the P-value is >0.05; the null hypothesis cannot be rejected, that is, the sequence is not stationary; while the ADF value of the first-order difference series DLNZC of LNZC is less than the lower critical value of the 5% significance level, the P-value is <0.05. Reject the null hypothesis that the series is stationary. From Figure 5, it can be concluded that the other groups of variables are all first-order single integrations; it meets the requirements of the cointegration test.

Cointegration tests and error correction models
Cointegration test

If the linear combination of non-stationary time series is stationary, the combination reflects the existence of long-term stable relationship between variables.

Proportional relationship is also called as cointegration relationship. If the time series have the same single integer order, and some linear

If the combination (cointegration vector) reduces the single integer order of the combined time series, it is said that there is a significant cointegration relationship between these time series. From the analysis of the unit root test results, it can be known that LNZC, LNLS, LNJS, LNLD, LNXJ, LNCJ, LNNJ, LNNG, and LNJY are all first-order single integers; so, LNZC and LNLS, LNJS and LNLD and LNXJ and LNCJ can be performed; Engle-Granger cointegration tests can be performed between LNNJ, LNNG and LNJY. For the convenience of research, this paper directly uses the Eng1e-Granger test method in Eivews to test whether there is a long-term equilibrium relationship between each group of variable data. The long-term equilibrium equations for each group of variables are obtained as follows: L(41):LLNS=4.5432+0.2289LNZC+εt(41)L(42):LLND=3.6183+0.3559LNJS+εt(42)L(43):LLNCJ=2.5923+()0.1262LNXJ+εt(43)L(44):LLNG=1.2100+()0.2412LNNJ+εt(44)L(45):LLNJ=1.8866417+()0.4443LNNJ+εt(45) \matrix{{L(4 - 1):\quad LLNS = 4.5432 + 0.2289LNZC + {\varepsilon _t}(4 - 1)} \hfill \cr {L(4 - 2):\quad LLND = 3.6183 + 0.3559LNJS + {\varepsilon _t}(4 - 2)} \hfill \cr {L(4 - 3):\quad LLNCJ = 2.5923 + (-)0.1262LNXJ + {\varepsilon _t}(4 - 3)} \hfill \cr {L(4 - 4):\quad LLNG = 1.2100 + (-)0.2412LNNJ + {\varepsilon _t}(4 - 4)} \hfill \cr {L(4 - 5):\quad LLNJ = 1.8866417 + (-)0.4443LNNJ + {\varepsilon _t}(4 - 5)} \hfill \cr}

The residual sequence is saved, and the ADF test is performed on the residual sequence. The stationarity test results are shown in Table 1, which verifies the stability of the ADF residual test.

ADF variable residual test results

Grouping Variable ADF Critical value Conclusion

1 t −5.8 −1.6 Stable
2 t −2.3 −1.9 Stable
3 t −4.8 −1.9 Stable
4 t −3.1 −1.9 Stable
5 t −3.9 −1.9 Stable
Error correction model

The cointegration test results show the annual growth rate of the total agricultural output value and the comprehensive grain production capacity of a city over the years, the proportion of rural employees engaged in non-agricultural industries and agricultural labour productivity, the growth rate of agricultural added value and the ratio of urban and rural residents’ income. There is a long-term equilibrium relationship between the per capita operating net income and the Engel coefficient of rural residents and the proportion of rural residents’ expenditure on education, culture and entertainment, which means that there is an ECM. The result is as follows: L41:DLy=0.0079DLx0.1531DLy(1)0.0026ecm(1)L42:DLy=0.1329DLx0.0228Dly(1)0.0892ecm(1)L43:DLy=0.0621DLx+0.0316DLy(1)0.0763ecm(1)L44:DLy=0.3876DDx0.0719DLy(1)0.5873ecm(1)L45:DLy=1.4612DLx+0.1084DLy(1)0.8992ecm(1) \matrix{{L4 - 1:\quad DLy = 0.0079DLx - 0.1531DLy(- 1) - 0.0026ecm(- 1)} \hfill \cr {L4 - 2:\quad DLy = - 0.1329DLx - 0.0228Dly(- 1) - 0.0892ecm(- 1)} \hfill \cr {L4 - 3:\quad DLy = 0.0621DLx + 0.0316DLy(- 1) - 0.0763ecm(- 1)} \hfill \cr {L4 - 4:\quad DLy = 0.3876DDx - 0.0719DLy(- 1) - 0.5873ecm(- 1)} \hfill \cr {L4 - 5:\quad DLy = - 1.4612DLx + 0.1084DLy(- 1) - 0.8992ecm(- 1)} \hfill \cr} (where ECM(−1) is the one lag of the residual term in the association.)

The signs of the correction coefficients of ECM(−1) in each group are all negative, which conforms to the reverse correction mechanism. From the above test results, it can be seen that the residual ɛt of each group is a stationary time series, and there is a cointegration relationship, which can reflect the existence of a long-term equilibrium relationship between the variables in each group. The constructed model is reasonable and has economic significance.

Granger causality test

The principle of the Granger causality test is as follows: if variable X is the cause of the change in Y, then adding the lag of X to the regression can significantly improve the prediction of Y. The Granger causality test is ‘Granger causality’ in the statistical sense, and its test conclusion is only a prediction not a causal relationship in the true sense, but this does not hinder its reference value. In the regression model, some variables do not necessarily have a certain causal relationship but are significantly correlated. We have found through regression analysis that there is a long-term stable equilibrium relationship between the variables in each group. Therefore, the test of the causal relationship between variables is as follows: meaningful and necessary. The Granger causality test results for each group of variables are shown in Table 2, where F is the family income variable and P is the labour force variable.

Gran causality test results

Grouping Original hypothesis F P

1 LNLS 2.4 0.06
LNZC 1.07 0.40
2 LNLD 2.92 0.09
LNIS 0.11 0.73
3 LNCI 3.15 0.06
LNXJ 0.21 0.82
4 LNNJ 3.19 0.08
LNGJ 1.63 0u.22
5 LNJY 5.19 0.02
LNNJ 5.54 0.03

As far as the model constructed in this paper is concerned, the rural revitalisation strategy is the reason for the optimisation of the industrial structure.

AHP

The tomographic analysis method has been applied by scholars as early as the 1970s. It is a method that combines qualitative and quantitative methods for hierarchical weight decision-making and an analysis to draw conclusions. AHP is suitable for solving a series of complex problems with many objectives, many criteria, difficult quantification and weak structural characteristics and provides simpler solutions for such problems. Hierarchical gaps can to a certain extent avoid the deviations caused by the subjective judgements of decision makers. It is a mistake in thinking to avoid decision makers from facing many criteria and problems. This is also an important reason why the analytic hierarchy process is used by many scholars.

For the assignment of importance, the scale method of 1–9 is usually used to construct an n-order judgement matrix. The scales at all levels are shown in Table 3.

Scales and meanings at all levels

Serial number I and j importance Assignment

1 Equally important 1
2 Slightly important 3
4 Extremely important 7
5 Slightly unimportant 9
6 Obviously unimportant 1/3
7 Strong is not important 1/5
8 Extremely unimportant 1/7
9 In the middle 2, 4, 6, 8
10 In the middle 1/2, 1/4, 1/6, 1/8

Because each index needs to be compared, it is necessary to standardise the elements of each column in the judgement matrix, using formula (8), where xij x_{ij}^{'} is the standardised value of the judgement matrix element and xij is the element in the judgement matrix. xij=xiji=1nxij x_{ij}^{'} = {{{x_{ij}}} \over {\sum\limits_{i = 1}^n {x_{ij}}}}

Use formula (9) to calculate the weight of each element wi w_i^{'} , and the wi w_i^{'} vector composed of it is the eigenvector: wi=1nj=1nxij w_i^{'} = {1 \over n}\sum\limits_{j = 1}^n x_{ij}^{'}

Use formula (10) to calculate the maximum eigenroot of the judgement matrix: λmax=i=1nj=1nxij*wjn*wj {\lambda _{\max}} = \sum\limits_{i = 1}^n {{\sum\limits_{j = 1}^n {x_{ij}}*{w_j}} \over {n*{w_j}}}

Using formula (11), CI, CI is calculated as the smaller the consistency index value of the judgement matrix, the better the consistency of the judgement matrix. CI=λmaxnn1 CI = {{{\lambda _{\max}} - n} \over {n - 1}}

Use formula (12) to calculate the consistency ratio, where CR is the average random consistency index, and RI is used to measure whether the matrix is consistent. However, for matrices below the second order, it has no practical significance and is generally zero. For matrices above the second order, if the value of CR is <0.1 then it is considered that the judgement moments are consistent. The values of which are shown in Table 4. CR=CIRI CR = {{CI} \over {RI}}

RI value

n 1 2 3 4 5 6 7 8 9
RI 0 0 0.5 0.9 1.1 1.2 1.3 1.4 1.4
Entropy method

The concept of entropy originates from statistical physics and heat and is used to quantitatively describe the degree of uncertainty of a system. This paper uses the entropy method to determine the weight of each index, which provides the basis for quantitative analysis for comprehensive evaluation.

Standardisation of indicators

Due to the inconsistency of the measurement units of various indicators, it is impossible to compare them accurately and objectively. Therefore, before calculating specific indicators, it is necessary to convert the absolute value of the indicator into a relative value and let xij=|xij| {x_{ij}} = \left| {{x_{ij}}} \right| in order to solve the problem that the indicators of different units cannot be compared. Since the values of positive and negative indicators have different meanings, it is necessary to perform dimensionless data processing on them respectively. The relevant algorithm formula is as follows:

Positive indicators: Xij=Xijmin(X1j,X2jXnj)max(X1j,X2jXnj)min(X1,,X2jXnj) X_{ij}^{'} = {{{X_{ij}} - \min \left({{X_{1j}},{X_{2j}} \ldots \ldots {X_{nj}}} \right)} \over {\max \left({{X_{1j}},{X_{2j}} \ldots \ldots {X_{nj}}} \right) - \min \left({{X_{1,}},{X_{2j}} \ldots {X_{nj}}} \right)}}

Negative indicators: Xij=max(X1,,X2jXnj)Xijmax(X1j,X2jXnj)min(X1,,X2jXnj) X_{ij}^{'} = {{\max \left({{X_{1,}},{X_{2j}} \cdots \ldots {X_{nj}}} \right) - {X_{ij}}} \over {\max \left({{X_{1j}},{X_{2j}} \ldots \ldots {X_{{\rm{n}}j}}} \right) - \min \left({{X_{1,}},{X_{2j}} \cdots \ldots {X_{{\rm{nj}}}}} \right)}} where xij is the value j of the first indicator of the i year. (i = 1,2…, n; j = 1,2,…, m)

Calculate the proportion i of the first evaluation j object under the first index

pij=Xiji=1nXij,(i=1,2,n;j=1,2,,m) {p_{ij}} = {{{X_{ij}}} \over {\sum\limits_{i = 1}^n {X_{ij}}}},\quad ({\rm{i}} = 1,2 \ldots,{\rm{n}};\;{\rm{j}} = 1,2, \ldots,{\rm{m)}}

Calculate the entropy value j of the first index

ej=ki=1npijln(pij)lnk>0,k=1/ln(n),ej0 \matrix{\hfill {{e_j} = - k\sum\limits_{i = 1}^n {p_{ij}}\ln \left({{p_{ij}}} \right)} \cr \hfill {\ln k > 0,\;k = 1/\ln (n),{e_j} \ge 0} \cr}

Calculate the coefficient of difference j of the first index

For the first index j, Xij is the smaller the difference and ej is the greater the difference. When Xij all are equal (ej = emax = 1(k = 1/lnn)), the index has no effect on the comparison Xij of the evaluated objects. When Xij is the larger difference and ej is the smaller distance, the index has a greater effect on the comparison of the evaluated objects. Therefore, gj = 1 − ej, gj the greater the definition difference coefficient, the more attention should be paid to the role of this indicator.

Determine the weights

wj=gjj=1mgj(1jm) {w_j} = {{{g_j}} \over {\sum\limits_{j = 1}^m {g_j}}}\quad (1 \le j \le m)

Among them, wj is the normalised weight coefficient

Calculate the comprehensive score of the evaluated object

si=j=1mwjpij(i=1,2,n) {s_i} = \sum\limits_{j = 1}^m {w_j} \cdot {p_{ij}}\quad (i = 1,2, \ldots n)

Calculation results of entropy value of each index

Criterion layer Index layer Increment Difference coefficient

Economic and social development level Per capita GDP 0.51 0.47
Per capita fiscal revenue 0.79 0.16
Proportion of tertiary industry 0.69 0.28
Total power of agricultural machinery 0.65 0.34
Grain yield per unit area 0.72 0.25
Urbanisation level 0.74 0.22
Per capita income of rural residents 0.69 0.25
Per capita cultivated land area 0.77 0.24
forest coverage 0.71 0.26
Industrial carbon dioxide emissions 0.73 0.21
Ecological environment security Hydroxide emission 0.79 0.20
Soot emission 0.90 0.12
Effective irrigation area 0.82 0.17
Fertiliser use intensity 0.75 0.27
Pesticide use intensity 0.86 0.15
Analysis of measurement results
Analysis of the optimised structure of the agricultural industry

From the cointegration regression equation, there is a long-term equilibrium relationship between the indicators of agricultural industrial structure optimisation and rural revitalisation indicators, and from the results of the Granger causality test, the optimisation of agricultural industrial structure is the Granger cause of rural revitalisation. In terms of each group of indicators, it can be considered that the optimisation of the agricultural industrial structure has effectively promoted rural revitalisation. Among them, the promotion of the prosperity of the industry is the greatest. The specific performance is that the annual growth rate of the total agricultural output value increases by 1 unit, and the comprehensive grain production capacity increases by 0.22 units; the proportion of rural employees engaged in non-agricultural industries increases by 1 unit, and agricultural labour productivity rose by 0.35 units. Promoting the affluence of life comes second, as shown in the fact that the growth rate of agricultural added value increases by 1 unit, and the income ratio of urban and rural residents decreases by 0.12 units; the per capita operating net income of rural households increases by 1 unit, and the Engel coefficient of rural residents decreases by 0.24. units. Then, there is the rural style and civilisation. The per capita net operating income of rural households increased by 1 unit, and the proportion of rural residents’ expenditure on education, culture and entertainment decreased by 0.44 units.

Analysis of optimisation strategies for agricultural economic development

Agricultural economy is an important part of my country's economic development. Relevant departments must strengthen their understanding of agricultural economic management work. Various situations in the economic development have contributed to the healthy and sustainable development of my country's rural economy.

Optimise the management mechanism

The development of agricultural economy needs a strong theory as a support, which requires the establishment of a corresponding management mechanism. In the new era, driven by the rural revitalisation strategy, a systematic and institutionalised economic situation has gradually emerged. Relevant departments have also stepped up their efforts to continuously adjust and optimise the management mechanism based on the actual situation, promote industrial upgrading and then ensure the management system. It can match the reality of rural economic development and meet the diversified needs of agricultural economic development. In the process of optimising the management mechanism, the transformation of farmers’ production concepts is also an important part of agricultural economic development. Relevant departments should continuously disseminate advanced production concepts among farmers through training courses and on-the-spot explanations by experts to assist in optimising the management mechanism. In addition, it is necessary to continuously update agricultural production equipment. Farmers themselves and relevant departments must do their homework in this regard. Advanced equipment can improve the efficiency of agricultural production and create more production capacity within a certain period of time. The agricultural management department should exchange feedback with farmers to form a good connection so that the excellent management mechanism can truly take root and germinate in the fields, so as to better serve the development of the agricultural economy.

Strengthen management innovation

Science and technology are the boosters of all productive forces, and the continuous improvement of the level of science and technology is an effective means for the continuous development of my country's agricultural economy. The agricultural management department must learn advanced technical means before farmers, strengthen the work of agricultural economics and do a good job in the overall management of the standardised development of agriculture through the use of high-tech means. On this basis, agricultural management departments should also make scientific predictions on agricultural development, and based on this, they must formulate relevant policies to adapt to the general trend of agricultural economic development. It is necessary to give full play to the advantages of scientific and technological means, make scientific judgements through reasonable calculation, comparison and data analysis, change the traditional way of relying on experience and old farmers, establish and improve a networked supervision system and better solve problems in agricultural development, which is an outstanding issue.

Strengthen the introduction of special talents and capital investment

The implementation of agricultural economic development work needs to rely on talents; talents are the carrier of management technology, and the quality of talents is directly related to the quality of agricultural economic development work. The management department should also regularly organise management personnel to carry out systematic work training to ensure the work quality and professional ethics of all personnel and always maintain the advanced level of management concepts and methods. The actual and future development directions of agricultural development in different regions are different. Managers in each region should have an in-depth understanding of the local agricultural situation so as to ensure the effectiveness of their own management. In addition, it is also necessary to combine the local agricultural development with national policies, formulate clear development plans based on the actual situation, give full play to advantages and avoid disadvantages, allow farmers to participate more actively in agricultural management and promote the better development of regional agricultural economy. The construction of rural infrastructure and informatization network construction is inseparable from the investment of funds. Only when the funds are in place, can a unique information network in the area be formed and provide farmers with more targeted agricultural information. Therefore, whether the capital investment in the preparatory stage is sufficient is an important factor for the smooth development of rural agricultural economic development.

Conclusion

In recent years, the issue of agricultural economic development has attracted attention from all walks of life. As a basic industry, agriculture is an important part of the national economy and directly determines the quality of life of our farmers. For so many reasons, it is extremely important to deeply explore the advantages of agricultural economic development under the rural revitalisation strategy. Guided by the basic national conditions, rationally plan the development goals of the agricultural industry, relying on the existing agricultural development indicators, and find a way more suitable for agricultural economic development through scientific calculation methods. Guide the rural economy to develop more scientifically, more modernly and more profitably by formulating scientific agricultural policies. In order to make up for the shortage of agricultural development, this paper proposes an analysis of agricultural economic development and optimisation measures under the rural revitalisation strategy. First, the practical and theoretical significance of agricultural economic development is pointed out, and the co-integration analysis is carried out to test the stationarity of the time series. Secondly, the ECM is introduced, and the data are supplemented. Prosperity promotes the greatest degree. Finally, using Granger causality test and analytic hierarchy process to analyse the indicators of agricultural industrial structure optimisation, the optimisation strategy of the optimising management mechanism, strengthening management innovation and strengthening the introduction of special talents and capital investment, is obtained.

Fig. 1

Eng1e-Granger cointegration analysis flow chart
Eng1e-Granger cointegration analysis flow chart

Fig. 2

The proportion of non-agricultural labour force and agricultural labour productivity data among rural employees in a city (1981–2021)
The proportion of non-agricultural labour force and agricultural labour productivity data among rural employees in a city (1981–2021)

Fig. 3

Data on per capita net operating income of rural households and Engel's coefficient of rural residents in a city (2001–2021)
Data on per capita net operating income of rural households and Engel's coefficient of rural residents in a city (2001–2021)

Fig. 4

Per capita operating net income of rural households and Engel's coefficient of rural residents in a city (2001–2021). (a) Per capita operating net income of rural households. (b) Engel's coefficient of rural residents
Per capita operating net income of rural households and Engel's coefficient of rural residents in a city (2001–2021). (a) Per capita operating net income of rural households. (b) Engel's coefficient of rural residents

Fig. 5

(a) ADF test results. (b) Numerical changes of 10% and 5%. (c) Change curve when P-value is 0.05
(a) ADF test results. (b) Numerical changes of 10% and 5%. (c) Change curve when P-value is 0.05

Scales and meanings at all levels

Serial number I and j importance Assignment

1 Equally important 1
2 Slightly important 3
4 Extremely important 7
5 Slightly unimportant 9
6 Obviously unimportant 1/3
7 Strong is not important 1/5
8 Extremely unimportant 1/7
9 In the middle 2, 4, 6, 8
10 In the middle 1/2, 1/4, 1/6, 1/8

ADF variable residual test results

Grouping Variable ADF Critical value Conclusion

1 t −5.8 −1.6 Stable
2 t −2.3 −1.9 Stable
3 t −4.8 −1.9 Stable
4 t −3.1 −1.9 Stable
5 t −3.9 −1.9 Stable

Gran causality test results

Grouping Original hypothesis F P

1 LNLS 2.4 0.06
LNZC 1.07 0.40
2 LNLD 2.92 0.09
LNIS 0.11 0.73
3 LNCI 3.15 0.06
LNXJ 0.21 0.82
4 LNNJ 3.19 0.08
LNGJ 1.63 0u.22
5 LNJY 5.19 0.02
LNNJ 5.54 0.03

RI value

n 1 2 3 4 5 6 7 8 9
RI 0 0 0.5 0.9 1.1 1.2 1.3 1.4 1.4

Calculation results of entropy value of each index

Criterion layer Index layer Increment Difference coefficient

Economic and social development level Per capita GDP 0.51 0.47
Per capita fiscal revenue 0.79 0.16
Proportion of tertiary industry 0.69 0.28
Total power of agricultural machinery 0.65 0.34
Grain yield per unit area 0.72 0.25
Urbanisation level 0.74 0.22
Per capita income of rural residents 0.69 0.25
Per capita cultivated land area 0.77 0.24
forest coverage 0.71 0.26
Industrial carbon dioxide emissions 0.73 0.21
Ecological environment security Hydroxide emission 0.79 0.20
Soot emission 0.90 0.12
Effective irrigation area 0.82 0.17
Fertiliser use intensity 0.75 0.27
Pesticide use intensity 0.86 0.15

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