The Role of Extension and Education in Agricultural Development: Empirical Evidence from Iran

Abstract

For several years, economic sanctions imposed by the US have created considerable pressure on Iran’s economy. Thus, Iran has continuously been seeking ways that improve productivity progress in order to acquire greater production by consuming fewer resources. This study aimed to explain the design of agricultural extension in the resistive economics development in Iran and present a practical model. The general approach of the present research is quantitative and inferential and has a mixed nature. Indeed, this investigation is semi-experimental in terms of the possibility of controlling variables, and data collection has been performed through the assessment of documents, context, and library. For sampling, the stratified random sampling method was used, while the sample size (151) was determined using the modified Cochran’s formula. Structural equation modeling (SEM) and LISREL software were applied to analyze the collected data, and finally, the structural equation modeling was achieved through regression and path analyzes. The validity of the questionnaire through expert opinion and average variance extracted (AVE) index, and the reliability of the questionnaire through Cronbach’s alpha coefficient (89%) were obtained. The findings of the goodness-of-fit test for the deterrent factor of agricultural extension affecting resistive economics show that the set of independent variables could explain 61–83% of the probability of variance of the dependent variable. For the leading factor of agricultural extension affecting the resistive economics, between 68.8 and 99.6%, and for the factor of resistive economics affecting the agricultural development sector, between 69.1 and 99.8% were calculated.

1. Introduction

A disrupted economy causes many problems for society. Nowadays, increasing population growth has increased the need for agricultural products and, subsequently, basic resources used for production. Also, economic sanctions have been known as a disruptive factor to economic growth in general and agricultural development in particular. In contrast, resistive economics is the concept and strategy that has been considered in the case of international sanctions. Resistive economics deals with the methods of economic development in the form of enlarging dimensions of the economy. In order to improve economic variables, and extend economic capacities, improved economics is the opportunities and evolution in the context of economics. It is recommended that the capacity of investment be concentrated in the field of agriculture, and the presence of farmers in the economy is needed. In this regard, the existence of economic planning plays an important role in the regulation of supply, demand, optimum utilization of available resources, and production factors [1].

On this basis, it can be concluded that agricultural economics is associated with the methods of optimal use of natural resources in agriculture [2]. Agriculture, if there is an advance in the next stage there, becomes a driving force of community development [3]. Extenstion trends and policies in the world indicate that at the current stage, a series of forces and factors that themselves are signs of vast forces in society affect the evolution and extension in terms of conception, politics, and structure [4]. The need for agricultural extension stems from the belief that the life of villagers and farmers should be improved. The deep gap that exists between the current and desired situations is mainly filled by the use of science and technology in economic and social activities and through changes in the behavior of the villagers. In this regard, agricultural extension plays a vital role. Nowadays, one of the most important needs agriculture sector is the increased economic power [5].

One of the necessary measures to design a model of progress is to have a strategic plan for the development and progress of the country. It is necessary to be aware of the current situation according to internal factors (strengths and weaknesses) and external factors (opportunities and threats); the correct strategic analysis should be done [6].

2. Theoretical framework

In world economic literature, resistive economics is a new concept, although it is less emphasized [7]. Resistive economics means the pressure of other countries and the attempt to control and mitigate these pressures in ideal conditions and turn threats into opportunities. Also, it can reduce dependencies and emphasize the benefits of domestic production and self-reliance efforts [8]. Resistive Economics is an approach to counteract economic and strategic sanctions, especially in cases where the exports and imports of a country are limited [9]. Increasing the need for crops due to some factors, such as urban development, income growth, changing food consumption patterns, the need to increase the productivity of agricultural lands, and also the resources available to farmers, is vital. Meanwhile, the existence of economic planning has an important role in regulating the supply, demand, and optimal use of available resources and factors of production [1].

“Terms and theories can be mentioned that are close and similar to it. The theory of economic resilience is one of them. State that the term economic is used in two senses: First, the ability of the economy to recover quickly from destructive external economic shocks and, Second, the ability of economics to withstand the effects of these shocks [10].” Resistance economics also has positive aspects, such as the progress of science, and more productions, and also has many aspects such as reducing. “In Iran, agricultural development is still a fundamental means of poverty alleviation, economic development and, in general, sustainable development [11]. The agricultural sector in Iran has played a key role in providing food security by relying on domestic resources, providing foreign exchange through increased exports, supplying the raw materials needed by the industry, and helping to develop dependent productive activities, efficient employment [12]. In resistive economics, the opportunities, capabilities, strengths and potentials of the agricultural sector should be used and the threats that have driven farmers out of the field should be turned into opportunities [13]”. From the point of view of economists, resistive economics is a space to face dependent independence and consumerism. Such an economy is not passive and stands against the goals of economic domination and he tries to change his economic structure based on his own goals and ideology data and tries to put the economy in line with its values and attitudes [14].

In Figure 1 shows that the expansion of agriculture extension through resistive economics as a solution to the problems of the agricultural sector leads to agricultural development.

Figure 1.

Role of agricultural extension affecting in agricultural development.

“In Iran, agricultural development is still a fundamental means of poverty alleviation, economic development and, in general, sustainable development [11]. The agricultural sector in Iran has played a key role in providing food security by relying on domestic resources, providing foreign exchange through increased exports, supplying the raw materials needed by the industry, and helping to develop dependent productive activities, efficient employment [12]. In resistive economics, the opportunities, capabilities, strengths and potentials of the agricultural sector should be used and the threats that have driven farmers out of the field should be turned into opportunities [13].” From the point of view of economists, resistive economics is a space to face dependent independence and consumerism. Such an economy is not passive and stands against the goals of economic domination and he tries to change his economic structure based on his own goals and ideology data and tries to put the economy in line with its values ​​and attitudes [14].

“With the assistance of resistive economics, which aims to empower farmers, water, land, and available facilities can be used to the greatest extent and can be used to turn threats into opportunities and strengths (SWOT). So, resistive economics with the extension of agriculture has common goals about farming and empowering farmers, and creating a balance between deterrent and leading factors [15]. The opportunities, capabilities, strengths, and potential of the agricultural sector should be used, and the threats that have pushed farmers out of the field into turned into an opportunity; strengths must be strengthened and weaknesses reduced to reduce pressure, and strengthen the contribute leading factors [16].”

“Necessity and importance of paying attention to the resistive economy in the field of agriculture means expanding efforts to make maximum use of existing facilities to produce strategic and basic products to reduce dependence on foreign countries, increase productivity as much as possible, produce goods that reduce foreign dependence provide the necessary input in a complete and timely manner and identify problems and challenges [17].” Such a situation indicates the pivotal role of resistive economics in the agricultural sector in recent years and reveals the existence of a targeted plan for the extension and resistive economics in the agricultural sector [18]. For data analysis, they have used statistical and structural equation modeling (SEM) [19]. Also, they showed the relationship between variables and a conceptual model (Raza et al. 2019). The main purpose of SEM is significant for modeling the extension of agriculture to achieve sustainable development. The hypotheses of the research were assessed by studying relationship between variables and direct and indirect effects, and then they were analyzed by SEM analysis. After data extracting, statistics and structural equivalence using SPSSV19, R, and AMOSv23 softwares were described.

3. Methods

“The present work is a mixed-method study, the qualitative part of which was based on the grounded theory of the foundation [20, 21]. Foundation Grounded theory is one of the research strategies through which theorizing is formed based on the main concepts obtained from the data in the field [22]. A total of 31 interviews were conducted in this study. Data quality analysis software MAXQDA12 was used to facilitate the data analysis process.” “Three coding technologies were proposed: open coding, axial coding, and selective coding [23]. Sorting the list of codes and analytical notes and all the sentences and text sections that were marked were retrieved through the MAXQDA software. In the next step, the code pairings and analytical notes, as well as family and categorization [24], were discussed (34 categories), and by examining the relationship between conceptual codes that were conceptually similar to each other, they are in a category [25, 26].”

In the last stage, which is called “selective” coding, in order to validate the data, the researcher’s acceptance method, checking the manuscripts with the participants and taking advantage of the additional opinions of the professors in the field of information Agricultural promotion was done. Also, with a complete explanation of the path and by analyzing the results with the help of a number of experts who did not participate in the research but were familiar with qualitative research, the credibility of the research was increased. Then, information was obtained from the interviewees who participated in the National Conference on Management and Resistance Economy who either had scientific articles or participated as producers and industrialists, knowledgeable people in the field of resistance economy [27].

The introduction of the method represents an important part of the research mission. The present study aimed to design a model for agricultural extension in the resistive economics of Iran. Achieving this goal is not possible unless the most appropriate method is chosen for research with the correct methodology and application and according to the subject of the research and its goals. The research perspective was a mixed-method or intertwined. In this investigation, we used the quantitative research method of structural equation modeling, which was done through confirmatory factor analysis and path analysis. The method of data collection was done through online exploration of documentary and library studies and field studies. This is a research with a survey method and is a method for collecting, analyzing, and interpreting data. The main instrument for collecting the data was a questionnaire, and the Likert scale was used to measure the questions.

Research variables were independent and dependent variables. Independent research variables include (extension of resistive economics) the deterrent and leading factors. The dependent variable also includes the resistive economics affecting agricultural development, whose ultimate goal is to achieve sustainable development, and the method of controlling variables was also quasi-experimental. The research tool was a researcher-made questionnaire that included a 5-point Likert scale from very low = 1 to very high = 5). The statistical population was a set of subjects and individuals who have the required information, which in this study included all experts in agricultural extension who have knowledge and information in the field of resistive economics and its role in the agricultural sector [28]. Through their research papers on agricultural extension and resistive economic, we learned that they have enough information on these concepts.

Initially, a pre-test was used to determine the sample size. The minimum number of samples for the pre-test, which represents the target population, was calculated as 31 people, including agricultural extension and education specialists who have information in the field of resistance economics. Sampling was also done by simple stratified random sampling. Data collection was done through Internet exploration, documentary and library study, and field study.

External validity means the formal and content validity of the questionnaire through the recorded opinion of experts, entrepreneurs of agricultural economy, and confirmatory factor analysis. In order to measure the validity of the structure, the extracted mean-variance index of AVE (0.92) was used, and calculating sequential theta for all question coefficients (θ = 0.98) was calculated. This index showed the percentage of the variance of the studied structure, which was affected by its markers. After analysis, research variables were refined and categorized by MAXQDA12 software, and the most important variables were identified. The reliability of the questionnaire was obtained by answering the questions in three main sections by asking questions in the following areas: Personal characteristics of the respondents, eight questions; agricultural extension and resistive economic including resistive economic, 29 questions; and questions related to the agricultural extension factor affecting the resistive economic, 23 questions; and the impact of resistance economy on the economic prosperity of the agricultural sector, 20 questions.

The method of data processing in inferential statistics in the first stage of the research, after identifying and eliminating additional variables, formulating and testing hypotheses using appropriate statistical tests, was used, and in the second stage, structural equation modeling (SEM) was utilized, and for data analysis, modeling was performed using LISREL software and structural equation modeling (confirmatory factor analysis and path analysis). Also, fitness indices were used. The goodness-of-fit test for the inhibitory extension deterrent factors affecting resistant economy development in the agricultural sector showed that the set of the research independent variables could explain 61–83% of the probability of changing levels of the dependent variable. In addition, inhibitory extension leading economic factors explained 68–99.6% and resistive economic factors explained 69.1–99% of the probability of highering the level of agricultural development in a country.

4. Discussion

In the qualitative part of the research, the grounded theory was first used; in the Grounded theory, the data analysis process began with open coding. Creativity is one of the important components of Grounded theory. The procedures of this method make the researcher break the assumptions and create a new order from the old elements [22].

The formation of a theory begins with conceptualization. Once we got the data, we looked for examples that would help us put the concepts into their respective categories. Accordingly, some concepts can be categorized in the category of higher abstraction than those concepts. In the coding process, it should be noted that at the conceptual level, as well as categorization according to the specific conjectures of the researcher, a bunch of concepts or categories are created. Although their terms and titles have a theoretical background, the content is unique and based on the collected data of the research.

“Field information collected from the target community, in order to achieve the field model ‘Modeling Agricultural Extension in the Resistive Economics of Iran using software MAXQDA12’ was analyzed. Then, in the axial coding step, after analyzing the collected data, items were categorized as main components, such as deterrent factors, leading factors, the definition of resistive economics, and the role of resistive economics in agriculture. In the following, based on the Grounded theory to do open coding, the field observations of the research and the collected data were reviewed several times, and after the extraction of their original sentences, similar and meaningful components of the topics were coded (122 final codes, 34 Categories, and 4 main categories included; Deterrent factors; Leading factors; Meaning resistive economic; The role of resistivee economics in agriculture), all codings focus on a category as the main category, and then other categories are associated as subcategories.”

Field information collected from the target community was presented below in order to achieve the field model “Structural Equation Model of Education of Agricultural Resistive Economics Extension to Secure Agricultural Development in Iran.”

In Figure 2, in the theoretical model of research, the most important variables are the deterrent factor (high cost, pests, diseases, and water scarcity). The most important factor for the leading factor is the (proper use of resources and products). The agricultural extension can play a positive role in resistive economics (food security) and can be effective against the most threats to the agricultural sector (climate change and waste). Also, by transforming threats into opportunities, it can play an important role in the advancement of the agricultural sector through its extension and facilitation.

Figure 2.

Theoretical model of resistive economics in agriculture.

“In the third step, using the MAXQDA12 qualitative statistical analysis software, the analysis was performed. After reviewing and analyzing the collected data, the items were arranged in the form of leading factors, deterrent factors, the definition of resistive economics, and the role of resistive economics in agriculture. In the following, based on the Grounded theory, open coding, field research notes, and data collection were done. Several times the review was assessed, and after the main sentences extraction, similar and meaningful components were registered in codes (122 final codes and 34 categories), and finally, the software showed important factors in each category.”

Results in Figure 3, in the theoretical model of research, show the most important variables is the deterrent factor (high cost, pests, diseases, and water scarcity), of the qualitative part of the research, and factors influencing the extension of resistive economics:

1. Deterrent factor: Smuggling of goods with a frequency of 62.9% has the highest deterrent role among the factors, and creating a space for special investment in the agricultural sector with a frequency of 23.8% causes the lowest deterrent rate among deterrents.

2. Resistive economics: Two variables related to economic stability in the agricultural sector to achieve products with high quality and the use of opportunities with a frequency of 37.7% have the highest percentage and rank of importance among the variables of resistive economics affecting development and increasing employment. In the profitable sectors of agriculture, with a frequency of 20.5%, it has the lowest percentage and the rank of importance among the variables of resistive economics affecting development.

3. Leading factor: Regarding the distribution and ranking of a leading factor of extension of resistive economics, creating a competition to empower farmers at the beginning of production with a frequency of 54.3% has the highest percentage and rank of importance for the extension of resistive economics, and the use of efficient officials with a frequency of 23.2% has the lowest percentage and rank of importance among the leading factors of extension of resistive economics.

Figure 3.

Paradigm model of qualitative analysis as a summary of software output MAXQDA12.

In the quantitative part of the research, Factor analysis for the variables of deterrent factor agricultural extension, leading factor agricultural extension, and resistive economics influencing development: The Kaiser-Mayer-Olkin (KMO) test is a method used to ensure the adequacy of the selected sample in exploratory factor analysis. The KMO value was equal to 0.524and also the value of the Bartlett test statistic was equal to 16876.723 (P = 0.000), which means the suitability of the data for factor analysis.

Table Communalities: all initial hold and all extracted hold (R2) for the relevant variables using factors as a predictor are showing. The first column shows the initial subscriptions. The second column shows the extraction subscriptions, which must be more than 0.5. In this study, 0.5 was considered the minimum number of extractive subscriptions, and other items that were lower than this value were removed from the model.

Table 1, the dispersion of the eigenvalue of accepted factors. The total scatters of the specific value of the accepted factors are 57%, which shows that the identified factors express about 57% of the common answer of the extension experts. Since the Kaiser-Guttman criterion is used to select the factors, the first three factors with a specific value greater than five were accepted and their values were displayed on the right side of the graph.

Component	Initial eigenvalues			Extraction sums of squared loadings			Rotation sums of squared loadings
	Total	% of Variance	Cumulative%	Total	% of Variance	Cumulative%	Total	% of Variance	Cumulative%
1	21/314	602/29	602/29	314/21	602/29	602/29	880/18	222/26	222/26
2	409/11	846/15	449/25	409/11	846/15	449/45	912/11	545/16	767/42
3	8/974	464/12	913/57	974/8	464/12	913/57	905/10	146/16	913/57
4	899/2	4/026	939/61
5	275/2	159/3	098/65
6	107/2	972/2	025/68
7–72	942/1	697/2	722/70

Table 1.

Exploratory factor analysis for changes in deterrents factor agricultural extension, leading factor agricultural extension, and resistive economics influencing development.

Rotated component matrix: Load factor indicates the degree of similarity or correlation of a graph with a factor. Component matrix after rotation: The load factor shows the degree of similarity or correlation of ideas and the division of variables in a factor and determines in which factor the variables are placed. In the section on variables related to the deterrent factor: four variables were eliminated, and the other 25 variables are included in the variables related to the deterrent factor. In the section of variables leading factor: three variables were eliminated, and the other 20 in the leading factor remained. In the section of variables related to the factor of resistive economics: two variables were eliminated, and the other 18 variables in the resistive economics factor remained. Confirmatory factor analysis for the variables of deterrent factor, extension, and resistive economics: After exploratory factor analysis, identification of factors and variables, and the number of removed variables in each factor, confirmatory factor analysis was performed on the remaining variables in the model.

Table 2 shows the significance levels remaining variables from factor analysis.

Model	NPAR	CMIN	DF	sig	CMIN/DF
Basic model	129	8840.122	1887	0.000	4.684
Standard model	2016	0.000	0
Independent model	63	15713.261	1953	0.000	8.046

Table 2.

Significance levels remaining variables from factor analysis.

CMIN = 880.12 P-value = 0.0000 RMSEA = 0.157 Chi² = 4.685.

Figure 4 means that the above model shows the changes in each factor, the standardized factor load of these variables, and the significance levels remaining variables from factor analysis. The factor load value of all factors was greater than 0.4, which indicates that all variables are correctly placed in the factors, and there is no need to delete any of the variables. Heuristic factor analysis was performed correctly and confirmed. According to the heuristic factor analysis, three factors were extracted, for better analysis of the data, and the rankings obtained by the variables of each factor were tested by (Friedman rank test).

Figure 4.

Fit measurement model in standard mode in confirmatory factor analysis.

Examining the research hypotheses and the relationship between research variables:

(1) Investigating the relationship between the factor of inhibition of agricultural extension and the resistive economics affecting agricultural development: The results show that the Spearman correlation coefficient between extension inhibitors and resistive economics affecting agricultural development is −0.314, and the significance level of the test is 0.010, which is significant at the level of 1% error. Between resistive economics affecting for the agricultural development and agricultural development there is an inverse and significant relationship.

Sequential regression was used to investigate the role of independent variables as a deterrent to the development of resistive economics in the agricultural sector. In order to perform sequential regression at the beginning, the probability ratio test of the model was investigated. The value obtained for the chi-square statistic (142.1) showed that the regression model is a suitable model (sig = 0.000).

In Table 3, the non-significant value of the chi-square statistics indicates that the independent variables can well predict the probability of dependent variable variability.

Sig	DF	Chi-square	−2 Log Likelihood	Model
			173.571	Basic model
0.000	84	142.107	31.464	Final model

Table 3.

Probability ratio test of the extension deterrent model.

In Table 4, the non-significant value of the chi-square statistics indicates that the independent variables can well predict the probability of dependent variable variability, and independent variables have been able to explain between 61 and 88.3% of the variance probability of the dependent.

Sig	DF	Chi-square	Model
0.000	112	65.542	Pearson
0.000	112	29.641	Deviation

Table 4.

Goodness-of-fit criteria on the resistive economics affecting agricultural development.

Pseudo${\mathrm{R}}^2$ NagelKerke = 0.883 Pseudo${\mathrm{R}}^2$McFadden = 0.802 Pseudo${\mathrm{R}}^2$ Cox and Snell = 0.610.

In Table 5, deterrent factor variables of agricultural extension affecting the resistive economics have been introduced.

Variables
Increasing the cost of production in the agricultural sector	Quality of production inputs
smuggling goods	Conditions for the production of agricultural inputs
Increase bank interest rate	Sanctions
Low financial ability of farmers to start production	Creating space for private investment in the agricultural sector
Not paying attention to the climate of each area when planting	Investing in loss-making sectors
Increased pest damage to farms	Inefficiency of extension managers
Poor optimal management of resources on farms	Poor optimal cost management
Climate change effect	Lack of privatization in the agricultural sector
Inadequate economic planning in the field of marketing of agricultural products	Underdevelopment of culture
Lack of competition between production units	The rate of development of mechanical infrastructure in the field of agricultural products
How to manage water transfer to lands	The spread of crop diseases
The rate of development of mechanical infrastructure in the field of agricultural products	The extent of using new knowledge in the production sector
The degree of mechanization of the agricultural sector

Table 5.

Deterrent factor variables agricultural extension affecting the resistive economics.

In Table 6, deterrent factor variables of agricultural extension affecting the resistive economics have been shown. The P-value for the path related to the deterrent factor and the resistive economics affecting agricultural development is less than 0.05, which means a negative effect of the deterrent factor on the resistive economics affecting agricultural development.

Model	NPAR	CMIN	DF	P	CMIN/DF	RMSEA	PCLOSE
Basic model	89	4154	901	0.000	4.611	0.155	0.000
Standard model	990	0.000	0
Independent model	44	9490.0383	946	0.000	10.032	0.245	0.000

Table 6.

Statistics and significance levels of variables that deterrent factor agricultural extension and the resistive economics affecting the agricultural development.

CMIN = 41540.85 P-value = 0.000 RMSEA = 0.155 Chi² = 4154.85.

(2) Investigating the relationship between leading factor agricultural extension and resistive economics affecting agricultural development: The Spearman correlation coefficient between leading factor and resistive economics affecting agricultural development is equal to 0.320, and the significance level of the test is equal to 0.003, which is significant at 99% confidence level. Sequential regression was used to investigate the role of independent variables as a leading factor in the agricultural extension and resistive economics affecting agricultural development in the agricultural sector. In order to perform sequential regression at the beginning, the probability ratio test of the model was investigated. The value obtained for chi-square statistics in the table showed that the regression model is a suitable model (sig = 0.000).

In Table 7, the non-significance of the chi-square statistics indicates that the independent variables can well predict the probability of variability of the dependent variable.

Model	−2 Log Likelihood	Chi-square	DF	Sig
Basic model	175.769
Final model	0.000	175.769	72	0.000

Table 7.

Probability ratio test of the leading factor model of agricultural extension affecting the resistive economics affecting agricultural development.

In Table 8, the non-significance of the chi-square statistics indicates that the independent variables can well predict the probability of variability of the dependent variable using the above statistics to calculate the coefficient of determination in sequential regression; it was indicated that the independent variables could explain the probability of variance of the dependent variable between 68.8 and 99.6%.

Model	Chi-square	DF	Sig
Pearson	33.141	72	1.000
Deviation	18.442	72	1.000

Table 8.

Goodness of the fitness test leads to effective resistive economics for agricultural development.

PseudoR2NagelKerke = 0.688 PseudoR2 Cox and Snell = 0.996 PseudoR2McFadden = 0.992.

In Table 9, the non-significance of the chi-square statistics indicates that the independent variables can well predict the probability of variability of the dependent variable using the above statistics to calculate the coefficient of determination in sequential regression; it was indicated that the independent variables could explain the probability of variance of the dependent variable between 68.8 and 99.6%.

Variables
Creating facilities to increase production	Use of new knowledge in productions
Indigenous volunteer	Scientific planning
Proper marketing	Recognize internal potential
Managers’ views on the agricultural economic sector	Target contacts or groups
Capable managers	Cooperation of different government departments
Use efficient officials	Development of publishing technology with the help of agricultural extension
Market transparency	Organizational policies
Executors of extension programs	Advanced technology
Philosophical foundations of agricultural promotion	Creating strong competition in products
Use of new technology in production	Extension organization

Table 9.

Leading factor variables of agricultural extension based on resistive economics affecting agricultural development.

Introducing the leading factor variables of agricultural extension based on resistive economics affecting agricultural development.

Table 10 indicates that the model fits and does not need to be modified.

Model	NPAR	CMIN	DF	P	CMIN/DF	RMSEA	PCLOSE
Basic model	77	3750.245	664	0.000	5.648	0.176	0.000
Standard model	741	0.000	0
Independent model	38	6778.186	703	0.000	9.642	0.240	0.000

Table 10.

Significant levels of the variables of the leading factor and the resistive economic factor affecting agricultural development.

RMSEA = 0.176 Chi² = 3750.24 CMIN = 3750.24 P-value = 0.000 Sig = 0.000.

The P-value for the path related to the leading factor and the resistive economics affecting agricultural development is less than 0.05, which means a positive and significant effect of the deterrent factor on the resistive economics affecting agricultural development.

Extension model of agricultural resistive economics in agricultural development: Sequential regression was used to investigate the role of leading and deterrent variables of extension on the development of resistive economics in the agricultural sector. In order to perform sequential regression at the beginning, the probability ratio test of the model was investigated. The value obtained for the chi-square statistic (155/175) showed that the regression model is a suitable model (sig = 0.000).

Table 11 indicates that the model fits and does not need to be modified.

Model	−2 Log Likelihood	Chi-square	DF	Sig
Basic model	175.155
Final model	0.000	175.155	80	0.000

Table 11.

Probability ratio test of leading factor agricultural extension and deterrent factor agricultural extension model to achieve the resistive economics influencing development.

In the above table, the insignificance of the chi-square statistics indicates that the independent variables can well predict the probability of dependent variable variability.

Table 12, was shown that the independent variables of leading factor agricultural extension and deterrent factor agricultural extension of agricultural extension have been able to explain between 69.1 and 100% of the probability of variance of the dependent variable of resistive economics influencing development.

Model	Chi-square	DF	Sig
Pearson	000.4	108	1.000
Deviation	5.546	5.546	1.000

Table 12.

Goodness-of-fit test of the leading factor agricultural extension and deterrent factor agricultural extension model to achieve the resistive economics influencing development.

PseudoR2NagelKerke = 0.691 PseudoR2Cox and Snell = 1.000 PseudoR2McFadden = 1.000.

In Figure 5, Structural equation modeling is shown using LISREL software.

Figure 5.

Standardized regression coefficients of the final model.

In Figure 6, structural equation modeling with the most important variables shown.

Figure 6.

Structural equation modeling.

Significance test: SEM and LISREL were used to verify the research model. As shown in Table 15, some paths are significant, and others are not significant.

In Table 13, the results of the quantitative part of the research, in general. It was found that the leading variables of extension have a positive and significant effect on the environment of resistive economics affecting agricultural development. The variables of the deterrent factor of agricultural extension on the environment of resistive economics have a negative effect. The variables of the leading factor of agricultural extension on the deterrent factor of agricultural extension have a negative effect. In total, all the mentioned variables have the ability to explain 0.98% of the variance changes of the dependent variable (resistive economics affecting agricultural development).

Table 13.

Path parameters with significant level.

5. Conclusion

The sanctions of several years in a row by the United States and some European countries have caused the idea of ​​optimal economic resources to grow in a resistant economy, and this research was conducted in this field and the need to optimize the use of resources economically. The present work is a mixed-method study, the qualitative part of which was based on the grounded theory of the foundation. After analysis, research variables were refined, and categorized by MAXQDA12 software, and the most important variables were identified. The variable most deterrent factor to resistive economics was misguided investment policies and the variable most important agricultural extension affecting the resistive economics was the economy with quality productions. The variable most leading factor to resistive economics paying more attention to the role of producers was and the variable most resistive economics in agriculture extension and development was. The qualitative part of which was based on a model obtained from structural equation modeling. The factor leading the load has a factor of 0.82% in explaining the resistive economics, and the deterrent factor explains the factor of non-realization of the resistive economics in the agricultural extension sector. But the results of path analysis showed that in the general model of deterrent variables, that have the greatest impact on the deterrent factor, there is an increase in bank profits (0.91), quality of production inputs (0.90), climate change (0.90), poor management resources on farms (0.89), and smuggling (0.88) percent, respectively. However, in the leading sector, the greatest impact was related to the variables of native change agent extension (0.79), marketing of substandard products in the agricultural production sector (0.76), cooperation of different government sectors (0.75), and extension organization (0.74). But in the sector of resistive economics, the greatest impact was related to strengthening the private sector (0.57), using domestic resources for agricultural products (0.45), changing the use of opportunities (0.45), and encouraging the consumption of domestic products of the sector agriculture (0.36) percent, respectively.

6. Suggestions

We make suggestions based on the findings obtained in this study for the policymakers of the agricultural sector, farmers, extension services, the Ministry of Agriculture, non-governmental organizations, and others:

1. Suggestions based on the obtained results for the deterrent factor of agricultural extension:

By reducing bank interest and preventing the increase of bank interest to farmers and other people working in this sector, we can take steps toward achieving a sustainable economy in the agricultural sector.

By increasing the quality of production inputs, it is possible to take steps in the marketing of products and the creation of conversion and processing industries of agricultural products in order to achieve a sustainable economy and reduce waste and wastage of agricultural products.

Due to climate change in different regions, solutions such as (1) using change agent indigenous due to the knowledge of the region and the knowledge of farmers and (2) it is possible to take steps toward the realization of a sustainable economy in the agricultural sector by providing agricultural extension training to adapt the planting of agricultural products to climate changes and reduce the dependence of agriculture on water resources.

2. Suggestions for the results obtained for the leading factor of agricultural extension:

The cooperation of different government sectors, the cooperation of different government and non-government sectors took a step toward sustainable economic improvement in the agricultural sector.

By using the change agents, indigenous, due to the knowledge of the region and the farmers, took a step in the direction of advancing sustainable economic goals in the agricultural sector.

Appropriate marketing in the agricultural production sector, by trained and educated marketers, has taken steps to advance sustainable economic goals in the agricultural sector.

3. Suggestions based on the obtained results for the resistive economics affecting the extension of agriculture:

Strengthening the private sector, through the granting of banking facilities, various concessions, discounts, and tax exemptions in order to realize a resistive economics.

Using domestic resources for agricultural products and encouraging the consumption of domestic products, and building culture in the field of using domestic products in the agricultural sector for the prosperity of agriculture and the realization of resistive economics.

Creating a change in the amount of using opportunities, including the creation of transformation industries and processing of agricultural products in order to reduce waste and wastage of agricultural products and realize resistive economics.