Agricultural Economics
M. Rafiee Sefid Dashti; S.M. Mirdamadi; S.J. Farajollah Hosseini; S. Shokri
Abstract
IntroductionEvery country in the path of sustainable development needs capacity building and empowerment of human resources, organizations and environmental and ecological conditions, for this reason capacity building has a significant impact on the empowerment of people and groups. Smart climate agriculture ...
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IntroductionEvery country in the path of sustainable development needs capacity building and empowerment of human resources, organizations and environmental and ecological conditions, for this reason capacity building has a significant impact on the empowerment of people and groups. Smart climate agriculture is a method that focuses on agriculture and seeks to improve the productivity and income of farmers, in order to increase the productivity and adaptability of agricultural products in Iran, it is necessary to implement smart climate farming methods by building the capacity of human resources to make decisions and take action. Agricultural extension system is considered as one of the key tools for realizing sustainable development and has capabilities such as improving livelihoods, training farmers, establishing social justice, empowering farmers, and increasing production and productivity. Considering the importance of building capacity in the food supply and security sector, which is facing many threats day by day, the role of extension training in promoting agricultural innovations and new perspectives and training farmers in order to improve their knowledge, information and skills are considered the important and effective factors in capacity building and development of the agricultural sector of Iran. Materials and MethodsIn this research the statistical population of the research was formed by extension experts in the northwest of the country, which includes the three provinces of East Azarbaijan, West Azarbaijan and Ardabil, with 4256 people. The sample size was also calculated based on Cochran's formula (n=354). In this way, according to the number of centers in each province and proportionally to the size of the statistical population and the sample size from each province, the required sample was randomly selected according to the number of employees in that provinceTo address the research problem and objectives, a questionnaire was developed as the primary research tool, consisting of four sections, seven items, and 31 questions tailored for experts in agricultural promotion and development. Aside from questions on personal and professional characteristics (gender, age, major, education level, work history, organizational position, employment status), all items were presented on a five-point Likert scale (1: very low, 2: low, 3: moderate, 4: high, 5: very high). In this research, to determine the face validity of the questionnaire, it was approved by the opinions of the research committee as well as managers and experts of agricultural extension and education after several stages of modification and revision. In order to measure construct validity, average variance extracted index (AVE) was used using SmartPLS software. To determine the reliability of the questionnaire, Cronbach's alpha and composite reliability coefficients were used, and for this purpose, 25 questionnaires were completed by a group identical to the research group. In this research, two main and secondary independent variables were investigated. Our dependent variable in this research is "smart climate agricultural development" (12 items) which are influenced by two independent variables including educational factors and promotion factors. Structural equation modeling (SEM) method was used in this research. Results and Discussion Education, promotion, and capacity building of human resources are essential strategies for sustainable development (Sulaiman, 2021). Therefore, building human resource capacity is critical for the economic growth and prosperity of any country (Notenbaert et al., 2017). In analyzing the eight hypotheses, Table 8 shows that the path coefficients for infrastructural, economic, social, organizational, cultural, educational, legal, and technical factors in the capacity building of extension experts for the development of smart climate agriculture are 0.120, 0.115, 0.114, 0.168, 0.143, 0.132, 0.147, and 0.104, respectively. Additionally, the t-statistics for these coefficients are 3.087, 3.120, 3.123, 7.17, 2.710, 2.468, 4.002, and 3.267, all exceeding the threshold of 1.96, indicating significance at the 5% error level. The model estimates suggest that infrastructural, economic, social, organizational, cultural, educational, legal, and technical factors have a positive and significant impact on capacity building among extension experts in developing smart climate agriculture. In general, based on the results obtained in the current research, it can be said that the identification of factors that create and facilitate the development of extension experts' capacities is very necessary and necessary for the development of smart climate agriculture. Research findings show that infrastructural factors, economic factors, social factors, organizational factors, cultural factors, educational factors, legal factors and technical factors have an effective and significant role in building the capacity of extension experts in the development of smart climate agriculture. In fact, increasing and improving the capacity of extension experts has direct and indirect benefits for the members of the Jihad Agricultural Organization and the villagers, and increases cooperation and interaction between them. The findings of this research help the policy makers and planners to identify the weaknesses and shortcomings to improve the performance of the Agricultural Jihad Organization and achieve the objectives of the Extension Unit. The analysis of the factors in this study helps to get a better understanding of improving the capacity of extension experts and consequently, it helps to increase the income, productivity and food security of the people with the development of smart climate agriculture. ConclusionFor data analysis, the method of structural equation modeling with the approach of partial least squares based on PLS3 software was used. Therefore, first, in order to enter the structural equation modeling test, it is necessary to make sure that the data is normal or not. By using the Kolmogorov-Smirnov and Shapiro-Wilk tests, the normality of the data can be checked, and this test is performed at the 95% confidence level, in other words, it is our significance level. According to the findings in Table 6, the significance level (p) for each variable is less than the threshold of 0.05 (P < 0.05), indicating that the null hypothesis (H0) is accepted, while the alternative hypothesis (H1) is rejected. This suggests that the research variables do not follow a normal distribution. To assess normality, the Kolmogorov-Smirnov and Shapiro-Wilk tests were employed, conducted at a 95% confidence level, aligning with the significance level of the study. Before proceeding with factor analysis, it was necessary to confirm the adequacy of the data. The Kaiser-Meyer-Olkin (KMO) index and Bartlett's Test of Sphericity were used for this purpose. As shown in Table 7, the sample size adequacy (KMO = 0.988) and the significance of Bartlett's test (652.537) both indicate that the sample is suitable for factor analysis. To investigate causal relationships between the research variables and assess the fit of the data to the conceptual model, structural equation modeling (SEM) was applied. Specifically, this research utilized partial least squares (PLS3) for hypothesis testing and model fitting. Figures 2 and 3 present the results from the software output, following the testing of the conceptual model. According to the results of Table 8, the results of the significant coefficients for each of the hypotheses, the standardized coefficients of the paths related to each of the hypotheses, and the results of the examination of the hypotheses are presented. According to Figures 2 and 3, it can be said that the standardized coefficient (path coefficient) between the variables (educational, infrastructural, economic, social, technical, organizational, legal, cultural factors with smart climate capacity building) is significant, so at the 99% confidence level Hypothesis H0 is rejected and hypothesis H1 is confirmed, and it can be concluded that educational factors; infrastructural; economic; social; technical, organizational; legal; culture have significant effects on the capacity building of extension experts in the development of smart climate agriculture, and therefore the eight hypotheses are confirmed. GOF criterion: To evaluate the model, the GOF criterion is used, which three values of 0.01, 0.25 and 0.36 are introduced as weak, medium and strong values for GOF. According to Table 6, the GOF is 0.865, confirming the very good fit of the overall model.