Research Article
Agricultural Economics
W. Qelich
Abstract
Introduction
Global environmental changes have become a significant challenge for humanity, highlighting the need for robust support for environmental projects across all dimensions, including financial and economic. Integrating ethics and human and social values with environmental concerns in economic ...
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Introduction
Global environmental changes have become a significant challenge for humanity, highlighting the need for robust support for environmental projects across all dimensions, including financial and economic. Integrating ethics and human and social values with environmental concerns in economic activities creates a new approach for sectors such as banking, manufacturing, industry, and insurance, reshaping their operations in response to these challenges. It is necessary to put aside the purely market-oriented approaches focused on the rapid development of financial markets at any cost, so that other policies with greater health can replace them. Meanwhile, the concept called "green banking" is one of the most important examples of this support. This kind of banking, as an important part of ethical banking, plays a special role in protecting the environment. With a comprehensive explanation of green banking and by using theoretical studies and international experiences and obtaining opinions from relevant experts and experts, this research identified the factors affecting the trend of the country's banking network towards green banking by using the Delphi method, questionnaire analysis and Friedman's test.
Materials and Methods
The current research is completely practical in terms of its purpose and qualitative and descriptive-analytical in terms of implementation method. First, by using the Delphi method, the factors affecting the tendency of the banking to implement green banking are identified, and then the relevant data is collected using a questionnaire. In the following, the known factors are ranked and prioritized with Friedman's test. The statistical population of this research was all managers and banking experts in Tehran. In the Delphi method, a standard statistical sample typically ranges from 10 to 30 questionnaires, with 28 initially considered for this research. After follow-up, 23 questionnaires were completed and included in the analysis. Additionally, 142 questionnaires were prepared and collected for Friedman's test implementation. At this stage, a Likert scale was used for the research questions, scored by managers, branch heads, and banking experts. A combined method has been used in the collection of research statistical data. At first, the concepts of green banking and ethical banking have been explained by using the library method and conducting free theoretical and field studies. In the following, with the Delphi technique and obtaining the opinions of relevant experts and experts, the most important factors affecting the tendency of the banking network to implement green banking have been calculated. In the following, the remaining important factors have been added to the set of factors with the method of intellectual generation.
Results and Discussion
Based on the results of the research, four main economic, structural, managerial and social criteria were identified in order to influence this tendency. In the sub-criteria section, the high inflation rate, the relative cheapness of energy prices and the presence of profitable parallel markets along with green deposits are mentioned as the most important reasons for the low tendency towards green banking. Also, the laws and regulations and the legal system, the recruitment system, the promotion and encouragement of bank managers and employees, the central bank's supervisory system, the senior managers' attitudes towards environmental issues, corporate governance, the bank's internal supervision, the attention to green banking in the selection and decision Customers, society's attitude towards environmental issues and the culture of demand among the most important sub-criteria affecting the trend of the Iranian banking towards green banking have been evaluated and introduced.
Conclusion
This research tried to identify and analyze the factors affecting the tendency of Iranian banking network towards green banking with a more comprehensive explanation of green banking and by using theoretical studies and international experiences and obtaining opinions from relevant experts and experts. Based on the research results, four main structural, economic, managerial and social factors influencing this trend were identified. Surveys showed that in the first place, economic factors were more effective than other factors in the trend of the banking towards green banking. Among the study factors categories, structural, managerial and social factors have the most influence on the trend of the country's banking network towards green banking. It is suggested that for the greater desire and tendency of the banking to implement and realize green banking, it is necessary to improve the economic components with the aim of more stabilization, inflation control, strengthening of supervisory dimensions and balance sheet reform of the banking network. Also, reforming the legal system in the supervision and support of green bankers, reforming the incentive and recruitment system, strengthening the attitude of senior bank managers and the general public to the necessity of protecting the environment, as well as reviewing the frameworks and processes of corporate governance in banks with a green perspective to encourage Iranian banking, is necessary towards green banking. In the meantime, undoubtedly the role of the central bank as the supervisory body and upstream regulatory body in reforming the general structures of the banking system and improving the management situation of the public sector of the banking network can be useful and effective in increasing the tendency of banks to establish green banking and comply with its criteria.
Research Article
Agricultural Economics
S. Kalhori; L. Abolhasani; M. Sabouhi Sabouni; M. Sarkhosh
Abstract
Introduction
Given the rapid process of industrialization, expansion of agriculture, increased reliance on fossil fuels, and the intensification of climatic conditions, air quality has rapidly deteriorated in recent years. One of the most important issues and challenges facing the world today is air ...
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Introduction
Given the rapid process of industrialization, expansion of agriculture, increased reliance on fossil fuels, and the intensification of climatic conditions, air quality has rapidly deteriorated in recent years. One of the most important issues and challenges facing the world today is air pollution, particularly PM2.5 pollution. This problem has evolved into one of the most complex and serious dilemmas affecting the lives of people worldwide. Exposure to high levels of air pollution has negative health implications. The present study aims to measure the willingness to pay of Mashhad city residents for the improvement of PM2.5 pollution and identify the factors influencing this willingness to pay.
Materials and Methods
This study used contingent valuation and the multiple-bound discrete choice model to calculate individuals' willingness to pay. The research focused on the certainty level of "definitely yes" and generated 13 different proposals ranging from 10,000 Toman to 200,000 Toman. The ordered logit regression model was employed to analyze the factors influencing the willingness of Mashhad citizens to pay for air quality improvement. The study collected 343 questionnaires from Mashhad city residents, considering variables such as education level, age, gender, marital status, family size, presence of children, chronic respiratory diseases and individuals' income. The dependent variable was the public's willingness to pay for improving air quality regarding PM2.5.
Results and Discussion
The study found that a significant portion of respondents were willing to pay for air quality improvement. About 22.45% were willing to pay less than 10,000 Toman, 60.06% were willing to pay between 45,000 and 58,000 Toman, 5.83% were willing to pay between 95,000 and 120,000 Toman, and 11.66% were willing to pay between 155,000 and 200,000 Toman. The average willingness to pay for PM2.5 pollutant improvement in Mashhad was estimated to be 55,488 Toman. Education, age, respiratory diseases, income, and family size were found to affect willingness to pay.
Conclusion
Improving air quality and reducing pollution requires costly efforts and collaboration from society. This research examines individuals' willingness to financially contribute to air quality enhancement. Factors influencing their willingness to pay are also studied. Based on the findings, it is recommended that the government and municipal authorities impose taxes and levies on polluting sectors, considering the calculated value of air pollution and its sources. Educational programs tailored to diverse educational backgrounds, along with technology and social media, can raise environmental awareness among youth. Developing cost-effective public transportation systems and providing discounts for low-income individuals can also help reduce pollution. Financial programs and incentives for cleaner resources are another solution for improving air quality.
Research Article
Agricultural Economics
M. Shabanzadeh-Khoshrody; E. Javdan; K. Shemshadi
Abstract
Introduction
During the last decade, due to the increase in food prices, the cost of a healthy diet in Iran has greatly increased. Although the government's support policies have aimed at improving the living conditions of households, but the cost and income information of the Iranian Statistics Center ...
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Introduction
During the last decade, due to the increase in food prices, the cost of a healthy diet in Iran has greatly increased. Although the government's support policies have aimed at improving the living conditions of households, but the cost and income information of the Iranian Statistics Center shows that due to inflationary conditions and its impact on real income and purchasing power of consumers, these programs have not had the necessary effect in reducing poverty and food insecurity. Reducing poverty and increasing the food security index is a requirement for independent countries like Iran. In this regard, knowing the current situation of poverty, food insecurity and factors influencing it, is not only the main condition for preparing future plans, but is necessary to continue this work with the aim of monitoring and evaluating the results of implemented plans and actions.
Materials and Methods
In the present study, the spatial distribution of poverty and food insecurity in the urban areas of Iran in 1401 has been investigated and then the factors affecting food insecurity have been identified. In order to achieve these goals, the nutritional performance matrix was drawn and calculated per capita calories in 1401 using the household income-expenditure information of Iran Statistics Center. The Aggregate Household Food Security Index (AHFSI) and the Foster, Greere and Thorbeke (FGT) poverty index were calculated and based on these indices, the spatial distribution of poverty and food insecurity in urban areas of Iran was analyzed. Finally, the impact of economic and demographic variables on food insecurity was analyzed in the framework of the logit model.
Results and Discussion
According to the results, the urban areas of the country are in low food security conditions; so that, only 45% of people have food security and about 55% of the residents of urban areas are either facing food insecurity or are on the border of food insecurity. On the other hand, the per capita calorie intake in the urban areas of the country is 2540 kcal, and generally these calories are supplied from cereals. In addition, there is inequality in the intake of calories in different provinces of the country, and the average intake of calories varies between 1988-3196 kcal among the provinces. Examining the status of food poverty indicators also shows that the average head count, gap and intensity of poverty in urban areas are 55.1%, 15.2% and 6% respectively. Based on these indicators, it can be said that 55.1% of the population of the urban areas of the country had food poverty in 1401 and the calorie intake of the poor households in these areas was 15.2% less than the minimum required daily calories; therefore, to eliminate poverty, the caloric intake of poor households should be increased by 15.2%. Finally, the results of the logit model estimation showed that the variables of age, employment status, working hours of the head of the household, subsidy, income and food diversity have a positive and significant effect on the food security of the households, but the Family size has a negative effect on the food security. In addition, the two variables gender and literacy of the head of the household did not have a significant effect on the food security in urban areas of Iran.
Conclusion
In this regard, although the long-term solution is to increase household purchasing power, stabilize and reduce commodity prices through strengthening production and supply, but in the short-term, increasing salaries and wages in line with the inflation rate and increasing social support programs for the low income deciels and weak society should be taken into consideration. In other words, income policies that can directly or indirectly increase the level of income and thus the purchasing power of the household, can be considered as a scientific and effective solution for food security. Moreover, the social support and poverty alleviation programs should be targeted and applied according to the needs and deficiencies in different geographical, demographic and income conditions. Finally, it should be acknowledged that improving the nutritional literacy of households can increase the nutritional knowledge and awareness of households, and therefore, by improving the variety and quality of the food they consume, it can lead to an increase in food security in urban areas of Iran.
Research Article
Agricultural Economics
F. Razzaghi Borkhani; T. Azizi Khalkheili; A.A. Barati
Abstract
Introduction
The shortage of freshwater resources is one of the primary crises the world faces, despite the constant availability of renewable water sources. As a result, the rising risks associated with water scarcity are a critical concern. The water crisis reduces crops production and negatively ...
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Introduction
The shortage of freshwater resources is one of the primary crises the world faces, despite the constant availability of renewable water sources. As a result, the rising risks associated with water scarcity are a critical concern. The water crisis reduces crops production and negatively affects food security. Due to the increase in demand for food, agriculture section is under more pressure because of both water crisis and more demand for food. Agricultural sector has been also facing with water shortage due to climate changes caused by the more global warming and low precipitation. Water crisis and climate changes leading to a decrease in the crops production. Now, agriculture and livelihood of villagers has become unstable more than any time. Considering the importance of irrigated farming in Mazandaran province in the country's food security, the present study was conducted with the aim of identifying the most important variables that affecting water security in Mazandaran province.
Materials and Methods
The statistical population of the research included 16 subject experts with research or executive experience in the fields related to water studies, water security and climate change. The selection of them was done in a purposeful way. The data collection tool was a researcher made questionnaire and the data collection method was face-to-face interview. At first, to identify the variables involved in water security a subject literature review and several semi-structured interviews with subject experts were conducted. Then, the experts were asked to evaluate the cross-effects of the identified variables through pairwise comparisons and in the form of the MICMAC questionnaire. Finally, the data were analysis using MICMAC software.
Results and Discussion
According to the results, among the studied variables, "knowledge and environmental literacy of villagers" and "reduction of precipitation due to climate change" (input variables) are two important key variables that directly and indirectly affect water security and therefore should be considered. The variables "best management of appropriate farm operations", "volume and diversity of water resources" and "good management and governance of agricultural water" are intermediate variables, with high impact and high dependence. Based on the direct influence network intensity of the key variables involved in water security, variables such as “best management of suitable farm operations”, “good management and governance of agricultural water”, “the degree of resilience of farmers to adapt to climate change” play a central and sensitive role. Based on the indirect relationships, “best management of appropriate farm operations”, “the degree of resilience of farmers to adapt to climate change”, “risk management of ecological hazards and climate change” have the greatest indirect effect on other variables and should be considered by policy makers and planners in this field.
Conclusion
Water crisis is a major challenge for agricultural activities and consequently for food security. Considering the vital role that Mazandaran province plays in the agricultural products production and as a result food security, the present study examined the most important variables affecting food security. The findings of this study showed that "good management and governance of agricultural water" has the most direct impact on water crisis management. Good water governance can be taken into consideration with the relative strengthening and synergistic participation of public and private sectors and non-governmental organizations in line with the planning and implementation of food security policy with the water-energy-food nexus approach. The role of increasing the environmental knowledge and literacy of villagers by providing effective educational-promotional services such as farm filed school is very important on the farmer's resilience and adaptability. On the other hand, variables such as good water management and governance, development of new irrigation systems and technologies, zoning of agricultural lands and the explanation of the appropriate cultivation pattern for each zone (such as planting crops with low water demand and high added value include medicinal plants) are undeniable impact on the livelihood resilience of the farmer's family and adaptation to climate change conditions. Diversify the livelihood resources of farming households with the participation of household women, promoting climate-oriented businesses that are compatible with climate changes (such as agricultural tourism and handicrafts), using drought-resistant species, changing the date of cultivation, developing greenhouse cultivation, medicinal plants and modernization of irrigation, change of history and cultivation pattern play important roles on the resilience of farmers to adapt climate change.
Research Article
Agricultural Economics
A. Sani Heidary; M. Daneshvar Kakhki; M. Sabouhi Sabouni; H. Mohammadi
Abstract
Introduction
Considering being located in arid and semi-arid regions of the world, Iran is influenced by the most severe impacts of drought. Drought is considered a major threat to the livelihood of rural households. During the recent drought, rural households faced significant losses and hardships, ...
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Introduction
Considering being located in arid and semi-arid regions of the world, Iran is influenced by the most severe impacts of drought. Drought is considered a major threat to the livelihood of rural households. During the recent drought, rural households faced significant losses and hardships, underscoring their lack of preparedness for this natural hazard. Consequently, every society must take proactive measures to manage changes, mitigate threats, and respond effectively. A review of the country's drought management programs reveals that policymakers have consistently prioritized increased production, even amid the critical conditions of recent droughts. This focus on boosting production to meet the basic needs of a growing population has taken precedence over enhancing rural households' livelihoods and resilience. However, improving rural households' resilience in drought conditions hinges on prioritizing their capacity for adaptability and flexibility. Therefore, considering the sensitivity of the issue of resilience as a dominant approach effective on the dimensions of life and livelihood of rural households on the one hand and the lack of a comprehensive study on its underlying factors, on the other hand, this research seeks to answer two questions: First, what is the resilience level of rural households against drought? Second, what factors influence the resilience levels of rural households in drought conditions?
Materials and Methods
The statistical population of this study is 16,817 rural households in Zehak city, located in Sistan and Baluchistan province, which are strongly influenced by different climatic events such as drought, excessive heat, low rainfall and 120-day winds. A stratified random sampling method was used to determine the sample size. According to Cochran's formula, the sample size is estimated to be 376 households. Data were collected by completing multidimensional questionnaires along with semi-structured interviews from households in 2023. To measure the resilience capacity of rural households, the theoretical framework of TANGO based on the estimation of the three capacities of absorption, adaptation and transfer was used through the factor analysis method, in which attitudinal and mental aspects of resilience are also taken into account. Finally, partial proportional odds model has been used to evaluate the influencing factors on the resilience capacity of rural households.
Results and Discussion
The results of the state of resilience capacity of rural households in the region indicated that the average value of their resilience capacity is 26.27, which shows the low level of resilience capacity in the region. Also, the households of the region are in a bad situation based on the absorption, adaptation and transmission capacities, and the households of the region have a stronger transmission capacity than the absorption and adaptation capacity against drought. The results of grouping the resilience capacity of households reveal that 32.45% are in the vulnerable group, 28.19% are in the relative resilience group, 22.61% are in the resilient group and 16.76% are in the high resilience group. The results show that more than 60% of households are at very low levels of resilience. Finally, the partial proportional odds model results demonstrated that the variables of education of the head of the household, skill level in agricultural activities, savings, household income, number of household contacts with agricultural extension, membership of the head of the household in social groups and access to microcredits have a positive effect and variables of the value of the loss of agricultural products and the number of livestock lost have a negative effect on the resilience capacity of rural households against drought.
Conclusion
According to the findings, policy-makers should prioritize strengthening the variables that determine the resilience capacity and its dimensions in the implementation of drought management programs so that households can absorb drought shocks without damaging their basic components. Policy-makers should also target specific categories of risks, dimensions of vulnerability and resilience in different time periods (before, during, and after shock) in order to choose comprehensive strategies to build and increase resilience. For instance, before a shock, better access to early detection of emerging climate risks could help farmers plan their cropping activities accordingly. Access to climate information allows for forward-looking adaptation that reduces the impact of shocks and increases resilience.
Research Article
Agricultural Economics
M. Rafiee Sefid Dashti; S.M. Mirdamadi; S.J. Farajollah Hosseini; S. Shokri
Abstract
Introduction
Every 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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Introduction
Every 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 Methods
In 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.
Conclusion
For 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.
Research Article
Agricultural Economics
H. Fouladi; H. Amirnejad; S. Shirzadi Laskookalayeh
Abstract
Introduction
In recent decades, the issue of climate change has become one of the global issues and has affected the agricultural sector. The continuation of agriculture regardless of the water shortage crisis has had an inappropriate effect on the sustainability and growth of this sector. On the other ...
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Introduction
In recent decades, the issue of climate change has become one of the global issues and has affected the agricultural sector. The continuation of agriculture regardless of the water shortage crisis has had an inappropriate effect on the sustainability and growth of this sector. On the other hand, the destructive effect of excessive use of chemical fertilizers and pesticides on water, soil, health of ecosystems, humans and other living beings is undeniable. For this reason, the void of using an efficient model that can provide all economic and environmental aspects at the same time was completely felt. The aim of this study was to provide an optimal cropping pattern using the integrated method of Goal and Grey planning. For this purpose, the farmers of the agronomy sub-sector of Tajan Basin were selected as the statistical population. In this regard, time series information was collected from the aggregation of the average data of 401 settlements located in this area during the years 2017-2021 from the annual reports of experts.
Materials and Methods
The Linear Programming (LP) Model quantifies an optimal way of integrating constraints to satisfy the objective function to optimize crop production and profits for irrigation farmers. To use LP, one must convert the problem into a mathematical model. To do this, an objective such as maximizing profit or minimizing losses is required. The model must also include decision variables that affect those objectives, and constraints that limit what user can do. Therefore, the LP Model is a single-objective method. Goal Programming (GP) is an extension of LP in which targets are specified for a set of constraints. GP is used to perform three types of analysis: Determining the required resources to achieve a desired set of objectives. Determining the degree of attainment of the goals with the available resources. Providing the best satisfying solution under a varying amount of resources and priorities of the goals. Thus, the GP model is a multi-objective method. The Grey system theory is identified as an effective methodology that can be used to solve uncertain problems with partially known information. Grey modelling approach uses accident data for estimating the model parameters. The model can reflect the dynamics, balance the conflicting the multidimensional targets of cropping patterns, and promoting the sustainable use of cultivated land. For achieving different goals in unstable economic and environmental conditions, we used a Goal-Grey model that was obtained from the integration of Goal programing and Grey Programing. The Goal-Grey model, by considering the uncertainty in the data, leads to overlap between the economic and environmental goals and provides the scope of cultivation for the selected products.
Results and Discussion
By estimating the Linear Programming (LP) Model, crops like wheat and canola are removed from the cropping pattern, while the cultivation areas for barley and high-yielding long-grain rice increase by 644% and 31%, respectively. In contrast, the cultivation areas for high-quality long-grain rice and maize decrease by 89% and 10%, respectively. Implementing this model boosts the gross profit of farmers in the Tajan region by 14% solely through adjusting the crop composition, without altering the current input levels. Additionally, the findings show that applying the LP Model results in fertilizer savings of 5%, 13%, and 10% for phosphate, nitrogen, and potash, respectively. The amount of herbicide and fungicide consumption in the LP Model is exactly equal to the current model of the region. However, the implementation of this model will lead to a 5% increase in the consumption of insecticides poison. The amount of irrigation water consumption in the LP Model was calculated to be 2% less than the current model of the region. In addition, the results indicate that by estimating the Goal-Grey Model, only canola is removed from the cropping pattern. Also, in order to achieve the defined goals in this study, the cultivation area of wheat and maize should be equal to 208 and 7356 hectares respectively. However, the flexibility of input usage enables adjustments to other crop cultivation areas, facilitating high-quality long-grain rice production on 970 to 18,157 hectares. Plus, the cultivation area of long-grain rice can vary from 7654 to 9995 hectares. In this model, barley can be removed from the crop composition like the linear pattern or cultivated on a maximum of 2553 hectares. The implementation of the Goal-Grey model will lead to a maximum 2% increase in the gross profit of the farmers of Tajan region compared to the current model of this region. Also, by implementing the Goal-Grey Model, on average, phosphate, nitrogen, and potash fertilizer consumption is saved by 16, 27, and 20 percent, respectively. In addition, with the implementation of the Goal-Grey Model, the consumption of agricultural pesticides will decrease from 733 to 355 thousand liters on average.
Conclusion
The LP Model is designed based on current regional conditions; however, as a single-objective model with fixed parameters, it lacks the flexibility to offer an adaptable program for farmers during drought or wet periods or when inputs are limited. Findings indicate that under current conditions, there is excessive use of chemical inputs and irrigation water. By accounting for data uncertainty, the Goal-Gray model addresses these limitations, balancing economic and environmental objectives and defining a cultivation range for selected crops.
Acknowledgement
We are grateful to the experts of agronomy management and plant conservation management of Mazandaran Province Agricultural Jihad Organization and Sari City Agricultural Jihad Management who cooperated in data collection. This article is taken from the preliminary results of a doctoral dissertation with material and intellectual rights related to Sari Agricultural Sciences and Natural Resources University, which is gratefully acknowledged.