فهرست مطالب z. farajzadeh∗

Production process including agricultural production plays a significant role for some pollutants like Methane (CH4) and Nitrous Oxide (N2O), leading to the necessity of supplying safe drinking water in rural areas due to pollutants leakage to water resources. Thus, in addition to the policies at the agricultural sector level, other measures should be taken at the farm level. In this context, the current study aims to develop strategies in order to mitigate pollutants emissions emitted from agricultural activities. The possible strategies to dampen the pollutants emissions at farm level were developed based on the current literature and the survey implemented throughout the selected experts in Fars province. The survey was conducted to provide information to rank the strategies. The strategies were ranked using FAHP and TOPSIS techniques. Based on the results, for both agronomy and horticultural, and husbandry activities, effectiveness and cost were ranked more important, assigning three-fourth of the total weight. The results obtained for strategies ranking revealed that for activities in agronomy and horticultural subsector, the prioritized strategies are agricultural machinery and equipment management, plant residue management and managerial activities in general at the farm level. For the husbandry activities, manure scraping method and the animal housing management were ranked with higher importance followed by alteration in animal feed diet.

The rank of Iran in terms of pollutant emissions, which mainly originate from the consumption of energy products, is much higher than the rank of gross domestic product, placing Iran the fourth in the production and consumption of gas and oil, among the cases with the highest emission intensity in the world. Different driving forces account for the high emission intensity. This study decomposes the changes in the aggregate emission intensity of the selected pollutants into a broader scope of driving forces including energy, urbanization, output, labor, and trade-related variables. The examined pollutants were far beyond carbon dioxide, including nitrogen oxides, sulphur dioxide, and carbon monoxide, emitted from energy product consumption. The aim of this study was to investigate the emission intensity of the selected pollutants and their components.  Decomposition analysis was done to decompose the emission intensity into a broader scope of the driving forces far beyond what examined in the literature. For this purpose, two well-known artificial neural networks, multilayer perceptron, and wavelet-based neural network were applied to forecast the emission intensity of the selected pollutants and their components. The emission intensity of nitrogen oxides and sulphur dioxide illustrated a decreasing trend. In contrast, a general increasing trend with significant fluctuation was observed for carbon monoxide and carbon dioxide emission intensity. Among the components, energy structure, population-labor ratio, and trade openness showed an intensity decreasing effect, while urban per capita output, urbanization, energy intensity, and industrial output-trade ratio contributed to higher emission intensity of the pollutants. Moreover, the multilayer perceptron and wavelet-based neural networks were recommended to examine the predictability of the emission intensity and its components. It was found that intensive and extensive growth and energy structure were the most significant driving forces of the emission intensity. The forecast results indicated that the emission intensity of nitrogen oxides, sulphur dioxide, and carbon monoxide might be predicted by the applied networks with a prediction error of less than 0.2 percent. However, the prediction error for carbon dioxide emission intensity was much higher.

Red meat plays an important role in providing animal protein of Iranian households. However, its supply has experienced some challenges stemming from exchange rate fluctuations, smuggling livestock to the neighboring countries, and importing frozen meat financed by subsidized currency. In addition, there has been significant emphasis on self-sufficiency and providing the red meat domestically. On the other hand, based on Falcon Mark index, Iran has encountered water tension, the situation that may be getting worse as self-sufficiency is more focused. Regarding the problem, this study aimed at investigating different scenarios of domestically-produced and import of red meat with due consideration of simultaneously minimizing the costs of red meat and water use. For this purpose, multiobjective programing was applied using data for 2000-2018 in which minimization of spending for domestic consumption and water footprint embodied in domestic production was included. The selected scenarios were different combinations of the weights for minimization objectives. The results showed that depending on the weights assigned to the objectives, providing 30-40 percent of domestic need from domestically produced red meat was recommended while the remaining need might be provided via imports. The findings revealed that recommended combination for domestically-produced and imported red meat could reduce the spending on domestic need and water footprint significantly, resulting in 63 percent and 42 percent reduction in water use and cost spending, respectively; in addition, greenhouse gas emissions would be reduced. These results are the result of importing sheep meat from Australia or cattle meat with bones from Brazil. In spite of a significant reduction in the objectives’ values, the selected countries for import revealed a low diversity.

Energy products are the main sources of emissions for most of the pollutants in Iran. However, for some pollutants like Methane (CH4) and Nitrous Oxide (N2O), the production process, including the agricultural production process, plays a significant role. The aims of this study were to analysis the emissions intensity of the selected pollutants and to introduce the determinants in Iranian agricultural sector. The emission intensity in the agricultural sector was decomposed into its components using decomposition analysis. Then, the regression analysis was applied to investigate the emission intensity determinants. The selected pollutants are Carbon Dioxide (CO2), CH4, and N2O emitted from agricultural production process. The applied data cover 1973-2016. The findings showed that CH4 emission intensity has been decreasing over the study horizon by 3.9% annually. For N2O, the corresponding value was 2.6%. Based on the results, output level in agricultural sectors is an important driving factor in the emission intensity. It was found that 1% increase in livestock output level is expected to increase CH4 emission intensity by 0.9% while it will dampen the N2O emissions intensity by more than 3.3%. By contrast, the same percentage of increase in the output level of agronomy and horticultural subsector will induce an increase of 3.3% in N2O emission intensity and will reduce the CH4 emission intensity more than 0.9%. Macroeconomic variables including urbanization and trade openness failed to affect the agricultural emission intensity significantly. The emission intensity of all pollutants, measured in CO2 equivalent, has been decreasing over the study period by 3.5% annually. It was also found that, in terms of aggregated emission, output expansion in livestock and forestry sectors may induce higher emission intensity, while agronomy and horticultural output expansion can reduce the emissions intensity. Given that the output level plays a significant role in emission intensity while the macroeconomic variables have nothing to do with emission intensity, the measures taken to reduce the emission intensity in the agricultural sector should be sector-specific. Moreover, the measures should focus on each subsector individually.

The resilience economy and the related policies and strategies have been one of the most important concerns of the policy makers over recent years. This issue is especially important in the agricultural sector due to its significant importance in the Iranian economy. Therefore, this study aimed at investigating the importance of government policies and requirements to achieve the goals of resilience economy in the agricultural sector of Iran using Analytical Network Process (ANP) method. For this purpose, a list of the most important policies and economic requirements affecting the resilience economy in the short term, mid-term and long-term was initially selected based on the related literature and experts opinions; then through questionnaire, the concerned experts' opinions were obtained and the extracted policies were ranked. The study results showed that the most important requirements to achieve resilience economy included 'supporting national production and efficiently use of time, resources and facilities' in short term and 'reforming the Iranian economic system and privatization' in mid-term and long-term. Also, it was found that food security and reduction of imports in the short run as well as reducing government intervention in price and wage determination in the long run would be the most important government policies.

Iranian per capita forest area is as low as one-fourth of the global one. However, the Iranian forests have experienced a rapid degradation and frequently-occurred fire events. Therefore, this study aims to consider driving factors of forest fires in the Iranian forests as well as to investigate the applicable strategies expressed by the selected experts to control and tackle fire. Regression analysis based on provincial data over 2001-2015 was applied to examine driving factors of forest fires. Fire control management analysis is implemented based on experts’ judgment on the offered strategies applying a combined framework of Fuzzy Analytic Hierarchy Process (FAHP) and TOPSIS model. The results indicated that socio-economic variables are relatively more determinant in forest fires compared to the environmental variables. As for environmental factors, the variable found to be significant is the temperature. It was also found that higher population and road density will increase the possibility of fire events since it gives rise to more usage of the forests. However, higher development level is expected to induce a reduction in fire events as it is associated with higher level of education and income. It was also found that the strategies with higher priority in fire control are more focused on human resources rather than equipment. Pre-fire strategies including “meeting with fire extinguishing body”, “imposing fire fines” and “using forest patrol” are the most prioritized ones while for during-fire events “keeping self-possession” and “fireman on time attendance at the fire place” are the highly recommended strategies.

The current study aim is to introduce, analyze and examine the possibility of forecasting these indices in Iranian economy. To get the objective, time series data for 1980-2015 were applied. The introduced indices for natural capital are ecological footprint, biological capacity, ecological tension, ecological occupancy, and ecological-economic coordination. The findings showed that ecological footprint as the main index has experienced an annual growth of 2 percent. In terms of the contribution, carbon dioxide accounts for the most part of the ecological footprint growth. On the other hand, biological capacity showed a decreasing trend over the study horizon such that it has been exceeded by ecological footprint, giving rise to an ecological deficit. The ecological tension index showed that services demanded from the environment have exceeded its capacity as well as those of the global average, leading to ecological insecurity. The forecasting results also indicated a higher forecasting ability for neural network model compared to the ARMA one, providing forecasts with errors of as low as less than 3 percent for the most of the indices.

Financial and credit institutions including agricultural banks encounter limited resources. So, to keep their financial ability to support agricultural production sustainably as well as to allocate the financial resources optimally, they should prioritize the activities based on their capital returns. In this regard, this study aimed at analyzing the capital returns of the selected agricultural products in Fars province of Iran. For this purpose, an individual production function comprising both physical and human capitals was estimated for all selected products including agronomical, horticultural and livestock products using panel data of 2005-2014. The findings showed that for all activities, the physical capital had the most contribution to production; however, this contribution varied among the products significantly. Comparatively, the capital returns for horticulture and livestock activities tended to be higher. The returns ranged from 0.45 to 0.8 for agronomical products while the corresponding ranges were 0.5-0.9 and higher than 0.7 for livestock and horticultural products, respectively. In addition to the physical and human capitals, as an innovative and control variable, De Martonne climatic index was used in estimated equations. This allowed an assessment of the geographical implications, indicating that regions with higher precipitation, possibly northern and western regions, had better condition to grow output, deserving to be taken into consideration in allocating the credit resources. Based on the capital returns, among the livestock products, red meat might be prioritized while among the horticultural products, citrus, walnuts, peach, apple, pistachios and apricot would be preferred over others. As for agronomical activities, investment priority belongs to machineries.

The limited financial and credit resources of banks need to be allocated optimally. In addition, in order to exit from the financial freezing situation, banks and other financial institutions, such as Agriculture Bank, have to allocate resources to the economic activities and sectors in a way that it entails the highest return on capital. In this regard, the objective of this study is to analysis capital returns in Fars province agriculture subsectors using neoclassical growth model. The models include physical and human capital for each subsector. The applied data are panel data covering period of 2005-2014 throughout Fars province counties. The results showed that physical capital returns for agriculture subsectors are around 0.4 percent. It was also found that knowledge of labor or human capital does not substantially contribute to productions, indicating the traditional production method and mismatch of labor knowledge with production activity.

Nurses plays an important role in the development of health care and treatment. Thus, a main task of a nursing director is to pay attention to the motivational energies of nurses. This study aimed to determine the job motivation of nurses working in Valiasr Hospital of Birjand in 2013.

This descriptive cross-sectional study was conducted on 117 nurses in Valiasr Hospital of Birjand who were selected by census. Data were collected using the Herzberg's Motivation- Hygiene Questionnaire which consisted of 30 items and 11 components with 5 Likert scale. Its reliability and validity were confirmed in previous studies. Data were analyzed in SPSS (version 16) using independent t test and ANOVA at the significant level of p<0.05.

In this study, nurse's overall score for job motivation was moderate with a mean score of 2.9±0.5. Intrinsic motivation al factors were more important than health factors (extrinsic) in creating job motivation. The most and least important motivational factor from among the components of motivational factors were the nature of work (3.8±0.8) and identification (2.3±0.9) and from among the components of health factors, related cooperation (3.8±0.7) and job security (1.8±0.8). There was no statistically significant difference in the overall motivation mean score in terms of demographic characteristics.

In light of nurse's job motivation and intrinsic motivational factors, it is recommended that nurse managers pay more attention to factors such as staff appreciation, economic problems, security, and working conditions.

Regarding the imprecise or fuzzy data on the agriculture activities, applying models based on fuzzy data may be useful. In line with this condition this study tries to compare the ability of possiblistic programming approach in developing optimal solution with fuzzy and deterministic programming. To get this objective a data set obtained from 90 farmers of Kohgilooye region, gathered in 1387(2008), was applied. Goals regarded in the models are mainly to increase gross margin (GM), decrease risk and to decrease water consumption. In framework of possiblistic programming approach these goals are maximizing average GM, minimizing difference between average and minimum GM, maximizing difference between average and maximum GM. The goal of reducing water consumption also contains minimizing average water consumption, maximizing difference between average and minimum water consumption and minimizing difference between average and maximum water consumption. Different optimal solutions was developed using the above models and then some of them were chosen based on the goals. Findings showed that possiblistic programming has preference over the two other models in that is able to get the various and conflict goals and its superiority to other models is more noticeable as the number of goals increases. High ability in developing desired solution in bad condition is another superiority of possiblistic programming. It was also found that watermelon, rice and melon are more appropriate to get the goals. The results suggeat that in order to lessen the pressure on water resources, some efforts is needed to decrease the price and revenue fluctuations.