International Journal of Agricultural Management and Development
Volume:14 Issue: 2, Spring 2024

Lakes, vital for human survival, are highly productive ecosystems providing services such as water supply and nutrient regulation. However, escalating water scarcity, fueled by global population growth and climate change, necessitates a comprehensive, long-term approach. The research method used in the study is an integrated framework of hydrology and ecological response components to determine environmental water requirements for Ovan Lake, Iran. The research delves into Ovan Lake's hydrological components, exploring precipitation, evaporation, surface water, and groundwater exchange. Identifying the great reed warbler breeding operation on the lake's margin reed lands as a key ecological relationship, the study highlights its role in reflecting the favorable status of other species and providing conservation benefits. Maintaining a water depth between 50 and 70 cm near the reeds, along with a reed cover width of approximately 20 m in March, as the breeding month, is recommended to ensure optimal breeding conditions for great reed warbler. Correlations between hydrological conditions (water depth, area, and volume) and ecological water depth are established, estimating environmental water requirements at 241 and 207 thousand m3 during drought and wet periods, respectively. The analysis computes water shortfalls needed to meet environmental water needs under different scenarios and ecological conditions. It matches minimum and optimum water levels to drought and wet periods based on available conditions. Results indicate challenges in maintaining ecological conditions in late spring and summer due to reduced rainfall and increased evaporation, emphasizing the need for proactive management strategies to sustain Ovan Lake's environmental health and restoration.

Occupational safety risk management in agricultural and horticultural activities is profound for preserving safety, increasing productivity, and enhancing yields. So, this research focused on identifying and assessing different occupational risks among grape growers. The statistical population (N=2145) was composed of grape growers in northwest of Iran (Meshginshahr County in Ardabil province). The sample (n=325), whose size was determined by Bartlett et al.’s (2001) table, was taken from the regions of Lahrud (222 people), Arjaq (58 people), and Fakhrabad (45 people) by the multi-stage randomization technique. The indices for measuring occupational risks were identified by the Delphi technique (37 skillful grape growers), and the occupational safety risks were assessed by the 3D method of risk assessment presented by Melbourne University. According to the results, most respondents (31.38%) were at a high occupational risk level. The most important occupational risks included incidents when working with manual and mechanical tools, incidents when working with chemical pesticides, and ergonomic problems. Also, the most effective variables in distinguishing the respondents included perceived severity (0.554), response cost (0.553), and self-efficacy (0.509). Developing compulsory safety regulations on the necessity of using protective covering on mechanical garden tools, providing supportive subsidies to procure safety equipment, and holding technical training courses about occupational safety and ergonomic principles will effectively mitigate occupational safety risks of grape growers.

The rural areas of Ilam province are facing many problems in the field of employment and production, and the development of entrepreneurial activities in these areas is very necessary. Opportunity identification has a very fundamental role in entrepreneurial activities.The study was carried out to home business opportunities for rural areas in Ilam province. The research used a combined qualitative and quantitative methodology. The methodology was qualitative in the first phase using semi-structured interviews and focus group discussions. The second phase was run by a descriptive-survey methodology. Participants were purposefully selected from village administrators, experts of the Coordination Office of Economic Affairs Governor, experts of the Jahad-e Agriculture organization, experts of the Cultural Heritage, Handicrafts and Tourism Organization, and experts of the Industry, Mining and Trade Organization. Data were collected from 72 participants in nine sessions as focus group discussion attended by 6-11 people. They were qualitatively analyzed by Strauss and Corbin’s constant comparison method in three coding phases. They were quantitatively analyzed with SPSS Software Package. In the end, the home businesses in priority in each Township were introduced for agricultural sector, processing industries, and handicraft industries.

This study aimed to use artificial neural networks (ANNs) to predict output energy and economic indicators. So, data on two fish breeding sites were collected with a questionnaire, directly from the site owners and administrators, and from the records. Then, the input and output energy of cold-water and hot-water fish were calculated. The cold-water fish were found to have a more favorable energy ratio (ER) (2.24), energy productivity (EP) (0.04 kg MJ−1), specific energy (SE) (26.83 MJ kg-1), and net energy gain (NEG) (33222.16 MJ kg-1). According to the results, fish feed and electricity are two factors among energy consumption inputs whose proper management will increase energy efficiency. The benefit-to-cost ratio was positive for cold-water fish (1.54) and hot-water fish (2.45). The productivity of cold-water and hot-water fish was 0.58 kg $-1 and 0.52 kg $-1, respectively. The results of ANNs showed that R2 varied from 0.947 to 0.993 overall, from 0.912 to 0.964 for the training stage, and from 0.978 to 0.980 for the testing stage in the case of cold-water fish. Regarding hot-water fish, these values were 0.885-0.998, 0.923-0.952, and 0.952-0.995, respectively. So, ANNs can be used to predict output energy and economic productivity.

Financial development can greatly contribute to alleviating environmental pollution by providing ready access to modern technologies. In contrast, it can aggravate environmental pollution by boosting production activities. Since CO2 emission is the main factor responsible for climate change, it is of crucial importance to study the factors affecting its emission. The present study mainly aims to explore the impact of financial development, gross domestic production (GDP), and energy use on CO2 emission in the agricultural sector of Iran from an environmental economics perspective. Accordingly, the impact of the financial index on CO2 emission is analyzed using time-series data of Iran for the period from 1991 to 2022 on the basis of an autoregressive distributed lag (ARDL) model. The results show an inverse relationship between financial development and CO2. This means that a 1% increase in the financial development of the agricultural sector results in a 0.27% reduction in CO2 emission in the long run. Also, the increase in the GDP of the agricultural sector and population growth will increase CO2 emission. There is also an inverse relationship between financial development and greenhouse gas emissions in the short run. Energy use and population growth are directly related to CO2 emission in the short run. So, the reduction of CO2 emission needs to make use of advanced technologies. Pollution emission planning should be on the agenda of planners and politicians.

Ensuring food safety, preventing market imbalance of supply and demand and market inflammation as well as improving production efficiency and productivity, transparency, traceability of distribution networks and supply chains of active agri-businesses in the field of basic and strategic products such as rice, tea, olives and citrus and the realization of many positive micro and macro consequences depend on intelligent supply chain management. Therefore, the purpose of this applied research was to present a model based on the effective factors of intelligent SCM of agri-businesses and to present SC strategies and effective actions. In this regard, in addition to library studies, field studies were conducted through in-depth interviews with 33 public and private sector experts from Guilan, Mazandaran and Zanjan provinces who were selected by theoretical and non-probabilistic sampling. In order to analyze the data, qualitative data-based and coding methods were used. The validity and reliability of the data collection tool were confirmed. Based on the research findings, 1556 open codes, 75 axial codes and 9 selective codes including economic and financial factors, marketing and sales, production and operational, institutional, infrastructure and logistics, communication and information, technological and innovative, climatic, environmental, biological and political were identified and the initial model designed based on effective factors by qualitative analysis method and using Maxqda2020 software. After removing 29 sub-factors with a repetition rate of less than 7, the final model was presented based on 46 sub-factors. In final, 8 SC strategies and 34 effective actions in order to succesfull intelligent SCM proposed.