Agricultural systems engineering (greenhouse, fish farming, mushroom production)
M. Zangeneh; N. Banaeian
Abstract
IntroductionSo far, many studies have been conducted to evaluate the impact of input consumption patterns on energy, economic, and environmental indicators on horticultural and greenhouse crops in Iran. A review of these studies shows that the causes of the current situation in the systems have not been ...
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IntroductionSo far, many studies have been conducted to evaluate the impact of input consumption patterns on energy, economic, and environmental indicators on horticultural and greenhouse crops in Iran. A review of these studies shows that the causes of the current situation in the systems have not been investigated. These studies are mostly reporting the current situation and the interventions and their effect on improving the input consumption pattern in the sustainability of the system have not been considered by researchers. Also, studies showed that the study location and products do not fit well with the volume of production in the horticultural and greenhouse sector of Iran. Therefore, in order to increase the effectiveness and future direction of studies in this field, this review study was conducted. In this article, Iranian horticultural and greenhouse production systems were reviewed and analyzed by reviewing the published articles between 2008 and 2018, using the PRISMA method. The PRISMA method is a well-known method for conducting systematic review studies. The PRISMA method includes the following sections: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions, and implications of key findings. In this article, 16 types of garden products and 6 types of greenhouse products were studied.Material and MethodsIn this study, the methods used to determine the status of energy consumption, economic and environmental patterns for horticultural and greenhouse crops were analyzed. For this purpose, the indicators of total energy consumption (TEI), energy efficiency (EUE), net energy (NE), and energy efficiency (EP) were examined in the section of energy. The issue of sensitivity analysis of energy inputs was also examined and the highest values of t-statistic and MPP were reported for products. In some articles, the data envelopment analysis method was used in systems performance analysis. The indicators used included technical efficiency (TE), pure technical efficiency (PTE), scale efficiency (SE), and energy-saving target ratio (ESTR). The results of them were summarized and reported. In some studies, the method of artificial neural networks and the Adaptive Neuro-Fuzzy Inference System were used. In general, in the present article, the challenges and risks in the methods used in previous studies were considered. The issue of sampling in the analysis of agricultural systems was discussed in detail and a new sampling procedure was proposed. To draw a general picture of energy and environmental indicators of orchard and greenhouse systems in Iran, the results published in the articles were reviewed. Not all researchers use the same equivalents in calculating the indices, and this makes the results of the studies slightly different from each other. The existence of such differences causes some deviations in comparing the results of similar articles in the same products. However, to adjust for these differences, averaging was used in the index report.Results and DiscussionThe study of the share of inputs in the total energy consumption shows that for horticultural products, the share of fertilizer and electricity inputs is very significant. In the case of greenhouse products, fuel input, which is mainly diesel, has the largest share of energy consumption. Walnuts have the lowest energy consumption and strawberries have the highest energy consumption among orchard products. Grapes, apples, and walnuts also have positive net energy, so they have the highest energy efficiency compared to other products. The most important inputs that have the greatest potential for energy savings in most products are diesel fuel and electricity. Among greenhouse crops in cucumber production, diesel fuel has great potential for energy savings that need to be reduced in future research. In the case of strawberry and rose products, electricity input has the greatest potential for energy savings. Knowing the potential of inputs that can be saved can be effective in changing the behavior of producers.ConclusionTo increase the effectiveness of research in this area, such studies should be done dynamically and for at least two or more years. In the first year, the input consumption pattern should be extracted and after performing the consumption pattern modifying interventions, the effect of these actions should be evaluated in the following years. Data envelopment analysis methods and multi-objective genetic algorithm can be well used to develop solutions to improve input consumption patterns. The review of articles showed that the study of the effect of social factors on the behavior of various production systems has been neglected. Since the pattern of energy consumption in the agricultural sector is significantly dependent on the behavior of users and the characteristics of systems and methods of production, it seems necessary to pay attention to this factor to prepare and design any process improvement strategy in the system. In this study, a new procedure including three stages of analysis, redesign, and evaluation was proposed to complete the studies related to the analysis of agricultural systems.
M. Zangeneh; A. Akram
Abstract
Introduction In this research, a part of the requirements for the establishment of a network of consultancy, agricultural engineering and technical services in the agricultural sector, which is related to the location of these centers, has been reviewed. The location of these centers has been done through ...
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Introduction In this research, a part of the requirements for the establishment of a network of consultancy, agricultural engineering and technical services in the agricultural sector, which is related to the location of these centers, has been reviewed. The location of these centers has been done through the determination of the field of operation and the appropriate establishment of consulting, engineering, and agricultural consulting companies based on regional capacities and taking into account the distance between the types of customers of such centers. Materials and Methods In the issue of locating service centers three main types of customer can be classified. First-class customers, which have the largest number among different types of customers, are farms and units that produce agricultural products. Each point of demand for these categories of customers may require different types of services at different times. Due to the large number and dispersion, these category of customers are considered as a focal point for ease of modeling in rural areas where they are located. Also, due to various reasons, including access to various facilities, security, traffic congestion and etc., the nominations for deployment of service centers are also considered in the same rural areas. In order to transport agricultural products from the place of production, the current location is considered to be the distance from the manufacturer's place, and the destination of the product is not studied in this issue. Second and third-type customers are demanding access to services at their own place. These types of customers may exist in some areas and agricultural supply chains. These two groups of customers include refineries, warehouses and silos mainly operating in the post-harvest of agricultural production. To meet the demand for each of the different demand points of different types of customers, the number of different trips from service centers to customer premises or vice versa is required. Each service center does not offer the same type of service to its customers. A total of 127 service packages are available for provision at a service center. Results and Discussion The main basis for choosing the optimal location for covering models is the placement of demand points in the defined coverage radius for the candidate points. Different radius were tested to find the perfect coverage radius in each of the studied villages. For this purpose, a radius of five to 160 kilometers was examined. In some coverage radius, not only does the optimal location not change, but the number of served points is also fixed. The location of different types of customers is different, so that the first type of customers are fully located in the village, but second and third type customers are widespread in the Hamedan province. Conclusion To conclude, it is necessary to consider the demand of customers located in the further distances of the service center due to the nature of the agricultural service, which requires inevitable traffic over long distances, when adjusting the operational plans of the agricultural service centers. To provide sufficient justification for the distance, though within the radius of coverage. Thus, the results of this research show that if all service centers cover 130 kilometers of radius, the largest number of customers will be covered. It should be noted that for the full coverage of all customers, the coverage radius of the service centers varies, but with the same radius, the 130 km radius is the largest coverage of the agricultural service centers in the Razan city.
