Modeling
M. Sami; A. Akram; M. Sharifi
Abstract
IntroductionThe need to develop alternative energy sources especially renewable energy has become increasingly apparent with the incident of fuel shortages and escalating energy prices in recent years. With the advent of renewable energy, various studies have been conducted to investigate the potential ...
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IntroductionThe need to develop alternative energy sources especially renewable energy has become increasingly apparent with the incident of fuel shortages and escalating energy prices in recent years. With the advent of renewable energy, various studies have been conducted to investigate the potential of biogas production from agricultural waste. Considering the importance of retention time and methane production potential for designing industrial digesters, many studies on potential analysis and modeling of the digestion process of different products have been carried out by various researchers. These studies are valuable for the design and implementation of anaerobic digesters. Apple is one of the most popular fruits in many parts of the world and is widely cultivated in many temperate regions of the world. Considering the large volume of apple waste in Iran, this study was designed based on potential evaluation and modeling of biogas production from apple pulp.Materials and MethodsIn order to measure the potential of biogas production from apple pomace, a number of lab-scale digesters with a capacity of 600 ml and a working capacity of 400-500 ml were made. pH and C/N ratio were modified by adding NaOH and urea solution, respectively. Three different temperature treatments including psychrophilic (ambient temperature), mesophilic (37ºC), and thermophilic (47ºC) were applied to the substrate. Used pomace samples were collected from the output of an apple juice factory in southern Isfahan province, Iran. Anaerobic Biodegradability (ABD) was obtained by dividing the experimental methane production potential (BMP) obtained from the experimental results on the theoretical methane production potential. Three most common kinetic models of Gompertz, Logistic, and Richards were used to predict and stimulate the cumulative methane production of treatments.Results and DiscussionUnder ambient temperature, the digestive process took a longer time, and the time of maximum dilly biogas production was considerably more than the other two treatments. Statistically, production time and peak time of this treatment was higher than the other two treatments at 1% significance level. Maximum daily biogas production in the ambient treatment was observed on day 37th with a volume of 6.99 g-VS-1 ml, while maximum daily biogas production in the treatments of 37 °C and 47 °C were observed on days 22th (20.16 ml g-VS-1) and 20th (25.57 ml g-VS-1), respectively. In all three treatments, daily biogas production increased sharply in the first incubation days and after that reduced and then production increased again. In mesophilic and thermophilic treatments, the production of biogas modestly stopped after 35 days, but under the ambient temperature, the process of production continued after 55 days. The methane concentration of biogas in the psychrophilic treatment was significantly lower than the other two treatments at 1% level. Two treatments of 37°C and 45°C have a significant difference in methane yield at 1% level. Nevertheless, the production of biogas in two treatments was not statistically different. In all three treatments, the lowest pH was recorded after 7 days of production and the highest pH was recorded on days 34-40. All three kinetic equations were able to simulate the methane production process with high precision, although the results of the Logistic model provided higher accuracy. In the treatment 47 °C, the efficiency of the studied equations was higher than other treatments and models were able to predict the production process with higher accuracy. Results of the experiment show the high biochemical methane production potential of apple pomace (473.17 ml g-VS-1), which under laboratory condition of this study up to 63.9% of this potential (302.70 ml g-VS-1) was obtained. ConclusionThis study results are valuable for the design and implementation of industrial digesters. The results indicate the apple pomace has a high potential for the production of methane and its biodegradability is high. Apart from pH that is acidic, other apple pulp factors are appropriate for the activity of methanogenic bacteria. In terms of nutrients, apple pomace is also a good environment for the growth of anaerobic bacteria.
N. Loveimi; A. Akram; N. Bagheri; A. Hajiahmad
Abstract
Introduction Remote sensing and using satellite images have been widely considered due to the high speed of measurement and great area of coverage. Canola is a source of edible oil and its cultivation in Iran and the world is developing. Comparing with other crops, canola, because of its yellow flowers, ...
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Introduction Remote sensing and using satellite images have been widely considered due to the high speed of measurement and great area of coverage. Canola is a source of edible oil and its cultivation in Iran and the world is developing. Comparing with other crops, canola, because of its yellow flowers, has a different canopy color, and only a few researches have been carried out in order to assess the spectral indices for prediction of its yield. Therefore, the main objective of this research is to evaluate some spectral vegetation indices to estimate the yield of canola in different growth stages. Materials and Methods The study was performed in 2016-2017 in Karaj, Iran. Three canola farms were chosen for the evaluation of the relationship between yield and some vegetation indices derived from the Sentinel-2 sensor. The sensor data were processed in five stages: before flowering, early flowering, peak of flowering, green and dry maturity, and the vegetation indices were extracted for each of them. This research was pixel-based and the pixels network of each studied farm was determined by RTKGPS. During harvesting time, for measurement of grain yield, five samples, four from the corners and one from the center of the pixel, were taken and their average was considered as the representative amount of the pixel. Totally, 112 pixels from three studied farms were used to calibrate the predictive models. By using Simple Linear Regression (SLR) models, ten new and conventional vegetation indices were assessed. Also, Multivariate Linear Regression (MLR) models and Artificial Neural Net (ANN) models with four bands, three visible bands and NIR band, as inputs, were used to predict the canola yield. In order to validate the SLR and MLR models, the "K-Fold" method of cross-validation was used, and for the validation of ANN models, 15% of data were used; 70% for the train, 15% for validation, and 15% for the test. Results and Discussion The results showed that, on the basis of SLR models, among the growth stages, the highest coefficient of determination (R2) in each of the vegetation indices belonged to one of the two stages: the peak of flowering and green maturity. According to SLR models, among the vegetation indices in different stages, the NDYI in the peak of the flowering stage had the highest correlation with yield (R2 = 73%). Also, the RVI with 29%, BNDVI with 52%, NDVI with 56%, and GNDVI with 35% had the highest R2 in the before flowering, early flowering, peak of flowering, green and dry maturity stages, respectively. MLR models resulted to the best yield predictive model at the peak of flowering stage (R2 = 76% for the calibration and R2 = 73% and RMSE = 0.641 for the validation). For ANN models, the strongest model achieved at peak of flowering stage (R2 = 92% for the calibration (train) and R2 = 77% and RMSE = 0.612 for the validation (test)). It seems that the results are affected by yellow flowers of canola, and absorption of blue light by their petals. Therefore, in the peak of the flowering stage, the reflection of the blue light is more likely to belong to green leaves and stems. Therefore, any index such as NDYI, which the blue reflection is subtracted in its equation, represents better the number of flowers, and since the density of flowers is directly related to the yield, the yield will be predicted with more precision. Conclusion The results of the analysis of the indices by SLR models showed that the correlation of each of the vegetation indices with the canola yield, in different stages of growth, has a considerable difference. Based on this model, the highest R2 in each of these indices happened in the peak of flowering or green maturity stage, and among these indices in different stages, the NDYI in the peak of the flowering stage had the highest R2. Finally, in both of the MLR and ANN models, with four bands, three visible bands and near-infrared band, as inputs, the best yield predictive model resulted in the peak of the flowering stage.
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.
K. Afsahi; A. Akram; R. Alimardani; M. Azizi
Abstract
For improvement or change in a plowing system, it is crucial that all important parameters to be taken in account. Recommendation of a tillage system should receive supports from research data as well as from skilled farmers in order to find a resolution to problems of that system. In this study, strengths, ...
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For improvement or change in a plowing system, it is crucial that all important parameters to be taken in account. Recommendation of a tillage system should receive supports from research data as well as from skilled farmers in order to find a resolution to problems of that system. In this study, strengths, weaknesses, opportunities and threats (SWOT) of different tillage systems for wheat cultivation in the Khodabandeh region (Zanjan province, Iran) were identified and ranked using Analytic Hierarchy Process (AHP). Based on the viewpoints of skilled farmers, the main threats in tillage systems, which include small farm lands in the region, lack of qualitative research on new tillage systems and lack of government support, affected the system selection (32 percent), relative strengths(26 percent), opportunities (22 percent), and weakness(20 percent). Because of these threats, farmers keep using conventional tillage method (with the value of 47 percent) in spite of their awareness about the benefits of conservation tillage and no-tillage methods. In this situation, the recommended measures are; making new policies for the land integration, performing qualitative research specially on new machinery, clarifying the government's policies on exporting and importing agricultural products and on the amount of guaranteed prices of products before starting the growing season. By these activities the threats can be replaced by opportunities and strengths.
M. Sharifi; A. Akram; Sh. Rafiee; M. Sabzehparvar
Abstract
Alborz province with an area of about 5121.7 km2 has about 0.31% of the total area of the country. The total arable area of the province is about 48954 hectares. Water, land and capital are the most important factors for agricultural production. By understanding the subjective beliefs, decision-making ...
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Alborz province with an area of about 5121.7 km2 has about 0.31% of the total area of the country. The total arable area of the province is about 48954 hectares. Water, land and capital are the most important factors for agricultural production. By understanding the subjective beliefs, decision-making criteria and economic incentives of local farmers, the priority of crops can be achieved with the maximum profitability of farmers and the least damage to the resources (water and land). The combination of Fuzzy Delphi techniques and methods of integrating analytical hierarchy process (AHP) can be an appropriate approach for achieving this goal. By employing the above combination of Fuzzy and AHP techniques, the priorities of the strategic agricultural crops in Alborz province achieved as wheat, barley, corn silage, alfalfa, cotton and canola, with final priority weighting factors of 0.496, 0.403, 0.354, 0.320, 0.183, and 0.090, respectively. By comparing the decision criteria it has been determined that the farmers prefer the amount of cultivation area, net income, production costs and livestock needs with the relative importance factors of 0.487, 0.410, 0.346 and 0.188, respectively. Among all prioritization criteria, the cultivated area had the highest priority. Water shortage, labor costs, lack of financial support, and governmental purchase allowance for wheat, were the main reasons for shifting the cultivated area towards wheat cultivation with total area of 14350 hectares.
