جستجوی مقالات مرتبط با کلیدواژه « تحلیل حساسیت » در نشریات گروه « مکانیزاسیون کشاورزی »

The Maximum energy loss in flat-plate solar collectors is due to top heat loss. The variables of absorber plate temperature, ambient temperature, wind speed, the distance between the absorber plate and the cover, collector length, and tilt angle are effective. In this research, considering the long-term meteorological data and developed relations for the top loss in flat-plate solar collectors in previous researches, including convection, conduction and radiation losses, calculations were performed in the form of Fortran codes. Results showed that the proper value for the distance between the absorber plate and one-layer cover has the range of 11 to 38 mm for some cities of the country. In addition, the sensitivity analysis of the top loss showed that the order of variables from maximum to minimum is wind speed, the distance between the absorber plate and the cover, collector tilt angle, collector length, absorbent plate temperature and ambient temperature.

Since white berry has much medical and edible benefit, it is known as a popular fruit. This research is carried out in order to classify ripe and unripe berries from each other using active thermography method. In this research 70 berries have been used as experimental samples randomly selected from a tree. They were sorted to “ripe” and “unripe” class by 5 expert people with attention to the color and texture of fruits. The temperature changes of samples by heat shock induction were recorded with a thermal camera and coefficients of first order, second order and logarithmic equations fitted to temperature-time graphs were employed for classification in MATLAB software. Using Principal Component Analysis (PCA) method, in MATLAB software, causes to use only first order equation coefficients, with an accuracy of 90 percent. Using the results of this research, the speed and accuracy of classification can be effectively increased in berry classification lines.

In this article a machine vision system and an artificial neural network (ANN) for classifying the strawberry based on maturity and shape features were used. First an image processing algorithm for extracting the color and shape features was investigated and then for grading the strawberry into three classes based on shape features and three classes of maturity based on colors features were done. The sensitivity analysis indicated that shape grading had highest sensitive to area, parameter, large and minor diameters features. Also a* and S color features had better correlation coefficient than other color features with total solid soluble and therefore were selected as supreme features for grading the strawberry based on maturity. Finally, results demonstrated that the ANN was able to classify with 94.04 and 95.14 total accuracy rate for shape and maturity grading.

This study was conducted to investigate and model the energy consumption and greenhouse gas emissions of irrigated chickpea cultivation in Isfahan province using multilayer perceptron artificial neural network (ANN). The amount of each consumed inputs in production were collected from 110 producers of chickpea randomly by a questionnaire. The total energy consumption, product yield and energy ratio in chickpea production were calculated as 33211.18 MJ/ha, 2276.36 kg/ha, and 1.02, respectively. Nitrogen fertilizer with 9808 MJ/ha had the highest amount of consumed energy. Total greenhouse gas (GHG) emissions were calculated 965.20 kg CO2eq. ha-, in which, electricity and diesel fuel had the highest amount of total GHG emissions with 36% and 34%, respectively. An ANN model with 13-7-2 topology was recognized as the best model for prediction of yield and total GHG emissions. Based on this ANN model, the values of determination coefficient in prediction of yield and total GHG emissions were determined as 0.929 and 0.979, respectively. The results of sensitivity analysis of the model showed that agricultural machinery inputs had the highest impact on yield and total GHG emissions.

The aims of this study were to investigate the energy flow and its modeling in Shablon cultivar of plum production in Golestan province of Iran. Data were collected through questionnaires and also interviews with producers. Cobb-Douglass model and sensitivity analysis were used to determine the effects of energy inputs on plum yield. The results revealed that the total energy inputs and energy use efficiency of plum production in the region were 25870.33 MJha-1 and 1.04, respectively. Diesel fuel and chemical fertilizer as the highest energy inputs were 33 and 30 percent, respectively.The shares of renewable energy and non-renewable energy of production were 88 and 12 percent, respectively. The Cobb-Douglas model results showed that the effects of human labor, agricultural machinery, diesel fuel, biocide and farmyard manure were positive on the yield while the effect of chemical fertilizers on plum yield was negative.

Golestan province is one of Northern provinces in Iran. The area under cultivation of agricultural products in this province is 724.697 hectares, of which about 694.618 hectares are used for farm products (AJMDC, 2011). Cotton as one of oilseed is a potential feedstock for biodiesel production (Ahmad et al., 2011). In the study of energy consumption and greenhouse gas emissions for cotton production in Alborz province, results showed that the total energy input was 31.237 MJ ha-1. Energy efficiency and energy productivity were 1.85 and 0.11, respectively, and greenhouse gas emissions of cotton production in Alborz province were 1195.25 kg CO2eq ha-1 (Pishgar-Komleh et al., 2012). Another study on energy analysis, greenhouse gas emissions and economic analysis of agricultural production was performed in Northern Iran (AghaAlikhani et al., 2013; Royan et al., 2012; Pishgar-Komleh et al., 2011; Mohammadi et al., 2010; Taheri-Garavand et al., 2010). The aims of this study were to determine the energy flow, greenhouse gas emissions and economic analysis of cotton production in the Golestan province and also to determine the effect of energy inputs on cotton yield. This research was conducted during 2011-2012 in three areas including Gorgan, Aq’qala and Gonbad in the Golestan province. The primary data were collected from the rice producers through a field survey with the help of a structured questionnaire. The number of subjects were studied by the Cochran formula (Snedecor and Cochran, 1980). Accordingly, 43 cotton producers were determined. In this study, eight energy inputs including seed, labor, machinery, diesel fuel, chemical fertilizers, chemicals, water for irrigation and farmyard manure for cotton production system were considered as independent variables. The outputs of the system including lint and seed were considered as dependent variable. Energy indices including energy efficiency, energy productivity, specific energy and net energy were calculated. In this study, the effect of energy inputs on yield was estimated using the Cobb-Douglas function. In order to determine the sensitivity of energy inputs in the production of cotton in the Golestan province, the marginal physical productivity method was applied. Greenhouse gas emissions, inputs of agricultural machinery, fuel, chemical fertilizers, chemicals and farmyard manure in cotton production in the Golestan province were calculated by the coefficients of each of these inputs. For economic evaluation of cotton production in the Golestan province, the variable costs, fixed and total production per unit area were considered. Economic indices of total production value, gross income, net income, economic productivity and benefit to cost ratio were estimated. Data analysis was performed using JMP8 software. Cotton yield in the Golestan province was about 2650 kg ha-1. Average cotton yield in the Alborz province was reported to be 3430 kg ha-1 (Pishgar-Komleh et al., 2012). In this study, diesel fuel had the highest energy consumer among other inputs like the other studies that have been done on energy crop production in Iran. Labor energy input with energy consumption of 2413 MJ ha-1, is known to be the fourth high-energy input in cotton production in the Golestan province. However, in many studies in Iran, this input was accounted to be less than one percent of the energy consumption in the production of agricultural products (Saeedi et al., 2013; Khoshnevisan et al., 2013; Mobtaker et al., 2012; Mobtaker et al., 2010). Chemical energy input with 1036 MJ ha-1, was allocated as 3.6% of energy consumption in the cotton production in the region. Seed energy input was the lowest energy among the other inputs in cotton production in the Golestan province. The results revealed that the total energy inputs for cotton production in the Golestan province was 28.898 MJ ha-1. The average energy efficiency for cotton production in the Golestan province was obtained to be 1.58. Energy productivity for cotton production in the Golestan province was calculated to be 0.092. From the results of Cobb-Douglas function to determine the relationship between energy input and yield of cotton in Golestan province, the effects of human labor, diesel fuel, water for irrigation, chemical fertilizers and farmyard manure inputs on the yield were positive, and the effects of agriculture machinery and chemicals inputs on cotton yield were negative. Greenhouse gas emission from diesel fuel input hadthe highest value of 646.23 kg CO2eq ha-1 with a 45.2 percent share. Farmyard manure with 23.5 percent of greenhouse emissions was identified as the second largest input in greenhouse gas emissions in cotton production. Variable costs, fixed and total cotton production in the Golestan province were calculated to be 3042429, 851880 and 3894309 Toman ha-1, respectively. Benefit to cost ratio for the cotton production in the Golestan province was calculated as 1.16. The results of this study showed that the energy efficiency for cotton production in the Golestan province was less than the energy efficiency for cotton production in the Alborz province, Hatay province of Turkey, and canola, soybean and sunflower production in the Golestan province. Also, the energy efficiency of cotton production was less than that of cotton production in Antalya Turkey and canola in the Mazandaran province. The highest share of energy consumption and greenhouse gas emissions belonged to diesel fuel with the share of 45.6 and 45.2 percent, respectively. However, this input accounted for 2.7 percent of variable costs.