The relationship between machine and soil
J. Taghinazhad; S. Rahmani
Abstract
IntroductionThe harvesting stage is the most crucial phase in peanut production. In other words, one of the critical stages in producing this product is the harvest stage. Although it has its difficulties, this stage is associated with significant losses, which experts attribute to the high economic ...
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IntroductionThe harvesting stage is the most crucial phase in peanut production. In other words, one of the critical stages in producing this product is the harvest stage. Although it has its difficulties, this stage is associated with significant losses, which experts attribute to the high economic value of peanuts. In recent years, farmers in the Moghan Plain have also started considering this product due to the special conditions of the Iranian economy. In 2020, this study investigated three methods of peanut harvesting in two stages: manual, tractor-mounted thresher (semi-mechanized), and harvesting with a pull-type combine. The first stage involves the complete removal of the plants from the soil, while the second stage involves drying and separating the peanut pod from the plant in Moghan.Methods and MaterialsThe experiment followed a split-plot design in the form of randomized complete blocks with four replications. The main plot consisted of soil moisture levels at harvest time, which were tested at three different levels: a1- 21%, a2- 18%, and a3- 15%. The sub-plot involved testing the separation of peanut pods from the plant using three different methods: b1- combine harvesting, b2- harvesting with a tractor-mounted thresher, and b3- manual harvesting. The study evaluated important harvest indicators such as quantitative loss (first and second-stage losses), actual field capacity, harvest time, and the number of required laborers. The results led to the identification of the best harvesting system.Results and DiscussionThe study revealed that the optimal soil moisture content for the initial stage of harvest was 18%. For most parameters, there was a significant difference observed among treatments at the 1% level. The pull-type combine method had the highest farm capacity with a maximum of 0.46 ha per hour, while the manual harvesting method had the lowest capacity with a minimum of 0.006 ha per hour. The total losses ranged between 5.95% and 10.58%, with the manual harvesting method exhibiting the lowest loss and the pull-type combine method showing the highest loss. Furthermore, the manual harvesting method required more labor compared to the other methods.ConclusionBased on the obtained results, it is recommended to use a pull-type combine for the early harvesting of peanuts and a manual method for obtaining high-quality peanuts in the Moghan region.
R. Karmulla Chaab; S. H. Karparvarfard; M. Edalat; H. Rahmanian- Koushkaki
Abstract
Introduction One of the problems which considered in recent years for grain harvesting is loss of wheat during production until consumption and tenders the offers for prevention of its especially in harvesting times by combine harvesting machine. Grain harvesting combines are good examples of an operation ...
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Introduction One of the problems which considered in recent years for grain harvesting is loss of wheat during production until consumption and tenders the offers for prevention of its especially in harvesting times by combine harvesting machine. Grain harvesting combines are good examples of an operation where a compromise must be made. One would expect increased costs because of natural loss before harvesting, because of cutter bar loss, because of threshing loss, because of greater losses over the sieve and because of the reduced forward speed necessary to permit the through put material to feed passed the cylinder. The ability to recognize and evaluate compromise solutions and be able to predict the loosed grain is a valuable trait of the harvesting machine manager. By understanding the detailed operation of machines, be able to check their performance, and then arrive at adjustments or operating producers which produce the greatest economic return. Voicu et al. (2007) predicted the grain loss in cleaning part of the combine harvester by using the laboratory simulator based on dimensional analysis method. The obtained model was capable to predict the grain loss perfectly. Soleimani and Kasraei (2012) designed and developed a header simulator to optimize the combine header in rapeseed harvesting. Parameters of interest were: forward speed, cutter bar speed and reel index. The results showed that all the factors were significant in 5% probability. Also in the case of forward speed was 2 km h-1, cutter bar speed was 1400 rpm and reel index was 1.5, the grain loss had minimum quantity. The main purpose of this research was to develop an equation for predicting grain loss in combine header simulator. Modeling of the header grain loss was conducted using dimensional analysis approach. Effective factors on grain loss in combine header unit were: forward speed, reel speed and cutter bar height. Materials and Methods For studying the effective parameters on head loss in grain combine harvester, a header simulator with the following components was built in Biosystems Engineering Department of Shiraz University. Reel unit The reel size was 120 cm length and 100 cm diameter. This reel was removed from an old combine header and installed on a fixed bed. For changing the rotational speed of the reel, an electrical inverter (N50-007SF, Korea) was used. Cutter bar unit The cutter bar length was 120 cm. Knifes were installed on this section. Reciprocating motion was transmitted to the cutter bar through a slider crank attached to a variable speed electric motor (1.5kw, 1400 rpm, Poland). The motor was fixed on the bed. Feeder unit This section was consisted of a rail and a virtual ground. This ground was a tray that the wheat stems were staying on it manually. The rail was the path of virtual ground. Treatments consisted of three levels of rotational speed of reel (21, 25 and 30 rpm), three levels of forward speed of virtual ground (2, 3 and 4 km h-1), three levels of cutter bar height (15, 25 and 35 cm) and three replications. In other words, 81 tests were done. The basis of choosing levels of treatments was combine harvester manuals and driver’s experiences. The dependent variable (H.L) was calculated as below: (1) Where L.G is the mass of loss grains and H.G is the mass of harvested grains. Results and Discussion Generally results of ANOVA test showed that the cutter bar height, rotational speed of reel and forward speed had significant effect on head loss. Also interaction of rotational speed and forward speed, cutter bar height and forward speed had significant effect on head loss. These findings were based on Soleimani and Kasraei (2012) research. Therefore, the cutter bar height, rotational speed of reel and forward speed were three independent parameters on head loss as a dependent parameter. By results of laboratory data, the equation for predicting grain loss by header simulator was obtained. Conclusion The statistical results of F- test in 5% probability showed that there were no significant difference between measured and predicted amounts for laboratory data.
F. Mahdiyeh Broujeni; A. Maleki
Abstract
Introduction Nowadays, many studies were performed about noise source and its type and effects related to duration of sound emission. Most of these researches just report sound pressure level in frequency or time domain. These researches should be continued in order to find better absorber material in ...
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Introduction Nowadays, many studies were performed about noise source and its type and effects related to duration of sound emission. Most of these researches just report sound pressure level in frequency or time domain. These researches should be continued in order to find better absorber material in noise pollution. Use of fractal geometry is a new method in this filed. Wave fractal dimension value is a strong tool for diagnosis of signal instability and fractal analysis is a good method to finding sound signal characteristics. Therefore the aim of this study is on the fractal geometry of SAMPO 3065 combine harvester signals and determine the fractal dimension value of these signals in different operational conditions by Katz, Sevcik, Higuchi and MRBC methods. Materials and Methods In this research, sound signals of SAMPO 3065 harvester combine that were recorded by Maleki and Lashgari (2014), were analyzed. Engine speed (high and low), gear ratio (neutral, 1st, 2nd, 3rd gear), type of operation (traveling and harvesting) and microphone position (in and out of the cabin) were the main factors of this research. For determining signal fractal dimension value in time domain, wave shape supposed as a geometrical shape and for calculation of fractal dimension value of these signals, total area of wave shape was divided into boxes in 50, 100, 200 milliseconds with an interval 25 millisecond box. Then Fractal dimension value of these boxes was calculated by Katz, Sevcik, Higuchi and MRBC methods using MATLAB (2010a) software. SPSS (Ver.20) software was used for further analysis. Results and Discussion Results showed mean effects of engine speed, microphone position, gear ratio, type of operation, box length, calculation method and all of two way interaction effects were significant. Means of Fractal Dimension in the road and field position were 1.4 and 1.28 respectively. The Maximum growth ratio of fractal dimension value during engine speed levels was related to road position. By increasing of box length and number of data points in each box, the fractal dimension value was increased. Investigation of fractal dimension methods showed changes of box length did not affect fractal dimension value in Higuchi method and range of this factor while box length varied were 0.001, 0.171, 0.005 and 0.024 in Higuchi, Katz, MRBC and Sevcik method respectively. These results showed that Katz method has maximum sensitivity and MRBC method like Higuchi method had the minimum sensitivity by changing of box length. In this research fractal dimension value of SAMPO Combine signals in the time domain in different operation conditions were investigated by Katz, Sevcik, Higuchi and MRBC methods. These values varied from 1 to 1.5 in different conditions. Maximum fractal dimension value was 1.63 in case of no cabin by MRBC method. Increasing of box length or further the data point cause of increasing fractal dimension value with increasing of sound pressure level of combine due to increasing of engine speed and working of different parts of harvesting combine. Due to define of sound pressure level, and increasing of this item in each gear ratio ,this can be justify that in high engine speed, wave turbulent is higher than low speed and this turbulent appeared in fractal dimension value. Conclusion One of the important factors in the evaluation of the time series disturbance is fractal dimension. Therefore, the study of sound signals can be an effective role in this regard. Factors such as the cabin existence, gear type, engine speed and operational state of combining parts had a considerable role in distribution of combinimg sound signals and fractal dimention of these signals. For example cabin acts as a barrier in the sound wave and decrease the sound pressure level near driver ear and cause decrease fractal dimention of signals. The study of time series with different lengths have shown that the duration time of the calculation in various methods had a significant effect. Increasing the length of signals due to a higher number of signal data cause to increase calculation time of fractal dimension calculation, while the changes of fractal dimension in increasing of the number of data is minimum and negligible. Therefore, the choice of the appropriate length of the signal is important.
M. R. Mostofi Sarkari; M. S. Valiahdi; I. Ranjbar
Abstract
Grain loss monitors are installed on combine harvester and make it possible to measure grain loss on different parts of the combine. The instrument permits the operator to adjust a proper ground speed to keep grain loss within an acceptable range. In this study a loss monitoring system was implemented ...
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Grain loss monitors are installed on combine harvester and make it possible to measure grain loss on different parts of the combine. The instrument permits the operator to adjust a proper ground speed to keep grain loss within an acceptable range. In this study a loss monitoring system was implemented to measure grain losses continuously on straw walker and sieves. Two grain loss monitors (KEE and TeeJet) were installed behind the straw walker and the sieves of JD-955 and JD-1165 combine harvesters. Harvesting performance parameters such as combine total and processing losses were then measured. To evaluate the precision and accuracy of the instruments, the measured and monitored losses were compared and investigated. The results of a two-year research showed that the average processing loss of the combine harvesters with 10-12% grain moisture content and 750 rpm drum speed was 0.82% which is whitin the acceptable range recommended by ASAE Standard No. S343.3. Furthermore, there was no significant difference between the measured and monitored values of processing loss.
A. Rohani; H. Ghaffari; R. Felehgari; Kh. Mohammadi; H. Masoudi
Abstract
Farm machinery managers often need to make complex economic decisions on machinery replacement. Repair and maintenance costs can have significant impacts on this economic decision. The farm manager must be able to predict farm machinery repair and maintenance costs. This study aimed to identify a regression ...
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Farm machinery managers often need to make complex economic decisions on machinery replacement. Repair and maintenance costs can have significant impacts on this economic decision. The farm manager must be able to predict farm machinery repair and maintenance costs. This study aimed to identify a regression model that can adequately represent the repair and maintenance costs in terms of machine age in cumulative hours of use. The regression model has the ability to predict the repair and maintenance costs for longer time periods. Therefore, it can be used for the estimation of the economic life. The study was conducted using field data collected from 11 John-Deer 955 combine harvesters used in several western provinces of Iran. It was found that power model has a better performance for the prediction of combine repair and maintenance costs. The results showed that the optimum replacement age of John-Deer 955 combine was 54300 cumulative hours.
J. Taghinazhad; M. R. Mostofi Sarkari
Abstract
Rapeseed cultivation in Iran is growing rapidly while this product has been facing specific problems. Every year a significant portion of edible oil is imported to the country from other countries. Despite this deficit, a great amount of canola is being lost every year. Therefore, in compliance with ...
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Rapeseed cultivation in Iran is growing rapidly while this product has been facing specific problems. Every year a significant portion of edible oil is imported to the country from other countries. Despite this deficit, a great amount of canola is being lost every year. Therefore, in compliance with technical points, adding a suitable platform to the exisiting machineries may reduce the losses. A field study was conducted in Moghan Agricultural Research Centre to study the technical and economical characteristics of harvesting machineries and evaluate Canola harvesting losses in different maturity stages, using three different combine harvester heads. The experiments were conducted in a completely randomized split split plot design with four replications. The main plot included seed maturity stage at three levels: A) 60%, B) 70% and C) 80%, and the subplot was the harvester’s ground speed at three levels: A) 1.5, B) 2.5 and C) 3.5 km h-1. The sub-subplot was combine head type with three forms: A) Mechanical, B) Hydraulically Joybar and C) Hydraulically Biso's Head. The results of ANOVA showed that maximum cutter bar losses occurred with Mechanical Head (5.36%) while the loss of Hydraulically Joybar's and Biso's head were 4.28 and 4.13 %, respectively. The results also showed that the maximum cutter bar losses occurred when 80% of seeds were matured and adequate time for canola harvesting was 70% of seeds maturity. The results of analysing the effects of harvesting ground speeds showed that the maximum cutter bar losses occurred with the speed of 3.5 km h-1. Finally, the results showed that the minimum cutter bar loss was obtained with Hydraulically Joybar's head considering the benefit per cost ratio. The cost for Mechanical head and Hydraulically Biso's head were 13500 and 262500 Rial ha-1, respectively.
