عنوان مقاله [English]
Peanut (Arachis hypogaea L.), which is also called ground pistachio in Iran, is one of the most important and economical oilseeds in tropical and subtropical regions, which is rich in minerals, vitamins, fatty acids, fiber and compounds. It is phenolic. Peanut production is important for consumption, income generation and improving the food security of smallholder farmers and due to its high quality of oil and protein, it is cultivated in at least 109 countries of the world. In recent years, due to changes in the price of dried fruits, this product has also attracted the attention of the farmers of Maghan Plain (North of Ardabil Province, Iran) and in the crop year of 2019-1400, with the cultivation of more than 10,000 hectares, the amount of production reached 30,000 tons of peanut products in Mughan has been harvested. Previously, Gilan province was in the first place of this product with the cultivation of 2,500 hectares. Despite the fact that about a century has passed since the cultivation of peanuts in Iran, not much research has been done on its sustainable production. One of the critical stages of production of this product is the harvest stage. This stage, while having its own difficulties, is associated with significant losses, which are considered by experts due to its high economic value and direct and indirect adverse environmental effects. In recent years, this product has also been considered by farmers in the Moghan Plain due to the special conditions of the Iranian economy. Peanut harvesting in Moghan is done manually and three machine methods including semi-mechanized, pull type mechanized and self-propeller. Each of these methods has unique features. Therefore, in this study, while evaluating important harvest indicators such as quantitative loss, quality loss, actual field capacity, number of labor required, cost of harvest operations and price of machines required, the best harvesting system using the Multiple Criteria Decision Making Matrix (TOPSIS model) was introduced.
The present research was carried out in Pars Abad, Moghan, in the crop year of 2018-2019, which was cultivated with Astana flower variety and mechanized. The final product was harvested on the 10th of October 2019 with approximately 19% soil moisture. Harvesting options were done randomly and with three repetitions as follows:
1- Manual method (A1): Using a V-shaped chisel, the root of the crop was loosened and harvested by human labor. Separation of Almond kernels from Materials Other than Grain (MOG) and cleaning of the final product was done using manual methods using a special sieve.
2- Semi-mechanized (A2): In this method, the peanut thresher behind the tractor manufactured by Dezful Machine Company, model PPH70 was used. This device does not have a pick-up platform. Therefore, feeding the device was done by two human labours at the same time as the tractor moved forward.
3- Pull type mechanized harvesting system (A3): In this method, the peanut harvesting pull type combine machine behind the tractor made by Bacanaklar Company in Turkey was used. This device is equipped with a pick-up platform and does not require human labors.
4- Self-propeller system (A4): In this method, a self-propeller combine harvester (special peanut harvesting combine) made in China (Henan Longfei 4HZJ-2500) was used. Due to its self-driving nature, it does not need a tractor.
The parameters considered in this research are:
1- Quantitative losses (%C1): These losses include the pods dropped on the ground and the pods left on the plant, which were not collected due to the improper operation of the machine harvesting system and the improper operation of the worker in manual harvesting, and are considered among the crop losses.
Where: B (kg/m^2) is the weight of the sample (loss) collected inside the fram with dimention 1×1 m^2 and P is the estimated yield of the product (kg/ha).
2- Quality loss (%C2): Quality loss is an estimate of the percentage of non-grain material among the pods of the product in the grain tank.
Where: Ws is the mass of the sample taken from inside the tank and WMOG is the mass of Material Other than Grain (MOG) in the sample.
3- Field capacity (C3) (ha/h): The actual capacity (taking into account the time intervals caused by the operation of the machine and the operator from the moment the machine entered the farm) was calculated as the amount of machine operation based on the area (A) per time unit (T).
4- Number of human labours (C4) (person days per hectare): This index was also calculated by considering the amount of work done by one worker in 8 hours of working day.
5- The final cost of harvesting operations (C5) (million Tomans per hectare): The price index was calculated based on the regional rate of peanut harvesting operations.
6- The amount of initial fixed investment including the purchase of related tools and machines (million Tomans) (C6)
In order to choose the best harvesting and ranking system, the multi-criteria decision matrix method and TOPSIS model were used.
The results showed that the lowest quantitative loss with C1=10.5% was for the manual method and the highest value C1=20.4% was for the semi-mechanized method. Manual and semi-mechanized harvesting systems had the highest harvest quality index and the lowest non-grain material (approximately 11.5%). Also, self-propeller system with Ca=0.7 ha/h and manual harvesting with Ca=0.0033 ha/h had the highest and lowest actual field capacity, respectively. The manual harvesting method required more labors than other methods and had the highest cost of harvesting operations. The results of using the TOPSIS method also showed that considering all indicators, the pull type mechanized harvesting system with a value of CL* = 0.79 is in the first place of options and manual harvesting is in the last option of this study. Semi-mechanized and self-propeller systems were also ranked second and third, respectively. Despite the fact that the pull type mechanized harvesting system with a value of CL* = 0.79 was ranked first in harvesting systems, but its difference of 0.21 with the ideal option, indicates that to improve this system, a lot of work is needed, including quantitative and qualitative decline should be reduced, which needs to be seriously considered by researchers in this field.