alireza allameh

The most important factor in timely tillage operations is the estimation of the number of working days. Probability of working days (PWD) is the ratio of workable days to days available during the working season for the desired operation. The PWD is used in some relationships in agricultural mechanization and is one of the effective factors in calculating the optimal size of the required machines, the farm capacity of the existing machines as well as the cost of timeliness. Therefore, for each day or each time period, the possibility of performing the operation is checked separately.
There are usually two methods for determining the probability of a working day which are statistics from real conditions and estimation of the feasibility of operations in the past years.
Sometimes the weather conditions indirectly prevent the operation. Soil moisture content is one of the most important factors for most agricultural operations but it cannot be directly extracted from meteorological data. Therefore, it should be initially estimated by practical methods and then the feasibility of the operation determined by comparing the moisture obtained with suitable moisture conditions. A soil water balance model is needed to determine soil moisture content. In these models, by considering the humidity in a day or a period of time and also by calculating the inputs and outputs of water to the soil profile, they determine the amount of humidity for the next time step.
To calculate the possibility of working day, daily soil moisture prediction models are often used which are based on long-term meteorological data and soil characteristics of the region. Estimating the number of working days is one of the effective factors in calculating the optimal size of required machines, the farm capacity of existing machines, as well as the cost of timeliness. The purpose of this study is to validate the proposed model to determine the probability of suitable working days for tillage operation.

Qazvin province, with an area of 15805 square kilometers, having 488 thousand hectares of agricultural land possesses more than three percent of Iran's total production. The total cultivated area of agricultural crops is roughly 259,088 hectares from which 56.57% is irrigated and the rest designated to rainfed.
Climatic conditions and soil moisture content are key factors in the production of agricultural products. Due to the difficulty and high costs of measuring meteorological parameters and soil moisture content in different conditions, models have been proposed for estimating them in the last few decades. Recent models require input data for implementation including meteorological variables (such as rainfall, air temperature and humidity, wind speed and radiation), soil physical properties, soil moisture content and plant residues. The difficulty of field measurement and their time-consuming nature has led to the expansion of the design and application of these models. To determine the probability of working days, a model is needed that can estimate the moisture regime of bare soil in different years and determine the possibility of working in a day by determining the soil moisture content in different layers and comparing it with the humidity limits. Then, by adding up the working days, the number and probability of working days would be determined. To determine the probability of suitable working days for tillage operations, a model was designed by the Delphi software. Also, this model could be used to estimate evaporation, run-off, soil moisture content in other regions providing that those variables were available.
After dividing the soil into several layers, this model estimates soil moisture content in different time steps. In this way, having the initial soil moisture content for whole soil profile and using meteorological data and soil properties, input (rain, deep infiltration) and output (evaporation, run-off), the moisture of each soil layer is computed and then added to the initial moisture. This cycle continues repeatedly and soil moisture content is determined in each time step and in each day. In this model, according to meteorological data and soil surface wetness, evaporation from the soil surface is calculated and it is subtracted from the wetness of the surface layers from which evaporation takes place. Moisture transfer between soil layers is estimated according to the total suction difference (sum of suction and gravity) between two layers and water conductivity in the soil (hydraulic conductivity). As a result, the input and output of each soil layer is specified in each stage, and according to the initial moisture of that layer at the beginning of the stage, the moisture at the end of the stage is determined and used as the initial moisture in the second stage of simulation. The wetness of each layer in each day is determined by averaging the time steps during the working hours of that day. By comparing soil moisture content with workability limits, the possibility of working on that day is determined. By adding up working days, the number and probability of working days is determined.

To check the validity of the model, it is necessary to compare its results with real measurements. For this purpose, the average standard deviation, the coefficient of variation, the average percentage of relative deviation, the regression of the sum of squares and the coefficient of determination were determined as the main indicators of model validation. The measured and estimated humidity are very close to each other in most places. The average value of the standard deviation in the cities of Buin Zahra and Abyek showed the underestimation of the model and in the city of Qazvin it showed the overestimation of the model. In general, the deviation from the observed values is very small. Since this value tends to zero, it indicates a good estimation of the model and very little deviation is observed in it.
Considering that the percentage of coefficient of variation in the studied cities is low, it shows the high accuracy of the model in estimating soil moisture content.
The efficiency value of the model, which shows the quality and how to fit the observed and estimated data, varies from -0.12 to -0.47. Due to the closeness of the model efficiency values to the number 1, there is a good fit between the measured and estimated moisture values which indicates the high accuracy of the model in estimating soil moisture content.
The results obtained from calculating the average percentage of relative deviation show the acceptable fit of the data obtained from the model and the model can be used to measure the amount of soil moisture content.
The regression value of the sum of squares squares (SSE) in the fitting process should be as low as possible, because it indicates the amount of random errors. The results obtained for the regression of the sum of squares squares show the high accuracy of the model. The coefficient of determination of the model in three cities ranges from 0.81 to 0.88, which indicates the probability of correlation between two categories of data in the future. About 81-88% of the variance is shared between the estimated value and the measured value. In the process of fitting the moisture curve and unsaturated hydraulic conductivity in the designed model, the variables are investigated separately. To ensure the validity of the output of the model, three available databases were used (Shapp et al., 2001; Rawls et al., 1982 and Marcel and Parrish, 1988). From the obtained results, the ratio of the calculated value to the observed one, it is realized that the percentage of remained moisture, the percentage of moisture in the saturated state, the inverse of the potential of air entering the soil, the experimental coefficient that determines the shape of the curve, the hydraulic conductivity of saturated soil and the calculated experimental parameter (tortuosity coefficient) are close to the value of the databases, which indicates the accuracy of the model in predicting the parameters. The results show that the mean value of the bias deviation (MBE) in the maximum temperature, maximum relative humidity and wind speed was negative, which indicates the underestimation of the model. In other cases, it is positive which indicates overestimation of the model. Considering that these values are close to zero, it confirms a good estimate of the model and very small deviation is observed. The model efficiency (ME) value, which indicates the quality and how to fit the observed and estimated data, varies between -0.2 and 0.9. Due to the closeness of the model efficiency values to the number 1, there is a good fit between the measured and estimated moisture values. This result indicates the high accuracy of the model in estimating meteorological data. The regression value of the sum of squares squares (SSE) is between 0.26 and 1.83, which indicates the low amount of random errors. In addition, its lower level indicates the high accuracy of the model in predicting meteorological data. According to the results, this model can be used with high accuracy in determining the probability of working days of tillage operations.

Mechanization is one of the main factors in the development of agriculture and is one of the examples of the application of technology in the agricultural sector, which makes it possible for the agricultural sector to reach the stage of industrial and commercial production. Agricultural mechanization, as a basic approach in the production of agricultural products, provides goals such as timely performance of agricultural operations, reduction of production costs, reduction of labor intensity, better management of agricultural inputs, quantitative and qualitative improvement of production and, in principle, the possibility of economic and mass production of products. There are inequalities in the development of agricultural mechanization, which is partly affected by natural factors, but human factors also play a significant role in its occurrence. Planning for the development of mechanization is one of the most important components in the development plan of the agricultural sector.The requirement for correct planning regarding agricultural mechanization depends on recognition of the existing situation. In order to determine the existing situation and comparing the mechanization status of each region to another, there is a need to have defined and meaningful indicators and criteria. The consciousness of the current situation and the distance between different regions as well as obtaining the optimal level can be used to provide a suitable program and development of mechanization for finding and resolving the disturbances and inequalities. In this research, the indicators of rice mechanization in central and southern regions of Guilan province were investigated and compared. According to the results, the current state of mechanization of rice has been determined and the necessary solutions for their improvement have been provided.

Guilan province is one of the northern provinces of Iran, with an area of 14711 square kilometers which stands the second ranking (31% of total) in terms of area harvested. A study was conducted during the years 2020 and 2021 for determination of indicators that govern the mechanization development in the central and southern regions of Guilan province. The studied areas were as rasht and khomam (in the central areas of guilan province) with an area under rice cultivation of 62430 hectares and roudbar (in the southern areas of guilan province) with an area under rice cultivation of 3375 hectares. The field method or field study was employed in terms of broad-based (holistic) and deep-based (depth-based) methods and its subset based on questionnaire for data collection in this research. Due to the lack of access to all villages of each city, one village was randomly selected and after checking their conditions, the relative homogeneity of the area was determined and the obtained information was generalized to other places. Collecting of data was done by completing the questionnaires through available statistical sources, field surveys and interviews with farmers. Data were collected from reliable authorities such as the Guilan agricultural jihad organization, agricultural jihad management of the cities, agricultural jihad centers, and the statistics of the Ministry of Agricultural Jihad. From the obtained data, the mechanization indices including degree of mechanization, mechanization level, mechanization capacity, machine power, machine productivity level, mechanization economic efficiency and machine farm efficiency were calculated.The results revealed that in the central and southern regions of Guilan, the degree of mechanization was 65.1 and 78.9 percent, the level of mechanization was 2.71 and 9.12, horsepower per hectare, the economic efficiency of mechanization was 0.89 and 0.83 tons per horsepower, the average capacity of mechanization was 431.73 and 853.20 horsepower in hour per hectare, respectively. Transplanting by a 4-row rice transplanter in both regions had the highest productivity coefficient. The lowest productivity coefficient assigned to the spraying operation by a motorized backpack sprayer (4.72%) in the central areas and the mouldboard plow in primary tillage by a tractor (1.79%) in the southern region. On average, in the central and southern regions, there was one tractor for every 35 and 5 hectares, a tiller for every 5 and 11 hectares, a transplanter for every 46 and 31 hectares, and a combine harvester for every 88 and 56 hectares, respectively. For every 100 farmers, there were 3 and 5 tractors, 24 and 2 tillers and 2 and 1 transplanters, respectively.

The degree of mechanization for tillage and transplanting operations in the central and southern regions of Guilan province demonstrated a good circumstance based on the sixth state plan of development. According to the expectations, by the end of the sixth plan, the degree of mechanization in harvesting operation was acceptable in the south of Guilan, but in the central, in order to reach the expectations, there is a need to reinforce and import more machines for improving the level of mechanization. The degree of mechanization in plant protection operation for both regions had unfavorable situation. Therefore, measures should be taken for replacing appropriate machines. The level of rice mechanization was higher in the south region than the central. from the above-mentioned reasons, the level of mechanization of rice in the southern region can be attributed to the multiple usage of the driving machines for paddy fields and other crops, the low area under rice cultivation and the large number of tillers and tractors, the lack of companies providing mechanized services, and little time available to farmers to carry out land preparation, transplanting, protection, and harvesting in these regions. The findings also show that tractors and tillers, which are the most important sources of power supply, are not evenly distributed across the central and southern regions. tractors and other self-propelled machines have not been distributed based on the area under cultivation and the economic, climatic, and cultural conditions of the farmers. The smallholder farmers tended to possess a self-propelled machinery while this caused either unused power in rural areas or used only for a short period of time. In some cases, tractors and tillers were used in unrelevant tasks such as transportation and handling. The highest productivity coefficient in the central and southern regions were related to the transplanting by a 4-row rice transplanter. But the lowest productivity coefficient was assigned to the spraying operation by a motorized backpack sprayer (4.72%) in the central areas and the mouldboard plow in primary tillage by a tractor (1.79%) in the southern region. The low productivity coefficient of these machines has represented their lower usage in paddy fields. The highest mechanization capacity in the studied regions was related to the primary tillage by a tractor mounted moldboard plow. The lowest consumed energy in the central and southern regions were related to weeding by a three-row power weeder and spraying by a motorized backpack sprayer which were 18.25 and 8.32 horsepower-hour per hectare. Due to the high cost of purchasing self-propelled machinery and the smallness of the land, the average ratio of self-propelled machinery to operator was not appropriate, which brought the operators a great deal of weakness in performing operations at the proper time.

The results of this study show that the degree of mechanization of tillage, planting with transplanter, spraying, weeding and harvesting in the eastern areas of Gilan province is 100%, 70.41%, 37.32%, 3.85% and 91.12% respectively. The level of rice mechanization in these regions is 3.95 horsepower per hectare. Roudsar city has the lowest level of mechanization and Siahkal city has the highest level with 2.75 and 5.14 horsepower per hectare, respectively. The average mechanization capacity of rice in the eastern regions of Gilan province is 646.51 horsepower-hour per hectare. The highest amount of mechanization capacity is related to the Primary tillage (Spring) with a tractor-turned plow and the lowest is related to the harvesting operation with rice self-propelled reaper, respectively, equal to 1323.09 and 30.80 horsepower-hour per hectare has been obtained. The lowest economic efficiency for Siahkal city and the highest for Roudsar city were calculated as 0.43 and 0.74 tons per horsepower, respectively. The results of this study show that the number of tillage machines and transplanters available in the eastern regions is suitable, and only by improving the management of machines, the level of implementation of mechanized transplanting operations should be increased. In the case of harvesting machines, there is a need to strengthen and introduce more machines to improve the degree of mechanization, and in the case of weeding operations, due to the low degree of mechanization, there is an urgent need to plan for the introduction of suitable machines.

Mechanization is one of the main factors in the development of agriculture. Agricultural mechanization, as a basic approach in the production of agricultural products, provides goals such as timely performance of agricultural operations, reduction of production costs, reduction of labor intensity, quantitative and qualitative improvement of production and, in principle, the possibility of Economic production. There are inequalities in the development of agricultural mechanization, which is partly affected by natural factors, but human factors also play a significant role in its occurrence. Planning for the development of mechanization is one of the most important components in the development plan of the agricultural sector. The requirement for correct planning regarding agricultural mechanization depends on recognition of the existing situation. Knowing and evaluating the development indices of rice mechanization is necessary for the correct selection and optimal use of rice machines and timely and quality agricultural operations to be used as basic information in the calculation of rice mechanization projects and economic analyses. In this research, the indices of rice mechanization in the central and southern regions of Gilan province were studied with the aim of estimating the number of machines needed in rice cultivation.

Gilan province is one of the northern provinces of Iran, with an area of 14711 square kilometers which stands the second ranking (31% of total) in terms of area harvested. A study was conducted during the years 2020 and 2021 for determination of indices that govern the mechanization development in the central and southern regions of Gilan province. The studied areas were as rasht and khomam (in the central areas of Gilan province) with an area under rice cultivation of 62430 hectares and roudbar (in the southern areas of Gilan province) with an area under rice cultivation of 3375 hectares. The field method or field study was employed in terms of broad-based (holistic) and deep-based (depth-based) methods and its subset based on questionnaire for data collection in this research. Due to the lack of access to all villages of each city, one village was randomly selected and after checking their conditions, the relative homogeneity of the area was determined and the obtained information was generalized to other places. Collecting of data was done by completing the questionnaires through available statistical sources, field surveys and interviews with farmers. Data were collected from reliable authorities such as the Gilan agricultural jihad organization, agricultural jihad management of the cities, agricultural jihad centers, and the statistics of the Ministry of Agricultural Jihad. From the obtained data, the indices determining the state of mechanization, working days and farm productivity were calculated.

The results revealed that in the central and southern regions of Gilan, the degree of mechanization was 65.1 and 78.9 percent, the level of mechanization was 2.71 and 9.12, horsepower per hectare and the average capacity of mechanization was 415.74 and 782.10 horsepower in hour per hectare, respectively. On average, in the central and southern regions, there was one tractor for every 35 and 5 hectares, a tiller for every 5 and 11 hectares, a transplanter for every 46 and 31 hectares, and a combine harvester for every 88 and 56 hectares, respectively. According to the results, the number of machines in the tilling and spraying stages is more than the estimated number of machines in the studied areas. The number of available machines in the central areas was 77.1 and 55% more in tillage and 35.6 and 41.2 percent less in planting and 25.8 percent more in the southern areas in tillage and 79.7 percent and in 56.4 plantings and 2.3 percent less than the estimated number.

The degree of mechanization for tillage and transplanting operations in the central and southern regions of Gilan province demonstrated a good circumstance based on the sixth state plan of development. According to the expectations, by the end of the sixth development plan, the degree of mechanization in plant protection and harvesting operations, there is a need to reinforce and import more machines. The level of rice mechanization was higher in the south region than the central. From the above-mentioned reasons, the level of mechanization of rice in the southern region can be attributed to the multiple usage of the driving machines for paddy fields and other crops, the low area under rice cultivation and the large number of tillers and tractors, the lack of companies providing mechanized services, and little time available to farmers to carry out land preparation, transplanting, protection, and harvesting in these regions. The findings also showed that tractors and tillers, which were the most important sources of power supply, were not evenly distributed across the central and southern regions. In some cases, tractors and tillers were used in irrelevant tasks such as transportation and handling. According to the results, in the stages of tillage and spraying, the number of available machines is more than the estimated ones in the studied regions. According to the results, the number of machines available in the central areas in Tillage (Primary tillage, Secondary tillage, Puddling, Leveling) is 77.1% and Plant Protection (spraying and weeding) 55% more and in planting 35.6 and harvesting (Rice reaper, rice combine harvester, baler) 41.2 percent less than the estimated number. The number of machines available in the southern regions in tillage is 79.7% and harvesting 25.8% percent more and in planting 56.4 and Plant Protection 2.3% percent less than the estimated number. The comparison of the current conditions of these areas with the estimate shows that there is no proper planning in the supply and distribution of agricultural machines according to the cultivated areas. This shows the necessity of planning to establish more balance to create appropriate and homogeneous conditions for the distribution of agricultural machines in the studied regions.

To realize the current state of mechanization, defined and meaningful indices and standards are needed. Evaluation of these indices can be used for correct estimation and optimal use of agricultural machines. In this study, by collecting data through questionnaires and available statistical sources, the indices determining the status of rice mechanization (including the degree, level and capacity of mechanization, executive level), working days and farm productivity were calculated. The number of agricultural machines needed to perform mechanized operations in different stages of rice production were estimated using the time opportunity method. The results showed that in the eastern and western regions the degree of mechanization was 74.8 and 66.9%, the level of mechanization was 3.95 and 3.54 horsepower per hectare, the average mechanization capacity was 390.48 and 391.80 horsepower per hectare, respectively. Also, there were a tractor for every 29 and 27 hectares, a tiller for 3 and 4 hectares, a transplanter for 29 and 25 hectares, a weeder for 81 and 189 hectares, and a combine for every 39 and 77 hectares, on average. The number of machines available in the eastern regions in tillage are 81.5%, Transplanting 2.5%, spraying 71%, and harvesting 38.5% more and in weeding 9% less than the estimated number. the number of machines available in the western regions in tillage are 80.6%, Transplanting 15.9%, spraying 70.8%, weeding 42.3% and harvesting 51.5 % more than the estimated number.