Optimization of Energy Consumption and Analysis of the Emission of Greenhouse Gases in the Production of Rice (Case Study of Mazandaran Province)”


 The present study has been done with the aim of investigating the energy consumption pattern, the economic analysis of production and assessing the greenhouse gas emissions (GHG) resulting from the production of rice in the Mazandaran province. The input and output energies were calculated and energy indices were determined. The efficiency of the rice farms and the optimization of energy consumption were estimated by using Data Envelopment Analysis (DEA) method and the amount of greenhouse gas emissions was determined after improving the consumption of inputs. According to the results, the total energies of consumed inputs and outputs for the production of paddy were calculated to be 91,061.5 and 100,967.95 MJ ha-1, respectively. The energy input of diesel fuel, machinery and chemical fertilizers were determined to be 41.27%, 19.63% and 19.58% out of the total energy inputs, representing the most highly consumed energy inputs in the production of paddy.


Introduction
The efficient use of energy in agriculture results in the sustainable development of agriculture. The sustainable crop cultivation necessitates considering input and output energy flows, in the given production system [21].
The modern agriculture, as a result of excessive dependence on numerous inputs, is one of the most intensive energy consuming sectors [46]. Today, inputs such as fuel, electricity, machinery, seed, fertilizers and chemicals take significant share of the energy supplies in the production system of modern agriculture [20]. The diversity of inputs has triggered remarkable changes in the energy consumption pattern of the agricultural sector and resulted in greater dependence on fossil fuel energy resources [34]. This can leave negative impacts on the environment and public health, resulting in excessive use of natural resources. Therefore, this point underlines the importance and necessity of investigating the energy consumption pattern in order to effectively employ it in the agriculture sector [42].
The limitation of farmlands, population growth, variation in infrastructure and tendency to high living standards are factors that have increased the consumption of energy in the agricultural sector. The effective use of energy in agriculture is one of the most important needs of sustainable development in agriculture [32].
Energy flow in agriculture can be categorized as direct and indirect, renewable and non-renewable. Energy and environmental audit are the most prevalent methods for investigating the efficiency of energy and environmental impacts of any production system [20]. The analysis of energy would illustrate how much energy could be used efficiently. Therefore, agriculture interacts with energy and they have a complementary structure [31].
The world population is projected to reach approximately 9 billion in 2050 [14]. It's estimated that the nutritional requirements of this population would be possible to be met through increasing the area under cultivation and increasing yield of production, necessitating further consumption of resources including fossil fuels, machinery, fertilizers, chemicals and other resources and from two perspectives, the use of primary resources aimed at ensuring the growth and survival of human being and the production of waste materials including various solid, liquid and gas pollutants have resulted in negative impacts on the environment. Raising awareness of the importance of the environment and the relevant impacts of the produced crops and consuming them motivates the provision of newer and more precise methods to alleviate these impacts. One of these methods that are expanding and being developed is the Life Cycle Assessment method.
Analyzing the environmental impacts, determining the energy and economic indices are important imperatives in evaluating the systems of crop productions.
In 2017, the worldwide rice production equaled 769.657 million tons, out of which more than 90.5% was produced in Asia. The area under cultivation of rice is 167.249 million hectares and the average yield totals 4,602 kg ha -1 [14].
The area under cultivation of crops in Iran is 11.7 million hectares of which 42.6% represents irrigated farms and 57.4% represents dryland farming. The area under cultivation of cereals in Iran totals 8.44 million hectares (71.7%). The area under cultivation of wheat, barley, paddy and corn respectively comprise 70.22%, 20.84%, 7.06% and 1.88% of the total area under cultivation of cereals in the country.
The quantity of crop productions in Iran equals 83 million tons, of which 68.2% belongs to irrigated agriculture and 31.8 % belongs to dryland farming. Out of the total produced crop in the country, 22.41 million tons (27%) are cereals, where the shares of wheat, barley, paddy and corn are 65.12%, 16.62%, 13.04% and 5.22%, respectively.
The area under cultivation of rice in Iran amounts to 596,035 ha, which is equivalent to 7.06% of area under cultivation of cereals in the country. Total quantity of the paddy produce in Iran adds up to 2,921,046 tons with an average yield of 4.9 tons per hectare.
Mazandaran province is one of the most important rice production regions in Iran where 218,293 ha of land is annually dedicated to paddy cultivation with a production quantity of 1,187,481 tons and an average yield of 5.44 tons per hectare, while the area under cultivation of rice in the Guilan province In investigating the efficiency of the consumptive energy, the emission of greenhouse gases and carbon efficiency of rice fields in the Sari County, the energy use efficiency and the net energy gain were reported to be 1.83 and 27,932 MJ.ha -1 . The fitted econometrics model, assisted by the Cobb-Douglas production function, showed that the impact of the machinery's input energy and human labor on the yield of the crop is significant. The largest elasticity of production (regression coefficient) for the machinery's input energy was obtained to be 0.64, showing that with a 1% increase in the energy consumption of this input, the value of the yield of rice increases by 0.64%. The elasticity of production of human labor input energy was determined to be -0. 37. In other words, with a 1% increase in the energy consumption of this input, the value of the yield of rice decreases by 0.3%. The analysis of the sensitivity of the energy of seed input, human labor, machinery, diesel fuel, fertilizers and chemicals were reported to be 0.058, -0.992, 0.078, 0.004, 0.027 and 0.089, respectively. In other words, with the rise of the consumption of 1 MJ of these inputs, provided that the energy consumption of other inputs remains unchanged, the rice yield changes by 0.058, -0.992, 0.078, 0.004, 0.027 and 0.089 kg ha -1 [13].
In a study, the input and output energies for the production of three varieties of local, high yield and hybrid rice using two traditional and mechanized cultivation methods in the Mazandaran province were investigated. The energy use efficiency for the production of rice using two traditional and mechanized cultivation methods were expressed to be 1.72 and 1.63, respectively [1].
Chauhan et al. [5] investigated the improvement of the energy consumption of paddy production in the alluvial regions of India's West Bengal state by taking into account the efficiency of different units and by using the input-oriented BCC and CCR models of the Data Envelopment Analysis method. In this study, input parameters including the consumptive energy of the inputs of human labor, machinery, diesel fuel, seed, organic fertilizers, chemical fertilizers and the yield of the crop as the output were investigated. They reported that 11.6% of the total consumptive energy in rice production is savable without leading to any decline in the rice yield in case the farmers employ the suggestions presented in the research. They also suggested that better use of consumptive energy in the tillage stage and primary modifications in the machinery can improve the efficiency of energy in the region. They expressed that better use of the tiller and machinery power improves the energy use efficiency, followed by an improvement in the energy productivity in rice production.
Technical efficiency, pure technical efficiency and scale efficiency of rice producers in India were investigated in terms of the quantity of energy consumption in a non-parametric analysis using the Data Envelopment Analysis method as well as BCC and CCR models, and the efficiency of farmers was determined in terms of energy consumption for the production of rice in five different sizes and four different climatic regions. Technical efficiency, pure technical efficiency and scale efficiency for the region 2, which was higher than other regions, were reported to be 0.88, 0.91 and 0.96 respectively. Moreover, out of the total 363 farmers studied in the region, 26 people were efficient in the BCC model and 13 people were efficient in the CCR model [38].
Considering the lack of comprehensive studies on the stream of energy consumption, optimization of the use of production inputs and energy, the economic analysis and the emission of pollutants resulting from the production of paddy, and by taking into account the importance of producing this crop, the present study has been carried out with the aim of investigating the energy pattern of consumptive inputs and outputs, optimizing the consumptive energy using the Data Envelopment Analysis method, and to conduct economic assessment and analysis on the emission of greenhouse gases resulting from the production of paddy in the Mazandaran province.

Particulars of the region and the type of agricultural management
The present research is a field survey carried out in the rice production farms of Mazandaran province in the year 2018. In the Figure 1, the geographical position of Mazandaran province is illustrated. This province is situated between 35° 47' to 36° 35' northern latitude and 50° 34' to 54° 10' eastern longitude from the prime meridian. The population of Mazandaran is 3.28 million people and the area of the province is 23,750 km 2 (making up 1.46 percent of the country's area), and in terms of area, it's 18 th largest province of the country. Toward the north, Mazandaran province borders the Caspian Sea, on the south it borders Tehran, Qazvin and Semnan provinces, on its Western side lies the Guilan province and on its east, there is the Golestan province. Mazandaran Province comprises 22 counties, 60 cities, 60 districts and 132 villages [50].
Of the total lands of Mazandaran, 74 percent are foothill and mountainous lands while 26 percent are plain lands. One of the most important features of the province is its high cultivation coefficient, i.e., 1.4, against the national average of 0.7, which means annually 610,000 hectares of the provincial lands are used for agricultural crops and orchards. In the agricultural year 2017, Mazandaran boasted 213,157 hectares of paddy lands in which 1,261,847 tons of paddy (taking into account the harvesting of ratoon and renewed seedling) were produced, accounting for more than 40% of rice production nationally.
Representing a 37.5% share of the harvest area of the lands under cultivation of rice, Mazandaran province ranks first nationally, followed by Guilan Province, which comes second in the countrywide ranking by harvesting 31.2 percent of the nation's rice fields. These two provinces together boast 68.7 percent of the country's rice cultivation area [48].

The methods of data collection and analysis
The collection of data required in the research was made possible through filling out questionnaires and face-to-face interviews with rice producers in the Mazandaran province. Other information required were obtained through literature and library research as well as agricultural experts.
In the present research, the simple random sampling technique was used. The population of the research comprised the entire rice-producing farmers in Mazandaran province. In order to determine the sample size and the number of questionnaires that had to be filled out, the Cochran formula, as described in the Equation 1, was employed [18,17,8].

Equation 1
Here, n is the sample size, N is the population size, S is the population's standard deviation, d is the favorable probable accuracy and t is equivalent to a value of 1.96.
Accordingly, the required sample size was determined to be 100 rice fields. The input energies to produce rice included the energy of labor, machinery and tools, fuel, irrigation water, electricity required to pump water, chemical fertilizers (nitrogen, phosphate, potassium and micronutrients), chemical pesticides (herbicides, insecticides and fungicides) and seeds. The output energy includes the energy of paddy and straws. To calculate the input and output energies, the quantity of each consumptive input or output was multiplied by its energy equivalent (energy coefficient). The equivalent values of the consumptive inputs and outputs are presented in the Table 1. Table 1

Calculating the energy indices
In order to determine the relationships between input and output energies, following indices are defined and employed using which it's possible to compare the status of energy for different crops in various agricultural systems. These indices are shown in the Equations 2 to 9 [18,30,33,49].

Equation 2
Equation 3 Equation 4 Equation 5 Equation 6 Equation 7 Equation 8 Equation 9 The energy cost is obtained by converting the total input energy into Barrel of Oil Equivalent (BOE) and calculating the price value of crude oil [33]. Every barrel of crude oil is approximately 159 liters, and the energy released from the complete burning thereof is 5.8 BTU (British Thermal Unit), or an equivalent of 6.117 gigajoules (GJ).
Researchers consider the energy use efficiency a criterion for the advancement of technology; therefore, energy use efficiency is the most important index in assessing the energy of agricultural systems. The entire data collected were entered in the Excel application and the computations required were carried out.

Data Envelopment Analysis
Farrell [11] determined relative efficiency in a production unit like engineering topics with a single input and an output. In later, Charnes et al. [4] improved Farrell method and presented a pattern that could be determined relative efficiency with multiple inputs and outputs. This method was known as DEA. DEA is a relatively new data oriented approach for evaluating the performance of a set of peer entities called Decision Making Units (DMU) which convert multiple inputs into multiple outputs. Recent years have seen a great variety of applications of DEA for use in evaluating the performance of many different kinds of entities engaged in many different activities in many different contexts in many different countries [7].
In DEA, all collected observations are used for measuring efficiency. DEA is making and solving n numbers of models to evaluate n numbers of productivity. In this method, a virtual unit, with the highest relative efficiency, is defined by combining all understudy units. So, inefficiency of units can be identified by comparing them with the virtual unit [43].
In fact, the Data Envelopment Analysis method doesn't calculate the efficiency of one unit; rather, it calculates the efficiency of one unit against the efficiency of other units and assesses it with other units. The advantages of using the Data Envelopment Analysis method include its comprehensibility, being easy-to-use, realistic assessment, the simultaneous assessment of the entire set of factors influencing the model, being needless of weights and pre-determined production function (nonparametric), portraying the best performance situation instead of the favorable situation, the ability to enter multiple inputs and multiple outputs, not requiring the same measurement units for the inputs and outputs and the direct comparison of units with one level or a combination of corresponding collections [19].
Efficiency by DEA is defined in three different forms: Technical Efficiency (TE), Pure Technical Efficiency (PTE) and Scale Efficiency (SE) [9]. TE represents the potency of a DMU to produce maximum output given the set of inputs and technology (output-oriented) or to obtain minimizing inputs while maintaining the same level of outputs (input-oriented) [11]. Technical efficiency is a measure evaluating DMU performance relative to that of other DMUs in consideration; it is also called global efficiency.
Pure technical efficiency separates technical and scale efficiencies in other words pure technical efficiency is technical efficiency that has the effect of scale efficiency removed. The advantage of this model is that it compares scale inefficient farms only to efficient farms of a similar size. The main advantage of the VRS model is that scale inefficient farms are only compared to efficient farms of a similar size [2]. The scale efficiency will also be obtained through dividing technical efficiency by pure technical efficiency [34]. In this study, the most comprehensive models of Data Envelopment Analysis, namely the return to constant scale model (CCR) and return to variable scale model (BCC) have been used to calculate efficiency. Each of these models include two input-oriented and outputoriented study directions. This means an inefficient DMU can turn into an efficient unit as a result of the reduction of the levels of input while the output is constant (input-oriented), or conversely, turn into an efficient unit by keeping the level of inputs constant and increasing the values of output (output-oriented) [37]. Choosing between an input-oriented study and an output-oriented study depends on the distinct characteristics of decision-making units being studied. In agricultural studies, input-oriented methods appear to be more appropriate as the number of outputs is limited, while several inputs are used for the production of agricultural crops and there is greater control over the consumption of inputs [31].
The variable return to scale model is used to calculate the pure technical efficiency and constant return to scale is used to calculate the technical efficiency [6].
The technical efficiency of an index is aimed at determining the efficiency of units based on the CCR model. The values of technical efficiency may range between zero and one. Technical efficiency is defined in the Equation 10 [34].

Equation 10
In the Equation 10, u 1 , u 2 , …, u r are the weights given to outputs for j th unit (r=1,2,…,s); y 1 , y 2 , …, y r are the amount of outputs for j th unit; v 1 , v 2 , …, v i are the weights given to inputs for j th unit (i= 1,2,…,m) and x 1 ,x 2 ,…,x i are the amount of inputs for j th unit, and j (j=1,2,…,n) is the number of the decision-making unit.
In order to solve the Equation 10, linear programming has been used, which is presented in the Equation 11 [16].

Equation 11
Where θ is the technical efficiency, u is the weight of outputs, y represents the outputs, v is the weight of inputs, x represents the inputs, n is the number of decision-making units, s is the number of outputs and m is the number of inputs.
Moreover, to calculate the pure technical efficiency, the linear programming model which is presented in the Equation 12 has been used [34].

Equation 12
Where z and u 0 are scalar and free in sign. Eventually, the scale efficiency was calculated through dividing the technical efficiency by pure technical efficiency [9]. The scale efficiency is indicative of the impact of the size of decision-making units on the productivity of a system and shows that some inefficient parts point to the inappropriate size of the decision-making units, and if each decision making unit moves toward the best size, it's possible to improve the overall productivity (technical efficiency) to the same extent as the level of technology (input) [38]. If a DMU is fully (100%) efficient in both the CRR and BCC scores, it is operating at the most productive scale size. If a DMU has the full BCC score, but a low CCR score, then it is locally efficient but not globally efficient due to its scale size. Thus, it is reasonable to characterize the scale efficiency of a DMU by the ratio of the two scores [44].
The relationship between scale efficiency, technical efficiency and pure technical efficiency is presented in the Equation 13.

Equation 13
In the present study, the efficiency of decision-making unit (paddy fields) has been analyzed using the Dea-Solver software and the farms were assessed in terms of the consumption of energy. Subsequently, efficient and inefficient units in the consumption of energy were determined, and eventually the quantity of the consumption of inputs, while the efficiency of decision-making units stood at 100 percent, was investigated.

Greenhouse gas emissions
Agricultural production necessitates employing a multitude of input materials (fertilizers, biocides, seeds, etc.) and energy carriers (natural gas, diesel fuel, etc.). Production, formulation, storage, distribution of agricultural inputs and their applications with agricultural machinery lead to combustion of fossil fuel, and use of energy from alternate sources which emit CO 2 and other greenhouse gases(GHGs) into the atmosphere [26]. To quantify the GHG emissions of rice production, carbon emission coefficients of agricultural inputs were applied. GHG emission coefficients are depicted in Table 2. GHG emissions were worked out by multiplying the input application rate (diesel fuel, chemical fertilizers, machinery, pesticides, electricity and natural gas) by its corresponding emission coefficient. Table 2 3

. Results and discussion
The average consumption of inputs and outputs and their total energy equivalent of paddy production in Mazandaran province are presented in Table 3. According to the results obtained, the average yield of paddy in the Mazandaran province equals 5,253.5 kg ha -1 and the average yield of rice straw amounts to 1,899.3 kg ha -1 . Table 3 The total input and output energies were determined to be 91,061.50 and 100,967.95 MJ ha -1 . The contribution of consumptive inputs to the total input energy is shown in Figure 2. Given the results obtained in this study, diesel fuel was considered to be the most widely used energy input with a share of 41.27%. The results achieved herein were in line with those of many previous studies showing that diesel fuel was the most widely used energy input in agricultural crop production. Despite the limited application of agricultural machinery during growing stage, compared with the extensive application of machinery in the land preparation stages, planting and harvesting, the diesel fuel came to be known as the most widely used energy input in the production of rice. After diesel fuel, the largest shares out of the total input energy in the production of paddy belonged to the energy inputs of machinery and chemical fertilizers, standing at 19.63% and 19.58%, respectively. One of the reasons for the largeness of the share of the consumptive energy of machinery input was the application of different tools frequently over a span of several hours for the preparation of land. After the inputs of fossil fuel, machinery and chemical fertilizers, the irrigation energy, with a 16.31% share out of the total input energy, was determined to be an energy-consuming input in the production of paddy in Mazandaran Province. Chemical fertilizers, human labor, seed and animal manure had the smallest share out of the total input energy with values of 1.19%, 1.09%, 0.9% and 0.02%, respectively.

Figure 2
The energy indices, i.e., energy use efficiency, energy productivity, specific energy and net energy gain are presented in Table 4.
According to the results obtained, energy use efficiency was estimated to be 1.11. Energy productivity was calculated to be 0.058 kg MJ -1 . In fact, in order to produce one kilogram of paddy, 17.24 MJ of energy were consumed. Based on the studies done, energy use efficiency for the production of rice using two methods of traditional and mechanized cultivation in the Mazandaran province stood at 1.72 and 1.63 respectively while the energy efficiency of rice production in the Guilan Province was expressed to be 1.53 [1,41]. Table 4 The positivity of the net energy demonstrated that the production of rice in studied area was justifiable in terms of energy balance.
In Figure 3, the shares of each energy types including direct energy and indirect energy as well as renewable and non-renewable energies are presented. Direct energy included the energy of fuel, irrigation, human labor and animal manure, while indirect energy included the energy of machinery, chemical fertilizers, chemical pesticides and seeds. Moreover, the resources of renewable energy included the energy of irrigation, seeds, labor and organic fertilizers whereas other inputs including fossil fuels, machinery, chemical fertilizers and chemical pesticides constituted the sources of nonrenewable energy. The results indicated that with a consumption value of 74,368.90 MJ ha -1 (81.67%) out of the total energy used, non-renewable energies retained a higher share than the renewable energy boasting a consumption value of 16,692.61 MJ ha -1 (18.33%). According to the results, the direct and indirect energy quantities involved in the production of paddy were calculated to be 53,451.86 and 37,609.64 MJ ha -1 , indicating the 58.70% share of direct energies and 41.30% share of indirect energies out of the total input energy involved in the paddy production in the region. The reason why the share of direct energy is higher than that of indirect energy is the high percentage of the consumption of irrigation and fuel inputs. According to studies carried out in the Guilan Province, the shares of renewable and non-renewable energies out of the total consumptive energy were found to be 11.22% and 88.78%, respectively. Moreover, 50.7% and 49.3% of the input energy represented the shares of indirect and direct energies respectively [41].

The results of Data Envelopment Analysis for rice production in the Mazandaran Province
The results of technical efficiency, pure technical efficiency and scale efficiency of one hundred rice production units studied in the Mazandaran province using the input-oriented Data Envelopment Analysis are given in Table 5 however, what has caused them to have efficiency score less than 1, was the lack of performance in the optimal scale. These items require a long-term planning to make the units efficient through moving toward optimal scale.
The results shown in Table 5 demonstrated that out of 66 technically inefficient farms, 63 units have increasing return to scale and 3 units have decreasing return to scale. In units with increasing return to scale, the production unit needed to be enlarged so that efficiency improves while in units with decreasing return to scale the production unit needed to be downsized. In other words, given the definition of technical efficiency, the reduction of production scale for the above mentioned units with decreasing return to scale, leads to a growth of efficiency as a result of which, the efficiency of these units will increase. In units where there is a decreasing return to scale, adding for example 1% to the quantity of the inputs will result in the increasing of the crop to an extent less than 1%. Therefore, the managers of these units should make efforts to increase their output to input proportion. Table 5   Table 6 shows the average technical efficiency, pure technical efficiency and scale efficiency of the paddy fields studied. The results indicated that the average values of these indices were 0.970, 0.996 and 0.974, respectively. Moreover, the standard deviations of these indices are shown in Table 6. The average technical efficiency of one hundred paddy fields investigated was determined to be 97%. In other words, they can reach the threshold of efficiency with 97 percent of production inputs and save three percent of inputs with an increase in their efficiency. As it can be observed, the technical efficiency score and scale efficiency range between 0.4812 and 1 and their standard deviation were greater than the pure technical efficiency score, indicating that farmers were not fully aware of the production techniques or didn't employ the techniques accurately. Table 6 In Figure 4, the technical efficiency, pure technical efficiency and scale efficiency of one hundred rice producers studied in the Mazandaran province have been categorized in different groups. It can be seen that in the constant return to scale, 34 units retained absolute efficiency while more than 82% of the units had a technical efficiency score of more than 95%.

Figure 4
This figure illustrates that after removing the scale inefficiency in the variable return to scale method, 85 paddy fields turned efficient while 98% of the farms investigated had a pure technical efficiency of more than 95%. Out of one hundred farms investigated, 34 farms retained scale efficiency and 86% of the farms had a scale efficiency of more than 95%. The fact that unit 4 should achieve maximum efficiency with these values of input in order to become efficient means that this unit should increase its rice production capacity, and for this purpose plant breeding and crop improvement are appropriate. Table 7   Table 8 The other production units whose efficiency is less than 100 percent can identify their pattern unit or units with an analysis like the one given above, and on that basis, identify the analysis of inputs and then carry out a suitable targeting for the production inputs in order to achieve a certain level of output.
The optimal value of energy consumption and the quantity of energy saving for each of the inputs of rice production are presented in Table 9. These values have been calculated in terms of the variable return to scale model. The results showed that the average quantity of energy consumption under optimal circumstances equals 86,115.829 MJ ha -1 , which compared to the status quo is 4,945.675 MJ ha -1 lower. This articulates that by taking into account the recommendations of this study and without reducing the current yield level of rice, it's possible to save 5.43% of consumptive energy.
The highest quantity of saving was observed in the consumption of fuel input energy at 2,796.09 MJ ha -1 followed by the energy of irrigation inputs and chemical fertilizers at 917.23 and 914.47 MJ ha -1 .
Considering the detrimental environmental impacts of the consumption of fuel and chemical fertilizers, the optimization and consumption management of these inputs can result in greater sustainability of rice production in the region studied in addition to lowering the consumption of energy. Table 9 In Figure 5, the percentage of energy saving for each of the consumptive inputs of rice production out of the total savable energy is shown. As it can be seen, the fuel energy, irrigation and chemical fertilizers boasted the highest percentages of energy saving at %56.54, %18.55 and %18. 49. This demonstrated that in inefficient farms, these inputs were not employed appropriately and it's imperative that a suitable consumption pattern for these inputs is promoted in the region. Singh et al. [47] investigated the consumption of energy in wheat production in different regions of India and reported that with the optimal use of energy in the regions, it's possible to increase the performance of wheat between 4.2% and 22.3%.

Figure 5
The Data Envelopment Analysis technique leads to the optimization of energy indices in the production of rice crop. The results of the optimization of the energy indices in the production of rice are presented in Table 10. As it can be observed, energy efficiency was calculated to be 1.172 under optimal circumstances, which shows a 5.38% improvement compared to the status quo (1.09). Moreover, the productivity of rice seed under the current and optimal circumstances was found to be 0.059 and 0.061 kg MJ -1 respectively, indicating that with the optimization of energy consumption, the quantity of produced crop increases by 3.28 percent per each unit of consumptive energy. Evidently, with the optimization of input energy, the share of indirect and non-renewable energies out of the total input energy increases. Moreover, the shares of direct energy and renewable energies have been on a declining course in a reverse manner and this is due to the large share of irrigation energy in improving energy. Table 10 In Table 11 detailing the pure technical efficiency (PTE), the current use of energy, and the optimal energy required from different energy sources for the inefficient farmers are shown. Moreover, their average values are presented. Using such information, it's possible to recommend to the decision making units to reduce the quantity of input energies by taking into account the operational methods in such a way that the output quantity (yield) remains constant. Therefore, the publication of these results contributes to the improvement of the productivity of energy for the production of rice in the region studied. In the last column of   Table 12 shows 71.43% of paddy fields boast excessive consumption in the use of seed input, as is the case with 73.21% of farms in the consumption of fuel input, 37.50% in the use of machinery input, 32.14% in the use of chemical fertilizers, 19.64% in the use of animal manure, 28.57% in the use of pesticides input, 60.71% in the use of irrigation input and 28.57% in the use of human labor input. Table 11   Table 12 3.

Economic analysis
The production costs included the wages of the human labor, land preparation, agricultural inputs, the costs of transplanting, cultivation and harvesting, conversion and processing. In the present research, the conventional average rental fee for each hectare of paddy field amounted to 823.53$ and the conventional interest rate was considered to be 15%. The results of this research showed that the gross production value and the total rice production cost in the Mazandaran province amounted to 3394.776 $ ha -1 and 2116.862 $ ha -1 , and the benefit to cost ratio was estimated to be 1.604 (Table 13). The fixed costs comprised 37.5% and the variable costs comprised %62.5 of the total production costs. The net return resulting from the production of rice equaled 1277.914 $ ha -1 , signifying the economic justifiability of the production of rice in the Mazandaran province. Table 13 In Table 13, the economic productivity of rice production was found to be 1.024 kg $ -1 . In other words, per each dollar of production costs, 12.04 kilograms of rice would be produced. The energy intensiveness index showed that on average, per each dollar of rice production costs, 43.017 MJ of energy from different inputs would be supplied. The energy intensiveness value index expresses that on average, for the supplying of 26.824 MJ of energy from different energy inputs to be used, one dollar of revenue would be made. The energy intensity cost index showed that for each kilogram of produced rice, the equivalent value of consumptive energy would be 0.481$ and the energy cost ratio showed that for each unit of rice production cost, the equivalent value of the energy supplied from different inputs to be used in the paddy field equaled 0.492 units. In the research by Pishgar et al. [41] the gross income, gross profit and net profit were obtained to be 4,095.6 $ ha -1 , 1,641.98 $ ha -1 and 939.71$ ha -1 , respectively. In their research, Pishgar et al. [41] reported the fixed and changing costs of rice production in the Guilan province to be 702.27 $ ha-1 and 2,453.62 $ ha-1, respectively. They also reported the total cost of rice production in the Guilan province to be 3,155.89 $ ha-1 and 0.9 $ kg-1. The benefit to cost ratio equaled 1.3 and the economic productivity of rice production was expressed to be 1.12 $ kg -1 .

The results of greenhouse gas emissions of rice production
The quantities of GHG emissions under real and optimal circumstances are presented in Table 14. The The GHG emissions under current conditions amounted 4,251.33 kg CO 2 eq. ha -1 , while declined to 4,146.21 kg CO 2 eq. ha -1 under optimal cultivation system. In other words, GHG emissions would be dropped by 2.7% if agricultural inputs used for rice production in the studies area were employed efficiently. The largest values of the decline of greenhouse gas emissions for the electricity input, diesel fuel and machinery were obtained to be 35.76, 31.01 and 27.06 kg CO 2 eq. ha -1 .
The use of chemical fertilizers (particularly nitrogen fertilizer) more than what is required for the plant leads to the pollution of water and soil at the same time as emitting a great load of greenhouse gas emissions. Moreover, the emissions related to the consumption of diesel fuel have to do with the use of outdated tractors in different agricultural operations, the incompatibility of power capacities in the machinery and tractors and the extensive consumption of energy resulting from intense tillage operations.
The use of appropriate tillage operations such as minimum tillage method and the use of combined machinery would result in the reduction of fuel consumption. Moreover, making use of new electromotors with a high working safety, together with the farmers' awareness of the real water requirements throughout the different stages of rice production would result in the reduction of greenhouse gas emissions pertaining to the consumptive electricity.
Khakbazan et al. [23] compared the greenhouse emissions in the shifting cultivation of wheat and chickpea crops. On this basis, the entire greenhouse gas emissions in producing one hectare of chickpea and wheat were obtained to be 250 and 768 kg CO 2 eq. ha -1 , respectively.
Moreover, the proportion of the yield of crops to the total greenhouse gas emissions in the chickpea and wheat crops were estimated to be 12.6 and 3.68 kg (kg CO 2 eq.) -1 , which was indicative of the higher yield of chickpea as compared to wheat [23].
In a study performed to calculate the greenhouse gas emissions of wheat production in Isfahan province, electricity and nitrogen fertilizer boasted the largest consumptive shares among other inputs with 2,400.7 (74%) and 371.2 (14%) kg CO 2 eq. ha -1 , respectively [28].
As it can be seen in Table 14, the Data Envelopment Analysis method was capable of reducing the total greenhouse gas emissions by 105.118 kg CO 2 eq. ha -1 . In a study on the reduction of greenhouse gas emissions resulting from strawberry production in the Guilan province, it was shown that the Data Envelopment Analysis method is capable of reducing 5,774 kg CO 2 eq. ha -1 of cultivation of the crop. Moreover, natural gas, with a 65% share, had the highest potential for the reduction of greenhouse gas emissions [25].
In a study on the amount of the emission of greenhouse gases resulting from cultivating Sorghum in the Sistan region, electricity retained the highest quantity of greenhouse gas emissions with 2,981.27 kg CO 2 eq. ha -1 out of a total of 3,746.7 kg CO 2 eq. ha -1 of greenhouse gases. After that, animal manure and diesel fuel retained the highest quantity of greenhouse gas emissions at 277.72 and 258.83 kg CO 2 eq. ha -1 .  Figure 6 illustrates the share of inputs out of the reduction of greenhouse gas emissions after the improvement of consumption. The electricity input, diesel fuel and machinery had the largest share in the reduction of greenhouse gas emissions resulting from the production of rice at 34.02%, 29.5% and 25.75%, respectively.

Figure 6
In another study the optimization of GHG emissions associated with plant and ratoon farms of sugarcane in the Khuzestan province has been studied using the Data Envelopment Analysis method. Based on the results obtained, the total greenhouse gas emissions in one hectare of plant and ratoon farms amounted to 5,825.25 and 4,310.7 kg CO 2 eq. where electricity, diesel fuel and nitrogen fertilizer in the plant farms retained the largest shares out of the total greenhouse gas emissions at 74.37%, 17.22% and 4.1%, respectively, while in the ratoon farms, electricity and diesel fuel had the largest shares at 79.29% and 11.7%. The emission of greenhouse gases in the plant and ratoon farms under optimal circumstances dropped by 633.12 and 110.01 kg CO 2 eq.

Conclusion
In the present study, the total input and output energies in the rice production process in the Mazandaran province were determined to be 91,061.50 and 100,967.95 MJ ha -1 respectively and the input of diesel fuel, boasting a 41.27% share out of the total input energy, is considered to be the most highly consumed energy input, the reason for which is the extensive use of rice machinery and tools in different stages of land preparation, transplanting, cultivation and harvesting of rice due to the special characteristics of paddy production. The energy use efficiency and energy productivity of rice in the Mazandaran province were calculated to be 1.11 and 0.058 kg MJ -1 . The positivity of the net energy demonstrates that the production of rice in the Mazandaran province is justifiable in terms of energy balance. Non-renewable and renewable energies respectively accounted for shares of 81.67% and 18.33% out of the total input energy in the production of paddy while direct and indirect energies accounted for shares of 58.70% and 41.30%. The reason why the share of direct energy is larger than that of indirect energy is the high percentage of the consumption of irrigation inputs and fuel.
The average technical efficiency, pure technical efficiency and scale efficiency of one hundred rice producers were obtained to be %97, %99.6 and %97.4, respectively. With the improvement of the consumption of inputs, the average energy consumption quantity will be 86,115.829 MJ ha -1 which is 4,945.675 MJ ha -1 less than the status quo. Therefore, considering the recommendations of this study and without reducing the current outputs level, it's possible to save %5.43 of energy. The highest rate of saving was observed in the fuel input energy valued at 2,796.09 MJ ha -1 , followed by the energy of irrigation inputs and chemical fertilizers with 917.23 and 914.47 MJ ha -1 , respectively. Given the detrimental environmental impacts of using fuel and chemical fertilizers, optimization and consumption management of these inputs can lead to increased sustainability in rice production in the region studied as well as reducing the consumption of energy.
Under optimal conditions, the energy efficiency will be 1.172, which shows a 5.38% increase compared to the current 1.09 value. Moreover, the quantity of energy productivity of rice under the current and optimal circumstances were obtained to be 0.059 and 0.061 kg MJ -1 , respectively. As a result, by optimizing the consumption of energy, the quantity of crops produced increases by 3.28 % for each unit of consumptive energy. Evidently, by optimizing the input energy, the share of indirect and non-renewable energies in proportion to the total input energy has increased, and also the share of direct energy and renewable energies in proportion to the reverse state has experienced a decline, which is due to the large share of irrigation energy in improving energy.
The benefit to cost ratio stood at 1.604 and the net return resulting from the rice production equaled 1277.914 $ ha -1 , signifying the economic justification of the production of rice in the Mazandaran province. On average, per each dollar of rice production cost, 43.017 MJ of energy is consumed from different production inputs in paddy fields, and on average, for the entry of 26.824 MJ of energy of different production inputs into the paddy fields of Mazandaran Province, one dollar of revenues would be made. Estimates on the energy intensity cost index revealed that the equivalent value of consumptive energy for the production of each kilogram of crops is 0.481 $, and the energy ratio cost index showed that for each unit of rice production cost, the equivalent cost of the energy procured from different production units for being used in the rice field equals 0.492.
With the optimization of the consumption of inputs, the greenhouse gas emissions drop by 2.7 percent. The highest reductions in the greenhouse gas emissions for the electricity input, diesel fuel and machinery were found to be the equivalent of 35.76, 31.01 and 27.06 kg CO 2 eq. ha -1 . The inputs of electricity, diesel fuel and machinery, with values of 34.02%, 29.5% and 25.75% represented the highest shares in the reduction of greenhouse gas emissions.
The optimization of the fuel consumptive energy, agricultural machinery, chemical fertilizers, most notably nitrogen and water for irrigation are among the important pathways to the better management of energy in producing rice in the paddy fields of the province studied. Replacing machinery and new tools with outworn machinery will result in the reduction of energy consumed by the machinery and diesel fuel and result in the lessening of greenhouse gas emissions. In order to reduce the fuel consumption and machinery energy, striking a balance between the machinery and tractor and lessening the extremely high energy resulting from the tillage operation is necessary.
Making use of suitable tillage methods such as the method of conservation tillage, dry working and using combined machinery result in the reduction of fuel consumed in the province. The inefficiency of irrigation systems, using outworn electromotors and the farmers' unawareness about the plant's genuinely required water are among the reasons for the growth of consumptive energy in the region studied. Therefore, the revision of outworn and dilapidated systems in the irrigation sector and using modern irrigation methods are recommended.
The management of the use of chemical fertilizers, applied research to determine the extent of the plant's need for nutrients during the different stages of growth and determining the appropriate quantity of chemical fertilizers required by the soil using the soil test play a substantial role in reducing the greenhouse gas emissions in the region studied as well as reducing the consumptive energy of this input. In order to reduce the emission of greenhouse gases, efforts should be made to optimize the consumption of chemical fertilizers and replace the organic fertilizers with chemical fertilizers. Reducing the use of chemical pesticides and making use of bio-inhibitors instead of chemical pesticides is effective in optimizing the consumption of energy and the reduction of greenhouse gases.

Acknowledgements
The authors would like to acknowledge the financial support provided by Agricultural Engineering Research Institute, Karaj, Iran.

Authors' contributions
All the authors have contributed to the structure, content, and writing of the paper. All authors read and approved the final manuscript.

Funding
The author's research is funded and supported by Agricultural Engineering Research Institute, Karaj, Iran.