ABSTRACT
The present study evaluated energy flow and the emission of greenhouse gases in the major crop production of the Ardabil plain, Iran, including wheat, potato, alfalfa, barley, and canola. The information required for this study was obtained using a questionnaire and face-to-face interviews with 1,078 farmers in the Ardabil plain during the crop year 2017–2018. Indices of input energy, output energy, specific energy, energy use efficiency, energy productivity, and global warming potential were calculated. The results showed that among the studied crops, the highest energy use efficiency was obtained with wheat and barley, being 5.2 and 5.14, respectively, and the lowest energy efficiency was obtained with alfalfa, rapeseed, and potato, being 4.6, 4.5, and 2.15, respectively. Potato (equivalent to 3,186.2 kg CO2/ha) and wheat (equivalent to 1,727.5 kg CO2/ha) had the largest share in the potential of global warming and greenhouse gas emissions compared to canola (equivalent to 1,326.22 kg CO2/ha), alfalfa (equivalent to 1,157.04 kg CO2/ha), and barley (equivalent to 952.39 kg CO2/ha). The production of crops with high water requirements and high chemical fertilizer consumption using old machines had a greater share in the amount of energy consumption and global warming compared to other crops.
HIGHLIGHTS
Energy indices and greenhouse gas emissions of major crops in the Ardabil plain were assessed in this study.
The highest input energy was related to potato, wheat, and alfalfa crops, while the lowest amount belonged to barley and canola crops.
In the output energy section, the highest energy was related to wheat, potato, and alfalfa crops and, the lowest energy was related to rapeseed and barley crops.
INTRODUCTION
In today's world, the challenges related to agriculture, food security, water and energy resources, productivity, and greenhouse gas emissions have become increasingly critical for global communities. These issues have prompted widespread economic, social, and environmental changes due to their far-reaching international impacts. Among these, the scarcity of water resources stands out as one of the most pressing concerns. Water is a fundamental element in agriculture and food production, making its availability crucial. As the global population grows and water consumption patterns shift, water resources face significant pressure, particularly in arid and semi-arid regions. This scarcity has profound implications for agricultural production and, consequently, the food security of societies. Food security is further complicated by the rising global population and evolving dietary habits. The need to produce more agricultural products to meet these demands, coupled with the limitations of water and soil resources, has led to the unbalanced exploitation of these resources, potentially reducing productivity and causing environmental degradation. In this context, sustainable agriculture and efficient resource use are essential (De Pinto et al. 2020; Bruhwiler et al. 2021). By strategically managing water, fertilizers, agricultural inputs, and modern technologies, it is possible to boost crop yields while preserving the environment. Climate change and greenhouse gas emissions also significantly impact agriculture and crop production. Rising temperatures and shifting weather patterns pose substantial challenges. Efforts to mitigate greenhouse gas emissions and promote renewable energy sources are vital in addressing these issues. These challenges have far-reaching international implications for the sustainable development of global communities, making extensive cooperation between countries and international organizations crucial. Through the implementation of effective policies and programs, agriculture can be improved, food security-enhanced, resource productivity increased, and the negative impacts of greenhouse gas emissions reduced (Baccour et al. 2021). Globally, approximately 90% of freshwater consumption is dedicated to food production, while around 30% of energy consumption is used within the food production and supply chain. However, with the anticipated rise in global population and economic development, it is expected that by 2030, the demand for food will increase by 50%, energy by 40%, and water by 30%. This scenario presents significant challenges that will further strain water and energy resources. Therefore, the integrated management of these limited resources to meet the growing food demand in the agricultural sector is both essential and challenging (Deng et al. 2020). The importance of energy balance has become increasingly apparent with the growing awareness of the global crisis related to the depletion of fossil fuels. This has led to efforts to accurately plan and estimate energy consumption across various sectors (Platis et al. 2019). On a global scale, about 5% of total energy is used in the agricultural sector, and approximately 11% of greenhouse gas emissions are attributed to agriculture (Singh et al. 2020). Water and energy can be considered as key indicators and parameters in agriculture that have the potential to fundamentally transform the sector. In Iran, where traditional farming practices are prevalent, the amount of energy consumed does not align with agricultural performance. In some cases, agriculture is even the largest contributor to greenhouse gas emissions (Masaeli et al. 2023; Mousavi et al. 2023; Peyvandi et al. 2023). The study area ranks first in Iran for potato production, fifth for wheat and barley production, and first for canola production. Therefore, considering that most regions in Iran that cultivate potatoes and other crops obtain their seeds from this area and have similar climatic conditions, the results of this study can be generalized to other similar regions as well.
Soltani et al. (2014), in a study on wheat production in Gorgan, found that energy related to fuel consumption with an average of 3,390 MJ/ha among the direct input energy has the highest amount. Then, they evaluated the indirect input energies of nitrogen fertilizer with 5,964 MJ/ha and introduced the highest amount among other fertilizers. Vahedi & Zarifneshat (2021) estimated average input energy, energy output, energy use efficiency, energy productivity, and net energy as 5,830.83 MJ/ha, 136,092.15 MJ/ha, 2.87, 0.212 kg/MJ, and 77,783.31 MJ/ha, respectively, for irrigated wheat in some areas in Iran. Manafi Dastjerdi & Kohnavard (2020), in a study conducted to obtain energy balance for alfalfa production in Alborz province, found that about 65% of the input energy for alfalfa in Karaj, Eshtehard, Nazarabad, and Savojbolagh cities belongs to fuel and seed inputs. They also found that Eshtehard city with 0.095 kg/MJ and Nazarabad city with 0.076 kg/MJ had the highest and lowest energy productivity in alfalfa production. Among the four study areas, they found that Eshtehard city with a net energy of 39,241.9 MJ/ha and a specific energy of 10.57 MJ/kg has better conditions than the other three cities. Dargahi et al. (2016) conducted a study on canola in Golestan province, Iran, to obtain energy use efficiency, energy productivity, specific energy, and net energy, being 2.8 kg/MJ, 0.1 kg/MJ, 8.8 MJ/kg, and 37,436.9 MJ/ha, respectively. Ghaderzadeh & Pir Mohammadiani (2019) conducted a study to evaluate the energy balance of potatoes in Hamadan province. The total input energy was 69,249 MJ/ha. The chemical fertilizer energy had the highest share of input energy. They also obtained an energy ratio, energy productivity, and net energy equal to 2.224 kg/MJ, 0.671 kg/MJ, and 84,751.9 MJ/ha, respectively. In another investigation assessing energy consumption efficiency, greenhouse gas emissions, and sustainability on wheat and canola farms in Khorramshahr, the input energy per hectare was reported to be 41,810 MJ for wheat and 33,517 MJ for canola. The largest impacts were attributed to inputs such as electricity, nitrogen fertilizer, and fuel. Of the total energy input, 22,606 MJ per hectare for wheat and 19,434 MJ per hectare for canola were direct energy, while 19,204 MJ per hectare for wheat and 14,083 MJ per hectare for canola were indirect energy. The study also found that energy use efficiency was 1.32 for wheat and 2.15 for canola (Khodaei Joghan et al. 2022).
In a study conducted by Abbas et al. (2020) aimed at analyzing the sensitivity of greenhouse gas emissions at the farm level, it was found that the average yield of corn was 6,874 kg/ha with a total energy of 42,241.45 MJ/ha. The net energy, energy use efficiency (average), specific energy, and energy productivity were found to be 58,806 MJ/ha, 2.39 MJ/kg, 6.15 MJ/kg, and 0.16 kg/MJ, respectively. In another study, Abbas et al. (2022) aimed to investigate the sensitivity of greenhouse gas emissions at the farm level on rice and cotton crops. They found that in the rice crop, chemical fertilizers, diesel fuel, and water for irrigation are the primary energy inputs allocated to themselves with 15,721.55, 10,787.50, and 6,411.08 MJ/ha, respectively. Meanwhile, in the cotton crop, diesel fuel, chemical fertilizers, irrigation fertilizer, and water accounted for 13,860.94, 12,691.10, and 4,456.34 MJ/ha, respectively. Additionally, they found that the total greenhouse gas emissions were 920.69 and 954.71 kg CO2eq/ha from rice and cotton production, respectively.
Several studies by researchers, e.g. Khaledian et al. (2012); Khoshnevisan et al. (2013); Yousefi et al. (2016), Mohammadi et al. (2014), and Vafabakhsh & Mohammadzadeh (2019), were conducted on energy balance and greenhouse gas emissions in the production of crops. These studies show that the amount of energy flows, energy indices, and greenhouse gas emissions in the production of crops are so different in regions according to the management from planting to harvest. Fan et al. (2020) examined the impact of land–water–energy interactions on agricultural management to reduce greenhouse gases. Their study revealed that rice paddies, due to high input levels and flooded cultivation, produced 29% of the total crop protein but accounted for 51% of total water consumption, 43% of total energy use, and 54% of total greenhouse gas emissions among the four crops studied (rice, wheat, corn, and soybean).
Given the above issues, focusing on improving energy efficiency and reducing greenhouse gas emissions in agriculture is essential. Effective energy management is key to achieving high productivity while minimizing waste. Energy indicators can help evaluate and enhance efficiency in crop production, leading to cost-savings and more sustainable practices. Water scarcity is a significant challenge in many parts of the world. On the other hand, agricultural activities contribute to the emission of greenhouse gases such as carbon dioxide (CO2) and methane (CH4), which impact global warming and climate change. In light of this, the present study aims to examine the impact of inputs used on energy indicators and greenhouse gas emissions in cold and dry climates, a subject that has been less studied at the regional level, especially considering all major agricultural crops.
MATERIALS AND METHODS
Maps of Iran, Ardabil province, and the study area in the Ardabil plain.
The share of different inputs in the total input energy in the production of major crops in the Ardabil plain.
The share of different inputs in the total input energy in the production of major crops in the Ardabil plain.
To calculate the production energy and greenhouse gas emissions of major crops in the downstream lands of Yamchi and Ghorichai dams of the Ardabil plain, including wheat, potato, alfalfa, barley, and rapeseed, cross-sectional data of the 2017–2018 crop year were used. First, the sample size was determined based on Cochran's relationship and the volume determination method of Bartlett et al. (2001). Then sampling was performed based on a questionnaire designed by the researcher. Questionnaires consisted of 1,078 questionnaires (wheat = 296, potato = 301, alfalfa = 141, barley = 209, and rapeseed = 131) that the collected information included the amount of input consumption, amount of production, costs, and values of production. The present questionnaire, designed by the researcher, underwent a content validity assessment using the content validity method, reviewed by experts (five individuals), achieving a validity of 99.0%. Moreover, the reliability of the study was evaluated using Cronbach's alpha (α > 0.95). The questionnaire developed by the researcher was approved by experts in terms of validity and reliability. Inputs used to specify the function of the products mentioned in Ardabil include man-days of manpower, the cost of using machinery, the amount of nitrogen fertilizers, phosphate, and potassium in kg/ha, the amount of chemical agents, including herbicides, fungicides, and insecticides in l/ha, the amount of water consumed in m3/ha, and the amount of seeds consumed in kg/ha. EXCEL and SPSS software were used to analyze the data.
CALCULATION OF ENERGY INDICATORS
To calculate and obtain energy indices in major crops in the Ardabil plain, energy inputs including manpower, fertilizers, pesticides, machinery, and water were used in agricultural operations along with performance. The product was calculated according to the energy equivalents of those given in Table 1. In order to compare energy indices, all input and output data in the production of major crops are expressed as averages.
Equivalent input and output energies in the production of studied crops in the Ardabil plain
Inputs . | Unit . | Equivalent energy (MJ unit−1) . | References . |
---|---|---|---|
Human labor | h | 1.96 | De et al. (2001) |
Machinery | h | 62.7 | Mandal et al. (2002) |
Diesel | l | 47.8 | Kitani (1999) |
Nitrogen | kg | 66.14 | Hatirli et al. (2006) |
Phosphorus (P2O5) | kg | 12.44 | Hatirli et al. (2006) |
Potassium (K2O) | kg | 11.15 | Hatirli et al. (2006) |
Herbicides | l | 85 | Kitani (1999) |
Insecticides | l | 229 | Kitani (1999) |
Irrigation water | m3 | 1.02 | Acaroglu (1998) |
Alfalfa seed | kg | 28.1 | |
Wheat seed | kg | 15.7 | Mohammadzadeh et al. (2017) |
Barley seed | kg | 14.7 | Mohammadzadeh et al. (2017) |
Canola seed | kg | 3.6 | Mohammadzadeh et al. (2017) |
Potato seed | kg | 3.6 | Esengun et al. (2007) |
Outputs | |||
Alfalfa | kg | 15.8 | |
Wheat grain | kg | 14.7 | Mohammadzadeh et al. (2017) |
Barley grain | kg | 14.7 | Mohammadzadeh et al. (2017) |
Straw of wheat and barley | kg | 12.5 | Mohammadzadeh et al. (2017) |
Canola grain | kg | 27.6 | Mohammadzadeh et al. (2017) |
Potato | kg | 3.6 | Esengun et al. (2007) |
Inputs . | Unit . | Equivalent energy (MJ unit−1) . | References . |
---|---|---|---|
Human labor | h | 1.96 | De et al. (2001) |
Machinery | h | 62.7 | Mandal et al. (2002) |
Diesel | l | 47.8 | Kitani (1999) |
Nitrogen | kg | 66.14 | Hatirli et al. (2006) |
Phosphorus (P2O5) | kg | 12.44 | Hatirli et al. (2006) |
Potassium (K2O) | kg | 11.15 | Hatirli et al. (2006) |
Herbicides | l | 85 | Kitani (1999) |
Insecticides | l | 229 | Kitani (1999) |
Irrigation water | m3 | 1.02 | Acaroglu (1998) |
Alfalfa seed | kg | 28.1 | |
Wheat seed | kg | 15.7 | Mohammadzadeh et al. (2017) |
Barley seed | kg | 14.7 | Mohammadzadeh et al. (2017) |
Canola seed | kg | 3.6 | Mohammadzadeh et al. (2017) |
Potato seed | kg | 3.6 | Esengun et al. (2007) |
Outputs | |||
Alfalfa | kg | 15.8 | |
Wheat grain | kg | 14.7 | Mohammadzadeh et al. (2017) |
Barley grain | kg | 14.7 | Mohammadzadeh et al. (2017) |
Straw of wheat and barley | kg | 12.5 | Mohammadzadeh et al. (2017) |
Canola grain | kg | 27.6 | Mohammadzadeh et al. (2017) |
Potato | kg | 3.6 | Esengun et al. (2007) |
Calculation of greenhouse gas emissions
The greenhouse gas emissions of each of the chemical inputs were calculated according to the CO2, N2O, and CH4 emission coefficients in Table 2. Also, the global warming potential per hectare was calculated based on the emission rate of each of the greenhouse gases and their coefficient of effect for 100 years, which was 1 for CO2, 30 for N2O, and 21 for CH4 (IPCC 1995). Finally, the global warming potential of greenhouse gases emitted in major crops of the Ardabil plain per hectare was calculated and expressed based on CO2 equivalent. EXCEL 2016 software was used to calculate and draw graphs.
Gaseous emissions (g) per unit of input
Inputs . | Unit . | CH4 . | N2O . | CO2 . | References . |
---|---|---|---|---|---|
Diesel | l | 5.20 | 0.7 | 3,560 | Kramer et al. (1999) |
Nitrogen (N) | kg | 3.7 | 0.03 | 3,100 | Snyder et al. (2009) |
Phosphorus (P2O5) | kg | 1.8 | 0.02 | 1,000 | Snyder et al. (2009) |
Potassium (K2O) | kg | 1 | 0.01 | 700 | Snyder et al. (2009) |
Herbicide | kg | _ | _ | 6,300 | Lal (2004) |
Insecticide | kg | _ | _ | 5,100 | Lal (2004) |
CO2 equivalence factor | g | 21 | 30 | 1 | IPCC (1995) |
Inputs . | Unit . | CH4 . | N2O . | CO2 . | References . |
---|---|---|---|---|---|
Diesel | l | 5.20 | 0.7 | 3,560 | Kramer et al. (1999) |
Nitrogen (N) | kg | 3.7 | 0.03 | 3,100 | Snyder et al. (2009) |
Phosphorus (P2O5) | kg | 1.8 | 0.02 | 1,000 | Snyder et al. (2009) |
Potassium (K2O) | kg | 1 | 0.01 | 700 | Snyder et al. (2009) |
Herbicide | kg | _ | _ | 6,300 | Lal (2004) |
Insecticide | kg | _ | _ | 5,100 | Lal (2004) |
CO2 equivalence factor | g | 21 | 30 | 1 | IPCC (1995) |
RESULTS AND DISCUSSION
Energy indicators
The values of input, output, and equivalent energy in the production of major crops in the Ardabil plain are shown in Tables 3 and 4. The highest manpower belongs to potato (240 h/ha equal to 470.4 MJ/ha) and alfalfa (200 h/ha equal to 392 MJ/ha), and other crops required lower manpower, i.e. wheat (120 h/ha equal to 235.2 MJ/ha), rapeseed (100 h/ha equal to 196 MJ/ha), and barley (72 h/ha equals to 141.12 MJ/ha). The amount of manpower used in each crop can vary depending on the type of field operations, type of crops, and farm size (Mohammadzadeh et al. 2017). In a study conducted by Nouri-Khajebelagh et al. (2023) to investigate energy indicators and greenhouse gas emissions in the Tajan plain, the amount of human labor hours in various crops ranged from 380 to 840 h/ha. Their overall findings indicated that human labor hours have a direct relationship with the type of crop, cultivated area, accessibility to facilities and water, and the level of mechanization of the lands. Generally, an increase in the level of mechanization of the land reduces labor hours.
Amounts of input and output in the production of studied crops in the Ardabil plain
Inputs . | Unit . | Wheat (ha−1) . | Barley (ha−1) . | Canola (ha−1) . | Alfalfa (ha−1) . | Potato (ha−1) . |
---|---|---|---|---|---|---|
Human labor | h | 120 | 72 | 100 | 200 | 240 |
Machinery | h | 11 | 8 | 10 | 14 | 20 |
Diesel | l | 137.5 | 100 | 125 | 205 | 250 |
Nitrogen | kg | 169.3 | 73.8 | 95 | 20 | 277.2 |
Phosphorus (P2O5) | kg | 54.6 | 10 | 20.2 | 7.3 | 45.8 |
Potassium (K2O) | kg | 7.8 | 2.6 | 90.7 | 21.5 | 264.8 |
Herbicides | l | 1.8 | 0.4 | 0.97 | 1.1 | 3.24 |
Insecticides | l | 0.2 | 0 | 0.98 | 2.65 | 0.7 |
Irrigation water | m3 | 4,605 | 2,793.6 | 4,205.9 | 6,782.5 | 12,091 |
Seed | kg | 267 | 262 | 10 | 90 | 3,038.5 |
Outputs | _ | _ | _ | _ | _ | |
Alfalfa | kg | _ | _ | _ | _ | 6,250 |
Wheat grain | kg | 5,449 | _ | _ | _ | _ |
Barley grain | kg | _ | 3,209 | _ | _ | _ |
Straw of wheat | kg | 5,500 | _ | _ | _ | |
Straw of barley | _ | 3,300 | _ | _ | _ | |
Canola grain | kg | _ | _ | 3,100 | _ | |
Potato | kg | _ | _ | _ | _ | 35,368.8 |
Inputs . | Unit . | Wheat (ha−1) . | Barley (ha−1) . | Canola (ha−1) . | Alfalfa (ha−1) . | Potato (ha−1) . |
---|---|---|---|---|---|---|
Human labor | h | 120 | 72 | 100 | 200 | 240 |
Machinery | h | 11 | 8 | 10 | 14 | 20 |
Diesel | l | 137.5 | 100 | 125 | 205 | 250 |
Nitrogen | kg | 169.3 | 73.8 | 95 | 20 | 277.2 |
Phosphorus (P2O5) | kg | 54.6 | 10 | 20.2 | 7.3 | 45.8 |
Potassium (K2O) | kg | 7.8 | 2.6 | 90.7 | 21.5 | 264.8 |
Herbicides | l | 1.8 | 0.4 | 0.97 | 1.1 | 3.24 |
Insecticides | l | 0.2 | 0 | 0.98 | 2.65 | 0.7 |
Irrigation water | m3 | 4,605 | 2,793.6 | 4,205.9 | 6,782.5 | 12,091 |
Seed | kg | 267 | 262 | 10 | 90 | 3,038.5 |
Outputs | _ | _ | _ | _ | _ | |
Alfalfa | kg | _ | _ | _ | _ | 6,250 |
Wheat grain | kg | 5,449 | _ | _ | _ | _ |
Barley grain | kg | _ | 3,209 | _ | _ | _ |
Straw of wheat | kg | 5,500 | _ | _ | _ | |
Straw of barley | _ | 3,300 | _ | _ | _ | |
Canola grain | kg | _ | _ | 3,100 | _ | |
Potato | kg | _ | _ | _ | _ | 35,368.8 |
Input and output energies in the production of studied crops in the Ardabil plain
Energy . | Unit . | Wheat (MJ ha−1) . | Barley (MJ ha−1) . | Canola (MJ ha−1) . | Alfalfa (MJ ha−1) . | Potato (MJ ha−1) . |
---|---|---|---|---|---|---|
Inputs | ||||||
Human labor | h | 235.2 | 141.12 | 196 | 392 | 470.4 |
Machinery | h | 689.7 | 501.6 | 627 | 877.8 | 1,254 |
Diesel | l | 6,572.5 | 4,780 | 5,975 | 9,799 | 11,950 |
Nitrogen | kg | 11,197.5 | 4,881.13 | 6,283.3 | 1,322.8 | 18,334.01 |
Phosphorus (P2O5) | kg | 679.22 | 124.4 | 251.3 | 90.8 | 569.8 |
Potassium (K2O) | kg | 86.97 | 28.99 | 1,011.3 | 239.7 | 2,952.5 |
Herbicides | l | 153 | 34 | 82.45 | 93.5 | 275.4 |
Insecticides | l | 45.8 | 0 | 224.4 | 606.9 | 160.3 |
Irrigation water | m3 | 4,697.1 | 2,849.5 | 4,290.02 | 6,918.15 | 12,332.82 |
Seed | kg | 4,191.9 | 3,851.4 | 36 | 2,529 | 10,938.6 |
Outputs | ||||||
Alfalfa | kg | _ | _ | _ | 98,750 | _ |
Wheat grain | kg | 80,100.3 | _ | _ | _ | _ |
Barley grain | kg | _ | 47,172.3 | _ | _ | _ |
Straw of wheat | kg | 68,750 | _ | _ | _ | _ |
Straw of barley | kg | _ | 41,250 | _ | _ | _ |
Canola grain | kg | _ | _ | 85,560 | _ | _ |
Potato | kg | _ | _ | _ | _ | 127,327.7 |
Energy . | Unit . | Wheat (MJ ha−1) . | Barley (MJ ha−1) . | Canola (MJ ha−1) . | Alfalfa (MJ ha−1) . | Potato (MJ ha−1) . |
---|---|---|---|---|---|---|
Inputs | ||||||
Human labor | h | 235.2 | 141.12 | 196 | 392 | 470.4 |
Machinery | h | 689.7 | 501.6 | 627 | 877.8 | 1,254 |
Diesel | l | 6,572.5 | 4,780 | 5,975 | 9,799 | 11,950 |
Nitrogen | kg | 11,197.5 | 4,881.13 | 6,283.3 | 1,322.8 | 18,334.01 |
Phosphorus (P2O5) | kg | 679.22 | 124.4 | 251.3 | 90.8 | 569.8 |
Potassium (K2O) | kg | 86.97 | 28.99 | 1,011.3 | 239.7 | 2,952.5 |
Herbicides | l | 153 | 34 | 82.45 | 93.5 | 275.4 |
Insecticides | l | 45.8 | 0 | 224.4 | 606.9 | 160.3 |
Irrigation water | m3 | 4,697.1 | 2,849.5 | 4,290.02 | 6,918.15 | 12,332.82 |
Seed | kg | 4,191.9 | 3,851.4 | 36 | 2,529 | 10,938.6 |
Outputs | ||||||
Alfalfa | kg | _ | _ | _ | 98,750 | _ |
Wheat grain | kg | 80,100.3 | _ | _ | _ | _ |
Barley grain | kg | _ | 47,172.3 | _ | _ | _ |
Straw of wheat | kg | 68,750 | _ | _ | _ | _ |
Straw of barley | kg | _ | 41,250 | _ | _ | _ |
Canola grain | kg | _ | _ | 85,560 | _ | _ |
Potato | kg | _ | _ | _ | _ | 127,327.7 |
The results showed that the working hours of the machines were between 8 and 20 h, with the highest working hours used for potato production and the lowest working hours used for barley production (according to Table 3). The higher working hours for potato production are due to the method of planting and the initial harvesting done with machines. The results of studies conducted in Iran show that the share of machinery in potato production (Ghaderzadeh & Pir Mohammadiani 2019), alfalfa (Asgharipour et al. 2016; Mohammadzadeh et al. 2018; Manafi Dastjerdi & Kohnavard, 2020), wheat (Vahedi & Zarifneshat 2021), barley (Vafabakhsh & Mohammadzadeh 2019), and rapeseed (Dargahi et al. 2016) usually accounted for less than 5% of the total input energy. The reason for the low share of machinery in input energy can be attributed to farmers' reluctance to use new systems, up-to-date machinery, and a lack of sufficient financial means to purchase machinery.
Fossil fuels, especially diesel, are one of the most important sources of energy needed for machinery and electropumps to pump water to higher points. As a result, the volume of diesel used in the production of agricultural products is directly related to the type of machinery, the working hours of machinery, and also in the case of diesel pumps, the level of irrigation. In this study, among the studied crops, the lowest consumption of diesel was in barley production, and the highest consumption of diesel was in potato production. As mentioned before, the high number of working hours of machines in potato production increased the consumption of diesel (20.17% of the total input energy equivalent to 59,237.8 MJ/ha). In a study conducted by Jafari Talukolaee et al. (2024), aiming to analyze the physical productivity and energy indicators of major agricultural crops and citrus fruits in the Tajan plain, it was found that fuel consumption accounted for between 20 and 50% of the input energy. Specifically, wheat allocated approximately 21% of its energy input to diesel fuel. Rajabi et al. (2012), in a study conducted on wheat production in Gorgan, found that the highest amount of diesel was consumed in the preparation of the planting bed and the irrigation of the crop. Among the studied energy inputs, chemical agents had the lowest share of total input energy. Beheshti Tabar et al. (2010) also found that the share of pesticides in the total input energy in crop production is less than other inputs.
Seeds had the highest share of energy input in barley production (22.4%), considering that in barley in the Ardabil plain, farmers usually do not use much fertilizer and pesticides; furthermore, they use two or three irrigations during the growing season. In potato cultivation, farmers use about 3.5 tonnes of potato tubers per hectare, having a share of 18.5% of energy input.
Average share of different input energies in the production of major crops in the Ardabil plain.
Average share of different input energies in the production of major crops in the Ardabil plain.
Among the studied crops, the highest water consumption is related to the production of potatoes (12,091 m3/ha equivalent to 12,332.82 MJ/ha), alfalfa (6,782.5 m3/ha equivalent to 6,918.15 MJ/ha), and wheat (4,605 m3/ha equivalent to 4,697.1 MJ/ha) had the highest water consumption, and rapeseed (4,205.9 m3/ha equivalent to 4,290.02 MJ/ha) and barley (2,793.6 m3/ha equivalent to 2,849.47 MJ/ha) had the lowest water consumption. In similar studies, the amount of irrigation water and its percentage of total energy input were, respectively, for wheat 5,850 m3/ha and 3.2% (Yousefi et al. 2016) and for rapeseed: 4,934 m3/ha and 15.5% (Mohammadzadeh et al. 2017).
Table 5 shows the energy indicators in the production systems of the studied crops. As can be seen from the table, potatoes, wheat, and alfalfa had the highest input energy with 59,237.8, 28,548.9, and 21,435.64 MJ/ha, respectively, and rapeseed and barley with 18,976.78 and 17,192.11 MJ/ha, respectively, had the lowest input energy.
Energy indicators in the production of studied crops in the Ardabil plain
Energy indicators . | Unit . | Wheat . | Barley . | Canola . | Alfalfa . | Potato . |
---|---|---|---|---|---|---|
Input energy | MJ ha−1 | 28,548.9 | 17,192.11 | 18,976.78 | 21,435.64 | 59,237.8 |
Output energy | MJ ha−1 | 148,850.3 | 88,422.3 | 85,560 | 98,750 | 127,327.7 |
Net energy | MJ ha−1 | 120,301.4 | 71,230.2 | 66,583.22 | 77,314.36 | 68,089.88 |
Energy use efficiency | _ | 5.2 | 5.14 | 4.5 | 4.6 | 2.15 |
Specific energy | MJ kg−1 | 5.24 | 5.36 | 6.12 | 3.43 | 1.67 |
Energy productivity | kg MJ−1 | 0.19 | 0.19 | 0.16 | 0.29 | 0.60 |
Energy indicators . | Unit . | Wheat . | Barley . | Canola . | Alfalfa . | Potato . |
---|---|---|---|---|---|---|
Input energy | MJ ha−1 | 28,548.9 | 17,192.11 | 18,976.78 | 21,435.64 | 59,237.8 |
Output energy | MJ ha−1 | 148,850.3 | 88,422.3 | 85,560 | 98,750 | 127,327.7 |
Net energy | MJ ha−1 | 120,301.4 | 71,230.2 | 66,583.22 | 77,314.36 | 68,089.88 |
Energy use efficiency | _ | 5.2 | 5.14 | 4.5 | 4.6 | 2.15 |
Specific energy | MJ kg−1 | 5.24 | 5.36 | 6.12 | 3.43 | 1.67 |
Energy productivity | kg MJ−1 | 0.19 | 0.19 | 0.16 | 0.29 | 0.60 |
The results of the output energy show that the highest amounts belong to wheat (148,850.3 MJ/ha), potato (127,327.7 MJ/ha), and alfalfa (98,750 MJ/ha), respectively, and the lowest amounts belong to rapeseed (85,560 MJ/ha) and barley (88,422.3 MJ/ha), respectively. The high output energy in wheat is due to the presence of grain and straw in the output energy separately. In similar studies, the output energy for wheat was 123,430 MJ/ha (Vafabakhsh & Mohammadzadeh 2019), for alfalfa was 122,450 MJ/ha (Habibi et al. 2024), for rapeseed was 51,809 MJ/ha (Vafabakhsh & Mohammadzadeh 2019), for potato was 154,000.9 MJ/ha (Ghaderzadeh & Pirmohammadiani 2019), and for barley was 114,650 MJ/ha (Vafabakhsh & Mohammadzadeh 2019).
The results obtained for net energy show that in this section, wheat (120,301.4 MJ/ha), alfalfa (77,314.36 MJ/ha), and barley (71,230.2 MJ/ha) had the highest net energy, and rapeseed (66,583.22 MJ/ha) and potato (68,089.88 MJ/ha) had the lowest net energy.
As can be seen in Table 5 in terms of energy use efficiency, wheat and barley crops had the highest energy use efficiency with 5.2 and 5.14, respectively, and alfalfa, rapeseed, and potato had the lowest energy use efficiency with 4.6, 4.5, and 2.15, respectively.
The study of specific energy in the studied crops showed that rapeseed (6.12 MJ/kg), barley (5.36 MJ/kg), and wheat (5.24 MJ/kg) had higher specific energy than alfalfa (3.43 MJ/kg) and potato (1.67 MJ/kg). In similar studies, specific energy for wheat was 10.5 MJ/kg, for barley was 9.6 MJ/kg, for rapeseed was 20.7 MJ/kg, for alfalfa was 6.8 MJ/kg (Vafabakhsh & Mohammadzadeh 2019), and for potato was 1.62 MJ/kg (Ghaderzadeh & Pir Mohammadiani 2019). Comparing the results obtained in the current study and similar studies, it was found that rapeseed requires more specific energy than other crops. Also, comparing the results of rapeseed and potato with other studies showed that the results obtained in this study are consistent with the results of other research.
Global warming potential
Contribution of different inputs to the global warming potential in crop production in the Ardabil plain.
Contribution of different inputs to the global warming potential in crop production in the Ardabil plain.
The results showed that the amount of greenhouse gas emissions and the resulting global warming potential in potato crops (equivalent to 3,186.225 kg CO2/ha) and wheat (equivalent to 1,727.46 kg CO2/ha) had the largest share between crops. Rapeseed (equivalent to 1,326.22 kg CO2/ha), alfalfa (equivalent to 1,157.04 kg CO2/ha), and barley (equivalent to 952.39 kg CO2/ha) were in the next positions. Abbas et al. (2022) aimed to investigate the sensitivity of greenhouse gas emissions at the farm level for rice and cotton crops. They found that the total greenhouse gas emissions were 920.69 and 954.71 kg CO2eq/ha from rice and cotton production, respectively. In general, it can be concluded that globally, in the agricultural sector, the highest level of greenhouse gas emissions at the farm level is attributed to chemical fertilizers, followed by diesel fuel and electricity. Mohammadi et al. (2014) conducted a study to obtain global warming potential in northern Iran. They found that the global warming potential for wheat, barley, and rapeseed was 1,171.1 kg CO2/ha, 1,105.7 kg CO2, and 1,063.5 kg CO2/ha, respectively. Comparison of the share of different inputs in the total global warming potential of major crops in the Ardabil plain showed that in most of the studied crops, consumption of gasoline (diesel fuel) and nitrogen fertilizer played the most important role in greenhouse gas emissions. The share of these two inputs in barley, alfalfa, wheat, canola, and potato crops was 97.6, 94.12, 93.3, 88.6, and 87.2%, respectively. Due to the fact that machinery, diesel fuel, and chemical fertilizers, including nitrogen, have a great impact on greenhouse gas emissions and global warming, organic farming can be used to reduce greenhouse gas emissions, as well as using new machines with high energy efficiency to mitigate greenhouse gas emissions.
The study relies on data for 1 year (2017–2018). This is a limitation of this study. It is true that the data were related to 1 year, but it reflects the agricultural management routine in the study area and many similar places in the country. Therefore, it is possible to generalize the results to similar regions to a large extent. However, longer and more comprehensive studies are always recommended, and this study paves the way for more comprehensive studies in the future.
CONCLUSION
Agriculture, as one of the fundamental factors in food supply and the largest consumer of water, has significant implications for energy and water efficiency indicators. Assessing the increase in energy and water efficiency can move agriculture toward sustainability and contribute to food security and natural resource conservation. The present study was conducted to examine the level of physical water efficiency and energy indicators in the Ardabil plain. The results showed that the highest input energy in the Ardabil plain is attributed to potato production with 59,237.8 MJ/ha; the highest output energy is related to wheat, with 148,850.3 MJ/ha; and the highest net energy from wheat is 120,301.4 MJ/ha. In terms of energy efficiency, potato production allocated the highest amount with 0.60 kg/MJ. Additionally, according to the findings, in all crops, the four inputs of water, nitrogen fertilizer, machinery, and fuel allocated the highest amounts. The results for greenhouse gas emissions showed that the highest level of emissions in the Ardabil plain is attributed to potato production with 3,186.2 kg CO2/ha, while the lowest level of this parameter is related to barley production, with 952.39 kg CO2/ha. The reason for this disparity is the excessive use of fertilizers and diesel fuel. In the present study, due to existing challenges regarding insufficient information and the unavailability of transportation costs, as well as uncertainties about the accuracy of other expenses, productivity indicators, including economic efficiency, were not investigated. It is suggested that in future studies, these parameters be evaluated to enhance the content and scientific rigor of the articles. Based on the results of the present study, it can be concluded that in the Ardabil plain, increasing the cultivation area of crops such as wheat and rapeseed and reducing the cultivation area of potatoes, in addition to reducing water consumption, also increase the overall yield of crops. This leads to increased productivity, food security, sustainable development, and reduced dependence on imports of livestock and poultry inputs. Considering the obtained results, it is recommended that to increase productivity in this plain and other regions, diversification of crop cultivation, utilization of modern irrigation systems, and proper management of existing resources should be implemented. Furthermore, increasing farmers' awareness should be recommended to managers and stakeholders.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.