3.1. Energy Consumption in Production
The quantity of input and output and total input and output energy (MJ ha
−1) in cotton production is presented in
Table 3. The total input energy used in cotton production was calculated at 83,869.49 MJ ha
−1. Considering Türkiye, machinery had the highest share of energy consumption in cotton production (28.69%), followed by electricity (22.79%), nitrogen (20.75%), and diesel fuel (15.59%).
While producing cotton, a machine is needed at every stage, from planting preparation to harvest. Thanks to advanced machines, agricultural products are produced in a shorter time by consuming less energy [
78]. In this study, the percentage of farmers harvesting cotton by machine was 91%. For this reason, machines are expected to have the highest use of energy in cotton farming.
Electrical energy is used in cotton production, tube well energizing, lighting, and irrigation. The use of electricity varies and mainly depends on the type of irrigation. Surface irrigation methods are generally used in cotton farming in Türkiye. When the drip irrigation method is used, electricity consumption will also decrease. This study stated that electrical energy consumed an average of 19110.11 MJ ha
−1 for the cotton. Electricity use accounted for 22.79% of the total energy use in cotton production (
Table 3). Another study stated that electricity (28%) consumed the most energy after fertilizer in cotton farming [
34].
The use of chemical fertilizers has increased considerably in Türkiye, as well as in the world, to increase productivity. Previous researchers have reported that fertilizers account for most of the total energy input for cotton [
35,
36,
37,
40]. It was determined that 223.17 kg ha
−1 fertilizer was used in cotton farming in Punjab [
41], 120.8 kg ha
−1 in Gorgan, and 164.61 kg ha
−1 in Darab [
40]. In the current study, the use of fertilizers was higher than in other countries. This result may be a way that cotton farmers resort to obtaining more products, or it may be due to the lack of knowledge about fertilization in cotton.
Chemicals are used in cotton production for plant growth and disease and pest control. In recent years, there has been an increase in diseases and pests in cotton fields. For this reason, pesticide applications have increased [
79] in cotton farming areas. Herbicides are used in the control of narrow-leaved weeds in cotton production and in the fight against insects, such as aphids, green worms, and red spiders. Insecticides and plant growth regulators, including boll openers and defoliants, are used to ensure the homogeneous opening of maturing cotton bolls and the shedding of leaves. Chemicals covered 0.89% of energy consumption. Herbicides (380.59 MJ ha
−1) were found to be the most energy-consuming of chemicals.
Diesel fuel is commonly used in cotton farming, from irrigation to fertilization to harvesting to processing. In the research area, 232.26 lt ha
−1 of diesel was used during cotton cultivation. The energy value of this use was 13,078.52 MJ ha
−1; the use of diesel accounted for 15.59% of the total energy consumption (
Table 3). In a similar study, the average diesel fuel use per hectare in Pakistan was 78.35 lt ha
−1 [
41]. This result confirmed that using diesel fuel machines increased cotton farming in Türkiye.
In recent years, hand harvesting has left its place for machine harvesting in cotton production in Türkiye. The percentage of farmers harvesting cotton by hand was 9%. For this reason, it was expected that machines result in the highest use of energy in cotton farming. The energy output of cotton yield was 60,465.57 MJ ha
−1. In other studies, the cotton energy output was calculated as 36,729 MJ ha
−1, 95,800 MJ ha
−1, 36,189 MJ ha
−1, 36,882 MJ ha
−1, 56,050 MJ ha
−1, 33,314 MJ ha
−1, and 65,984 MJ ha
−1, respectively [
35,
36,
38,
39,
40,
41,
42]. The average cotton yield in the study area was 5124.2 kg ha
−1, higher than in other countries except for China [
31]. In addition, the mechanization of cotton production in Türkiye in recent years has caused the output energy to be higher than in other studies [
35,
36,
37,
39].
In the study area, more furrow irrigation techniques were used in cotton farming. Sprinkler and drip irrigation systems were used less frequently [
80]. Because of the less effective use of water consumption, drip and sprinkler irrigation methods consume less energy than furrow irrigation methods [
81]. According to the study results, an average of 10,995.22 m
3 ha
−1 water was consumed in cotton production. The energy consumed for irrigation was 6926.99 MJ ha
−1. It was 8.26% of the energy consumption in cotton agriculture (
Table 3).
Intensive soil cultivation is carried out in cotton farming (ploughing, planting, hoeing, etc.). Rusu [
82] and Lachuga [
83] reported that tillage constitutes a significant part of energy costs. For this reason, in cotton farming, it is thought that energy consumption can be reduced by using minimal tillage methods (stubble sowing, tillage, seed sowing with a single tool, etc.) instead of traditional tillage.
3.2. Energy Indices of Cotton Production
Energy use efficiency, energy productivity, specific energy, and net energy in cotton production are presented in
Table 4. The energy use efficiency of cotton production in Türkiye was calculated at 0.87. This value showed that the energy use in cotton farming is inefficient. Reducing agricultural inputs or increasing the cotton yield is necessary to increase energy efficiency [
40]. This is supported by Yilmaz et al. [
35], who stated that the EUE in cotton farms in the Antalya province of Türkiye was 0.74. Gokdogan et al. [
39] reported that the EUE in cotton farms in the Aydin province of Türkiye was 1.92. The EUE of cotton farms in the Sanliurfa province of Türkiye was calculated at 2.52 [
36] and 2.36 [
37]. The main reason for this difference in results is that climatic conditions have an impact on energy use efficiency. In other studies, Khan et al. [
84] in China, Zahedi et al. [
38] in Isfahan, Kazemi et al. [
40] in Darab, Kazemi et al. [
40] in Gorgan, and Imran et al. [
41] in Punjab calculated the energy use efficiency in cotton as 1.51, 0.70, 0.94, 1.11, and 0.70, respectively.
The energy productivity of cotton production in Türkiye was 0.07 kg MJ
−1. According to this finding, 0.07 units of output were obtained per unit of energy. Previous studies have shown that the energy productivity of cotton production in Isfahan was 0.10 kg MJ
−1 [
38] and 0.04 in Punjab [
41]. Kazemi et al. [
40] reported that the energy productivity of cotton farms in Darab was 0.08 kg MJ
−1 and 0.09 kg MJ
−1 in Gorgan.
The specific energy was 17.31 MJ kg
−1. This indicator was estimated at 12.53 MJ kg
−1 for Darab and at 10.67 MJ kg
−1 for Gorgan [
40]. As for the rate of net energy, it was found to be −23,043.92 MJ ha
−1. The negative value for net energy was also found by Zahedi et al. [
38], Kazemi et al. [
40], and Imran et al. [
41] for cotton. Energy conservation in these fields can be improved by increasing energy efficiency and implementing new technologies. As long as the energy output per unit of energy input rises, net energy increases [
40].
Total mean energy inputs as direct, indirect, renewable, and non-renewable are presented in
Table 5. The results showed that cotton consumed more indirect energy (51.99%) than direct energy (48.01%). Similarly, Yilmaz et al. [
35] and Zahedi et al. [
38] found that cotton consumed more indirect energy than direct energy. However, Kazemi et al. [
40] found the opposite result.
This study used human labour, seeds, and water as renewable energy sources. It was determined that renewable resources (10.04%) consumed less energy than non-renewable resources (89.96%). Yilmaz et al. [
35] and Gokdogan et al. [
39] also reported similar results. Because the energy consumption of renewable energy sources used in cotton production was low (
Table 2), no significant energy savings are expected from these sources. However, considerable energy savings can be expected using non-renewable energy sources (machinery, diesel, nitrogen, etc.).
Information about the descriptive statistics of the variables used in the efficiency analysis is presented in
Table 6. A seven-input and single-output model was created to measure the cotton production efficiency of farms with DEA. To obtain an average cotton yield of 5124.2 ha kg
−1 of cotton during the cotton production season, an average of 29.16 kg ha
−1 of seeds, 287.19 kg ha
−1 of N, 95.84 kg ha
−1 of P, 6.33 times of irrigation, 6.78 times of spraying, 585.94 h of labour, and 383.81 h of machine power are needed.
Economic, technical, allocation, scale, and pure technical efficiency values in cotton farms were estimated with DEA. The data obtained from the analysis are presented in
Table 7. The economic efficiency in the examined cotton farms varied between 0.156 and 1, and the average was 0.479. According to the results, ineffective cotton farms should reduce their costs in the production process by approximately 52% to reach the level of effective cotton farms by producing in the minimum cost input composition. The main reason for the inefficiency in cotton farms was inadequate allocation efficiency. The allocation efficiency of the examined farms varied between 0.103 and 1, and the allocation efficiency coefficient was determined to be 0.570 on average. Cotton farms produced 43% more cost than the minimum cost input composition. This finding means that some cotton farms produce with the wrong input combination considering the current input prices and technology level.
The technical efficiency of cotton farms varied between 0.116 and 1, and the technical efficiency coefficient was determined to be 0.838 on average. This result shows that inefficient farms can reduce their inputs by approximately 16% without reducing cotton production. Pure technical efficiency and scale efficiency were used to determine the technical efficiency coefficient of the examined cotton farms, and they were determined to be 0.539 and 0.640, respectively. The main reason for the lack of technical efficiency in cotton farms was the technical inadequacy of cotton farmers’ agricultural knowledge and skills. Although most of the examined farms were of appropriate scale, they could not technically carry out their activities. According to the results of the scale efficiency analysis, 85.8% (564 farms) of the cotton farms in the research area had increasing returns to scale, and 7.9% of those (52 farms) had decreasing returns to scale. In comparison, the rate of cotton farms with fixed returns to scale was 6.3% (41 farms). Binici et al. [
85], in their study of cotton farmers in the Harran Plain, determined that 70% of the farms were operated ineffectively. In similar research, technical efficiencies in cotton farms in the Hatay and Adana provinces ranged from 0.23 to 1.00, and the average technical efficiency was determined to be 0.79 [
47]. This result indicates that there are some opportunities to increase resource use efficiency. For instance, they can reduce their input costs by 21% on average to produce the same yield in cotton farms [
47]. Wei et al. [
86] found the technical efficiency, economic efficiency, and allocation efficiency of cotton mills in Punjab to be 0.95, 0.66, and 0.70, respectively. In a study comparing the technical efficiency of irrigated and non-irrigated cotton producers, it was found that 80% of irrigated cotton producers are efficient, and 70% of non-irrigated cotton producers are efficient. Findings showed that in Texas, irrigated farms can reduce their expenditure on inputs by 10% on average while producing at the same level [
45].
The energy consumption of technically efficient and inefficient farms is presented in
Table 8. Farms with an efficiency coefficient of <0.90 were grouped as inefficient, and farms with ≥0.90 were grouped as efficient farms. For inefficient farms to be effective, energy consumption should be reduced in all other inputs except potassium. Statistically, for inefficient farms to become efficient farms, it is necessary to reduce the use of human labour, machinery, diesel fuel, nitrogen, and phosphate energy by 27.6%, 29.4%, 10%, 19%, and 32.6%, respectively (
p < 0.01).