Today, responding to the growing need for food for increasing the world’s population and providing adequate food is one of the main causes of energy requirements in agriculture operations (Bishop, 1993). In order to meet the food requirements of this population, agricultural activities are highly dependent on energy consumption in different production- agriculture systems; this dependence not only depends on the type of product but also the type of inputs used in the manufacture of that product. The diversity in the type of behavior in different systems of using the inputs and energy sources leads to differences in energy efficiency in the production system so that it can lead cause the unstable agriculture. Sustainable agriculture is defined as supplying the present needs without compromising the ability of future generations to meet consumers' requirements without any trouble (Bishop, 1993; Dovers and Handmer, 1993). Energy can be exploited from different inputs such as human labor, animal, fossil-based fuels, electricity and machinery to perform various operations in dairy production. Implementing more automatic equipment and performing mechanized operations has led to a crisis of environmental deterioration (Kraatz, 2012). In addition, because of limitation and depletion in energy resources, the outlook of energy consumption needs to be optimized with correct decisions. Therefore, improving the management level of energy usage is crucial to combat the rising energy costs, depletion of natural resources and environmental deterioration (Dovì et al. 2009). Energy is one of the basic requirements for the economic and social development of a country or area. The analysis and scientific forecasts of energy consumption are important for the planning strategies and policies of energy usage (Liang et al. 2007). Therefore, effective energy usage is one of the key factors for sustainable agricultural production which provides financial savings, fossil resources preservation and air pollution reduction (Uhlin, 1998). On the other hand, livestock production is the poor converter of energy, because it is based on a double energy transformation. First, solar energy and soil nutrients are converted into biomass by green plants. Second, when crops are fed to livestock, a major share of energy intake is spent on keeping up body metabolism and only a small portion is used to produce meat and milk (Frorip et al. 2012). Increased energy efficiency and utilization of non-renewable energy are effective to improve the air quality and also to reduce greenhouse emissions. Of other effects of increased energy efficiency could point to the reduced operating costs as well as the costs arising from agricultural production (Meul et al. 2007). In order to meet the increasing demands of people to animal products, technology and methods need to be adopted to improve the efficiency of animal production; it would increase the production and reduce the environmental impacts (Capper et al. 2009). Moitzi et al. (2010) examined the energy used with focus on the concentrate level in dairy farms in Austria. Kraatz et al. (2009) examined the effects of different feeds and all inputs (Kraatz, 2012) on the energy indexes in dairy farms. Recently, Pagani et al. (2016) reported that a potential 40% reduction in total energy consumption could be achieved by shifting to organic farming and following some practices in dairy farms. Uzal (2013) reported that concentrates and forage accounted for the highest percentages (95.93%) in free stall dairy farms. In another study, the highest energy use of electricity was reported as 37% related to milk cooling and heating the water with 31% (Upton et al. 2010), respectively. The results of a study in Qazvin province of Iran showed that total energy consumed during one year was 147659.44 MJ calf-1 in fattening farms (Hosseinzadeh-Bandbafha et al. 2017). Sefeedpari et al. (2012) reported that the annual total energy consumption was 72816.7 MJ cow-1 in a dairy farm, where feed was the most energy consumer input (38.089 MJ cow-1); then fuel, electricity and machinery usage were 7824, 1698 and 475 MJ cow-1, respectively. Non-renewable and renewable energy also had 20% and 80% share of total energy consumption. Moreover, many studies (Pagani et al. 2016; Nacer et al. 2017; Koesling et al. 2017; Todde et al. 2018) were carried out about the energy consumption in dairy farms worldwide. However, to our knowledge, no research has been carried out so far about the effect of herd size on energy consumption in dairy farms of Iran.
MATERIALS AND METHODS
Selection of case study region and data collection
This study was conducted in Tehran province of Iran. Tehran has located within 35 ˚34' and 35 ˚50' north latitude and 51 ˚02' and 51 ˚36' east longitude (Anonymous, 2010). Required information was collected from dairy farms with face to face questionnaire in the west part of Tehran. The required sample size was calculated using Eq. 1 (Kizilaslan, 2009) and was estimated as 37 units but in order to have less inaccuracy, 45 questionnaires was completed and finally the information of 42 units were analyzed since some information was invalid.
n= Nt2S2 / Nd2 + t2S2
n: required sample size.
N: size of population or number of dairy farms in the study area.
t: t value at 95% conﬁdence limit (1.96), assuming a normal distribution.
s2: standard deviation.
d: acceptance error.
The studied dairy farms had an average milk yield of 31.94 ± 3.2 kg per day for milking cows and 195.42 ± 10.18 days in milk (Table 1). Since we aimed at comparing energy consumption and energy production among herds of different sizes, dairy farms were categorized to small farms (≤200 cows, N=24 farms), medium size (with 200-450 cows, N=11 farms) and large farms (≥450 cows, N=7 farms) based on the frequencies of each category.
Input and output energy
A number of different sources of input including human labor, machines, fuel, electricity, and feed are required for milk production. Also, energy output sources are considered as milk, meat, and manure. Energy sources can be classified into non-renewable sources such as fuel, and electricity and renewable sources such as forage, concentrate, machines, labor, and water.
Table 1 Specifications of livestock in the studied dairy farms
To calculate the indirect energy used by machineries such as tractors and stationary machinery, Eq. 2 was used (Kitani, 1999):
ME= G × Mp × t / T
ME: machinery energy (MJ cow-1).
G: material mass used for manufacturing (kg).
M: production energy of material(MJ kg-1).
t: time that machine used (h cow-1).
T: economic lifetime of machine (h).
Since milk is produced with different qualities in fat and protein content, as the first step, the total milk data was gained from questionnaire and then it was corrected based on fat and protein content using Eq. (3) (IDF, 2010):
Fat and protein corrected milk (kg Year-1)= milk production (kg year-1) × [0.1226 fat% + 0.0776 true protein% + 0.2534]
The products of a dairy farm consist of milk, meat and manure which were considered as energy outputs and estimated using the energy equivalent coefficients from Table 2.
In order to compare energy efficiency of dairy farms, energy indices (energy ratio (ER), net energy gain (NEG), energy productivity (EP), and specific energy (SE) were used, which are based on the Eqs. (4-7) (Mandal et al. 2002):
ER= Eout / Ein
NEG= Eout – Ein
EP=Y / Ein
SE=Ein / Y
ER: energy ratio.
NEG: net energy gain (MJ kg FPCM-1).
EP: energy productivity (kg FPCM MJ-1).
SE: specific energy (MJ kg FPCM-1).
Eout: output energy (MJ kg FPCM-1).
Ein: input energy (MJ kg FPCM-1).
Y: kg FPCM production.
Data were analyzed using the general linear model (GLM) procedure of SAS 9.4 (SAS, 2015). When a signiﬁcant (P<0.05) F-test was detected for the dairy farm sizes, the corresponding means were compared by the least signiﬁcant difference (LSD).
RESULTS AND DISCUSSION
The results of input and output energy analysis are presented in Table 3. All energy calculations were performed on an annual basis. As seen in Table 3, the amount of energy consumed per kg FPCM was significantly greater in small and medium dairy farms than in large dairy farms (P˂0.0001). The amount of energy consumed in small, medium and large dairy farms were 32.34, 21.94 and 15.30 MJ kg FPCM-1, respectively. And, the amount of energy produced in small, medium and large dairy farms were 3.34, 3.35 and 3.9 MJ kg FPCM-1, respectively. The results of this study show that the total energy inputs in large farms have produced better efficiency of consumption and energy production than small farms. For example, feed energy, which has the highest share of energy consumption in all farms of this study, shows a significant difference in large farms compared to small farms (P<0.0002). Generally, in large farms due to better and more rigorous management; because of the better feed management and high productive potential of cows hired in large dairy farms and given the smaller part of the feed needed for maintenance energy in high potential cows, efficiency in large farms is higher (Capper et al. 2009). The pattern of energy intake usage relates to the energy consumption of machinery, equipment and herd size (De et al. 2001). In a study, energy consumption of 60 rural dairy farms (low milk production) was evaluated in India (Divya et al. 2012).
Table 2 Energy equivalent coefficients, energy inputs and outputs
a*: economic life of machine (year).
Table 3 Energy inputs and outputs of dairy farms (MJ kg FPCM-1) (Mean±SE)
The means within the same row with at least one common letter, do not have significant difference (P>0.05).
SE: standard error.
The research was carried out in dairy farms at three levels of small, medium and large; the average total energy consumed per head in a period of the year was 53024.25 MJ and the highest inputs were related to feed intake with a share of more than 90%, followed by human labor. This shows that in India, because of the very cheap labor, it is not economical to use the equipment (Divya et al. 2012). However, in Iranian dairy farms, the use of equipment is highly logical and cost-effective due to the high salary of the skilled workers. The results of the study in Qazvin province of Iran showed that total energy consumed during one year was 147659.44 MJ cow-1 (Hosseinzadeh-Bandbafha et al. 2018). In Tehran province of Iran dairy farms consumes the total energy as 72816.7 MJ cow-1 (Sefeedpari, 2012) and in Guilan province of Iran, total energy consumption was calculated to be 52592.81 MJ cow-1 (Soltanali et al. 2015). In another study, the total energy consumption was reported to be 2.36 MJ kg FPCM-1 (Upton et al. 2013). As seen in Table 3, the highest energy consumed was related to feed intake (forage and concentrate) whereby the feed intake energy was significantly higher in small farms (P˂0.0002). However, large and medium sized dairy farms had no significant difference (Table 3). More energy has been used in dairy farms in Qazvin province of Iran; the feed energy was calculated 135079.31 MJ cow-1 and accounts for 91.5% of the total energy consumed (Hosseinzadeh-Bandbafha et al. 2017). In two similar studies in Iran, feed energy consumption was 41549 MJ cow-1 (Sefeedpari, 2012) and 42931 MJ cow-1 (Soltanali et al. 2015) with feed consumption energy as 78% and 82% of total energy consumed, respectively. In Finland, 67-71% of the total energy input was related to feed consumption energy (Frorip et al. 2012). In fattening calf farms in Qazvin province of Iran, feed energy consumption was 75% of total energy consumed (Hosseinzadeh Bandbafha et al. 2017). All of these observations were consistent with the results of the current study, meaning that feed has the highest share of energy consumption in dairy farms. Management of feed intake and grouping of animals in dairy farms according to their production and offering the appropriate rations to animals, increases feed efficiency and saves the amount of feed consumed, which in turn saves energy consumption in dairy farms. The second most important input was fossil fuels, which was used in small farms with a significant difference compared to medium and large farms (P˂0.0014), but there was no significant difference between large and medium farms. The results of this study indicate that the number of cows has a direct impact on the consumption of inputs, including fossil fuels. In Qazvin province of Iran, fossil fuels energy was estimated with an average amount of 9405.23 MJ cow-1 and is the second most energy-consuming input after feed (Hosseinzadeh-Bandbafha et al. 2018). In Guilan province of Iran, this value for each head was 6799.67 MJ (Soltanali et al. 2015); also, in Tehran province of Iran it was calculated as much as 8656 MJ cow-1 (Sefeedpari, 2012). The results of these researches, in terms of fossil fuels consumption, are in agreement with the present study, highlighting the need for the optimum use of fossil fuels, and improving or replacing the amortized machinery, equipment, and heating systems on the farm. The lowest energy consumption was related to the input of the machinery, and equipment, which in small, medium and large farms were 0.0024, 0.0019 and 0.0012 MJ kg FPCM-1, respectively (Table 3). The low consumption of machinery in farms is due to the fact that they were calculated per kg FPCM and also the use of machinery in small farms did not differ significantly from other farms. The third most consumable input was electricity (Table 3). Similar to other inputs, this input is consumed greater in small farms compared to large farms (P˂0.0001). Applying the same logic for abovementioned inputs, the results of this study indicate that the share of water energy consumption in small dairy farms is significantly higher than that of medium and large dairy farms (P˂0.0001). The use of human labor in dairy farms is inevitable, even in fully mechanized farms, human labor is one of the main principles. As shown in Table 3, the human labor in small and medium farms is significantly higher than the larger farms. A similar trend of fossil fuels, electricity, and human labor use in the dairy farms was observed while as the number of animals is elevating, the amount of consumption is not ascending and is almost following a step up condition. The share of energy inputs for dairy farms is shown in Figure 1. The main energy output of dairy farms was milk production (ranging from 89.86% to 91.19%). Hosseinzadeh-Bandbafha et al. (2018) reported that 91.36% of energy output was related to milk; and, 5.62% and 3.02% were related to meat and manure, respectively. According to the results of the current study (Table 4), energy indices (energy ratio and energy productivity) were better in large dairy farms than in medium and small dairy farms (P˂0.0001). Energy indices can be improved by increasing the inputs efficiency, by reducing losses of inputs per cow or by optimizing inputs. In large farms, losses of inputs are lower than small farms. Energy productivity for small, medium and large units is calculated as 0.033, 0.477 and 0.068 kg MJ-1, respectively (P˂0.0001). The energy indices obtained in this study also indicates that the large farms had better performance in terms of usage and production. Hosseinzadeh-Bandbafha et al. (2018) reported that energy productivity of dairy farms in Qazvin province of Iran was 0.054 kg MJ-1. This amount corresponded to the large and medium farms of the current study in one direction. In another study, the energy productivity was reported to be 0.12 kg MJ-1 (Sefeedpari, 2012). The share of renewable and renewable energy and direct and indirect energy are shown in Figure 2. Non-renewable energy consumption was significantly higher in smaller dairy farms (P˂0.0001). Non-renewable energy consumption directly affects environmental pollution and includes fossil fuels and electricity.
Table 4 Energy indices in dairy farms of different sizes (Mean±SE)
The means within the same row with at least one common letter, do not have significant difference (P>0.05).
SE: standard error.
Figure 1 The share of energy inputs in dairy farms of different sizes
Figure 2 The share of energy inputs in dairy farms of different sizes as direct (DE), indirect (IDE), renewable (RE) and non-renewable (NRE) forms
In this study, non-renewable energy consumption was higher in small farms than large farms, which can be attributed to the high consumption of fossil fuels and electricity in these farms per kg FPCM. In other studies the share of non-renewable energy was reported to be 8.18% (Hosseinzadeh-Bandbafha et al. 2018) and 44.89% (Sefeedpari et al. 2012). In the present study, the consumption of more non-renewable energy in small and medium-sized farms was related to the inefficient use of fossil fuels for heating systems and machinery such as tractors and equipment, the amount of electricity used to ventilate the animal pens, and milking parlor, the technical performance of the milking machine and the milk cooling system.
The results of this study indicate that energy consumption per kg FPCM in large dairy farms is more efficient. Among the inputs fed to the system, energy of feed (forage, and concentrate) accounts for the highest share of energy consumption in dairy farms. The amount of non-renewable energy was lower in large dairy farms, which indicates the impact of herd size on energy consumption. According to the energy indices in small dairy farms, lower energy productivity was observed per kg FPCM. The reason for the higher consumption of inputs in small dairy farms was due to the lower losses occurred in large dairy farms because of better management. In fact, there are other factors in improving the energy consumption per kg FPCM, including farm management, genetic potential of cows, feed quality, grouping cows, feeding management, etc., which has not been included in this study.