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Energy and economic analysis of rice production under different farm levels in Guilan province of Iran
Energy 36 (2011) 5824e5831
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Energy and economic analysis of rice production under different farm levels in Guilan province of Iranq S.H. Pishgar-Komleh*, P. Sefeedpari, S. Rafiee Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 March 2011 Received in revised form 25 August 2011 Accepted 28 August 2011 Available online 17 September 2011
The study was carried out on energy requirement and energy inputeoutput relationship of rice production in Guilan province of Iran. Data were collected from 105 farmers with face-to-face questionnaire method. The research results revealed rice production consumed a total energy of 39333 MJ ha1 which fuel energy use was 46% followed by chemical fertilizer (36%), seed (8%) and biocide (6%), respectively. The share of direct, indirect, renewable and non-renewable energies was 49%, 51%, 11% and 89% respectively. The energy use efficiency and energy productivity were found as 1.53, 0.09 kg MJ1, respectively. The econometric model was developed using CobbeDouglas type function and results showed that fuel and machinery energy inputs contributed significantly to the yield. The results of sensitivity analysis of the energy inputs showed that the MPP value of fuel was the highest (0.93), followed by machinery (0.23), biocide (0.17) and seed (0.15) energy inputs. Economic analysis indicated that total cost of production was 3156 $ ha1. Gross and net return were 1642 $ ha1 and 940 $ ha1, respectively and the benefit-cost ratio was calculated 1.29. Mainly, large farms (more than 1 ha) had better management and were more successful in energy use and economic performance. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Rice Energy ratio Sensitivity analysis Economic analysis Cultivated area
1. Introduction Agriculture is a process of energy conversion, the conversion of solar energy into food, feed and fiber through photosynthesis [1]. Energy use in agricultural production has become more intensive due to the use of fossil fuel, chemical fertilizers, pesticides, machinery and electricity to provide substantial increases in food production. However, more intensive energy use has brought some important human health and environment problems so efficient use of inputs has become important in terms of sustainable agricultural production [2]. Energy requirements in agriculture are divided into two groups being direct and indirect. Direct energy is required to perform various tasks related to crop production processes such as land preparation, irrigation, intercultural, threshing, harvesting and transportation of agricultural inputs and farm produce [3]. It is seen that direct energy is directly used at farms and in the fields. Indirect energy, on the other hand, consists of the energy used in the manufacture, packaging and transport of
q Financial support by: Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. * Corresponding author. Tel.: þ98 261 2801011; fax: þ98 261 2808138. E-mail addresses: [email protected] (S.H. Pishgar-Komleh), paria. [email protected] (P. Sefeedpari), shahinrafi[email protected] (S. Rafiee). 0360-5442/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2011.08.044
fertilizer, pesticide, seed and farm machinery [4]. Energy use patterns and contribution of energy inputs vary depending on farming systems, crop season and farming conditions. Calculating energy inputs of agricultural production is more difficult than in the industry sector due to the high number of factors affecting the production [5]. Rice (Oryza sativa) is the hugely important food crop for the world’s population, especially in East, South, Southeast Asia, the Middle East, Latin America, and the West Indies. It is the grain with the second highest worldwide production, after maize (Zea mays) [6]. Food security, which is the condition of having enough food to provide adequate nutrition for a healthy life, is a critical issue in the developing world. About 3 billion people, nearly half of the world’s population, depend on rice for survival. In Asia as a whole, much of the population consumes rice in every meal. In many countries, rice accounts for more than 70% of human caloric intake. In Asia in total, just over 30% of all calories come from rice [7]. Rice cultivation is well-suited to countries and regions with low labor costs and high rainfall, as it is labor-intensive to cultivate and requires plenty of water. Rice can be grown practically anywhere (under various soil conditions (salt, alkali, peat) and different water and temperature regimes), even on a steep hill or mountain [8]. Rice is one of the most important crops in Iran. The annual production of rice in Iran was more than 2.2 Mt in 2008 [9]. The worldwide average yield of rice was 4.15 t ha1 in 2007. However
S.H. Pishgar-Komleh et al. / Energy 36 (2011) 5824e5831
Nomenclature d D2 DE ei GM(Xj) GM(Yi) IDE MPPxj N Nh NRE
Precisionðx XÞ d2/z2 Direct energy Error term Geometric mean of jth energy input (the 0 j0 th root product of 0 j0 energy inputs) Geometric mean of yield (the 0 i0 th root product of 0 i0 yields) Indirect energy Marginal physical productivity of jth input Required sample size; Number of holdings in target population Number of the population in the h stratification Non-renewable energy
rice average yield in Iran was almost 5.56 t ha1 [10]. Iran with amount of 1.7 Mt is the second rice importer country after Philippine [11]. Guilan province with 34.2% share of total rice crop production is among the main rice production areas in Iran. Rice production share of this province was 30% (0.65 Mt) in 2008 [9]. Therefore, efforts are immediately required to increase production of rice crop. Furthermore, in order to sustain agricultural production, effective energy use is required, since it provides ultimate financial saving, preservation of fossil resources and reduction of environment distortion [12]. It has been realized that crop yields and food supplies are directly linked to energy [1]. The main objective in agricultural production is to increase yield and decrease costs. In this respect, the energy budget is important. Energy budget is the numerical comparison of the relationship between inputs and output of a system in terms of energy units [13]. Numerous researches have been conducted on energy and economic analysis to determine the energy efficiency of different crop production practices in the developed countries [14e24]. However, very few researches have been published on energy and economic analysis of rice crop with respect to Iran. Khan et al. (2009) studied energy use pattern and the relationship between energy inputs of two regime of rice cultivation (Bullock Operated Farms (BOF) and Tractor Operated Farms (TOF)) in Dera Ismail Khan, district of Pakistan. Consumption of animate energy on BOF was more than TOF due to heavy use of animate energy in land preparation operation and outputeinput ratio on BOF (6.32) was higher than TOF (4.16) [25]. Gajaseni (1995) analyzed energy usage of transplanting and direct seeding systems of wetland rice systems in Thailand [26]. The outputeinput ratio was 4.5 for the transplanting system and 2.7 for the direct seeding system. Singh et al. (1994) studied the relationship between rice yield and energy inputs. The results revealed that there are quadratic relations between yield and pre-harvest energy input. Moreover the yield shows Robb’s parabolic relation with irrigation, fertilizer and total energy input [27]. Bockari-Gevao et al. (2005) analyzed energy consumption in lowland rice-based cropping system of Malaysia and found that the highest average operational energy consumption was for tillage (48.6% of the total operational energy consumption) and followed by harvesting (32.6%) and planting (15.7%). The energy ratio value calculated 8.86 for rice production [28]. Bautista et al. (2010) studied the energy balance for different rice production systems in Philippines. Their research results indicated the energy ratio value of 9.0 and 7.5 for farms were irrigated by canal and pump facility. In all farms fertilizer (nitrogen), fuel and seed inputs had the highest energy use [29]. The results of
RE s S2h X1 X2 X3 X4 X5 X6 Yi z
ai aj bi gi
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Renewable energy Standard deviation Variance of h stratification Machinery energy Human labor energy Fuel energy Chemical fertilizer energy Biocide energy Seed energy Yield level of the ith farmer Reliability coefficient (1.96 in the case of 95% reliability) Coefficients of the exogenous variables Regression coefficient of jth input Coefficients of the exogenous variables Coefficients of the exogenous variables
energy consumption for production of five major crops (rice, maize, sugarcane, cassava and soybean) showed total energy input for rice production varied between 1790e18,490 MJ ha1. Chemical fertilizer (nitrogen) had the highest energy usage. Energy ratio was 4.0 and 2.8 for irrigated and rain fed rice [30]. A study was carried out Iqbal (2008) to find the energy inputs requirement for production of rice in different categories of farms in Bangladesh. The results indicated small farms (0.61e1.00 ha) had the highest energy use efficiency (4.14) in comparison with other groups [31]. Eskandari et al. (2011) considered the energy consuming process and factors influencing rice production in semi-mechanized and traditional systems in Mazandaran province of Iran. They found irrigation and fertilizer energy are the most energy consumers in rice production. Energy use efficiency calculated 3.00 and 3.08 for traditional and semi-mechanized farms due to better input energy management in semi-mechanized farms [8]. The results of Pathak et al. (1985) study on energy use pattern and potential for energy saving in rice-wheat cultivation revealed rice consumed more energy in comparison with wheat production [32]. The aim of this research was to determine the effect of farm size levels on energy use efficiency per hectare for the production of rice, study the energy balance and do economical analysis. Furthermore, this study reveals the relationship between energy inputs and yield by developing mathematical models based on rice farms of Guilan province, Iran. 2. Materials and methods 2.1. Study and location The study was conducted in Langroud city (37.167 N and 50.15 E) of Guilan province, Iran in 2009e2010 production year. Langroud is located in north of Iran on the south cost of Caspian Sea, 21 m above sea levels. The annual average rainfall is almost 1100 mm. The highest and lowest temperature is 33 and 0 Celsius for summer and winter respectively. The soil analysis showed the structure of soil is clay and clay loam. Guilan province was selected for this research because of its high rice cultivated area (34% of country area) [9]. 2.2. Experimental details and sampling procedure The data used in this study were based on cross sectional and data were collected from 105 farmers growing single rice by using a face-to-face questionnaire. The average size of the studied farms
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was 0.6 ha. The sample size was determined using a stratified random sampling technique [33]
P ð Nh Sh Þ2 n ¼ P N 2 D2 þ Nh S2h
(1)
where n is the required sample size; N is the number of total holdings in the target population; Nh is the number of the population in the h stratification; Sh is the standard deviation in the h stratification, S2h is the variance in the h stratification, D2 is equal to d2 =z2 ; d is the precision, where ðx XÞ (5%) is the permissible error and z is the reliability coefficient (1.96, which represents 95% reliability). 2.3. Methodology of energy budgeting The inputs and the energy requirements of each input for rice production were collected, determined and presented for every questionnaire as per the socio-economic structures of the farms. General inputs in rice production were machinery, human labor, chemical fertilizers, fuel, biocide and seed. Output was rice (grain) as a product. The energy equivalent of different inputs and output (Table 1) were used to estimate the energy values. The human energy as an energy input was calculated by multiplying the number of man-hours (h ha1) by estimated power rating of human labor (MJ h1) from Table 1. Energy used of machinery was estimated by [34].
ME ¼ E G T
(2)
Where ME is the machinery energy (MJ), E the production energy of machine (Table 1), G the mass of machine (kg), and T is the economic life of machine (year). Other inputs like fuel, seed, biocide and chemical fertilizers used in rice production were transformed to energy value (MJ ha1) by multiplying the quantity of the material used in the farms by the energy equivalent of each material. For example chemical fertilizer
Table 1 Energy equivalents of inputs and output in rice production. Inputs (unit) A. Inputs 1. Machinery Tractor and self-propelled (kg yra) Stationary equipment (kg yra) Implement and machinery (kg yra) 2. Human Labor Male (h) Female (h) 3. Fuel Diesel (L) Gasoline (L) Natural gas (m3) Electricity (kW h) 4. Chemical Fertilizer N (kg) P2O5 (kg) K2O (kg) 5. Biocide Insecticide (kg) Herbicide (kg) Fungicide (kg) 6. Seed (kg) B. Output 1. Rice (kg) a
Economic life of machine (year).
Energy equivalent (MJ unit1)
Reference
9e10 8e10 6e8
[34] [34] [34]
1.96 1.57
[27] [27]
47.8 46.3 49.5 12
[34] [34] [34] [34]
78.1 17.4 13.7
[34] [34] [34]
229 85 115 14.7
[34] [34] [34] [18,27]
17
[34]
(nitrogen) energy consumption calculated by multiplying the amount of nitrogen usage (kg ha1) by energy coefficient of nitrogen fertilizer production (78.1 MJ kg1 from Table 1); so the result is the energy consumption of nitrogen fertilizer (MJ ha1) in rice production. Also, other energy inputs can be estimated hereby. Because of using gasoline pumps to prepare irrigation water, irrigation energy was displayed as gasoline energy. The amount of output energy (MJ ha1) estimated by multiplying the rice yield (kg ha1) by rice energy equivalent (MJ kg1). The total energy input is also classified into direct and indirect and renewable and non-renewable forms. The direct energy (DE) includes human labor, diesel fuel, gasoline, electricity and natural gas energy that are used in the production process and indirect energy (IDE) consists of machinery, chemical fertilizer, seed and biocide energy. On the other hand, renewable energy (RE) consists of human labor and seed and non-renewable energy (NRE) inclusive machinery, diesel fuel, gasoline, natural gas, electricity, biocide and chemical fertilizer [35]. Renewable energy used to describe energy sources that are replenished by natural processes on a sufficiently rapid time-scale. So they can be used by humans more or less indefinitely, provided the quantity taken per unit of time is not too great. On the other hand non-renewable energy used to describe energy sources that exist in a limited amount on Earth [36]. The energy ratio (energy use efficiency), energy productivity, specific energy and net energy were calculated as per given below in (Eq. (3)e(6)) [12].
Energy Output MJ ha1 Energy Ratio ¼ Energy Input MJ ha1 Rice Output kg ha1 Energy Productivity ¼ Energy Input MJ ha1 Energy Input MJ ha1 Specific Energy ¼ Rice Output kg ha1 Net Energy ¼ Energy Output MJ ha1 Energy Input MJ ha1
(3)
(4)
(5)
(6)
In order to have a better analysis of energy consumption pattern, the whole energy inputs, output and indices (energy ratio, energy productivity, specific energy and net energy) were calculated for different rice farm sizes. For this purpose farms were classified (due to the frequency of farm size in the sample population) in three categories as small (<0.5 ha), medium (between 0.5 and 1 ha) and large farms (>1 ha). 2.4. Analysis of energy with mathematical models The different mathematical functions such as linear, linearlogarithmic, logarithmic-linear and second degree polynomial were tested to find and analyze the relationship between energy inputs and yield. CobbeDouglas function yielded better estimates in terms of statistical significance and expected signs of parameters among other functions. CobbeDouglas function has been used by several authors to examine the relationship between energy inputs and yield [35,37]. CobbeDouglas production function is expressed as;
S.H. Pishgar-Komleh et al. / Energy 36 (2011) 5824e5831
Y ¼ f ðxÞexp ðuÞ
(7)
Eq. (7) can be linearized and expressed in the following form;
ln Yi ¼ ln b0 þ
n X
bj ln Xij þ ei i ¼ 1; 2; .; n
(8)
j¼1
where Yi denotes the yield of the ith farmer, Xij the vector of inputs used in the production process, ln b0 the constant term, bj represent coefficients of inputs which are estimated from the model and ei is the error term. In this study with assumption that, when the energy input is zero, the crop production is also zero, Eq. (8) becomes shorter to Eq. (9):
ln Yi ¼
n X
bj ln Xij þ ei i ¼ 1; 2; .; n
(9)
j¼1
With assumptions that yield is a function of inputs energy, Eq. (9) can be expanded to Eq. (10);
ln Yi ¼ b1 ln ðX1 Þ þ b2 ln ðX2 Þ þ b3 ln ðX3 Þ þ b4 ln ðX4 Þ þ b5 ln ðX5 Þ þ b6 ln ðX6 Þ þ ei
(10)
Where, X1, X2, X3, X4, X5, and X6 are machinery, fuel, human labor, chemical fertilizer, biocide and seed energy respectively. In addition to influence of each energy inputs on rice yield, CobbeDouglas function was utilized to evaluate the impact of direct, indirect, renewable and non-renewable forms of energy on rice yield as a following forms;
ln Yi ¼ g1 ln ðDEÞ þ g2 ln ðIDEÞ þ ei
(11)
ln Yi ¼ d1 ln ðREÞ þ d2 ln ðNREÞ þ ei
(12)
where Yi denotes the yield of the ith farmer, DE, IDE, RE and NRE are direct, indirect, renewable and non-renewable energy that used for rice production respectively, gi and di are the coefficients of variables and ei is the error term. Eqs. (10)e(12) were estimated using ordinary least square (OLS) technique. Sensitivity analysis studies how the variation in model outputs can be due to different sources of variation (the change in the quantity of total physical product resulting from a unit change in a variable input, keeping all other inputs unchanged) [38]. To analyze the sensitivity of energy inputs on rice yield, the marginal physical productivity (MPP) value, based on the response coefficients of inputs was determined. Marginal physical productivity, usually abbreviated MPP, is found by dividing the change in total physical product by the change in the variable input. Marginal physical productivity, which more often goes by the name marginal productivity (MP), is one of two measures derived from total physical product. In other words, MPP factor express the changes of output with a unit change of input while other inputs are fixed in their geometric mean value. Positive value of MPP indicates with an increase in input value, output value will increase and negative value of MPP indicates with increasing in input value, output value will decrease. To calculate MPP value of each input aij was utilized as follow [39,40]:
MPPxj ¼
change in total physical product GMðYÞ aij ¼ change in variable input GM Xij (13)
Where MPPxj is marginal physical productivity of jth input, aij regression coefficient of jth input, GM(Y) geometric mean of crop yield and GM(Xij) geometric mean of jth energy input.
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In the last part of this study economic analysis was done, therefore the net return, gross profit and benefit to cost ratio were calculated for rice production in research area [41]:
Total production value ¼ Rice yield kg ha1 Rice price $ kg1
(14)
Gross return ¼ Total production value $ ha1 Variable production cost $ ha1
(15)
Net return ¼ Total production value $ ha1 Total production cost $ ha1
(16)
Total production value $ ha1 Benefit cost ratio ¼ Total production cost $ ha1 Rice yield kg ha1 Productivity ¼ Total production cost $ ha1
(17)
(18)
The ANOVA test and Duncan compare mean were utilized to analyze the differences between all values in three different farm sizes. To calculate the energy values and compare the values in different farm sizes, all data from rice farms were entered into Excel 2010, SPSS 19 and to study the relationship of energy inputs and crop yield Shazam 9.0 software was applied.
3. Results and discussion 3.1. Input-output energy analysis Table 2 displays the average energy consumption of each input and output for rice production in three groups of farm size. Total average energy input and output were calculated 39,333 and 60,341 MJ ha1. The results of Freedman (1980) study indicated, for all methods of rice production, there is a greater energy output of food produced compared to cultural energy used for production [42]. The results showed that the average total energy consumption for rice production in Guilan province was 39,333 MJ ha1 which is higher than other researches, due to high energy consumption in fuel and chemical fertilizer energy inputs [28,29]. The highest energy use in rice production belonged to fuel energy input (consist of diesel, gasoline, electricity and natural gas) which accounted about 46% of total energy consumption. The first rank in fuel category energy (Table 2) belongs to gasoline energy consumption (18%) and after that the diesel (11%), natural gas (11%) and electricity (6%) were in next ranks. The gasoline energy was consumed for irrigation while the diesel was mainly used for tractors and various machinery operations. Whereas, large amount of water was pumped in rice production and because of low price of fuel in result of government subsidies, high consumption of fuel energy was observed. The natural gas and electricity energy were mainly utilized by post-harvest operations such as rice cleaning, drying, milling and sifting. Fuel input plays an important role in total energy consumption for rice production [8,28,29]. Fifty years ago muscles power were just used in rice production but at the present time, human labor and animal power were replaced with heavy
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Table 2 Amounts of energy inputs and output in rice production. Inputs
Farm size groups (ha)
Total energy (MJ ha1)
Percentage
1.01 0.73 0.28 3.34 45.95 10.82 18.66 10.66 5.81 35.76 6.07 1.79 3.82 0.46 7.87 100
Small (<0.5)
Medium (0.5e1)
Large (>1)
A. Inputs 1. Machinery Tractor or Tiller Implement and Vehicle 2. Human Labor 3. Fuel Diesel Gasoline Natural gas Electricity 4. Chemical Fertilizer 5. Biocide Insecticide Herbicide Fungicide 6. Seed Total energy input
405.65a 295.19a 110.46 1317.9 18075.7 4257.47 7340.67 4192.08 2285.48 13211.4a 2406.11 712.71 1509.5 183.9 3112.1 41139.8a
398.6a 290.31a 108.29 1313.8 18075.09 4256.66 7340.35 4190.27 2287.81 14012.1b 2389.42 706.51 1500.41 182.5 3104.2 40432.8b
382.43b 275.83b 106.6 1312.7 18068.01 4249.55 7340.25 4191.49 2286.72 14976.2c 2366.07 691.91 1497.62 176.54 3073.38 36427.5c
395.56 287.11 108.45 1314.81 18072.93 4254.56 7340.42 4191.28 2286.67 14066.30 2387.2 703.71 1502.51 180.98 3096.56 39333.36
B. Output 1. Rice
59440.5
59671.9
61913.4
60341.90
Note: Different letters (a, b, c) show significant difference of means at 5% level.
machineries and equipment that consume high amounts of energy [26]. Old machineries and equipment are the reasons for high fuel energy consumption in rice production in Iran. Applying new machineries and irrigation pumps with more energy efficiency decrease the amount of energy usage. Fuel energy was followed by the chemical fertilizer (including N, P2O5 and K2O) energy with the share of 36% of total energy inputs. The result was similar to several researches [8,25,26,28,29,32,42] where chemical fertilizer had high consumption in rice production. There are two considerable reasons for the high chemical fertilizer consumption: farmer’s poor knowledge and subsidies price. With the lack of knowledge, most Iranian farmers don’t know the required amount of chemical fertilizer for different crops and it became a common belief between them that excessive use of chemical fertilizer will increase the yield [43]. Moreover, the government subsidies price had significant effect on fertilizers use. As a result of using chemical fertilizer inefficiently (more than plant need), soil and water pollution would be followed. According to Singh et al. (1998) research results, energy used in the production of chemical fertilizer accounts for about 40% of total energy used in agricultural production in developed countries [37]. The share of seed energy consumption was calculated about 8% of all energy inputs in rice
production. The amount of seed energy use can be reduced by applying fewer amounts of seed per hectare. The required quantity of seed will be reduced by using high quality seed. Moreover, qualified seed will help to reduce the chances of pest and weed infestation, reduce the energy needed in weeding and chemical application and increase yield. The wastage of energy in the form of seed could be reduced considerably by using good quality seeds [43]. In this study insecticide, herbicide and fungicide were utilized with share of 6%. Herbicide had the highest consumption value between biocides (4%) and followed by insecticide and fungicide (2% and 1% respectively). As shown in Table 2, machinery was the least demanding energy input among the whole specified inputs in rice production. Due to low mechanization level in rice production, machinery energy consumption was 396 MJ ha1 (1%) and human power was accounted for 3% of total input energy. The comparison of energy inputs consumption based on farm size showed that farmers with more than 1 ha cultivated area used the least amount of machinery energy (382 MJ ha1) significantly by using wider machinery and implement and less turning around. Nassiri et al. (2009) research concluded that larger farms required using proper machine size and suitable tractors by considering farm size [44]. The results (Table 2) revealed that in large farms, the
Table 3 Energy forms and indices in rice production. Item
Energy ratio Energy productivity Specific energy Net energy Direct energye Indirect energyf Renewable energyg Non-renewable energyh Total energy
Unit
e kg MJ1 MJ kg1 MJ ha1 MJ ha1 MJ ha1 MJ ha1 MJ ha1 MJ ha1
Average (MJ ha1)
Farm size groups (ha) Small (<0.5)
Medium (0.5e1)
Large (>1)
1.44a 0.08 11.76 18300a 19746a 20801a 4504a 35243a 41140a
1.47a 0.09 11.52 19239a 19275b 19725b 4350b 35500a 40433b
1.69c 0.1 10.0 25485b 19143c 19312b 4379b 34023b 36428c
Note: Different letters (a, b, c) show significant difference of means at 5% level. e Include human labor and fuel. f Include machinery, seed, chemical fertilizer and biocide. g Include seed and human labor. h Include machinery, chemical fertilizer, fuel and biocide.
1.53 0.09 11.09 21008 19388 19946 4411 34922 39333
Percentage (%)
49.3 50.7 11.22 88.78
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Table 4 Econometric estimation results. Independent variable
Coefficient
Model 1: ln Yi ¼ b1 ln ðX1 Þ þ b2 ln ðX2 Þ þ b3 ln ðX3 Þ þ b4 ln ðX4 Þ þ b5 ln ðX5 Þ þ b6 ln ðX6 Þ þ ei 1. Machinery 0.12 2. Fuel 0.83 3. Human labor 0.03 4. Chemical fertilizer 0.07 5. Biocide 0.12 6. Seed 0.11 Durbin Watson 1.97 2 0.99 R a b c
MPP
4.79a 21.56a 0.46 1.56 1.72c 2.39b
0.23 0.93 0.05 0.08 0.17 0.15
significant at 1% level. significant at 5% level. significant at 10% level.
amount of seed usage was least and as the farm size increases the yield of rice increases, as well but not significantly. Also, less chemical fertilizer was used in small farms (Table 2) indicating that higher level of management of chemical fertilizers were practiced in small farms. In the study carried out by Chauhan et al. (2006) in paddy production, it was concluded that potentially good operating practices include utilization of chemical fertilizer, human labor and seed sources lead to efficient production [43]. Therefore, inefficient farmers should learn the utilization way of these sources from the efficient ones. Totally it was revealed improved management in large rice farms (more than 1 ha) made considerable reduction in energy expenditure however, led to more amount of output energy (rice yield) in comparison with other levels of farms. It was similar to Iqbal (2008) research’s results that showed large farms (0.61e1.00 and more than 2 ha) had better use of energy management in rice production [31]. Table 3 shows energy indices of rice production and the forms of energy inputs as direct and indirect energy and renewable and nonrenewable energy for three sizes of farms. Energy ratio is one of the best energy indices that shows the efficient use of energy in rice production. The results indicated the average energy ratio of 1.53. Because of entering post-harvest operations (cleaning, drying, milling and sifting) in energy calculations the energy ratio estimate was lower than the other researcher’s reports (2.8 (rainfed rice) [30], 3 (traditional production method) [8], 3.08 (semi-mechanized production method) [8], 3.9 (irrigated rice) [31], 4 (irrigated rice) [30], 5.22 (irrigated rice) [25], 6.7 (irrigated rice) [45]). Lu et al. in their study showed that the long-term rice system had the highest energy output/input ratio (2.26) which was 8.69 and 1.95 times that of the long-term vegetable and rotation systems [46]. Eskandari et al. (2011) in their research found better use efficiency value for semimechanized in comparison with traditional system (energy ratio value of 3.08 and 3 respectively) [8]. Energy productivity, specific energy and net energy of rice production were calculated as Table 5 Econometric estimation results of direct, indirect, renewable and non-renewable energies. Independent variable
Coefficient
Model 2: ln Yi ¼ g1 ln ðDEÞ þ g2 ln ðIDEÞ þ ei 1. Direct 0.40 2. Indirect 0.14 Durbin Watson 1.74 R2 0.99 Model 3: ln Yi ¼ d1 ln ðREÞ þ d2 ln ðNREÞ þ ei 1. Renewable 0.02 2. Non-renewable 0.35 Durbin Watson 2.05 0.99 R2 a
t-Ratio
significant at 1% level.
t-Ratio 7.08a 4.37a
0.79 14.66a
MPP 0.45 0.15
0.03 0.36
0.09 kg MJ1, 11.09 MJ kg1 and 21,008 MJ ha1, respectively. Better management and using less energy input and producing more energy output (more yield) are two methods to reach higher energy ratio value. The amounts of direct and indirect were calculated 19,388 MJ ha1 (49%) and 19,946 MJ ha1 (51%), respectively. The share of renewable energy was 11% (4411 MJ ha1) while that of non-renewable form was 89% (34,922 MJ ha1). It is clear from Table 3 that in comparison with renewable energy, the portion of non-renewable energy is higher, so rice production is mostly depending on non-renewable energy sources (such as fossil fuels). Several researchers have found similar results that the portion of non-renewable form of energy was higher than that of renewable forms [47,48]. As farm size increases, energy ratio increases accordingly, reaching to 1.66 in the largest farm size group significantly (P < 0.05). As seen in Table 3, large farmers had better energy indices (energy ratio, energy productivity, specific energy and net energy) that it can be referred to the better management of large scale farms. The effect of rice farm size on energy indices of rice production in research area was similar to Iqbal (2008) results. They clarified small (0.61e1.00 ha) and large (2 ha) farms had better energy use efficiency value in contrast with landless (0.20 ha), marginal (0.21e0.61 ha) and medium (1e2 ha) farms [31]. 3.2. Econometric estimation and sensitivity analysis of rice production Regression results for model 1 showed the significant impact of machinery and fuel energy on rice yield at level of 1% (Table 4). In Table 6 Economic analysis of rice production. Cost and return components
Unit
Yield Sale price Gross value of production Variable cost of production Fixed cost of production Total cost of production Total cost of production Gross return Net return Benefit to cost ratio Productivity
Farm size groups (ha)
Average value
Small (<0.5)
Medium (0.5e1)
Large (>1)
kg ha1 $ kg1 $ ha1
3496.50 1.15 4052.98
3510.11 1.15 4057.63
3641.96 1.15 4176.2
3550 1.15 4095.60
$ ha1
2385.06
2432.51
2543.28
2453.62
1
702.27
702.27
702.27
702.27
$ ha1
3245.55
3134.78
3087.33
3155.89
$ kg1
0.93
0.89
0.85
0.89
1
1667.92 807.43 1.25 1.08
1625.12 922.85 1.29 1.12
1632.92 1088.87 1.35 1.18
1641.98 939.71 1.29 1.12
$ ha
$ ha $ ha1 e kg $1
5830
S.H. Pishgar-Komleh et al. / Energy 36 (2011) 5824e5831
addition, seed and biocide had significant impact at 5% and 10% probability level respectively. Other inputs such as human labor and chemical fertilizer had no significant impact on rice yield. Of all inputs, fuel had the highest impact (0.83) and followed by machinery (0.12) and biocide (0.12) energy inputs. The regression results (model 1) expressed with 10% increasing in fuel, machinery and biocide energy, rice yield will increase 8.3%, 1.2% and 1.2% respectively. The results of MPP values indicated 1 MJ increasing in fuel and machinery energy led to 0.93 and 0.23 kg ha1 increasing in yield of rice production respectively. To validate Model 1, Durbin Watson test was performed [16]. Analysis for model 1 resulted 1.97 for Durbin Watson value, i.e. there was no autocorrelation in the estimated model (significant level of 5%). The model’s coefficient of determination calculated 0.99 (R2 ¼ 0.99). To realize the relationship between rice yield and forms of energy (direct and indirect), regression analysis (model 2) was utilized. The results are given in Table 5. It became evident that the impact of direct and indirect energy on rice yield was significant at 1% level with coefficient values of 0.40 and 0.14 for direct and indirect energies respectively. The regression coefficients of renewable and non-renewable energies on yield were investigated (model 3). As it can be seen in Table 5, the impact of renewable and non-renewable was 0.02 and 0.35 respectively. Only nonrenewable form was significant (at 1% probability level). The MPP values specified, consuming more (1 MJ) non-renewable, direct and indirect energy lead to more (0.36, 0.45 and 0.15 kg ha1) rice yield while using more (1 MJ) renewable energy will decrease the rice yield (0.03 kg ha1). Durbin Watson values for model 2 and 3 were 1.74 and 2.05, respectively (significant at 5% probability level). In addition, the model’s coefficient of determination was same (0.99) for models 2 and 3. 3.3. The economic analysis of rice production The cost of each input and the market price of rice (two market price for two cultivar), in research region were used to calculate the cost and return components of rice production. As it is represented in Table 6, variable and fixed cost were 2453.62 $ ha1 and 702.27 $ ha1 with share of 78% and 22% of total cost, respectively. By multiplying sale price by rice yield, the gross value calculated (4095.6 $ ha1). Total cost of production was 3155.89 $ ha1 and 0.9 $ kg1. Gross and net return were 1641.98 $ ha1 and 939.71 $ ha1 respectively. The benefit-cost ratio of rice production was calculated to be 1.3 that was lower than khan et al. (2009) research results of benefit-cost ratio (2.7) [25]. The economic research in other crops revealed the benefit to cost ratio value of 1.10 [17], 1.43 [49], 0.86 [2], 1.17 [50] and 1.57 [51] for soybean, wheat, cotton, sugar beet and greenhouse tomato respectively. At the end of economic analysis of rice production, the economic productivity was calculated 1.12 kg $1. The effect of rice farm size on the benefit-cost ratio revealed that large farms (>1 ha) had better values because of better management in consumption of energy inputs. 4. Conclusion Based on the results, following conclusions are drawn: 1. Energy inputs and output of rice production calculated to be 39,333 MJ ha1 and 60,341 MJ ha1. Chemical fertilizer was the biggest energy consumer (36% of total energy usage) and followed by gasoline (19%), diesel fuel (11%), natural gas (11%) and seed energy (8%). Machinery was discovered as the least demanding energy input in all inputs (1%). Energy ratio, energy productivity, specific energy and net energy were 1.53,
0.09 kg MJ1, 11.20 MJ kg1 and 21,008 MJ ha1 respectively. Direct, indirect, renewable and non-renewable forms of energy were 19,388 MJ ha1 (49%), 19,946 MJ ha1 (51%), 4411 MJ ha1 (11%) and 34,922 MJ ha1 (89%), respectively. 2. Regression coefficient values for machinery, fuel, labor, fertilizer, biocide and seed were 0.12, 0.83, 0.03, 0.07, 0.12 and 0.11, respectively. Fuel, machinery and biocide MPP values calculated to be of 0.93, 0.23 and 0.17 respectively. The impact of direct (0.40), indirect (0.14) and non-renewable (0.35) energy was significant at 1% level while renewable energy had no significant impact (0.02) on rice yield. 3. The average value of total cost of production, gross return, net return, benefit-cost ratio and productivity of rice production calculated to be 3156 $ ha1, 1642 $ ha1, 940 $ ha1, 1.29 and 1.12 kg $1 respectively. 4. The effect of rice farm size studied and the results revealed large farms (<1 ha) have better efficiency for rice energy and economic analysis in the research area. At the end of this study, recommended that with fewer government subsidies, teaching farmers about the less chemical fertilizer consumption and increasing cultivated area, the high energy consumption of chemical fertilizer and fuel (diesel, gasoline, natural gas and electricity) energy will be controlled, hence this act leads to improvement in energy indices in rice farming. Energy management should be considered as an important field in terms of efficient, sustainable and economical use of energy. It is essential to use modern technologies and new machineries for post-harvest operations to decrease high amount of energy usage in rice production. High energy use in rice production is detrimental to the environment due to mainly excess input use. According to the results investigated for the effects of farm size on energy inputs consumption, economic analysis and small farm sizes in rice planting pattern, it is recommended to plant in larger farms in order to lower total energy input and vice versa higher total energy output. To reduce the amount of production cost it is recommended to substitute the obsolete and aged machinery and tractors with the new ones so that the production cost decreases by reducing the maintenance and timeliness costs. Also, using new machines cut costs of fuel consumption; hence it leads to efficient use of diesel fuel energy. References [1] Stout BA. Handbook of energy for world agriculture. London: Elsevier Applied Science; 1990. p. 21. [2] Yilmaz I, Akcaoz H, Ozkan B. An analysis of energy use and input costs for cotton production in Turkey. Renewable Energy 2005;30:145e55. [3] Singh JM. On farm energy use pattern in different cropping systems in Haryana, India. Germany: International Institute of Management University of Flensburg. Sustainable Energy Systems and Management; 2002. [4] Kennedy S. Energy use in American agriculture. Sustainable energy. Term Paper. Available from, http://web.mit.edu/10.391J/www/proceedings/ Agriculture_Kennedy2000.pdf; 2000. [5] Yaldiz O, Ozturk H, Zeren Y, Bascetincelik A. Energy usage in production of field crops in Turkey. In: Proceedings of 5th International Congress on Mechanization and Energy Use in Agriculture; 1993 Oct 11e14; Kusadasi, Turkey. [6] Food and Agriculture Organization (FAO) of the United Nation Statistics. Retrieved 2006-12-26. Available from: http://www.faostat.fao.org. [7] Anonymous. Rice genome landscape [Internet]. Australia: a free public resource for patent system navigation worldwide. Available from, http:// www.patentlens.net/daisy/RiceGenome/3649/3591.html; 2011. [8] Eskandari Cherati F, Bahrami H, Asakereh A. Energy survey of mechanized and traditional rice production system in Mazandaran Province of Iran. African Journal of Agricultural Research 2011;6(11):2565e70. [9] Anonymous. Annual agricultural statistics. Available from. Ministry of Jihad-eAgriculture of Iran, http://www.maj.ir; 2009. [10] Food and Agriculture Organization (FAO). 2008. Available from: http://www. fao.org.
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Energy and economic analysis of rice production under different farm levels in Guilan province of Iranq S.H. Pishgar-Komleh*, P. Sefeedpari, S. Rafiee Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 March 2011 Received in revised form 25 August 2011 Accepted 28 August 2011 Available online 17 September 2011
The study was carried out on energy requirement and energy inputeoutput relationship of rice production in Guilan province of Iran. Data were collected from 105 farmers with face-to-face questionnaire method. The research results revealed rice production consumed a total energy of 39333 MJ ha1 which fuel energy use was 46% followed by chemical fertilizer (36%), seed (8%) and biocide (6%), respectively. The share of direct, indirect, renewable and non-renewable energies was 49%, 51%, 11% and 89% respectively. The energy use efficiency and energy productivity were found as 1.53, 0.09 kg MJ1, respectively. The econometric model was developed using CobbeDouglas type function and results showed that fuel and machinery energy inputs contributed significantly to the yield. The results of sensitivity analysis of the energy inputs showed that the MPP value of fuel was the highest (0.93), followed by machinery (0.23), biocide (0.17) and seed (0.15) energy inputs. Economic analysis indicated that total cost of production was 3156 $ ha1. Gross and net return were 1642 $ ha1 and 940 $ ha1, respectively and the benefit-cost ratio was calculated 1.29. Mainly, large farms (more than 1 ha) had better management and were more successful in energy use and economic performance. Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: Rice Energy ratio Sensitivity analysis Economic analysis Cultivated area
1. Introduction Agriculture is a process of energy conversion, the conversion of solar energy into food, feed and fiber through photosynthesis [1]. Energy use in agricultural production has become more intensive due to the use of fossil fuel, chemical fertilizers, pesticides, machinery and electricity to provide substantial increases in food production. However, more intensive energy use has brought some important human health and environment problems so efficient use of inputs has become important in terms of sustainable agricultural production [2]. Energy requirements in agriculture are divided into two groups being direct and indirect. Direct energy is required to perform various tasks related to crop production processes such as land preparation, irrigation, intercultural, threshing, harvesting and transportation of agricultural inputs and farm produce [3]. It is seen that direct energy is directly used at farms and in the fields. Indirect energy, on the other hand, consists of the energy used in the manufacture, packaging and transport of
q Financial support by: Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran. * Corresponding author. Tel.: þ98 261 2801011; fax: þ98 261 2808138. E-mail addresses: [email protected] (S.H. Pishgar-Komleh), paria. [email protected] (P. Sefeedpari), shahinrafi[email protected] (S. Rafiee). 0360-5442/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2011.08.044
fertilizer, pesticide, seed and farm machinery [4]. Energy use patterns and contribution of energy inputs vary depending on farming systems, crop season and farming conditions. Calculating energy inputs of agricultural production is more difficult than in the industry sector due to the high number of factors affecting the production [5]. Rice (Oryza sativa) is the hugely important food crop for the world’s population, especially in East, South, Southeast Asia, the Middle East, Latin America, and the West Indies. It is the grain with the second highest worldwide production, after maize (Zea mays) [6]. Food security, which is the condition of having enough food to provide adequate nutrition for a healthy life, is a critical issue in the developing world. About 3 billion people, nearly half of the world’s population, depend on rice for survival. In Asia as a whole, much of the population consumes rice in every meal. In many countries, rice accounts for more than 70% of human caloric intake. In Asia in total, just over 30% of all calories come from rice [7]. Rice cultivation is well-suited to countries and regions with low labor costs and high rainfall, as it is labor-intensive to cultivate and requires plenty of water. Rice can be grown practically anywhere (under various soil conditions (salt, alkali, peat) and different water and temperature regimes), even on a steep hill or mountain [8]. Rice is one of the most important crops in Iran. The annual production of rice in Iran was more than 2.2 Mt in 2008 [9]. The worldwide average yield of rice was 4.15 t ha1 in 2007. However
S.H. Pishgar-Komleh et al. / Energy 36 (2011) 5824e5831
Nomenclature d D2 DE ei GM(Xj) GM(Yi) IDE MPPxj N Nh NRE
Precisionðx XÞ d2/z2 Direct energy Error term Geometric mean of jth energy input (the 0 j0 th root product of 0 j0 energy inputs) Geometric mean of yield (the 0 i0 th root product of 0 i0 yields) Indirect energy Marginal physical productivity of jth input Required sample size; Number of holdings in target population Number of the population in the h stratification Non-renewable energy
rice average yield in Iran was almost 5.56 t ha1 [10]. Iran with amount of 1.7 Mt is the second rice importer country after Philippine [11]. Guilan province with 34.2% share of total rice crop production is among the main rice production areas in Iran. Rice production share of this province was 30% (0.65 Mt) in 2008 [9]. Therefore, efforts are immediately required to increase production of rice crop. Furthermore, in order to sustain agricultural production, effective energy use is required, since it provides ultimate financial saving, preservation of fossil resources and reduction of environment distortion [12]. It has been realized that crop yields and food supplies are directly linked to energy [1]. The main objective in agricultural production is to increase yield and decrease costs. In this respect, the energy budget is important. Energy budget is the numerical comparison of the relationship between inputs and output of a system in terms of energy units [13]. Numerous researches have been conducted on energy and economic analysis to determine the energy efficiency of different crop production practices in the developed countries [14e24]. However, very few researches have been published on energy and economic analysis of rice crop with respect to Iran. Khan et al. (2009) studied energy use pattern and the relationship between energy inputs of two regime of rice cultivation (Bullock Operated Farms (BOF) and Tractor Operated Farms (TOF)) in Dera Ismail Khan, district of Pakistan. Consumption of animate energy on BOF was more than TOF due to heavy use of animate energy in land preparation operation and outputeinput ratio on BOF (6.32) was higher than TOF (4.16) [25]. Gajaseni (1995) analyzed energy usage of transplanting and direct seeding systems of wetland rice systems in Thailand [26]. The outputeinput ratio was 4.5 for the transplanting system and 2.7 for the direct seeding system. Singh et al. (1994) studied the relationship between rice yield and energy inputs. The results revealed that there are quadratic relations between yield and pre-harvest energy input. Moreover the yield shows Robb’s parabolic relation with irrigation, fertilizer and total energy input [27]. Bockari-Gevao et al. (2005) analyzed energy consumption in lowland rice-based cropping system of Malaysia and found that the highest average operational energy consumption was for tillage (48.6% of the total operational energy consumption) and followed by harvesting (32.6%) and planting (15.7%). The energy ratio value calculated 8.86 for rice production [28]. Bautista et al. (2010) studied the energy balance for different rice production systems in Philippines. Their research results indicated the energy ratio value of 9.0 and 7.5 for farms were irrigated by canal and pump facility. In all farms fertilizer (nitrogen), fuel and seed inputs had the highest energy use [29]. The results of
RE s S2h X1 X2 X3 X4 X5 X6 Yi z
ai aj bi gi
5825
Renewable energy Standard deviation Variance of h stratification Machinery energy Human labor energy Fuel energy Chemical fertilizer energy Biocide energy Seed energy Yield level of the ith farmer Reliability coefficient (1.96 in the case of 95% reliability) Coefficients of the exogenous variables Regression coefficient of jth input Coefficients of the exogenous variables Coefficients of the exogenous variables
energy consumption for production of five major crops (rice, maize, sugarcane, cassava and soybean) showed total energy input for rice production varied between 1790e18,490 MJ ha1. Chemical fertilizer (nitrogen) had the highest energy usage. Energy ratio was 4.0 and 2.8 for irrigated and rain fed rice [30]. A study was carried out Iqbal (2008) to find the energy inputs requirement for production of rice in different categories of farms in Bangladesh. The results indicated small farms (0.61e1.00 ha) had the highest energy use efficiency (4.14) in comparison with other groups [31]. Eskandari et al. (2011) considered the energy consuming process and factors influencing rice production in semi-mechanized and traditional systems in Mazandaran province of Iran. They found irrigation and fertilizer energy are the most energy consumers in rice production. Energy use efficiency calculated 3.00 and 3.08 for traditional and semi-mechanized farms due to better input energy management in semi-mechanized farms [8]. The results of Pathak et al. (1985) study on energy use pattern and potential for energy saving in rice-wheat cultivation revealed rice consumed more energy in comparison with wheat production [32]. The aim of this research was to determine the effect of farm size levels on energy use efficiency per hectare for the production of rice, study the energy balance and do economical analysis. Furthermore, this study reveals the relationship between energy inputs and yield by developing mathematical models based on rice farms of Guilan province, Iran. 2. Materials and methods 2.1. Study and location The study was conducted in Langroud city (37.167 N and 50.15 E) of Guilan province, Iran in 2009e2010 production year. Langroud is located in north of Iran on the south cost of Caspian Sea, 21 m above sea levels. The annual average rainfall is almost 1100 mm. The highest and lowest temperature is 33 and 0 Celsius for summer and winter respectively. The soil analysis showed the structure of soil is clay and clay loam. Guilan province was selected for this research because of its high rice cultivated area (34% of country area) [9]. 2.2. Experimental details and sampling procedure The data used in this study were based on cross sectional and data were collected from 105 farmers growing single rice by using a face-to-face questionnaire. The average size of the studied farms
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was 0.6 ha. The sample size was determined using a stratified random sampling technique [33]
P ð Nh Sh Þ2 n ¼ P N 2 D2 þ Nh S2h
(1)
where n is the required sample size; N is the number of total holdings in the target population; Nh is the number of the population in the h stratification; Sh is the standard deviation in the h stratification, S2h is the variance in the h stratification, D2 is equal to d2 =z2 ; d is the precision, where ðx XÞ (5%) is the permissible error and z is the reliability coefficient (1.96, which represents 95% reliability). 2.3. Methodology of energy budgeting The inputs and the energy requirements of each input for rice production were collected, determined and presented for every questionnaire as per the socio-economic structures of the farms. General inputs in rice production were machinery, human labor, chemical fertilizers, fuel, biocide and seed. Output was rice (grain) as a product. The energy equivalent of different inputs and output (Table 1) were used to estimate the energy values. The human energy as an energy input was calculated by multiplying the number of man-hours (h ha1) by estimated power rating of human labor (MJ h1) from Table 1. Energy used of machinery was estimated by [34].
ME ¼ E G T
(2)
Where ME is the machinery energy (MJ), E the production energy of machine (Table 1), G the mass of machine (kg), and T is the economic life of machine (year). Other inputs like fuel, seed, biocide and chemical fertilizers used in rice production were transformed to energy value (MJ ha1) by multiplying the quantity of the material used in the farms by the energy equivalent of each material. For example chemical fertilizer
Table 1 Energy equivalents of inputs and output in rice production. Inputs (unit) A. Inputs 1. Machinery Tractor and self-propelled (kg yra) Stationary equipment (kg yra) Implement and machinery (kg yra) 2. Human Labor Male (h) Female (h) 3. Fuel Diesel (L) Gasoline (L) Natural gas (m3) Electricity (kW h) 4. Chemical Fertilizer N (kg) P2O5 (kg) K2O (kg) 5. Biocide Insecticide (kg) Herbicide (kg) Fungicide (kg) 6. Seed (kg) B. Output 1. Rice (kg) a
Economic life of machine (year).
Energy equivalent (MJ unit1)
Reference
9e10 8e10 6e8
[34] [34] [34]
1.96 1.57
[27] [27]
47.8 46.3 49.5 12
[34] [34] [34] [34]
78.1 17.4 13.7
[34] [34] [34]
229 85 115 14.7
[34] [34] [34] [18,27]
17
[34]
(nitrogen) energy consumption calculated by multiplying the amount of nitrogen usage (kg ha1) by energy coefficient of nitrogen fertilizer production (78.1 MJ kg1 from Table 1); so the result is the energy consumption of nitrogen fertilizer (MJ ha1) in rice production. Also, other energy inputs can be estimated hereby. Because of using gasoline pumps to prepare irrigation water, irrigation energy was displayed as gasoline energy. The amount of output energy (MJ ha1) estimated by multiplying the rice yield (kg ha1) by rice energy equivalent (MJ kg1). The total energy input is also classified into direct and indirect and renewable and non-renewable forms. The direct energy (DE) includes human labor, diesel fuel, gasoline, electricity and natural gas energy that are used in the production process and indirect energy (IDE) consists of machinery, chemical fertilizer, seed and biocide energy. On the other hand, renewable energy (RE) consists of human labor and seed and non-renewable energy (NRE) inclusive machinery, diesel fuel, gasoline, natural gas, electricity, biocide and chemical fertilizer [35]. Renewable energy used to describe energy sources that are replenished by natural processes on a sufficiently rapid time-scale. So they can be used by humans more or less indefinitely, provided the quantity taken per unit of time is not too great. On the other hand non-renewable energy used to describe energy sources that exist in a limited amount on Earth [36]. The energy ratio (energy use efficiency), energy productivity, specific energy and net energy were calculated as per given below in (Eq. (3)e(6)) [12].
Energy Output MJ ha1 Energy Ratio ¼ Energy Input MJ ha1 Rice Output kg ha1 Energy Productivity ¼ Energy Input MJ ha1 Energy Input MJ ha1 Specific Energy ¼ Rice Output kg ha1 Net Energy ¼ Energy Output MJ ha1 Energy Input MJ ha1
(3)
(4)
(5)
(6)
In order to have a better analysis of energy consumption pattern, the whole energy inputs, output and indices (energy ratio, energy productivity, specific energy and net energy) were calculated for different rice farm sizes. For this purpose farms were classified (due to the frequency of farm size in the sample population) in three categories as small (<0.5 ha), medium (between 0.5 and 1 ha) and large farms (>1 ha). 2.4. Analysis of energy with mathematical models The different mathematical functions such as linear, linearlogarithmic, logarithmic-linear and second degree polynomial were tested to find and analyze the relationship between energy inputs and yield. CobbeDouglas function yielded better estimates in terms of statistical significance and expected signs of parameters among other functions. CobbeDouglas function has been used by several authors to examine the relationship between energy inputs and yield [35,37]. CobbeDouglas production function is expressed as;
S.H. Pishgar-Komleh et al. / Energy 36 (2011) 5824e5831
Y ¼ f ðxÞexp ðuÞ
(7)
Eq. (7) can be linearized and expressed in the following form;
ln Yi ¼ ln b0 þ
n X
bj ln Xij þ ei i ¼ 1; 2; .; n
(8)
j¼1
where Yi denotes the yield of the ith farmer, Xij the vector of inputs used in the production process, ln b0 the constant term, bj represent coefficients of inputs which are estimated from the model and ei is the error term. In this study with assumption that, when the energy input is zero, the crop production is also zero, Eq. (8) becomes shorter to Eq. (9):
ln Yi ¼
n X
bj ln Xij þ ei i ¼ 1; 2; .; n
(9)
j¼1
With assumptions that yield is a function of inputs energy, Eq. (9) can be expanded to Eq. (10);
ln Yi ¼ b1 ln ðX1 Þ þ b2 ln ðX2 Þ þ b3 ln ðX3 Þ þ b4 ln ðX4 Þ þ b5 ln ðX5 Þ þ b6 ln ðX6 Þ þ ei
(10)
Where, X1, X2, X3, X4, X5, and X6 are machinery, fuel, human labor, chemical fertilizer, biocide and seed energy respectively. In addition to influence of each energy inputs on rice yield, CobbeDouglas function was utilized to evaluate the impact of direct, indirect, renewable and non-renewable forms of energy on rice yield as a following forms;
ln Yi ¼ g1 ln ðDEÞ þ g2 ln ðIDEÞ þ ei
(11)
ln Yi ¼ d1 ln ðREÞ þ d2 ln ðNREÞ þ ei
(12)
where Yi denotes the yield of the ith farmer, DE, IDE, RE and NRE are direct, indirect, renewable and non-renewable energy that used for rice production respectively, gi and di are the coefficients of variables and ei is the error term. Eqs. (10)e(12) were estimated using ordinary least square (OLS) technique. Sensitivity analysis studies how the variation in model outputs can be due to different sources of variation (the change in the quantity of total physical product resulting from a unit change in a variable input, keeping all other inputs unchanged) [38]. To analyze the sensitivity of energy inputs on rice yield, the marginal physical productivity (MPP) value, based on the response coefficients of inputs was determined. Marginal physical productivity, usually abbreviated MPP, is found by dividing the change in total physical product by the change in the variable input. Marginal physical productivity, which more often goes by the name marginal productivity (MP), is one of two measures derived from total physical product. In other words, MPP factor express the changes of output with a unit change of input while other inputs are fixed in their geometric mean value. Positive value of MPP indicates with an increase in input value, output value will increase and negative value of MPP indicates with increasing in input value, output value will decrease. To calculate MPP value of each input aij was utilized as follow [39,40]:
MPPxj ¼
change in total physical product GMðYÞ aij ¼ change in variable input GM Xij (13)
Where MPPxj is marginal physical productivity of jth input, aij regression coefficient of jth input, GM(Y) geometric mean of crop yield and GM(Xij) geometric mean of jth energy input.
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In the last part of this study economic analysis was done, therefore the net return, gross profit and benefit to cost ratio were calculated for rice production in research area [41]:
Total production value ¼ Rice yield kg ha1 Rice price $ kg1
(14)
Gross return ¼ Total production value $ ha1 Variable production cost $ ha1
(15)
Net return ¼ Total production value $ ha1 Total production cost $ ha1
(16)
Total production value $ ha1 Benefit cost ratio ¼ Total production cost $ ha1 Rice yield kg ha1 Productivity ¼ Total production cost $ ha1
(17)
(18)
The ANOVA test and Duncan compare mean were utilized to analyze the differences between all values in three different farm sizes. To calculate the energy values and compare the values in different farm sizes, all data from rice farms were entered into Excel 2010, SPSS 19 and to study the relationship of energy inputs and crop yield Shazam 9.0 software was applied.
3. Results and discussion 3.1. Input-output energy analysis Table 2 displays the average energy consumption of each input and output for rice production in three groups of farm size. Total average energy input and output were calculated 39,333 and 60,341 MJ ha1. The results of Freedman (1980) study indicated, for all methods of rice production, there is a greater energy output of food produced compared to cultural energy used for production [42]. The results showed that the average total energy consumption for rice production in Guilan province was 39,333 MJ ha1 which is higher than other researches, due to high energy consumption in fuel and chemical fertilizer energy inputs [28,29]. The highest energy use in rice production belonged to fuel energy input (consist of diesel, gasoline, electricity and natural gas) which accounted about 46% of total energy consumption. The first rank in fuel category energy (Table 2) belongs to gasoline energy consumption (18%) and after that the diesel (11%), natural gas (11%) and electricity (6%) were in next ranks. The gasoline energy was consumed for irrigation while the diesel was mainly used for tractors and various machinery operations. Whereas, large amount of water was pumped in rice production and because of low price of fuel in result of government subsidies, high consumption of fuel energy was observed. The natural gas and electricity energy were mainly utilized by post-harvest operations such as rice cleaning, drying, milling and sifting. Fuel input plays an important role in total energy consumption for rice production [8,28,29]. Fifty years ago muscles power were just used in rice production but at the present time, human labor and animal power were replaced with heavy
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Table 2 Amounts of energy inputs and output in rice production. Inputs
Farm size groups (ha)
Total energy (MJ ha1)
Percentage
1.01 0.73 0.28 3.34 45.95 10.82 18.66 10.66 5.81 35.76 6.07 1.79 3.82 0.46 7.87 100
Small (<0.5)
Medium (0.5e1)
Large (>1)
A. Inputs 1. Machinery Tractor or Tiller Implement and Vehicle 2. Human Labor 3. Fuel Diesel Gasoline Natural gas Electricity 4. Chemical Fertilizer 5. Biocide Insecticide Herbicide Fungicide 6. Seed Total energy input
405.65a 295.19a 110.46 1317.9 18075.7 4257.47 7340.67 4192.08 2285.48 13211.4a 2406.11 712.71 1509.5 183.9 3112.1 41139.8a
398.6a 290.31a 108.29 1313.8 18075.09 4256.66 7340.35 4190.27 2287.81 14012.1b 2389.42 706.51 1500.41 182.5 3104.2 40432.8b
382.43b 275.83b 106.6 1312.7 18068.01 4249.55 7340.25 4191.49 2286.72 14976.2c 2366.07 691.91 1497.62 176.54 3073.38 36427.5c
395.56 287.11 108.45 1314.81 18072.93 4254.56 7340.42 4191.28 2286.67 14066.30 2387.2 703.71 1502.51 180.98 3096.56 39333.36
B. Output 1. Rice
59440.5
59671.9
61913.4
60341.90
Note: Different letters (a, b, c) show significant difference of means at 5% level.
machineries and equipment that consume high amounts of energy [26]. Old machineries and equipment are the reasons for high fuel energy consumption in rice production in Iran. Applying new machineries and irrigation pumps with more energy efficiency decrease the amount of energy usage. Fuel energy was followed by the chemical fertilizer (including N, P2O5 and K2O) energy with the share of 36% of total energy inputs. The result was similar to several researches [8,25,26,28,29,32,42] where chemical fertilizer had high consumption in rice production. There are two considerable reasons for the high chemical fertilizer consumption: farmer’s poor knowledge and subsidies price. With the lack of knowledge, most Iranian farmers don’t know the required amount of chemical fertilizer for different crops and it became a common belief between them that excessive use of chemical fertilizer will increase the yield [43]. Moreover, the government subsidies price had significant effect on fertilizers use. As a result of using chemical fertilizer inefficiently (more than plant need), soil and water pollution would be followed. According to Singh et al. (1998) research results, energy used in the production of chemical fertilizer accounts for about 40% of total energy used in agricultural production in developed countries [37]. The share of seed energy consumption was calculated about 8% of all energy inputs in rice
production. The amount of seed energy use can be reduced by applying fewer amounts of seed per hectare. The required quantity of seed will be reduced by using high quality seed. Moreover, qualified seed will help to reduce the chances of pest and weed infestation, reduce the energy needed in weeding and chemical application and increase yield. The wastage of energy in the form of seed could be reduced considerably by using good quality seeds [43]. In this study insecticide, herbicide and fungicide were utilized with share of 6%. Herbicide had the highest consumption value between biocides (4%) and followed by insecticide and fungicide (2% and 1% respectively). As shown in Table 2, machinery was the least demanding energy input among the whole specified inputs in rice production. Due to low mechanization level in rice production, machinery energy consumption was 396 MJ ha1 (1%) and human power was accounted for 3% of total input energy. The comparison of energy inputs consumption based on farm size showed that farmers with more than 1 ha cultivated area used the least amount of machinery energy (382 MJ ha1) significantly by using wider machinery and implement and less turning around. Nassiri et al. (2009) research concluded that larger farms required using proper machine size and suitable tractors by considering farm size [44]. The results (Table 2) revealed that in large farms, the
Table 3 Energy forms and indices in rice production. Item
Energy ratio Energy productivity Specific energy Net energy Direct energye Indirect energyf Renewable energyg Non-renewable energyh Total energy
Unit
e kg MJ1 MJ kg1 MJ ha1 MJ ha1 MJ ha1 MJ ha1 MJ ha1 MJ ha1
Average (MJ ha1)
Farm size groups (ha) Small (<0.5)
Medium (0.5e1)
Large (>1)
1.44a 0.08 11.76 18300a 19746a 20801a 4504a 35243a 41140a
1.47a 0.09 11.52 19239a 19275b 19725b 4350b 35500a 40433b
1.69c 0.1 10.0 25485b 19143c 19312b 4379b 34023b 36428c
Note: Different letters (a, b, c) show significant difference of means at 5% level. e Include human labor and fuel. f Include machinery, seed, chemical fertilizer and biocide. g Include seed and human labor. h Include machinery, chemical fertilizer, fuel and biocide.
1.53 0.09 11.09 21008 19388 19946 4411 34922 39333
Percentage (%)
49.3 50.7 11.22 88.78
S.H. Pishgar-Komleh et al. / Energy 36 (2011) 5824e5831
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Table 4 Econometric estimation results. Independent variable
Coefficient
Model 1: ln Yi ¼ b1 ln ðX1 Þ þ b2 ln ðX2 Þ þ b3 ln ðX3 Þ þ b4 ln ðX4 Þ þ b5 ln ðX5 Þ þ b6 ln ðX6 Þ þ ei 1. Machinery 0.12 2. Fuel 0.83 3. Human labor 0.03 4. Chemical fertilizer 0.07 5. Biocide 0.12 6. Seed 0.11 Durbin Watson 1.97 2 0.99 R a b c
MPP
4.79a 21.56a 0.46 1.56 1.72c 2.39b
0.23 0.93 0.05 0.08 0.17 0.15
significant at 1% level. significant at 5% level. significant at 10% level.
amount of seed usage was least and as the farm size increases the yield of rice increases, as well but not significantly. Also, less chemical fertilizer was used in small farms (Table 2) indicating that higher level of management of chemical fertilizers were practiced in small farms. In the study carried out by Chauhan et al. (2006) in paddy production, it was concluded that potentially good operating practices include utilization of chemical fertilizer, human labor and seed sources lead to efficient production [43]. Therefore, inefficient farmers should learn the utilization way of these sources from the efficient ones. Totally it was revealed improved management in large rice farms (more than 1 ha) made considerable reduction in energy expenditure however, led to more amount of output energy (rice yield) in comparison with other levels of farms. It was similar to Iqbal (2008) research’s results that showed large farms (0.61e1.00 and more than 2 ha) had better use of energy management in rice production [31]. Table 3 shows energy indices of rice production and the forms of energy inputs as direct and indirect energy and renewable and nonrenewable energy for three sizes of farms. Energy ratio is one of the best energy indices that shows the efficient use of energy in rice production. The results indicated the average energy ratio of 1.53. Because of entering post-harvest operations (cleaning, drying, milling and sifting) in energy calculations the energy ratio estimate was lower than the other researcher’s reports (2.8 (rainfed rice) [30], 3 (traditional production method) [8], 3.08 (semi-mechanized production method) [8], 3.9 (irrigated rice) [31], 4 (irrigated rice) [30], 5.22 (irrigated rice) [25], 6.7 (irrigated rice) [45]). Lu et al. in their study showed that the long-term rice system had the highest energy output/input ratio (2.26) which was 8.69 and 1.95 times that of the long-term vegetable and rotation systems [46]. Eskandari et al. (2011) in their research found better use efficiency value for semimechanized in comparison with traditional system (energy ratio value of 3.08 and 3 respectively) [8]. Energy productivity, specific energy and net energy of rice production were calculated as Table 5 Econometric estimation results of direct, indirect, renewable and non-renewable energies. Independent variable
Coefficient
Model 2: ln Yi ¼ g1 ln ðDEÞ þ g2 ln ðIDEÞ þ ei 1. Direct 0.40 2. Indirect 0.14 Durbin Watson 1.74 R2 0.99 Model 3: ln Yi ¼ d1 ln ðREÞ þ d2 ln ðNREÞ þ ei 1. Renewable 0.02 2. Non-renewable 0.35 Durbin Watson 2.05 0.99 R2 a
t-Ratio
significant at 1% level.
t-Ratio 7.08a 4.37a
0.79 14.66a
MPP 0.45 0.15
0.03 0.36
0.09 kg MJ1, 11.09 MJ kg1 and 21,008 MJ ha1, respectively. Better management and using less energy input and producing more energy output (more yield) are two methods to reach higher energy ratio value. The amounts of direct and indirect were calculated 19,388 MJ ha1 (49%) and 19,946 MJ ha1 (51%), respectively. The share of renewable energy was 11% (4411 MJ ha1) while that of non-renewable form was 89% (34,922 MJ ha1). It is clear from Table 3 that in comparison with renewable energy, the portion of non-renewable energy is higher, so rice production is mostly depending on non-renewable energy sources (such as fossil fuels). Several researchers have found similar results that the portion of non-renewable form of energy was higher than that of renewable forms [47,48]. As farm size increases, energy ratio increases accordingly, reaching to 1.66 in the largest farm size group significantly (P < 0.05). As seen in Table 3, large farmers had better energy indices (energy ratio, energy productivity, specific energy and net energy) that it can be referred to the better management of large scale farms. The effect of rice farm size on energy indices of rice production in research area was similar to Iqbal (2008) results. They clarified small (0.61e1.00 ha) and large (2 ha) farms had better energy use efficiency value in contrast with landless (0.20 ha), marginal (0.21e0.61 ha) and medium (1e2 ha) farms [31]. 3.2. Econometric estimation and sensitivity analysis of rice production Regression results for model 1 showed the significant impact of machinery and fuel energy on rice yield at level of 1% (Table 4). In Table 6 Economic analysis of rice production. Cost and return components
Unit
Yield Sale price Gross value of production Variable cost of production Fixed cost of production Total cost of production Total cost of production Gross return Net return Benefit to cost ratio Productivity
Farm size groups (ha)
Average value
Small (<0.5)
Medium (0.5e1)
Large (>1)
kg ha1 $ kg1 $ ha1
3496.50 1.15 4052.98
3510.11 1.15 4057.63
3641.96 1.15 4176.2
3550 1.15 4095.60
$ ha1
2385.06
2432.51
2543.28
2453.62
1
702.27
702.27
702.27
702.27
$ ha1
3245.55
3134.78
3087.33
3155.89
$ kg1
0.93
0.89
0.85
0.89
1
1667.92 807.43 1.25 1.08
1625.12 922.85 1.29 1.12
1632.92 1088.87 1.35 1.18
1641.98 939.71 1.29 1.12
$ ha
$ ha $ ha1 e kg $1
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addition, seed and biocide had significant impact at 5% and 10% probability level respectively. Other inputs such as human labor and chemical fertilizer had no significant impact on rice yield. Of all inputs, fuel had the highest impact (0.83) and followed by machinery (0.12) and biocide (0.12) energy inputs. The regression results (model 1) expressed with 10% increasing in fuel, machinery and biocide energy, rice yield will increase 8.3%, 1.2% and 1.2% respectively. The results of MPP values indicated 1 MJ increasing in fuel and machinery energy led to 0.93 and 0.23 kg ha1 increasing in yield of rice production respectively. To validate Model 1, Durbin Watson test was performed [16]. Analysis for model 1 resulted 1.97 for Durbin Watson value, i.e. there was no autocorrelation in the estimated model (significant level of 5%). The model’s coefficient of determination calculated 0.99 (R2 ¼ 0.99). To realize the relationship between rice yield and forms of energy (direct and indirect), regression analysis (model 2) was utilized. The results are given in Table 5. It became evident that the impact of direct and indirect energy on rice yield was significant at 1% level with coefficient values of 0.40 and 0.14 for direct and indirect energies respectively. The regression coefficients of renewable and non-renewable energies on yield were investigated (model 3). As it can be seen in Table 5, the impact of renewable and non-renewable was 0.02 and 0.35 respectively. Only nonrenewable form was significant (at 1% probability level). The MPP values specified, consuming more (1 MJ) non-renewable, direct and indirect energy lead to more (0.36, 0.45 and 0.15 kg ha1) rice yield while using more (1 MJ) renewable energy will decrease the rice yield (0.03 kg ha1). Durbin Watson values for model 2 and 3 were 1.74 and 2.05, respectively (significant at 5% probability level). In addition, the model’s coefficient of determination was same (0.99) for models 2 and 3. 3.3. The economic analysis of rice production The cost of each input and the market price of rice (two market price for two cultivar), in research region were used to calculate the cost and return components of rice production. As it is represented in Table 6, variable and fixed cost were 2453.62 $ ha1 and 702.27 $ ha1 with share of 78% and 22% of total cost, respectively. By multiplying sale price by rice yield, the gross value calculated (4095.6 $ ha1). Total cost of production was 3155.89 $ ha1 and 0.9 $ kg1. Gross and net return were 1641.98 $ ha1 and 939.71 $ ha1 respectively. The benefit-cost ratio of rice production was calculated to be 1.3 that was lower than khan et al. (2009) research results of benefit-cost ratio (2.7) [25]. The economic research in other crops revealed the benefit to cost ratio value of 1.10 [17], 1.43 [49], 0.86 [2], 1.17 [50] and 1.57 [51] for soybean, wheat, cotton, sugar beet and greenhouse tomato respectively. At the end of economic analysis of rice production, the economic productivity was calculated 1.12 kg $1. The effect of rice farm size on the benefit-cost ratio revealed that large farms (>1 ha) had better values because of better management in consumption of energy inputs. 4. Conclusion Based on the results, following conclusions are drawn: 1. Energy inputs and output of rice production calculated to be 39,333 MJ ha1 and 60,341 MJ ha1. Chemical fertilizer was the biggest energy consumer (36% of total energy usage) and followed by gasoline (19%), diesel fuel (11%), natural gas (11%) and seed energy (8%). Machinery was discovered as the least demanding energy input in all inputs (1%). Energy ratio, energy productivity, specific energy and net energy were 1.53,
0.09 kg MJ1, 11.20 MJ kg1 and 21,008 MJ ha1 respectively. Direct, indirect, renewable and non-renewable forms of energy were 19,388 MJ ha1 (49%), 19,946 MJ ha1 (51%), 4411 MJ ha1 (11%) and 34,922 MJ ha1 (89%), respectively. 2. Regression coefficient values for machinery, fuel, labor, fertilizer, biocide and seed were 0.12, 0.83, 0.03, 0.07, 0.12 and 0.11, respectively. Fuel, machinery and biocide MPP values calculated to be of 0.93, 0.23 and 0.17 respectively. The impact of direct (0.40), indirect (0.14) and non-renewable (0.35) energy was significant at 1% level while renewable energy had no significant impact (0.02) on rice yield. 3. The average value of total cost of production, gross return, net return, benefit-cost ratio and productivity of rice production calculated to be 3156 $ ha1, 1642 $ ha1, 940 $ ha1, 1.29 and 1.12 kg $1 respectively. 4. The effect of rice farm size studied and the results revealed large farms (<1 ha) have better efficiency for rice energy and economic analysis in the research area. At the end of this study, recommended that with fewer government subsidies, teaching farmers about the less chemical fertilizer consumption and increasing cultivated area, the high energy consumption of chemical fertilizer and fuel (diesel, gasoline, natural gas and electricity) energy will be controlled, hence this act leads to improvement in energy indices in rice farming. Energy management should be considered as an important field in terms of efficient, sustainable and economical use of energy. It is essential to use modern technologies and new machineries for post-harvest operations to decrease high amount of energy usage in rice production. High energy use in rice production is detrimental to the environment due to mainly excess input use. According to the results investigated for the effects of farm size on energy inputs consumption, economic analysis and small farm sizes in rice planting pattern, it is recommended to plant in larger farms in order to lower total energy input and vice versa higher total energy output. To reduce the amount of production cost it is recommended to substitute the obsolete and aged machinery and tractors with the new ones so that the production cost decreases by reducing the maintenance and timeliness costs. Also, using new machines cut costs of fuel consumption; hence it leads to efficient use of diesel fuel energy. References [1] Stout BA. Handbook of energy for world agriculture. London: Elsevier Applied Science; 1990. p. 21. [2] Yilmaz I, Akcaoz H, Ozkan B. An analysis of energy use and input costs for cotton production in Turkey. Renewable Energy 2005;30:145e55. [3] Singh JM. On farm energy use pattern in different cropping systems in Haryana, India. Germany: International Institute of Management University of Flensburg. Sustainable Energy Systems and Management; 2002. [4] Kennedy S. Energy use in American agriculture. Sustainable energy. Term Paper. Available from, http://web.mit.edu/10.391J/www/proceedings/ Agriculture_Kennedy2000.pdf; 2000. [5] Yaldiz O, Ozturk H, Zeren Y, Bascetincelik A. Energy usage in production of field crops in Turkey. In: Proceedings of 5th International Congress on Mechanization and Energy Use in Agriculture; 1993 Oct 11e14; Kusadasi, Turkey. [6] Food and Agriculture Organization (FAO) of the United Nation Statistics. Retrieved 2006-12-26. Available from: http://www.faostat.fao.org. [7] Anonymous. Rice genome landscape [Internet]. Australia: a free public resource for patent system navigation worldwide. Available from, http:// www.patentlens.net/daisy/RiceGenome/3649/3591.html; 2011. [8] Eskandari Cherati F, Bahrami H, Asakereh A. Energy survey of mechanized and traditional rice production system in Mazandaran Province of Iran. African Journal of Agricultural Research 2011;6(11):2565e70. [9] Anonymous. Annual agricultural statistics. Available from. Ministry of Jihad-eAgriculture of Iran, http://www.maj.ir; 2009. [10] Food and Agriculture Organization (FAO). 2008. Available from: http://www. fao.org.
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