Comparison of GHG Emissions of Efficient and Inefficient Potato Producers Based on Data Envelopment Analysis

Transcription

1 Journal of Agricultural Engineering and Biotechnology Nov 2013, Vol 1 Iss 3, PP Comparison of GHG Emissions of Efficient and Inefficient Potato Producers Based on Data Envelopment Analysis Benyamin Khoshnevisan 1, Shahin Rafiee *2, Mahmoud Omid 3, Hossein Mousazadeh 4 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran 1 b_khoshnevisan@utacir; *2 shahinrafiee@utacir; 3 omid@utacir Abstract- Data for this study were obtained from province of Esfahan in Iran 260 potato producers were randomly selected for data collection Energy efficiency of potato growers was studied and degrees of technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were determined using data envelopment analysis (DEA) Additionally, wasteful uses of energy by inefficient farms were assessed and energy saving of different sources was computed Furthermore, the effect of energy optimization on greenhouse gas (GHG) emission was investigated and the total amount of GHG emission of efficient farms was compared with inefficient ones It was revealed that 21% of producers were technically efficient and the average of TE was calculated as 083 Based on the BCC model 105 growers were identified efficient (40%) and the mean PTE of these farmers was found to be 098 Also, it was concluded that 13% ( MJ ha -1 ) of overall input energies can be saved if the performance of inefficient farms rose to a high level Finally it was concluded that, by energy optimization the total GHG emission can be reduced to the value of kg CO 2eq Keywords- DEA; Technical Efficiency; GHG Emission I INTRODUCTION Esfahan province with approximate production of 3 million tons (42%) various agricultural crops had the 9 th rank in 2010 among all provinces in Iran, while simultaneously it had the 3 th rank among big potato producers with tons (10%) per year [1] Potatoes are grown worldwide under a wider range of altitude, latitude, and climatic conditions than any other major food crop No other crop can match the potato in its production of food energy and food value per unit area [2] This plant has one of the heaviest demands for fertilizer inputs over other vegetable crops For instance, the percentage of nitrogen (N), phosphorus (P) and potassium (K) requirements for potato cultivation are, respectively, 100%, 100% and 33% greater than that required for tomato or pepper productions [3] Developing need of human beings for food production has resulted in the increased consumption of energy and natural resources because farmers have little knowledge or few incentives to use more energy-efficient methods [4] Agricultural production uses large quantities of locally available non-commercial energy, such as seed, manure and animate energy, as well as commercial energies, directly and indirectly, in the form of diesel fuel, electricity, fertilizer, plant protection, chemical, irrigation water, machinery, etc Efficient use of these energies helps to achieve increased production and productivity and contributes to the profitability and competitiveness of agriculture sustainability in rural living [5] On the other hand, it will minimize environmental problems and prevent destruction of natural resources [6] Energy use in agricultural production has been investigated in several studies [3-5, 7-11] Efficiency is defined as the ability to produce the outputs with a minimum resource level required [12] DEA is an analysis method to measure the relative efficiency of a homogeneous number of organizations that essentially perform the same tasks [13] It has been accepted as a major frontier technique for benchmarking energy sectors in many countries Given a sample of the DMUs, the purpose of the DEA is to establish the relative efficiency of each DMU within a sample [14] A big advantage of DEA is that it does not need any prior assumptions on the underlying functional relationships between inputs and outputs [15] Based on the literature many studies focused on energy use efficiency in agricultural productions [3, 16-20] Singh et al [21] investigated optimization of energy inputs for wheat production in Punjab They found that total energy input in different zones could be saved by 71%-223% Omid et al [14] studied energy use pattern of cucumber greenhouses in Iran They reported that technical, pure technical and scale efficiencies were 087, 097 and 09, respectively Also, they showed that 85% of overall resources could be saved by raising the performance of inefficient DMUs to the highest level In another study [22] DEA was applied to assess the technical efficiency of energy use in different barberry production systems in Iran Technical efficiency of producers in small and large farms was reported 066 and 050, respectively Based on the literature, there has been no study on application of DEA for assessing the energy use efficiency in potato production Accordingly, the objectives of this study were: (a) to determine the efficiencies of potato farmers; (b) to identify target energy requirement and wasteful uses of energy and (c) to assess the effect of energy optimization on GHG emission DOI: /JAEB

2 Journal of Agricultural Engineering and Biotechnology Nov 2013, Vol 1 Iss 3, PP A Selection of Case Study Region and Data Collection II MATERIALS AND METHODS This study was carried out in 260 potato producers in Esfahan province in Iran Fereydoonshahr with an average temperature of 16 degrees of Celsius was chosen to represent the whole study area The average size of potato farms in this area was estimated at 16 ha The province is located in the middle of Iran, within and north latitude and and east longitude Data were collected from the potato producers by using a face-to-face questionnaire method The sample size was calculated, using the Neyman method [23], to be equal to 260, then selection of 260 potato producers from the population was randomly carried out Input energies encompassed human labor, chemical fertilizer, biocides, farmyard manure (FYM), water for irrigation, machinery, diesel fuel, electricity and seeds while output energy included potato produced Energy conversion factors were used to convert each agricultural input and output into energy equivalents Energy equivalents were calculated by multiplying the quantity of the inputs used per hectare by their conversion factors (Table 2) Water for irrigation was extracted from agricultural well by electricity pumps Energy for pumping water was calculated as Eq 1 [24]: g H Q DE (1) where DE presents direct energy (J/ha), g is acceleration due to gravity (ms -2 ), H is total dynamic head (m), Q is volume of required water for one cultivating season (m 3 ha -1 ), γ is density of water (kg m -3 ), is pump efficiency (70%-90%) and p q is total power conversion efficiency (18%-20%) [25] B Data Envelopment Analysis The DEA is a non-parametric data analytic technique whose domain of inquiry is a set of entities, commonly called decision-making units (DMUs), which receive multiple inputs and produce multiple outputs [26] In this study, DEA methodology was applied to determine TE, PTE and SE of potato farmers in order to calculate the amount of energy saving and GHG emission reduction The input energies encompassed labor, chemical fertilizer, farmyard manure (FYM), machinery, diesel fuel, electricity, biocides, water for irrigation and seeds Input variables were considered based on energy per hectare (MJ ha -1 ) and potato yield (kg ha -1 ) was chosen as output variable CCR and BCC models were employed in the present study CCR model which was built on the assumption of constant returns to scale (CRS), was suggested by Charnes, Copper [27] It is also called the global efficiency model Later, Banker, Charnes [28] introduced the BCC model based on variable returns to scale (VRS) and it was also called the local efficiency model DEA models are broadly divided into two categories on the basis of orientation: input-oriented and output-oriented Input-oriented models have the objective of minimizing inputs while maintaining the same level of outputs, whereas outputoriented models focus on increasing outputs with the same level of inputs [29] In this study an input-oriented DEA model was used to determine efficient and inefficient DMUs Three different forms of efficiency are defined by DEA; technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) TE is defined as the DMU s ability to achieve maximum output from given inputs, while pure technical efficiency is defined as the DMU s success in selecting inputs in optimal proportions with respect to price [29] Inappropriate operation and inadequate scale of a farm are two main reasons for inefficiency of a DMU CCR model includes both TE and SE while BCC model calculates only PTE of DMUs In order to obtain SE in the present study, both CCR and BCC models were calculated and SE was defined as follows [30]: p q SE CCR (4) where CCR and BCC are the CCR and BCC scores of a DMU, respectively SE = 1 shows scale efficiency (or CRS) and SE < 1 indicates scale inefficiency Scale inefficiency can be due to the existence of either increasing returns to scale (IRS) or decreasing returns to scale (DRS) A shortcoming of the SE score is that it does not demonstrate if a DMU is operating under IRS or DRS This is resolvable by simply imposing a non-increasing returns of scale (NIRS) condition in the DEA model [18, 29] IRS and DRS can be determined by comparing the efficiency scores obtained by the BCC and NIRS models; so, if the two efficiency scores are equal, then DRS apply; else IRS prevail [14, 29] Energy saving target ratio (ESTR) helps to determine the inefficiency level of energy usage ESTR was calculated as [31]: BCC DOI: /JAEB

3 Journal of Agricultural Engineering and Biotechnology Nov 2013, Vol 1 Iss 3, PP ( saving target) ESTR (%) Energy j j 100 (5) (Actualenergy input) ESTR represents each inefficiency level of energy consumption The value of ESTR is between zero and unity A higher ESTR implies higher energy use inefficiency, and thus, a higher energy saving amount [31] C GHG Emissions Some problems in agricultural productions are mainly due to the high levels of dependency on fossil energies High use of fossil energies causes a lot of serious environmental problems among which global warming and greenhouse gas (GHG) emissions are counted as important ones Agricultural greenhouse gas (GHG) emissions account for 10%-12% of all manmade GHG emissions [32] On the other hand, agricultural productions have been identified as a major contributor to atmospheric greenhouse gases (GHG) on a global scale with about 14% of global net CO 2 emissions coming from agriculture [33] CO 2 emission coefficients of agricultural inputs were used for quantifying the GHG emissions of potato cultivation Table 1 summarizes GHG emission equivalents GHG emission was calculated by multiplying the input application rate (diesel fuel, chemical fertilizers, pesticides, electricity and water for irrigation) by its corresponding emission coefficient TABLE 1 GREENHOUSE GAS (GHG) EMISSION COEFFICIENTS OF AGRICULTURAL INPUTS Inputs Unit GHG Coefficient (kg CO 2 eq unit -1 ) Machinery MJ 0071 [34] Diesel fuel L 276 [35] Chemical fertilizers (a) Nitrogen (N) kg 13 [36] (b) Phosphate (P 2 O 5 ) kg 02 [36] (c) Potassium (K 2 O) kg 02 [36] Pesticides (a) Herbicide kg 63 [36] (b) Insecticide kg 51 [36] (c) Fungicide kg 39 [36] Electricity* kwh 0608 [37] * The power plant burns LNG j Reference The excel spreadsheet was used to analyze energy use pattern Also, in order to assess the efficiency indices of potato farms, the DEA software Efficiency Measurement Systems (EMS), Version 13, was applied A Energy Use Pattern III RESULTS AND DISCUSSIONS Table 2 summarizes the amount of inputs, output and their energy equivalents for potato production The total energy consumption was calculated as MJ ha -1 Electricity with a share of 37% was the most energy consumer and it was followed by chemical fertilizers and seeds The majority of electricity was used for electric pumps to extract water from agricultural well Among the chemical fertilizers, nitrogen played the most important role with a share of 28% The main reason for high contribution of chemical fertilizers is that the farmers did not have appropriate knowledge about the proper time and amount of fertilizers usage The contribution of biocides and human labor in comparison with other inputs in the total input energy was negligible As can be seen, the average of potato yield in the survived region was kg ha -1 Accordingly, total output energy was calculated as MJ ha -1 The last column of Table 2 represents the standard deviation of energy inputs and potato yield for different farms The wide variations in input energies accentuated that there was mismanagement of energy consumption, so it is evident that there is an enormous potential for enhancing energy use pattern of potato production in the studied region TABLE 2 ENERGY COEFFICIENTS AND ENERGY INPUTS/OUTPUT IN VARIOUS POTATO PRODUCTION OPERATIONS Inputs/output Unit Energy coefficients (MJ unit -1 ) A Input Quantity per unit area (ha) Total energy equivalent (MJ ha -1 ) Percentage 1 Human labor h 196 [38] Chemical fertilizer a N kg 781 [24] b P 2 O 5 kg 174 [24] SD a DOI: /JAEB

4 Journal of Agricultural Engineering and Biotechnology Nov 2013, Vol 1 Iss 3, PP c K 2 O kg 137 [24] FYM kg 03 [39] Biocides kg 120 [40] Machinery kg yrb 8-10 [24] Water for irrigation m3 102 [38] Diesel fuel L 478 [24] Electricity kwh 12 [24] Seed kg 36 [41] Total input energy B Output B DEA Results Potato kg 36 [41] a Indicates standard deviation of energy inputs (MJ ha -1 ) and potato yield (kg ha -1 ) b The economic life of machine (year) Using the CCR model, overall technical efficiencies of all DMUs were evaluated Additionally, PTE and SE were determined based on BCC model BCC model results illustrated that 105 farms (40%) out of 260 were efficient and their efficiency score was equal to unity (Fig 1) On the other hand, the remaining 155 potato producers which gained efficiency scores less than one were comparatively inefficient Table 3 summarizes the efficiency scores of some chosen farms Inefficient DMUs had efficiency score between 09 and 099 The results of CCR model showed that only 21% of farms (55 DMUs) were technically efficient and 15% of DMUs were in the range of 09-1 The average values of the PTE, TE and SE are presented in Table 3 The average values of PTE, TE and SE were calculated 098, 083 and 084, respectively The values of PTE less than one means that the target DMU is using more energy than required [27] Based on the literature, the technical efficiency scores of 074 for canola [18], 094 for kiwifruit [12] and 087 for cucumber [14] were reported Fig 1 Efficiency score distribution of potato producers in Iran The BCC model includes both IRS and DRS, while a NIRS model gives DRS To determine whether a DMU has IRS or DRS an additional test is required The values of TE for both BCC and NIRS were calculated and their calculated values were compared The same values of TE for NIRS and BCC models show that the DMU has DRS, while the different values imply that the farm has IRS [14] The last column of Table 3 indicates the results of RTS for some selected DMUs These results revealed that 56 farms (based on CCR model) had CRS while 204 DMUs were found to be operating at IRS Therefore, a proportionate increase in all inputs leads to more proportionate increase in outputs and for considerable changes in yield, technological changes in practices are required The information on whether a farmer operates at IRS, CRS or DRS is particularly helpful in indicating the potential redistribution of resources between the farmers, and thus, enables them to achieve to the higher yield value [42] TABLE 3 TECHNICAL AND SCALE EFFICIENCIES AND RETURNS TO SCALE DOI: /JAEB

5 Journal of Agricultural Engineering and Biotechnology Nov 2013, Vol 1 Iss 3, PP DMU Technical efficiency Scale efficiency CRS VRS NIRS (CRS/VRS) Return to scale Increasing Increasing Increasing Increasing Increasing Increasing Increasing Increasing Increasing Increasing Increasing Increasing Constant Increasing Constant Constant Constant Constant Constant Constant Average Max Min SD C Setting Realistic Input Levels for Inefficient Farmers Pure technical efficiency less than one indicates that DMUs use energy inefficiently Accordingly, finding the real amount of required energy which is used in every inefficient DMU can help us to reduce the wastage use of energy without any changes in production level Table 4 summarizes the present use, target use, energy saving and ESTR percentage for each energy source TABLE 4 ENERGY SAVING (MJ HA -1 ) FROM DIFFERENT SOURCES IF RECOMMENDATION OF STUDY ARE FOLLOWED Input Present use (MJ/ha) Target use (MJ/ha) Energy saving (MJ/ha) ESTR (%) 1 Human labor Chemical fertilizer a N b P 2 O c K 2 O FYM Biocides Machinery Water for irrigation Diesel fuel Electricity Seed Total input energy (MJ/ha) The results revealed that the total input energy can be reduced to MJ ha -1, while the current yield will not change On the other hand, it means that MJ ha -1 can be saved if all DMUs improved their conditions from energy use point of view According to the ESTR results, if all DMUs operate efficiently, 246%, 233% and 19% reduction of FYM, K 2 O and P 2 O 5 will be achieved while the maximum contribution to the total energy saving is the electricity (45%) and it is followed by nitrogen (185%), seeds (10%) and water for irrigation (8%) Fig 2 shows the share of different sources in the total energy saving Using more efficient electric pumps in irrigation systems can reduce the present energy use Improving the efficiency of irrigation systems is another appropriate way which can lead to improving energy use efficiency of potato production in the DOI: /JAEB

6 Journal of Agricultural Engineering and Biotechnology Nov 2013, Vol 1 Iss 3, PP studied area Applying chemical fertilizers especially nitrogen according to the plant needs is highly recommended to enhance the energy saving in the surveyed region Fig 2 Total potential improvement summary Based on the results which can be seen in Table 4, energy indices (energy ratio, specific energy, energy productivity and net energy ) were calculated as per details provided by Omid et al [14] Making a comparison between energy indices in the current energy use and target energy use showed the improvements of these indices (Table 5) The energy ratio of potato production in the studied area was calculated as 103 and it can be improved to the value of 112 Energy productivity, specific energy and net energy were found 029 kg MJ -1, 425 MJ kg -1 and -916 MJ ha -1, respectively and they can be enhanced to the values of 031 kg MJ -1, 393 MJ kg -1 and MJ ha -1 D GHG Emissions TABLE 5 COMPARISON BETWEEN ENERGY INDICES AND IMPROVED ENERGY INDICES FOR POTATO PRODUCTION Items Unit Quantity in present use Quantity in target use Difference (%) Energy ratio Energy productivity kg MJ Specific energy MJ kg Net energy MJ ha GHG emission of efficient and inefficient DMUs was investigated to determine the role of energy optimization in environmental condition of potato production in the studied area The total GHG emission of potato production was calculated as kg CO 2eq The results revealed that the most amount of CO 2 emission was related to the electricity with amount of kg CO 2eq and followed by chemical fertilizers As was mentioned above, the energy consumption can be reduced by improving some agricultural practices and technological changes in inefficient DMUs Subsequently, the emission of GHG can be decreased in the studied region The results revealed that Target GHG emission by decreasing of 9% can be reduced to the value of kg CO 2eq (Fig 3) The most reduction was observed in electricity by 82% of total reduced emission and was followed by fertilizers (11%) and diesel fuel (6%) Using renewable sources of energy for producing electricity like wind and solar energy sources, applying more efficient electric pumps and replacing diesel fuel by bio diesel can lead to cultivation with less GHG Fig 3 Present and target Greenhouse gas emissions of inputs in potato production IV CONCLUSIONS DOI: /JAEB

7 Journal of Agricultural Engineering and Biotechnology Nov 2013, Vol 1 Iss 3, PP This study was carried out in the province of Esfahan in Iran Data were collected from 260 farmers in Fereydonshahr region by a face-to-face questionnaire method Data envelopment analysis was used to determine the efficiency and inefficiency of DMUs Energy indices and GHG emission were compared based on present and target energy used The following conclusions were drawn: 21% of growers (55 farmers) were technically efficient, while based on the BCC model 105 producers were identified efficient (40%) The average of TE, PTE and SE were determined 083, 098 and 084, respectively Also, the results revealed that 56 farms had CRS and the rest had IRS Comparison between present and target energy use showed that MJ ha -1 can be saved if all inefficient DMUs use energy based on the recommendations of this study The maximum contribution to the total energy saving is the electricity (45%) and it is followed by nitrogen (185%), seeds (10%) and water for irrigation (8%) It was concluded that if all farmers use energy efficiently, the energy ratio, energy productivity, specific energy and net energy can improve to the value of 112, 031 kg MJ -1, 393 MJ kg -1 and MJ ha -1, respectively Based on the results it was observed that the total GHG emission in efficient farms was kg CO 2eq while it was calculated as 2415 kg CO 2eq in inefficient farms The total GHG emission can be reduced to the value of kg CO 2eq The most reduction was observed for electricity by 82% of total reduced emission and was followed by fertilizer (11%) and diesel fuel (6%) ACKNOWLEDGMENT The financial support provided by the University of Tehran, Iran, is duly acknowledged REFERENCES [1] Anonymous Annual agricultural statistics Ministry of Jahad-e-Agriculture of Iran [2] Zangeneh M, Omid M, Akram A A comparative study on energy use and cost analysis of potato production under different farming technologies in Hamadan province of Iran Energy 2010;35(7): [3] Mohammadi A, Tabatabaeefar A, Shahin S, Rafiee S, Keyhani A Energy use and economical analysis of potato production in Iran a case study: Ardabil province Energy Conversion and Management 2008;49(12): [4] Esengun K, Erdal G, Gündüz O, Erdal H An economic analysis and energy use in stake-tomato production in Tokat province of Turkey Renewable Energy 2007;32(11): [5] Singh H, Mishra D, Nahar NM Energy use pattern in production agriculture of a typical village in arid zone, India part I Energy Conversion and Management 2002;43(16): [6] Rafiee S, Mousavi Avval SH, Mohammadi A Modeling and sensitivity analysis of energy inputs for apple production in Iran Energy 2010;35(8): [7] Hatirli SA, Ozkan B, Fert C An econometric analysis of energy input output in Turkish agriculture Renewable and Sustainable Energy Reviews 2005;9(6): [8] Pishgar-Komleh SH, Ghahderijani M, Sefeedpari P Energy consumption and CO2 emissions analysis of potato production based on different farm size levels in Iran Journal of Cleaner Production 2012;33(0): [9] Pishgar-Komleh SH, Keyhani A, Mostofi-Sarkari MR, Jafari A Energy and economic analysis of different seed corn harvesting systems in Iran Energy 2012;43(1): [10] Singh S, Singh S, Pannu CJS, Singh J Energy input and yield relations for wheat in different agro-climatic zones of the Punjab Applied Energy 1999;63(4): [11] Singh S, Singh S, Pannu CJS, Singh J Optimization of energy input for raising cotton crop in Punjab Energy Conversion and Management 2000;41(17): [12] Mohammadi A, Rafiee S, Mohtasebi SS, Mousavi Avval SH, Rafiee H Energy efficiency improvement and input cost saving in kiwifruit production using Data Envelopment Analysis approach Renewable Energy 2011;36(9): [13] Cooper LM, Seiford LM, Tone K Introduction to data envelopment analysis and its uses New York: Springer2006 [14] Omid M, Ghojabeige F, Delshad M, Ahmadi H Energy use pattern and benchmarking of selected greenhouses in Iran using data envelopment analysis Energy Conversion and Management 2011;52(1): [15] Seiford LM, Thrall RM Recent developments in DEA: the mathematical programming approach to frontier analysis Journal of Econometrics 1990;46:7 38 [16] Dawson PJ, Lingard J, Woodford CH A generalized measure of farm-specific technical efficiency American Journal of Agricultural Economics 1991;73: [17] Fraser I, Cordina D An application of data envelopment analysis to irrigated dairy farms in Northern Victoria, Australia Agricultural Systems 1999;59(3): [18] Mousavi-Avval SH, Rafiee S, Jafari A, Mohammadi A Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach Energy 2011;36(5): DOI: /JAEB

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