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Sensitivity analysis of energy inputs for Maize Production System in Kermanshah Province of Iran
Abstract
The aims of this study were to investigate influences of energy inputs and energy forms on output levels and evaluation of inputs sensitivity for maize production in Kermanshah province, Iran. The sensitivity of energy inputs was estimated using the marginal physical productivity (MPP) method and partial regression coefficients on maize yield. Data were collected from 72 maize farms in August and September 2011. The sample volume was determined by random sampling method. The total energy input was 26.91 GJ ha −1. The chemical fertilizer (N and P) with 56 percent had the biggest share of total energy inputs. Econometric model evaluation showed that the human labor energy was the most significant input affecting the output level. Sensitivity analysis results indicate that with an additional use of 1 MJ for each of seeds, machinery and water for Irrigation energy would result in an increase in maize yield by 6.02, 5.57 and 5.14 kg, respectively.
... In recent years several mathematical methods have been used to examine the relationship between inputs consumption and agricultural production (Abdi et al., 2012;Antanasijević et al., 2015;Salehi et al., 2014). Khanali et al. (2022) develop a new eco-exergoenvironmental toxicity index to determine the most sustainable irrigation system, either surface or drip, for sunflower cultivation in Kurdistan, Iran. ...
This study aims to investigate the impact of renewable energy consumption on the economic growth of G7 countries and explore the potential nonlinear relationship between the two variables. Initially, the NARDL model is employed to analyze the G7 countries, allowing for the control of nonlinear relationships and considering asymmetric effects. The findings of the NARDL model reveal an asymmetric long run cointegration relationship between renewable energy consumption and economic growth in Canada and the US, while other countries show different dynamics. Subsequently, a causal dynamic impact analysis is conducted to gain further insights into the relationship between renewable energy consumption and economic growth. In the next step, the advantage of panel data analysis is utilized to investigate the overall impact across all G7 countries. For this purpose, the study extends the NARDL model to the PNARDL (Panel Nonlinear Autoregressive Distributed Lag) model, which facilitates the control of asymmetric effects and nonlinearity in the panel data model. In this context, this study is one of the first studies to control the nonlinearity in panel data analysis. The results from the PNARDL model demonstrate that renewable energy has a positive long-term relationship with economic growth in G7 countries; however, this relationship is statistically insignificant.
... In recent years several mathematical methods have been used to examine the relationship between inputs consumption and agricultural production (Abdi et al., 2012;Antanasijević et al., 2015;Kaab et al., 2019a;Salehi et al., 2014;Singh et al., 2007). In addition to mathematical methods, artificial neural networks (ANNs) are a suitable and accurate tool for forecasting the performance of various systems (Taki et al., 2012b). ...
Uncertainty about the energy use efficiency, lack of knowledge about economic outcomes, and its environmental consequences have always take risks in changing cultivation patterns and moving towards the optimal path. Accordingly, this study provided mathematical, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) methods to predict output energy, economic profit, and global warming potential (GWP) of wheat production. For this purpose, 75 wheat farms located in the central area of Hamadan province, Iran, were selected randomly, and data were gathered through oral interviews. After collecting input and output energies data, the averages of inputs and outputs energies were obtained about 43055 MJ ha−1 and 117407 MJ ha−1, respectively. Economic analysis has performed in the next step. Its results revealed that the benefit-to-cost ratio and net return were computed about 2.33 and 488.29 $ per ha for wheat production. Then, life cycle assessment (LCA) was utilized to specify the environmental effects of wheat cultivation, and its results demonstrated that GWP is the most important environmental impact which caused 624.29 kg CO2 eq. during 1 ton of wheat production. Modeling results illustrated R2 was varied between 0.264 and 0.978 in the linear regression, 0.313 and 954 in the best structure of ANN with two hidden layers, and 0.520 and 0.962 in the ANFIS with three-level structure. Modeling comparison indicated that generally, ANFIS model with considering all uncertainty items can be offered better prediction models among all and after that ANN with considering non-linear parameters is in the next rank.
... They also reported a high energy output of 83.11 GJ/ha, which contributed to the better overall energy efficiency. Abdi et al. (2012) recorded an energy consumption of 26.92 GJ/ha, whereas Lorzadeh et al. (2011) reported an energy input of 29.31 GJ/ha with fertilizers accounting for 48.25 %. ...
Efficiency of the primary agricultural production is under constant analysis regarding energy, economy and ecology. Regardless the innovation and technical progress, engineers and researchers are still looking for production system that will make primary agricultural production more energy efficient and ecology sustainable. This paper gives the results on the energy consumption and energy efficiency of crop production, emphasizing the importance of the mineral fertilizers use and their influence on the overall energy consumption. Results show that mineral fertilizers participate in the total energy consumption in crop production, from 40.53% (seed sunflower) up to 55.19% (maize). Statistical analysis showed that there is a correlation between energy input through the fertilizers and yield. The regression analysis showed that there is a positive relation between the crop yield and energy used through the fertilizer in all cases except in case of wheat.
... The results demonstrated that improving energy and water productivity in crop cultivation operations are the two possible approaches to decreasing the environmental effects of energy and water inputs in food production systems [12]. Abdi et al. [13] investigated sensitivity analysis of energy use for maize production. The results showed that total energy input for maize cultivate was about 26.91 GJ ha À1 , which about 56% of this was related to chemical fertilizers. ...
Today, intensive use of energy sources leads to environmental damages such as global warming and resource depletion. Hence, this study provided energy, environmental and economic overview of wheat cultivation in Hamedan province, Iran. The initial data were collected from 75 wheat farms applying face-to-face interview technique. The prepared data related to the 2017–2018 production cycle. The energy analysis results demonstrated that the total energy consumption and output energy in wheat cultivation were 43054.63 MJ ha−1 and 117407.13 MJ ha−1, respectively. Energy use efficiency, energy productivity and net energy gain were computed as 2.73, 0.12 kg MJ−1 and 74352.50 MJ ha-1, respectively. Economic analysis showed, total value and cost wheat production were 854.86 ha–1, respectively. Net return was 488.29 $ ha–1 and benefit to cost ratio computed as 2.33 in the investigated region. Wheat environmental impacts were evaluated by applying life cycle assessment methodology. Results of environmental impacts showed the largest emissions were related to marine aquatic ecotoxicity (319757.6377 kg 1,4-DB eq.), abiotic depletion (fossil fuels) (6673.1319 MJ) and global warming potential (624.2944 kg CO2 eq.). Electricity was a hotspot in abiotic depletion (fossil fuels), global warming potential, freshwater aquatic ecotoxicity, ozone layer depletion and photochemical oxidation impact categories. Cumulative exergy demand results indicated that the rates of non-renewable fossil (7088.05 MJ ha-1) for wheat farms mainly resulted from electricity and nitrogen fertilizer.
... FRONTIER, Version 4.1, developed by [10] was used to estimate the coefficients. Refer ence A. Inputs 1. Human labor h 1.96 [19] 2. Machinery h 62.70 [1] 3. Diesel fuel L 56.31 [5] 4. Chemical fertilizers kg ...
The study examined the energy forms and balance in peach production and determinants of energy use efficiency in Samsun province, Turkey. Research data were collected from 43 randomly selected peach producers by using a well-structured questionnaire. Both energy analysis and the stochastic production frontier approach were used to investigate the energy balance between the energy inputs and yield in peach production and reveal determinants of energy inefficiency. The research showed that output energy was greater than energy inputs for peach production and farm level energy efficiency scores differed associated with the characteristics of farms and peach producers in the research area. The energy productivity, specific energy, energy intensiveness and net energy of peach production were 0.60 kg MJ-1 , 1.67 MJ kg-1 , 1.73 MJ $-1 and 2429.96 MJ ha-1 , respectively. Energy analysis also showed that the main determinants, which influenced the energy use efficiency positively, were schooling, experience, the ratio of peach orchards to farmland and family size. However, the variables of total assets, working capital, number of trees and number of plots showed a negative relationship with energy efficiency. The study proposes strategies such as providing better extension services and farmer training programs, enhancing specialization in peach production, decreasing the number of plots and balancing the working capital use together with controlling the distribution of total assets in order to increase the energy use efficiency for peach production in Samsun province.
... Because of water deficiency in Iran, maize is transplanted in some parts throughout the country. This planting method is expensive but gives the maize a higher competition advantage against weeds (Abdi et al., 2012). ...
Weeds are a serious threat to crop production in Iran as they reduce the yield of wheat, barley, rice, maize, and chickpea on average by 23%, 21%, 35%, 20%, and 50%, respectively. Orobanche spp., Avena ludoviciana (Durieu.), Convolvulus arvensis (L.), Sorghum halpence (L.) Pers, and Cuscuta compestris (Y.) are the most important weed species that compete with major crops in Iran. Recently, some newly introduced and invasive weeds, including Hordeum spontaneum (K. Koch.), Cynanchum acutum (L.), Physalis divaricata (L.), and Azolla filiculoides (Lam.), have become a very serious problem in a wide range of crops in different parts of Iran. Chemical control is the predominant weed management practice in Iran. In addition, mechanical weed control methods including soil tillage and hand weeding are applied to a lesser extent. 2,4-D + MCPA (in cereal crops), clodinafop-propargyl (in wheat crop), haloxyfop-r-methyl ester (in broadleaved crops), tribenuron-methyl (in wheat), nicosulfuron (in maize), trifluralin (in oil crops), metribuzin (in potato), glyphosate (in orchards and non-cultivated areas), and paraquat (in waste lands and between crop rows) are the most commonly used herbicides in Iran. There are currently 14 unique cases (species x site of action) of herbicide-resistant weeds in Iran. The most important and newly emerged challenges in Iran are to manage the present noxious and invasive weed species. Increasingly, the evolution of herbicide-resistant biotypes in wheat and other important crops would be another challenge in the future. In addition, the adoption and extension of integrated weed management strategy, addition of suitable adjuvants to herbicide tank mixture, and use of proper sprayers would remain critical challenges in weed management practices in Iran. The integration of weed control methods such as crop rotation, tillage, planting date and pattern, herbicides, and allelopathy would lead to the effective and sustainable management of weeds.
... In some of similar studies, total energy consumption has been reported as 42819.25 MJ ha -1 for apple , 26917.47 MJ ha -1 for maize (Abdi et al., 2012), 62260.90 MJ ha -1 for tangerine (Mohammadshirazi et al., 2012), about 810570 MJ ha -1 for alfalfa , about 19248.04 ...
The objectives of this research were to investigate influences of energy inputs and energy forms on output levels and evaluation of CO2 emissions for hazelnut production in Guilan province of Iran. Moreover, the sensitivity analysis was done by marginal physical productivity (MPP) method for energy inputs and energy using linear regression. Initial data were collected from 120 orchardists in September and October 2012. The total energy of 2862.62 MJ ha-1 was calculated for gardening in one year. The results of energy forms analysis revealed the share of non-renewable and indirect energy was more than renewable and direct energy, significantly. The ratio of energy output to energy input was approximately 3.93. Total CO2 emissions of hazelnut production was calculated as 77.66 kgCO2eq. ha-1. Also, the diesel fuel had the highest share of emissions among all inputs with 33.84%. Econometric model estimation indicated that the impact of human labor, machinery, diesel fuel and pesticides energy inputs were significantly positive on hazelnut yield. The sensitivity analysis was presented that the marginal physical productivity (MPP) value of pesticides, farmyard manure and diesel fuel energy were the highest with 9.43 and -4.86 and 0.97, respectively. In energy forms econometric models, impact of direct, indirect and renewable energies were significantly. Furthermore, direct and renewable energies was the most sensitive groups in energy forms with MPP value of 0.98 and 1.19, respectively.
... It determines the relative efficiency of a number of decision making units (DMUs) and target values for their improvement (Malana and Malano, 2006). In recent years, many authors have applied DEA in agricultural Mohammadi et al., 2011;Mobtaker et al., 2012;Abdi et al., 2012;Taki et al., 2012); therefore the objectives of this study were to specify energy use pattern for wheat production, analyze the efficiencies of farmers and to identify target energy requirement for wheat production. ...
Energy use efficiency in the wheat production as one of the key indicators for developing more sustainable agricultural, was studied by analysis of energy flow in this crop in Kermanshah province of Iran in 2012. Data was collected using questionnaires and face to face interviews with 70 farmers. Total inputs and outputs of wheat systems were calculated and converted to their energy equivalent. Results indicated that total energy input and output in wheat production were 16762.8 and 143070.4 MJ/ha, respectively. The highest share of input energy was recorded for chemical fertilizer (31.5%), diesel fuel (30.04%) and water for irrigation (16.7%). The results also showed that energy ratio, energy productivity and net energy were 8.52, 0.25, and 126307.6 MJ/ha-1 respectively. In this study also the Data Envelopment Analysis (DEA) method is used to estimate the energy efficiencies of wheat producers the following results were obtained: 15.7% and 57.1% of farmers were found to be technically and pure technically efficient, respectively. The average values of technical, pure technical and scale efficiency scores of farmers were found to be 0.83, 0.97 and 0.86, respectively. Also, energy saving target ratio for wheat production was calculated as 2.2 %, indicating that by following the recommendations resulted from this study, about 371.99 MJ ha-1 of total input energy could be saved while holding the constant level of wheat yield.
... calculated as 1.88.In another study, Mobtaker et al. (2010) investigated the energy consumption and inputs sensitivity for barley production in Iran. Econometric model evaluation showed that machinery energy was the most significant input which affects the output level.As well, sensitivity analysis indicates that MPP of biocides energy wasnegative.Abdi et al. (2012) determined the energy balance between the energy inputs and yield for maize production in Kermanshah, Iran. Results showed the energy input of chemical fertilizers had the biggest share within the total energy inputs. In another work, Nabavi- Pelesaraei et al. (2013a) studied the energy inputs-yield relationship for peanut production in ...
The aim of this research was to examine the energy consumption and economic analysis of eggplant production in Guilan province of Iran. Also, the marginal physical productivity (MPP) method was applied to sensitivity of energy and cost inputs using linear regression. For this purpose, data were collected from 60 farmers by a face to face questionnaire. Total input energy and yield was found to be about 13911 MJ ha-1 and 21290 kg ha-1 for eggplant productions, respectively. Also, the highest share of inputs for total energy consumption was belonged to diesel fuel and chemical fertilizers (mainly nitrogen), respectively. The results of energy indices analysis revealed the energy use efficiency, energy productivity, net energy and energy intensiveness were found as 9.03, 1.53 kg MJ-1, 111701.33 MJ ha-1 and 5.19 MJ ha-1. Also the benefit to cost ratio was calculated as 1.67.
This study was conducted to analyze and model the energy consumption and environmental indicators for different wheat production systems in Qazvin province. Information needed for this study, related to 2021–2022, data was collected by monitoring machinery, and inputs during wheat production were interviewed by farmers and filled out questionnaires. The purpose of this study includes: (1) estimating the fuel consumption and energy of inputs in different wheat production systems, (2) estimating the environmental impact associated with input consumption using life cycle assessment (LCA), and (3) providing suggestions for optimization. The results showed wheat production in irrigated conservation tillage system has energy ratio (2.12) and energy productivity (0.18 kg MJ−1) suitable compared to conventional irrigated and rainfed tillage, and conservation rainfed tillage. Nitrogen fertilizer (55%–68%) and fuel consumption (15%–31%) of affect the energy consumption of four wheat cultivation systems. Three categories of damage to human health, ecosystem and resources in LCA were using the ReCiPe2016 method. Resource damage category for conventional tillage irrigation (76.05 USD2013) has significant pollution. The share of seed emissions, On-Farm emissions and nitrogen emissions affect the categories of damage to human health, ecosystems and resources, respectively. The results of the Cobb-Douglas model showed that T3 (fertilization and weed control) was significant at the level of 1% among other inputs. T1 (chisel packer roller), T2 (planting) and T4 (harvest and straw packaging) were significant at 5% level. The results of artificial neural network (ANN) of conventional rainfed cultivation showed that 6 input layers, 7 hidden layers with 10 hidden neurons and 1 output parameter were selected as the best structure. R2 and RMSE for fuel consumption were 0.95 and 43.65, respectively. As a result, the ANN results show higher accuracy than the Cobb-Douglas model.
In recent decades, the growth of energy consumption and environmental pollution has increased significantly. The purpose of this study is to provide a suitable solution for energy, economic and environmental analysis in grape and olive production. The energy output of grapes in traditional and semi-mechanized production was 253110 MJ ha⁻¹ and 284380 MJ ha⁻¹, respectively. Also, olive energy output in traditional and semi-mechanized production was 56286 MJ ha⁻¹ and 83780 MJ ha⁻¹, respectively. Nitrogen consumption in both production methods also has the largest share of energy among the total energy of inputs with about 32 to 47%. The energy use efficiency of semi-mechanized grape production, traditional grape production, semi-mechanized olive production and traditional olive production were reported as 11.26, 9.66, 3.11 and 1.63, respectively. In economic results, the semi-mechanized grape production had a high profit to cost ratio (5.83) and productivity (38.88 kg $⁻¹). The results of life cycle assessment of the two studied products showed that the highest value of aquatic eutrophication is related to the traditional production of olives (1051724.94 kg PO4- P limited) and the lowest value of this effect is related to the semi-mechanized production of grapes (106102.43 kg PO4- P limited). The values of global warming (865.01, 416.56, 143.23 and 122.17) kg CO2eq are related to traditional olive production, semi-mechanized olive production, traditional grape production and semi-mechanized grape cultivation, respectively. The most contaminants in the traditional method of olive (8688.52 PDF*m²*yr) and semi-mechanized olive (3869.91 MJ primary) were related to ecosystem quality and resources, respectively. The emissions of electricity, diesel fuel and machinery were higher in semi-mechanized production than in traditional production. According to results of energy, economic and environmental impacts semi-mechanized cultivation in two crops is better than traditional cultivation.
Forecasting crop yield and its environmental effects can help increase agricultural energy efficiency and reduce environmental impacts. This study provides mathematical, adaptive network-based fuzzy inference system (ANFIS) and neural networks (ANNs) techniques for forecast yield, economic profit, and global warming of wheat production. For this purpose, 75 wheat farms located in the central area of Hamadan province were selected randomly and data were gathered through oral interview. Then, computed the input and output energy, life cycle assessment (LCA) was utilized to specify the environmental effects of wheat cultivation. The calculations displayed that the averages of inputs and outputs energy were about 43055 MJ ha ⁻¹ and 117407 MJ ha ⁻¹ , respectively. The LCA results demonstrated that wheat cultivation cause to the emissions of 624.29 kg CO 2 eq. ton ⁻¹ . ANN structures for predicting yield, economic profit and global warming in wheat production with two hidden layers were the best topologies. ANFIS model results indicated that in the 3-level ANFIS model, the highest R ² is found for net return (0.962). The results comparison showed that ANN and ANFIS models outperform linear models to predict yield, economic profit, and global warming of wheat production.
The current mechanized system of crop production requires a considerable amount of energy. Finding the important factors that lead to improved crop yields is a necessary step towards reducing huge energy inputs and, subsequently, reducing environmental concerns and increasing agricultural sustainability. This research work explores the impact of energy resources and forms of energy on maize production in Nigeria was modeled using the Cobb-Douglas production function and validated by the Durbin-Watson procedure. The effect of input energy on maize yields was determined using partial regression coefficients and the Marginal Physical Product process. Data on agricultural inputs such as human labour, machinery, seed, fuel and agro-chemicals were obtained in 50 established farms, through interviews and using questionnaires. Data were transformed to energy suitable form using appropriate standard energy equations. Research results showed that maize production consumed 9803.78 MJ ha⁻¹ of energy, 45.36% of which was fertilizer followed by fuel (35.90%), machinery (10.72%), herbicide (3.88%) seed (3.61%) and human labour (0.53%) respectively. The contributions of direct and indirect energy were 36.44% and 63.56%, while renewable and non-renewable energy were 4.42% and 95.58%, respectively. The energy ratio, specific energy and productivity value were 2.46, 0.2851 MJ kg⁻¹ and 3.51 MJ kg⁻¹, respectively. The econometrics of the energy resources and out revealed that fertiliser, machinery and human labour energy resources with elasticity 12.98, 9.70 and 8.08, respectively, were the most significant energy resources that had a significant impact on output. The impact of fertilizer, seed fuel, and machinery was significant (p<0.05). The R² and Durbin-Watson values of the developed models indicate that the models were able to predict energy output at different inputs.
The aims of this study were to evaluate the sustainability and efficiency of tomato production and to investigate the determinants of inefficiency of tomato farming in Marand region of East Azerbaijan province, Iran. For these purposes, a two-stage methodology was applied, in which for the first time a fractional regression model (FRM) was employed in the second stage of analysis. So that, in the first stage a non-parametric Data Envelopment Analysis (DEA) was used to analyze the efficiencies of tomato production and in the second stage, farm specific variables such as education level, farmers’ age, total land size and use of manure were used in a fractional regression model to explain how these factors influenced efficiency of tomato farming. The results of the first stage showed that there are considerable differences between efficient and inefficient farmers in the studied area, so that the main differences were in the use of chemical fertilizers, biocides and water for irrigation. Also, the results of second stage revealed that farmers’ age, education level and total land size positively affected efficiency in tomato production. So, better use of land, chemical fertilizers, water for irrigation and improving the farmers’ educational levels through literacy campaign and land consolidation would probably increase the efficiency in the long term.
This study analyzes energy use and investigates influences of energy inputs and energy forms on output levels in Turkish agriculture during the period 1975–2000. The output level was calculated in the form of annual grain equivalent at aggregate level for 104 agricultural commodities except livestock products. Output level was specified as a function of total physical, fertilizer and seed energy, and ordinary least squares was employed to estimate equation parameters. The results show that total energy input has increased from 19.6 GJ/ha in 1975 to 45.7 GJ/ha in 2000, whereas total output energy has risen from 27.1 GJ/ha to a level of 39.1 GJ/ha. Energy efficiency indicators, input–output ratio, energy productivity and net energy have declined over the examined period. Total physical and fertilizer energy, particularly nitrogen, significantly contributed to output level with elasticities of 0.24 and 0.14, respectively. The results also revealed that non-renewable, direct and indirect energy forms had a positive impact on output level. Moreover, Turkish agriculture has experienced a substantial increase in non-renewable energy use. This inefficient energy use pattern in the Turkish agriculture can create some environmental problems such as increase in global warming, CO2 emissions, and non-sustainability. Thus, policy makers should undertake new policy tools to ensure sustainability and efficient energy use.
Energy in agriculture is important in terms of crop production and agroprocessing for value adding. Canola is one of important rapeseed that it is tilled in dry farming systems in north of Iran. The aims of this study were to determine direct input energy and indirect energy in dry farming canola production, to investigate the efficiency of energy consumption and to make an economic analysis of canola farming in Behshahr County of Iran. Data were collected from 62 canola farms by using a face to face questionnaire method. The results revealed that canola production consumed a total of 28705.3 MJ/ha of which chemical fertilizer and diesel fuel energy consumption was 65.5% and 30%, respectively. Output Energy was 41230 MJ/ha. Output– input energy ratio and specific energy of production were 1.44 and 15.1 MJ/kg, respectively. Nonrenewable energy was 99% total input energy that concluded that canola production needs to improve the efficiency of energy consumption in production and to employ renewable energy. Total cost ware was 641.1 USD and Benefit cost ratio and net income ware 0.86 and 550 USD respectively.
The aim of this study was to examine the energy equivalents of inputs and output in sunflower seed (Helianthus annuus L.) oil production in Tokat province of Turkey. Total energy consumption was calculated as 18,931.09 MJ ha. Chemical fertilizers were the highest energy consuming inputs of the different operations (51.28%). The energy consumption for diesel fuel was 28.55% of the total energy input. The energy use efficiency, defined as energy produced per unit of energy used, was 2.95. The specific energy of sunflower seed oil production was determined to be 8,489.28 MJ t. It was found that the direct and indirect energy inputs were 30.41% and 64.13% of the total energy input, respectively. The non-renewable form of energy input was 92.46% of the total energy input compared to only 2.08% for the renewable form.
The energy efficiency and economic benefits of agroforestry systems are key issues with respect to their actual sustainability as sound agricultural practices as well as to their potential for further development. Two typical agroforestry systems in China, the Paulownia [Paulownia elongta S.Y. Hu] intercropping system in the north and the Tea [Camellia sinensis O. Ktze] intercropping system in the south were chosen as research subjects. The studies were conducted to establish the energy balance and economic benefits to farmers of these two agroforestry systems in northern and southern China. The objectives were to determine the potential of developing the Paulownia intercropping system in the north and the Tea intercropping system in the south, and their respective sustainability. Two research sites were selected, and two intercropping systems were designed. All the inputs/outputs data of these two agroforestry systems were collected and documented. Input/output analysis and process analysis were conducted. Energy output/input ratio of Paulownia intercropping system was 1.39, of Non-Paulownia intercropping system (traditional cropping system) was 1.27, an increase of 9.45%. Economic output/input ratios of Paulownia and Non-Paulownia intercropping systems were 2.42 and 2.25, respectively, an increase of 7.56%. Ratios under Tea and Non-Tea intercropping systems reflected the same trend. The Tea energy output/input ratio record was 1.12, compared to 0.06 of the Non-Tea intercropping system, i.e. the former is 18.7 times higher than the latter. Economic output/input ratios under Tea intercropping system (2.3) was 64.29% higher than that of the Non-Tea intercropping system (1.4). It was concluded that these two typical agroforestry intercropping systems have a higher energy efficiency and also a better financial benefits to farmers.
A combination of process and I-O analyses is used to investigate relationships between several kinds of energy uses, product compositions and technical change in Swedish agriculture. Estimates are based on comparisons of three development stages in the transition period from 1956 to 1993. Energy productivity has changed dramatically. Cash crops and non-ruminant animals have been more efficient in their use of support energy, compared to other farm sectors; all sectors have increased their solar energy productivity. Support energy from fertilisers is increasingly a major contributor to solar energy productivity giving 20 kWh biomass per kWh input. Manure has become marginal as a substitute for external energy inputs through fertiliser. Transforming a larger population of produced biomass to useful products outside agriculture, changing from ruminants to non-ruminants production or finding fuel uses of manure will increase energy productivity and sustainability. Contradictions with recent analyses of energy or sustainability may be explained by differences in the perceived influences of technical and structural change or earlier incomplete systems approaches.
The analysis of models is a considerable problem when there is uncertainty in the model parameters and when the model parameters interact and determine the model output in a non-linear way. Usually, sensitivity analysis is applied to deal with the problem of uncertainty. However, if non-linearities and parameter interactions are strong, a sensitivity analysis is valid only in a small region around the chosen parameter combination. Moreover, the result of a sensitivity analysis will depend on the values assigned to the model parameters. A method is suggested in this paper which may be regarded as the `sensitivity analysis of a sensitivity analysis'. It determines how sensitively the output of a sensitivity analysis depends on the values assigned to the model parameters. The results of this analysis are used to identify those parameter combinations which encompass most of the variability in the output of a sensitivity analysis.
Modern farming has become very energy intensive. There is a great need to balance the use and availability of energy, especially in the agricultural sector, in such a manner so as not to affect production adversely. A study was conducted to optimise the energy inputs for the cotton crop in Punjab. Different mathematical functional relations were fitted between the yield and total energy input. An attempt was also made to optimise the energy inputs using frontier production functions (unconstrained frontier function and constrained frontier function). Seedbed preparation, irrigation and weeding consumed about 70% of the total energy input. The average yield of cotton can be increased by 6–8% with an additional energy input of 1–3%, mainly through tillage, irrigation and spraying.
This study examines energy use patterns and the relationship between energy inputs and yield for greenhouse tomato production in Antalya province of Turkey. The data used in this study were based on cross-sectional data collected from growers by using a face to face survey. The results revealed that diesel (34.35%), fertilizer (27.59%), electricity (16.01%), chemicals (10.19%) and human power (8.64%) consumed the bulk of energy. In the surveyed farms, average yield and energy consumption were calculated as around 160000kg/ha and 106716.2MJ/ha, respectively. The results also showed that output–input, specific energy and energy productivity were 1.2, 12380.3MJ/t and 0.09kg/MJ, respectively. The results implied that small size farms were more efficient than large ones in terms of output–input ratio. An econometric model was developed to estimate the impact of energy inputs on yield. Therefore, tomato yield, an endogenous variable was assumed to be a function of exogenous variables; fertilizer, chemicals, machinery, human, water for irrigation and seed energy. The empirical results indicated that all exogenous variables except seed energy were found statistically significant and contributed to yield. Among all statistically significant exogenous variables, human, fertilizer, water, chemicals and machinery were ranked in terms of elasticities. These results indicate that the Turkish greenhouse industry heavily depends on fossil fuels.
Energy balance sheets were calculated based on input data from 76 winter wheat and 21 sugar beet field experiments between 1989 and 1997. N rates applied varied from 0 to 230 kg/ha of N for winter wheat and between 0 and 200 kg/ha of N for sugar beet. The total energy consumption was calculated for the winter wheat and sugar beet production systems including the manufacture of fertilizers, seeds, plant protection substances and machinery, transport from factory to field and all on-farm activities. Energy output in the form of the harvested biomass was calculated using the physical combustion value of the different metabolic components of the grain and beet. A linear relationship was found between increasing energy input into the total system and increasing N fertilizer application. In the absence of N fertilization, total energy input was 7.5 GJ/ha for winter wheat and 8 GJ/ha for sugar beet. This increased to 17.5 GJ/ha and 16 GJ/ha at the highest rates of N fertilization. At each production intensity, defined as N fertilization level, energy output in the form of grain and beet was much higher than energy input. The energy output/input ratio varied between 6 and 13 for winter wheat and 11 and 29 for sugar beet. This variation was dependent on production intensities and growing conditions. Highest energy output/input ratio was observed at low production intensity. The net energy yield increased with increasing energy input. The energy input to obtain the maximum net energy yield was approximately equal to the energy required to achieve economic optimum grain or extractable sugar yield. The calculation of the energy efficiency factor of N fertilizer application revealed that the amount of energy obtained through the increase in harvested biomass due to N fertilization exceeds at least five times the energy input through N fertilizer application.
This study examines energy consumption of inputs and output used in kiwifruit production, and to find relationship between energy inputs and yield in Mazandaran, Iran. For this purpose, the data were collected from 86 kiwifruit orchards which were selected based on random sampling method. The results indicated that total energy inputs were 30285.62 MJ ha−1. About 47% of this was generated by total fertilizer including farmyard manure, 28% from diesel fuel and machinery. About 70% of the total energy inputs used in kiwifruit production was indirect while only about 30% was direct. Econometric estimation results revealed that energy inputs of human labour, water for irrigation, total fertilizer and machinery contributed significantly to the yield. The impact of human labour energy (0.17) was found the highest among the other inputs in kiwifruit production. The results also showed that direct, indirect and renewable and non-renewable, energy forms had a positive impact on output level. Cost analysis showed that total cost of kiwifruit production was obtained as 6063.81 −1) was obtained by dividing kiwifruit yield by total production cost.
This paper studies the energy balance between the input and the output per unit area for greenhouse cucumber production. For this purpose, the data on 43 cucumber production greenhouses in the Tehran province, Iran, were collected and analyzed. The results indicated that a total energy input of 148836.76 MJ ha−1 was consumed for cucumber production. Diesel fuel (with 41.94%) and chemical fertilizers (with 19.69%) were amongst the highest energy inputs for cucumber production. The energy productivity was estimated as 0.80 kg MJ−1. The ratio of energy output to energy input was approximately 0.64. Results indicate 10.93% and 89.07% of total energy input was in renewable and non-renewable forms, respectively. The regression results revealed that the contribution of energy inputs on crop yield (except for fertilizers and seeds energies) was significant. The human labour energy had the highest impact (0.35) among the other inputs in greenhouse cucumber production. Econometric analysis indicated that the total cost of production for one hectare of cucumber production was around 33425.70 $. Accordingly, the benefit–cost ratio was estimated as 2.58.
The objectives of this study were to determine the energy consumption and evaluation of inputs sensitivity for barley production in Hamedan Province, Iran. The sensitivity of energy inputs was estimated using the marginal physical productivity (MPP) method and partial regression coefficients on barley yield. The results revealed that total energy input for barley production was ∼25,027 MJ ha−1; the non-renewable energy shared about 66% while the renewable energy did 34%. Energy use efficiency, energy productivity, and net energy were 2.86, 0.19 kg MJ−1, and ∼46,498 MJ ha−1, respectively. Econometric model evaluation showed that machinery energy was the most significant input which affects the output level. Sensitivity analysis indicates that with an additional use of 1 MJ of each of the human labour, machinery and electricity energy would lead to an increase in yield by 7.37, 1.66 and 0.33 kg, respectively. Also, the MPP of biocides energy was calculated to be −1.97 implying that the use of biocides energy is in excess for barley production, causing an environmental risk problem in the region.
Scarce information is available about the energy use pattern of horticultural commodities in general and more specifically for peri-urban horticulture. Peri-urban horticulture in the outskirts of Bogota is an important source of vegetables for Colombia's capital city. Based on detailed follow-ups and periodic field measurements an output–input energy balance was performed with the main objective to study the energy use efficiency of those systems. An uncertainty analysis on the input factors and on the energy equivalents was then applied. Over a measurement period of 18-month, the energy use for coriander, lettuce, radish and spinach was investigated, respectively 12.1, 18.8, 6.6 and 10.7 GJ ha−1 were consumed in these cropping systems. Negative balances were observed for all species exception made for spinach where an output:input ratio of 1.16 was found. The two-way uncertainty analysis showed the highest uncertainty for N-based fertilization while no significant effect was observed for seeds in direct sowing crops. Sustainability of peri-urban horticulture around Bogota is compromised not only because of the city expansion but also due to its inefficient energy use. Technical improvements are required to ensure the environmental subsistence of this important sector for the metropolitan area of the city.
The Indian hot arid zone, occupying an area of 31.71 Mha, spreads over Western Rajasthan, North Gujrat, South West Haryana and Punjab, some parts of Andhra Pradesh and Karnataka State, but the major part of it (61.8%) lies in the western part of Rajasthan, covering 12 districts commonly known as “Thar Desert” and characterized by harsh climatic conditions with active dunal activities. To add to the misery, there is continuous occurrence of severe drought in the region since the last 2–3 years (1997–98 and 1999–2000).Data on the energy input for cultivating different selective crops for 1999–2000 (drought year) were collected, analysed and presented for the village “Siwas” district, Pali (Zone-IV, rainfall >400 mm/yr). Owing to the drought, farmers of the village have grown kharif crops (being rainfed) by providing life saving irrigation. The maximum energy is required for raising the cotton crop, followed by wheat, mustard, maize and cluster bean. There is more non-renewable form of energy input (73.2%) than renewable form (26.8%) in all the crops. Further, more non-renewable energy is required for cultivating rabi crops compared to kharif. Among the kharif crops, the energy ratio varied from 3.4 to 7.0, suggesting that cotton, having an energy ratio 7.0, is most profitable compared to other crops. However, among the rabi crops, mustard is found most profitable.The crop yield can be correlated with energy input in the form of a second degree polynomial. During a drought period, by providing life saving irrigation, the yields of kharif crops were in agreement with the average yield obtained during a normal rainfall year.
Use of high yielding varieties, increased use of fertilizers and chemicals and mechanized farming of the wheat crop result in high energy use. An energy scenario of wheat production in Punjab was constructed to establish optimum energy input levels and to know the sensitivity of a particular energy input level on productivity for five agro-climatic zones. The sensitivity of a particular energy input on production was assessed using the marginal physical productivity technique and partial regression coefficients. The linear programming technique based on the concept of one-to-one functions was used to optimize the various energy input uses. The study on sensitivity of energy inputs on productivity of wheat revealed that an additional 1 MJ of energy through fertilizers in zone 1, Diesel in zone 3 and chemicals in zone 4 would result in increase in the yield of wheat by 0.118, 0.219 and 0.610 kg, respectively. Statistically, a constant return to scale prevailed for wheat production in all the zones except zone 1, being rain fed. Wide variations in the efficiency rating of input use were observed in zones 1, 2 and 5. Using existing energy inputs, the yield of wheat can be increased by 22.3% in zone 1, 20.8% in zone 2, 6.1% in zone 3, 4.2% in zone 4 and 10.6% in zone 5. On average, the existing level of yield of wheat could be obtained while reducing the energy inputs use by 22.3%, 20.8%, 9.8%, 7.1% and 15.9% in zones 1, 2, 3, 4 and 5, respectively, over the actual energy use.
The aim of this study was to determine direct input energy and indirect energy in per hectare in cotton production and compare with input costs. The study also sought to analyse the effect of farm size. Data were collected from sixty five farmers using a face to face questionnaire. The sample farms were selected through a stratified random sampling technique. The results revealed that cotton production consumed a total of 49.73 GJha−1 of which diesel energy consumption was 31.1% followed by fertilizer and machinery energy. Output–input energy ratio and energy productivity were 0.74 and 0.06 kg of cotton MJ−1, respectively. Cost analysis showed that net return per kilogram of seed cotton was insufficient to cover costs of production in the research area. The most important cost items were labour, machinery costs, land rent and pesticide costs. Large farms were more successful in energy productivity, use efficiency and economic performance. It was concluded that energy management at farm level could be improved to give more efficient and economic use of energy.
The purposes of this study were to determine energy consumption of input and output used in sugar beet production, and to make a cost analysis in Tokat, Turkey. Data were collected from 146 sugar beet farms in Tokat, Turkey by using a face-to-face questionnaire performed in January and February 2005. Farms were selected based on random sampling method. The results revealed that total energy consumption in sugar beet production was 39 685.51 MJ ha−1, and accounted for 49.33% of fertilizer energy, and 24.16% of diesel energy. The output/input energy ratio was 25.75 and energy productivity was 1.53 kg MJ ha−1. Results further indicated that 82.43% of total energy input was in non-renewable energy form, and only 12.82% was in renewable form. Economic analyses showed that profit–cost ratio of farms was 1.17. The highest energy cost items were labor, land renting, depreciation and fertilizers. Although intensive energy consumption in sugar beet production increased the yield, it also resulted in problems such as global warming, land degradation, nutrient loading and pesticide pollution. Therefore, there is a need to pursue a new policy to force producers to undertake energy-efficient practices to establish sustainable production systems without disrupting the natural resources. In addition, extension activities are needed to improve the efficiency of energy consumption and to sustain the natural resources.
Integrated production requires constant improvement of methods employed to achieve high fruit quality and yield with minimal inputs. This work was undertaken to evaluate the energy inputs for apple production, to identify the highest energy consuming operations and propose ways to improve them. Integrated production methods were employed during 1999 and 2000 at 26 apple (Malus domestica Borkh.) orchards in Zagora Pelion (Central Greece). The inputs and outputs of major nutrients (only five farms) as well as energy were calculated and mean values are presented. N inputs were five times higher than outputs and reduction in N fertilization can be considered. Total energy inputs were 50.7 GJ ha−1 and outputs 118.5 GJ ha−1 (51.6 GJ ha−1 from fruit). Chemicals (pesticides, fertilizers), use of machinery and fuel were the most important inputs for apple production, while human labor, although intensively used, accounted for small energy inputs due to conversion factor used. Energy outputs could be improved, as the potential for fruit production is higher compared to fruit production of the study years. Energy productivity was calculated (average of the 2 years) as 0.42 kg MJ−1, energy intensity as 2.50 MJ kg−1 and energy efficiency (only fruit considered) as 1.0. Thus, energy savings could be obtained without significant yield reduction, mainly through reducing fertilizer inputs (especially N), diminishing pest control applications through proper techniques and improving the farm road network.
The objective of this study is to determine the energy use in the Turkish agricultural sector for the period of 1975–2000. In the study, the inputs in the calculation of energy use in agriculture include both human and animal labor, machinery, electricity, diesel oil, fertilizers, seeds, and 36 agricultural commodities were included in the output total. Energy values were calculated by multiplying the amounts of inputs and outputs by their energy equivalents with the use of related conversion factors. The output–input ratio is determined by dividing the output value by the input value. The results indicated that total energy input increased from 17.4 GJ/ha in 1975 to 47.4 GJ/ha in the year 2000. Similarly, total output energy rose from 38.8 to 55.8 GJ/ha in the same period. As a consequence, the output–input ratio was estimated to be 2.23 in 1975 and 1.18 in 2000. This result shows that there was a decrease in the output–input energy ratio. It indicates that the use of inputs in Turkish agricultural production was not accompanied by the same result in the final product. This can lead to problems associated with these inputs, such as global warming, nutrient loading and pesticide pollution. Therefore, there is a need to pursue a new policy to force producers to undertake energy efficient practices to establish sustainable production systems.
The aims of this study were to determine the amount of input–output energy used in dry apricot production, to investigate the efficiency of energy consumption and to make an economic analysis of dry apricot production in Malatya, Turkey. Data used in this study were obtained from 97 farmers using a face to face questionnaire method. The sample farms were selected through a stratified random sampling technique. The population investigated was divided into two strata based on the size of apricot farms as 0.1–3.0 ha (66 farms) and larger than 3.1 ha (31 farms). The results revealed that 28647.03 MJ ha−1 energy were consumed by the first group and 17884.72 MJ ha−1 by the second group of farmers. The input–output ratio and productivities were 1.24 and 0.24 in the first strata and 1.31 and 0.25 in the second strata, respectively. Results further indicated that in both types of farms, 3/4 of the total energy cost was in non-renewable energy forms, and only 1/4 was in renewable forms. The economic analyses showed that the profit-cost ratios of the farms were 1.11 and 1.19, respectively. Net returns calculated were 414.51 ha−1 in the farms investigated. It was concluded that extension activities are needed to improve the efficiency of energy consumption in dry apricot production and to employ environmentally friendly agricultural management practices and production methods.
In this study, the cherries production in Turkey, and the energy equivalences of input used in this production are investigated. The data were collected through a survey study from the business enterprises in the Tokat region, where intensive cherries production takes place. The following results were obtained at the end of the study: with 42%, the input of fertilizer is the highest within the energy equivalences of input used in cherries production. This is followed by electricity and fuel (diesel) with 22% and 21%, respectively. The energy equivalences of chemicals, human labour, machinery and irrigation water were found to be low. The input/output ratio in the production of cherries was found as 0.96. This shows that the inputs used in the production of cherries are not used efficiently. Hence, human resources should be improved, sustainable agriculture should be extended and conscious farming should be provided. The extension staff has very important responsibilities on this subject.
Impacts of organic farming on the efficiency of energy use in agriculture. An organic center state of science review Available in www.organic-center.org
- D Pimentel
Pimentel D. Impacts of organic farming on the efficiency of energy use in agriculture. An organic center
state of science review. 2006. Available in www.organic-center.org/reportfi les/ENERGY_SSR.pdf.
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