Efficient use of energy in agriculture is one of the conditions for sustainable production. In the present study energy use pattern for tomato production in Iran was investigated and a non-parametric ...data envelopment analysis (DEA) technique was applied to analyze the technical and scale efficiencies of farmers with respect to energy use for crop production. The energy use pattern indicated that diesel, electricity and chemical fertilizers are the major energy consuming inputs for tomato production in the region. Moreover, the results of DEA application revealed that of the average pure technical, technical and scale efficiencies of farmers were 0.94, 0.82 and 0.86, respectively. Also the results revealed that by adopting the recommendations based on the present study, on an average, about 25.15% of the total input energy could be saved without reducing the tomato yield.
► We used DEA technique to determine the efficiencies of farmers with regard to energy use in tomato production activities. ► The pure technical efficiency, technical efficiency and scale efficiency were found to be 0.94, 0.82 and 0.86, respectively. ► 25.15% of the total input energy could be saved without reducing the tomato yield.
Purpose
The main purpose of this study was to evaluate the use of an integrated life cycle assessment (LCA), artificial neural network, and metaheuristic optimization model to improve the ...sustainability of tomato-based cropping systems in Iran. The model outputs the combination of input usage in a tomato cropping system, which leads to the highest economic output and the least environmental impact.
Methods
The LCA inventory was created using data from 114 open-field tomato farms in the Alborz Province of Iran during one growing period in 2015. Among all management components, the main focus was on irrigation management systems. The optimization problem was designed by integrating three indicators: carbon footprint (CF), benefit-cost ratio (BCR), and energy use efficiency (EUE) as the objective of field tomato production. The functional unit was 1 kg of tomato aligned with the system boundary of the cradle to market life cycle. Three artificial neural networks (ANNs) were applied to model relationships between the inputs and three indices (CF, BCR, and EUE) as the objective functions. Multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) were used to minimize the CF and maximize the BCR and EUE indicators. The abovementioned aims have been pursued by developing codes in MATLAB software.
Results and discussion
CF, BCR, and EUE were calculated to be 0.26 kg CO
2−eq
(kg tomato)
−1
, 1.8, and 0.5, respectively. MOGA results envisage the possibility of an increase of 86% and 50% in the EUE and BCR and a 43% reduction in the CF of tomato production systems. Moreover, EUE and BCR increased by 83% and 49%, and CF was reduced by 39% from the optimum results obtained from the MOPSO algorithm. It was revealed that in order to optimize field tomato production with the target objectives of this study, a large additional use for irrigation pipes, plastic, and machinery in comparison to current situation is required, while a large reduction of biocide, chemical fertilizer, and electricity consumption is indispensable.
Conclusions
According to the results of our study, it was concluded that the optimal solutions require a modernization of irrigation systems and a decrease in the consumption of chemical fertilizers and pesticides. The implementation of management options for such solutions is discussed.
•Input and output data were collected from 140 lentil farms in Esfahan province of Iran.•The energy use in lentil production was inefficient considering the energy indices.•Acidification potential ...was the greatest environmental impact category.•Proper management of fertilizer, diesel fuel and machinery was suggested.•The ANN was useful model to predict the crop yield and environmental indices.
In this study, an Artificial Neural Network (ANN) was applied to model yield and environmental emissions from lentil cultivation in Esfahan province of Iran. Data was gathered from lentil farmers using face to face questionnaire method during 2014–2015 cropping season. Life cycle assessment (LCA) was applied to investigate the environmental impact categories associated with lentil production. Based on the results, total energy input, energy output to input ratio and energy productivity were determined to be 32,970.10MJha−1, 0.902 and 0.06kgMJ−1, respectively. The greatest amount of energy consumption was attributed to chemical fertilizer (42.76%). Environmental analysis indicated that the acidification potential was higher than other environmental impact categories in lentil production system. Also results showed that the production of agricultural machinery was the main hotspot in abiotic depletion, eutrophication, global warming, human toxicity, fresh water aquatic ecotoxicity, marine aquatic ecotoxicity and terrestrial ecotoxicity impact categories, while direct emissions associated with lentil cultivation was the main hotspot in acidification potential and photochemical oxidation potential. In addition, diesel fuel was the main hotspot only in ozone layer depletion. The ANN model with 9-10-6-11 structure was identified as the most appropriate network for predicting yield and related environmental impact categories of lentil cultivation. Overall, the results of sensitivity analysis revealed that farmyard manure had the greatest effect on the most of the environmental impacts, while machinery was the most affecting parameter on the yield of the crop.
•Energy and environmental assessment of a commercial hydroponic greenhouse was done.•PV system were simulated to supply energy demand of greenhouse.•About 120 m2 of solar cells are needed to provide ...electricity demand of greenhouse.•PV system application can improve environmental indicators in strawberry production.
Today, the growing demand for hydroponic greenhouses is driven by the limitations of fertile land and water. In light of the imperative for sustainable development, particularly in energy usage within these greenhouses, the integration of clean energy sources, especially solar power, is essential. This study aims to assess the energy and environmental aspects, as well as the practicality of employing photovoltaic cells to meet the energy requirements of a commercial hydroponic greenhouse in Alborz province. Data was gathered from a 3000 m2 strawberry hydroponic greenhouse through on-site visits, surveys, and measurements. The findings revealed that the total input energy during a production period amounts to 8652.20 GJ ha−1. The energy ratio, net energy, and energy productivity were calculated as 0.03, -8424.20 GJ ha−1, and 0.20 kg GJ−1, respectively. Natural gas accounted for the highest input energy use, followed by electricity, while biocides, human labor, and chemical fertilizers had the lowest energy consumption. According to the LCA results, the damage categories for resources amount to 724.69 USD2013 per 1 ton of strawberries, with natural gas exerting the greatest impact on this measure. The feasibility assessment for solar energy implementation in the greenhouse indicated that approximately 120 m2 of solar cells would be required to generate electricity from solar radiation, covering about 4 % of the greenhouse roof. Furthermore, the use of solar cells was found to enhance energy and environmental indicators.
•Inputs and outputs data were collected from 110 farmers in Esfahan province of Iran.•Energy productivity and energy ratio were computed 0.06kgMJ−1and 1.02, respectively.•Machinery and manure ...management were important to modify energy and environmental performance.•MOGA reduced the environmental impacts much larger than DEA.•Inputs usage obtained from MOGA was significantly lower than the results of DEA.
Energy consumption in agricultural products and its environmental damages has increased in recent centuries. Life cycle assessment (LCA) has been introduced as a suitable tool for evaluation environmental impacts related to a product over its life cycle.
In this study, optimization of energy consumption and environmental impacts of chickpea production was conducted using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) techniques. Data were collected from 110 chickpea production enterprises using a face to face questionnaire in the cropping season of 2014–2015. The results of optimization revealed that, when applying MOGA, optimum energy requirement for chickpea production was significantly lower compared to application of DEA technique; so that, total energy requirement in optimum situation was found to be 31511.72 and 27570.61MJha−1 by using DEA and MOGA techniques, respectively; showing a reduction by 5.11% and 17% relative to current situation of energy consumption. Optimization of environmental impacts by application of MOGA resulted in reduction of acidification potential (ACP), eutrophication potential (EUP), global warming potential (GWP), human toxicity potential (HTP) and terrestrial ecotoxicity potential (TEP) by 29%, 23%, 10%, 6% and 36%, respectively. MOGA was capable of reducing the energy consumption from machinery, farmyard manure (FYM) diesel fuel and nitrogen fertilizer (the mostly contributed inputs to the environmental emissions) by 59%, 28.5%, 24.58% and 11.24%, respectively. Overall, the MOGA technique showed a superior performance relative to DEA approach for optimizing energy inputs and reducing environmental impacts of chickpea production system.
The objective of this study was the application of non-parametric method of data envelopment analysis (DEA) to analyze the efficiency of farmers, discriminate efficient farmers from inefficient ones ...and to identify wasteful uses of energy for alfalfa production in Hamedan province, Iran. This method was used based on eight energy inputs including human labor, machinery, diesel fuel, fertilizers, farmyard manure, biocide, electricity and seed energy and single output of alfalfa yield. Technical, pure technical, scale and cross efficiencies were calculated using CCR and BCC models for farmers. From this study the following results were obtained: from the total of 80 farmers, considered for the analysis, 46% and 69% were found to be technically and pure technically efficient, respectively. The average values of technical, pure technical and scale efficiency scores of farmers were 0.84, 0.97 and 0.89, respectively. Also, energy saving target ratio for alfalfa production was calculated as 9.4%, indicating that by following the recommendations resulted from this study, about 75.90GJ ha−1 of total input energy could be saved while holding the constant level of alfalfa yield. Moreover the contribution of electricity input from total saving energy was 77.4% which was the highest share followed by chemical fertilizers (15%) and diesel fuel (4%) energy inputs. Optimization of energy use improved the energy use efficiency, energy productivity and net energy by 10.6%, 10.4% and 10.6%, respectively.
► We used a non-parametric method of DEA to analyze the efficiency of alfalfa farmers. ► About 46% of farmers were technically and 69% were pure technically efficient. ► Technical, pure technical and scale efficiency scores were 0.84, 0.97 and 0.89. ► Optimization of energy use improved the energy use efficiency by 9.4%. ► Contribution of electricity input from total saving energy was the highest.
Corn silage and alfalfa hay are the main feedstuffs for dairy farming, and due to the drop in corn price, there is a tendency toward replacing alfalfa hay with corn silage in dairy cow diets. This ...study focuses on the comprehensive assessment of alfalfa hay and corn silage production systems from energy, economic and environmental perspectives. The life cycle assessment (LCA) from raw material extraction up to delivery of forage to the dairy farms was conducted to quantify the ecoprofile of forage production. The results of comparative analyses revealed that corn production was more dependent on chemical fertilizers specially nitrogen, but alfalfa production was more dependent on electricity, water and diesel fuel for farm operations. Energy use per ton of alfalfa hay dry matter was higher than that of corn silage (2.6 vs. 2.4 GJ t−1). On the other hand, output energy of alfalfa hay was considerably higher than that of corn silage (15.8 vs. 8.0 GJ t−1). Consequently, energy use efficiency of alfalfa hay was almost two times of that of corn silage (6.1 vs. 3.3). Nevertheless, corn silage had higher benefit-to-cost ratio than alfalfa hay (2.55 vs. 2.27), and higher environmental impacts in the forms of abiotic depletion, global warming potential, marine aquatic ecotoxicity, photochemical oxidation, acidification and eutrophication potential. Finally, the effects of varying input parameters of machinery and diesel fuel, electricity, seeds, fertilizers, pesticides and transportation on LCA results were assessed via sensitivity analysis.
•Comprehensive energy, economic and LCA of dairy feedstuff were conducted.•Alfalfa hay is more energy efficient and environmentally friendly than corn silage.•Corn silage production system was more economically profitable than alfalfa.
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•Energy and life cycle assessment of walnut are computed by IMPACT 2002+.•MOICA is used to optimize output energy and total weighted damages.•Gasoline and On-Orchard emissions are the ...most effective factors in energy and LCA.•MOICA can save total energy and damages by about 19316 MJ ha−1 and 1.47 Pt.•Gasoline and ecosystem quality have most potential in saving by MOICA.
Although the agricultural sector is an important source of bioenergy production, this production can be considered sustainable when energy consumed and environmental emissions are optimal. As such, the assessment of energy flow, environmental emissions of walnut orchards in Alborz province of Iran and their simultaneous optimization by multi-objective imperialist competitive algorithm are the main goals of this investigation. Input-output energy analysis, IMPACT 2002+ method of life cycle assessment, and multi-objective imperialist competitive algorithm are used in the energy-environmental evaluation for optimization in this study. Results ascertain that energy uses of the entire output and input are computed to be 31,015 and 27200 MJ ha−1, respectively and that gasoline with 40% is the dominated consumer of energy. Moreover, energy use efficiency is 0.88, which indicates energy inefficiency in walnut production. Environmental results shows that On-Orchard emissions with a share more than 50% in ecosystem quality, human health, and climate changes and gasoline in resources category are the main hotspots. Multi-objective optimization illustrates that the reduction in total energy is 19316 MJ ha−1 (about 62%) and gasoline with 58% is the most energy saving input among all. On the other hand, the total weighted emission decreases by about 1.47Pt (about 40%). Generally, results reveal that timely maintenance can help orchardist attain close to optimal condition. Furthermore, the application of imperialist competitive algorithm not only can offer optimum pattern of walnut production, but also be extended to the world for different crops.
In this study, various Artificial Neural Networks (ANNs) were developed to estimate the production yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 ...greenhouses in the region during four periods of plant cultivation in 2009–2010. The total input energy and energy ratio for basil production were 14,308,998 MJ ha
−1 and 0.02, respectively. The developed ANN was a multilayer perceptron (MLP) with seven neurons in the input layer, one, two and three hidden layer(s) of various numbers of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chemical fertilizers, farm yard manure, chemicals, electricity and transportation. Results showed, the ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coefficient of determination (
R
2) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, respectively. Sensitivity analysis revealed that chemical fertilizers are the most significant parameter in the basil production.
► ANNs were adopted to predict production yield of greenhouse basil in Iran. ► Input energy and energy ratio were 14,308,998 MJ ha
−1 and 0.02. Diesel fuel was the main energy consuming input. ► A two hidden layer network having 7-20-20-1 topology was selected as the best model for estimating basil yield. ► For this model,
R
2, RMSE and MAE were 0.976, 0.046 and 0.035, respectively. ► Sensitivity analysis revealed chemical fertilizer is the most significant parameter in the basil production.
The aim of this study was to determine the amount of input–output energy used in potato production and to make an economic analysis of potato production in Hamadan province, Iran. Data for the ...production of potatoes were collected from 100 producers by using a face to face questionnaire method. The population investigated was divided into two groups. Group I was consisted of 68 farmers (owner of machinery and high level of farming technology) and Group II of 32 farmers (non-owner of machinery and low level of farming technology). The results revealed that 153071.40 MJ ha
−1 energy consumed by Group I and 157151.12 MJ ha
−1 energy consumed by Group II. The energy ratio, energy productivity, specific energy, net energy gain and energy intensiveness were calculated. The net energy of potato production in Group I and Group II was 4110.95 MJ ha
−1 and −21744.67 MJ ha
−1, respectively. Cost analysis showed that total cost of potato production in Groups I and II were 4784.68 and 4172.64 $ ha
−1, respectively. The corresponding, benefit to cost ratio from potato production in the surveyed groups were 1.09 and 0.96, respectively. It was concluded that extension activities are needed to improve the efficiency of energy consumption in potato production.
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