• Energy Research

Abstract The aims of this study comprise energy optimization, economic analysis and life cycle assessment in converting paddy to white rice by data envelopment analysis (DEA) and multi-objective genetic algorithm (MOGA). For these purposes, 60 milling factories in Guilan province in Iran are assessed. Results indicate that the amount of energy input and output are 68178.31 MJ TIP−1 and 11894.64 MJ TIP−1, respectively, in converting paddy to white rice, in which natural gas consumption has a very high contribution to the total energy inputs. Life cycle assessment results show that background system for natural gas in milling factories and combustion of natural gas inside factories are environmental hotspots. Based on optimization results in converting paddy to white rice (mainly with lower natural gas consumption), reductions in energy consumption are 6 and 24%, reductions of global warming potential are 8 and 9%, and increase net profits are 24 and 41% by using DEA and MGOA, respectively. It can be said that MGOA is an appropriate optimization method to find the best mix in converting paddy to white rice inputs in order to attain energy, environmental and economic efficiencies.

Abstract Eco-efficiency, defined as the economic profit per unit of environmental impact, can largely differ between farms that produce the same crop. Understanding the underlying drivers of differences in eco-efficiency can help to identify effective options for increasing environmental product performance. Here, we analyzed differences in eco-efficiency between 200 paddy farms in Iran. With multiple linear regression modeling, we assessed the influences of farming system (conventional, limited input, organic) and yield, including potential interactions, on economic profit per unit of impact on ecosystems (terrestrial, freshwater, marine) and human health. Our results showed that the eco-efficiency of organic farming systems is (i) positively associated with yield, and (ii) systematically higher compared to conventional and limited input farming systems. We also found that the eco-efficiency of conventional and limited input systems is positively associated with yield for impacts on terrestrial ecosystems, but not for impacts on freshwater and marine ecosystems and human health. Our results reflect both higher economic profits and lower environmental impacts of organic paddy farms per unit of rice production compared to the other two production systems.

Abstract The objective of this study is to comprehensively optimize energy usage and determine mitigation of greenhouse gas (GHG) emissions in agricultural and horticultural crops of Guilan Province, Iran. For this purpose, required data are collected from eggplant, garlic, tea, hazelnut, kiwifruit and tangerine producers through questionnaires. In this study, GHG emissions are investigated under both On-Farm and Off-Farm sectors. Data envelopment analysis method is employed for the optimization of GHG emissions and energy flow. The highest and lowest energy consumption are related to tea and kiwifruit production, respectively. Results show that kiwifruit and eggplant have the highest scores in technical efficiency whilst tangerine and tea have the highest values in pure technical efficiency. The largest amount of energy is saved in kiwifruit orchards with 8316.29 MJ ha−1. Nitrogen fertilizer and diesel fuel have the topmost energy saving potential in most crops. Kiwifruit orchards have the highest potential for mitigation of GHG gas emissions (520.79 kg CO2 eq. ha−1). Results show that an appropriate usage of nitrogen fertilizer and replacement by organic fertilizer will mitigate GHG emissions as well as energy consumption. It can be concluded that GHG emissions can be mitigated by energy optimization in all the studied crops.

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 CO2eq.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.

Abstract Climate change impacts, limited fossil fuel resources and escalating energy demand result in the use of clean and renewable energies such as solar thermal energy for sustainable agricultural production. In this study, the utilization of photovoltaic (PV) and photovoltaic/thermal (PV/T), which captures the remaining energy and removes waste heat from the PV module, is simulated by TRNSYS software as an alternative energy supplier in energy-environmental life cycle assessment (LCA) of greenhouse (GH) strawberry production in Alborz province, Iran. For this purpose, three scenarios, namely, present (Sc-1), PV (Sc-2) and PV/T (Sc-3) systems, are considered. Results show that the total input and output energy uses are examined to be 919250 and 142618.75 MJ ha−1, respectively, and diesel fuel with about 80% of the total energy usage is the most energy consuming input. Solar system simulation reveals 150 and 147 panels for PV and PV/T systems to supply energy, respectively. Environmental damages are investigated by IMPACT2002 + based on ten-ton of yield as the functional unit. LCA results indicate that diesel fuel and On-Farm emissions have most significant effects amongst damage categories in Sc-1. Moreover, applying solar technologies reduce total damage categories by about 16% and 6% in Sc-2 and Sc-3, respectively. The cumulative exergy demand (CExD) analysis indicates that diesel fuel is a significant portion of energy forms in Sc-1 and solar systems in Sc-2 and Sc-3 reduce the total CExD by about 50% and 33%, respectively compared to the Sc-1. Besides, Sc-2 with PV panels is the most energy-environmental-friendly scenario among them. It should be noted that Sc-3 has less efficiency compared to Sc-2 owing to additional equipment use and the temperate weather during the study. Finally, it can be concluded that trends of energy and environmental damage categories can be modified significantly by applying solar technologies.

AbstractTechnical management of agricultural units plays an important role in increasing the yield, energy efficiency, and decreasing the production costs. Based on that, the present study aimed to evaluate and optimize the technical and economic efficiency in Saffron farms in the 2019–20 cropping season in Iran. Required data were collected from 70 Saffron farms through interviews and questionnaires and were analyzed and compared using two optimization methods including data envelopment analysis (DEA), and multi‐objective genetic algorithm (MOGA). Based on the results related to the energy section, the total energy input was obtained as 43,578 MJ ha−1 before any optimization, while it was determined as 36,033 and 36,910 MJ ha−1 after optimization using DEA, and MOGA, respectively. Also DEA and MOGA methods improved the energy ratio index (ER) (0.002) by 50, and 159%, respectively. Results related to the economic section showed that the total production costs were mitigated from 1260 $ ha−1 to 863.5 and 1069 $ ha−1 after optimization by DEA, and MOGA, respectively. After optimization of revenue (using MOGA method), and total costs (using MOGA, and DEA), the benefit cost ratio index was improved from 1.43 to 2.09 (using DEA), and 3.3 (using MOGA). Consequently, MOGA optimization method showed better results compared to DEA in both energy and economic sections.

Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg-1 and 66,112.94MJkg-1, respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy.