Energy use and sensitivity analysis of energy inputs for alfalfa production in Iran

Mobtaker, Hassan Ghasemi; Akram, Asadollah; Keyhani, Alireza

doi:10.1016/j.esd.2011.10.009

Cited by 62 publications

(17 citation statements)

References 25 publications

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“…Similar findings were reported by other researchers that the ratio of DE is higher than IDE, and the rate of NRE and CE is greater than RE and NCE consumption in different cropping systems (Ozkan et al, 2004;Khoshnevisan et al, 2013b;Soltani et al, 2013). Ghasemi Mobtaker et al (2012) found that the share of DE, IDE, RE and NRE in alfalfa production systems of Hamedan province were 84%, 16%, 1% and 99%, respectively.…”

Section: Energy Use Pattern In Alfalfa Production Systemssupporting

confidence: 87%

“…Average yield of alfalfa (for 6 years) was found to be 65 500 kg ha −1 and the total energy equivalents was 962.85 GJ ha −1 (Table 4). This amount compares to 1525.21 and 1530.02-1491.52 GJ ha −1 in 7 years as reported by Ghasemi Mobtaker et al (2012Mobtaker et al ( , 2011, respectively.…”

Section: Energy Use Pattern In Alfalfa Production Systemssupporting

confidence: 84%

“…Our results of energy input is closer to the results (496.9 GJ ha −1 ) reported by Ghazvineh and Yousefi (2013) for 6 years from alfalfa agro-ecosystems in Kermanshah province of Iran. However, estimates of energy input from this study are lower than 810.57 GJ ha −1 for whole production life (7 years) of alfalfa hay production during 2001-2007 growing seasons (Ghasemi Mobtaker et al, 2012) and 72.33-82.16 GJ ha −1 for total energy used in 7 years production life of alfalfa in different irrigation systems (Ghasemi Mobtaker et al, 2011) in the Hamedan province of Iran. Also, Zahmatkesh et al (2013) in their research reported that the total energy input in alfalfa production was 62.7 GJ ha −1 while the output energy was 240.0 GJ ha −1 for whole production life and establishment in Zanjan province of Iran.…”

Section: Energy Use Pattern In Alfalfa Production Systemsmentioning

confidence: 85%

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Evaluation of energy input and greenhouse gases emissions from alfalfa production in the Sistan region, Iran

2016

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h i g h l i g h t s• The total input energy was 313.52 GJ ha −1 where the output was 962.85 GJ ha −1 .• Value of total GHGs emission was estimated at 181,190 kg CO 2 e ha −1 .• The highest share of input energy in the production systems belonged to electricity.• Alfalfa was fairly efficient in terms of energy consumption and GHGs emission.• In terms of CO 2 e, 95.3% of the GWP originate from N 2 O, 4.6% from CO 2 and 0.1% from CH 4 . a b s t r a c tThe recognition of forage production methods that maximize energy efficiency and minimize Greenhouse Gases (GHGs) emissions is essential. The aims of this survey were to assess the energy consumption, emissions of GHGs and global warming potential (GWP) of alfalfa production systems in Sistan region, Sistan and Baluchestan province in the South-east of Iran. Data were collected randomly from 110 alfalfa farm using face-to-face questionnaire survey. Energy inputs included chemical fertilizers, diesel fuel, pesticides, seed, machinery and human labor. The results indicated that average total input and output energies in alfalfa production during the entire lifetime of the farm were 313.52 GJ ha −1 and 962.85 GJ ha −1 , respectively. The most important energy inputs belonged to electricity (72.5%), followed by diesel fuel (12.3%) and N fertilizer (6.0%). Energy use efficiency and energy productivity were 3.07 and 0.209 kg MJ −1 , respectively. Share of direct and indirect energy were 85% and 15%, respectively. Total emissions of CO 2 , N 2 O and CH 4 in alfalfa farms were 8262.67 kg ha −1 , 557.31 kg ha −1 and 7.65 kg ha −1 , respectively. Hence, total GWP was 181190 kg CO 2 e ha −1 and 2.77 kg of CO 2 e kg −1 of dry hay produced. In terms of CO 2 e, 95.3% of the GWP originate from N 2 O, 4.6% from CO 2 and 0.1% from CH 4 . Accordingly, efficient use of energy is essential to reduce the greenhouse gas emissions and environmental impact in alfalfa agroecosystems.

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Section: Energy Use Pattern In Alfalfa Production Systemssupporting

confidence: 87%

Section: Energy Use Pattern In Alfalfa Production Systemssupporting

confidence: 84%

Section: Energy Use Pattern In Alfalfa Production Systemsmentioning

confidence: 85%

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Evaluation of energy input and greenhouse gases emissions from alfalfa production in the Sistan region, Iran

2016

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“…Recently published regional studies were used to obtain the inputs of resources and energy and yield output for corn silage (Pishgar Komleh et al, 2011), alfalfa (Mobtaker et al, 2012), barley (Azarpour, 2012), wheat (Shahan et al, 2008), sugar beet pulp (Bazrgar et al, 2011) and citrus pulp (Namdari et al, 2011). These feed items accounted for more than 70% of dry matter in the rations.…”

Section: Agronomy Stagementioning

confidence: 99%

“…In an Indian study, agricultural pumping contributed one-third of the overall energy (Pelletier et al, 2011). However, an Iranian investigation concluded that about 75% of total energy (both direct and indirect) for producing alfalfa was from electricity used to pump water from deep wells (Mobtaker et al, 2012). Recently, Rezae and Gholamian (2013) tested a new photovoltaic water pump system in Gorgan, Iran.…”

Section: Agronomymentioning

confidence: 99%

Greenhouse gas emissions of packaged fluid milk production in Tehran

Daneshi

Esmaili-Sari

Daneshi

et al. 2014

Journal of Cleaner Production

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Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

Nikkhah

Rohani

Rosentrater

et al. 2019

Env Prog and Sustain Energy

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There are some studies regarding the prediction of agricultural energy flows using artificial neural networks (ANNs). These models are quite sensitive to correlations amongst inputs, and, there are often strong correlations amongst energy inputs for agricultural systems. One potential method to remediate this problem is to use principal component analysis (PCA). Therefore, the purpose of this research was to predict energy flows for a specific agricultural system (Iranian tea production) via a novel methodology based on ANNs, and using principal components as model inputs, not raw data. PCA results showed that the first and second components could account for more than 99% of variation in the data, thus the dimensions of the data set could be decreased from six to two for the prediction of energy flows for Iranian tea production. Using these principal components as inputs, an ANN model with 2–15–1 structure was determined to be optimal for energy flow modeling of this system. To conclude, the results of this study highlighted that the use of PC as ANN inputs improved ANN model prediction through reducing its complexity and eliminating data colinearity. Many agricultural systems could benefit from using this methodology for energy modeling. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13130, 2019

show abstract

Energy use and sensitivity analysis of energy inputs for alfalfa production in Iran

Cited by 62 publications

References 25 publications

Evaluation of energy input and greenhouse gases emissions from alfalfa production in the Sistan region, Iran

Evaluation of energy input and greenhouse gases emissions from alfalfa production in the Sistan region, Iran

Greenhouse gas emissions of packaged fluid milk production in Tehran

Integration of principal component analysis and artificial neural networks to more effectively predict agricultural energy flows

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