Input–output energy analysis of cherries production in Tokat Province of Turkey

Kızılaslan, Halil

doi:10.1016/j.apenergy.2008.07.009

Cited by 272 publications

(108 citation statements)

References 28 publications

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“…Also the contribution of nonrenewable energy to the total energy input was 86% in the pistachio farms. Other reports similarly indicated that the ratio of direct to indirect energy and the rate of non-renewable energy were greater than that of renewable energy consumption in production of various agricultural commodities Kizilaslan, 2009). In developing countries with low levels of technological knowledge the utilization of non-renewable energy sources, is usually high and imposes a serious threat to environment (Fadai, 2007).…”

Section: Forms Of Energymentioning

confidence: 99%

Energy inputs-yield relationship and sensitivity analysis of pistachio (Pistacia vera L.) production in Markazi Region of Iran

Afshar

Alipour

Hashemi

et al. 2013

Span. j. agric. res.

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Pistachio is considered as an important agricultural commodity in Iran and ranks top amongst all exported agricultural products. Conducting an overall energy audit and economic analysis of pistachio production can provide useful information to help implement management strategies for improving energy efficiency. A study was conducted during 2009 and 2010 to evaluate the overall energy inputs and outputs and to perform an economic analysis of pistachio production in the Iranian province of Markazi. The results revealed that the total energy input for pistachio production was 54305 MJ ha -1 . Electricity, followed by diesel fuel and nitrogen fertilizer application were the highest contributors to energy input in pistachio orchards. The contribution of direct energy was higher than indirect energy and share of non-renewable energy was more than renewable energy. Net energy, energy use efficiency, energy productivity and specific energy were 7522 MJ ha -1 , 0.86, 0.08 kg MJ -1 and 13.69 MJ kg -1 , respectively. Econometric model based on Cobb-Douglas function indicated that impacts of electricity, diesel fuel and chemical fertilizer energy inputs on pistachio yield were positive while the impact of machinery was negative. The marginal physical productivity (MPP) value for diesel fuel was the highest among other variables, followed by energy inputs related to the chemicals and irrigation water. The results revealed that production of pistachios in Markazi Province, under current management practices, are not energy efficient and efforts should be made to improve energy use efficiency.Additional key words: econometric model; energy use efficiency; renewable energy; sustainability.

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Section: Forms Of Energymentioning

confidence: 99%

Energy inputs-yield relationship and sensitivity analysis of pistachio (Pistacia vera L.) production in Markazi Region of Iran

Afshar

Alipour

Hashemi

et al. 2013

Span. j. agric. res.

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“…Without energy, farming is impossible; especially, as modern agriculture depends totally on energy use and fossil resources. Energy consumption in agriculture has been increasing in response to the limited supply of arable land, increasing population, technological changes, and a desire for higher standards of living (Hatirli et al, 2006;Kizilaslan, 2008;Manaloor & Sen, 2009). Between 1900 and 2000, the global cultivated area increased 80-100% and energy harvested on farms grew six fold.…”

Section: The Role Of Energy In Agricultural Developmentmentioning

confidence: 99%

“…The inputs in energy analysis in wheat production include direct factors or operational energy consumption (field machinery, human labour, and irrigation pumps (electrical or fuel)) and indirect energy sources (fertilizer, pesticides, and seeds) (Bailey, Gordon, Burton, & Yiridoe, 2008;Kitani, 1999;Kizilaslan, 2008;Mohtasebi, 2008;Ozkan, Kurklu, & Akcaoz, 2004;Safa & Tabatabaeefar, 2002; University of Canterbury. Centre for Advanced Engineering., 1996).…”

Section: Energy Sources In Agriculturementioning

confidence: 99%

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Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Safa

Samarasinghe

2011

Energy

115

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New Zealand farmers practice a form of ‗industrialised' agriculture that relies on relatively high inputs of fossil fuels, not only to power machinery directly but also for manufacturing artificial fertilizers and agrichemicals (Wells, 2001). Consequently, New Zealand is one of the countries with the highest energy input per unit weight (in agriculture) in the world (Conforti & Giampietro, 1997). Furthermore, in terms of shipping, the influence of increasing global fuel costs is greater on New Zealand farming than in other countries. The main aim of this study was to estimate energy consumption in wheat production. Energy determination can give a clear picture of farms in order to compare different farming systems and energy inputs. The second main target of this study was to develop a neural network model to simulate and predict energy use in wheat production under different conditions incorporating social, geographical, and technical factors. Additionally, the interaction effects between different factors were examined in this study.This study was conducted on irrigated and dryland wheat fields in Canterbury, New Zealand, in the 2007-2008 harvest year. Canterbury represents 87% of the wheat area and 66% of the arable area harvested in New Zealand.Energy consumption here is defined as the energy used for the production of wheat until it leaves the farm. The data were collected from three different sources:questionnaire, literature review, and field measurements. The energy inputs estimated in this study are those that go into on-farm production systems before the post-harvest processes. The study considered only the energy used in wheat production, without taking into account the natural sources of energy (radiation, wind, rain, etc). IVA survey was conducted to collect the most important data and to identify farmers' attitudes and opinions about energy consumption. In this study, 40 arable farms were selected randomly, as far as possible. From the initial analysis, it was found that 30 farms were irrigated and the rest were dryland farms. Irrigated farms were irrigated between one to ten times annually depending on the rainfall. Some irrigated farms have also been converted to dryland farms, or vice versa, in different years. The data for a large number of farming factors were gathered in the survey.Average energy consumption for wheat production was estimated at around 22,600 MJ/ha. On average, fertilizer and electricity (mostly for irrigation) were used more than other energy sources, at around 10,654 MJ/ha (47%) and 4,870 MJ/ha (22%), respectively. The average energy consumption for wheat production in irrigated farming systems and dryland farming systems was estimated at 25,600 and 17,458 MJ/ha, respectively. This study is the first to create an appropriate Artificial Neural Network (ANN) model to predict energy consumption in wheat production with optimum variables. This study would be the first to investigate the factors related to the efficient use of energy in agricultural production. A careful study o...

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“…In an earlier and related study, determined using simple random sampling method. The DEA was applied to estimate the technical efficiency of equation is as below [17] input use in I rrigated dairy farms of Australia [10]. Also, Barnes assessed the technical efficiency scores of (1) Scottish dairy farms by applying the non-parametric method of DEA [11].…”

Section: Selection Of Case Study Region and Data Collectionmentioning

confidence: 99%

Assessment and Optimization of Energy Consumption in Dairy Farm: Energy Efficiency

Sefeedpari¹

2012

IJEE

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Abstract:Energy is considered as an important production factor in many systems and therefore should be managed in parallel with other main production resources including land, labor and capital. In this paper, the energy efficiency scores of some selected dairy farms, using the non-parametric data envelopment analysis approach was assessed. The results for efficient farmers based on two DEA models showed that with an energy conservation policy, farmers can perform better in stock farming. The total energy consumption was 72.8 GJ/cow and the top two energy consuming inputs as feed intake and electricity drew the fact that promoting input utilization efficiency seems critical. The mean technical efficiency score of 0.88 made this ample need more apparent. Machinery, electricity and feed were suggested as the main inputs for optimization due to their high contribution in energy saving (46 and 36%, respectively). The least amount of ESTR (Energy Saving Target Ratio) for human labor indicated that it is employed efficiently. By practicing efficiently, dairy farmers are potentially capable of reducing their energy consumption by 16.8%. In this study, adopting new methodologies, cutting the excess use of inputs such as feed stuffs through using standard feeding rations and providing consultancy and extension programs are recommended.

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Input–output energy analysis of cherries production in Tokat Province of Turkey

Cited by 272 publications

References 28 publications

Energy inputs-yield relationship and sensitivity analysis of pistachio (Pistacia vera L.) production in Markazi Region of Iran

Energy inputs-yield relationship and sensitivity analysis of pistachio (Pistacia vera L.) production in Markazi Region of Iran

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Assessment and Optimization of Energy Consumption in Dairy Farm: Energy Efficiency

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