Input–output analysis of energy use in agriculture

Karkacıer, Osman; Göktolga, Ziya Gökalp

doi:10.1016/j.enconman.2004.07.011

Cited by 84 publications

(26 citation statements)

References 3 publications

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“…The main reasons for this little scientific attention are data shortages and lower levels of multi-disciplinary work, which mean researchers, give little attention to marginal subjects in science. However, energy use in agricultural production has been increasing faster than that in many other sectors (Karkacier & Gokalp Goktolga, 2005). It is clear that energy use in modern agriculture has increased; however, the growth rate of production is higher.…”

Section: The Role Of Energy In Agricultural Developmentmentioning

confidence: 98%

“…As discussed before, some studies show that there is a positive relationship between energy usage and productivity (Baruah & Bora, 2008;Hatirli et al, 2006;Karkacier & Gokalp Goktolga, 2005;Karkacier et al, 2006;Outlaw et al, 2005;Singh et al, 2004;Smil, 2008). Also, there is a significant relationship between output energy and weather, price, yield, and technology (Ozkan et al, 2004).…”

Section: Energy Conservation In the Agriculture Sectormentioning

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

116

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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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Section: The Role Of Energy In Agricultural Developmentmentioning

confidence: 98%

Section: Energy Conservation In the Agriculture Sectormentioning

confidence: 99%

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

Safa

Samarasinghe

2011

Energy

116

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“…There have been many studies related to energy use in agriculture (Swanton et al 1996;Pimentel et al 1999;Uhlin 1999;Ozkan et al 2004;Hatirli et al 2005;Karkacier & Goktolga 2005;Kempen & Kraenzlein 2008;Kizilaslan 2009;Bekhet & Abdullah 2010;Unakitan et al 2010;Banaeian & Zangeneh 2011;Asgharipoura et al 2012;Tabatabaie et al 2013). Moreover, many researchers worldwide have studied EUE in the poultry production sector, including broiler (Sibbald 1982;Andrews & Zimmermann 1990;Grobas et al 1999;Atilgan & Koknaroglu 2006;Jekayinfa 2007;Heidari et al 2011a) and laying hens (Ojo 2003;Yusuf & Malomo 2007;Ashagidigbi et al 2011, Sefeedpari et al 2012.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the energy efficiency of poultry houses in the Bursa region of Turkey

Kılıç

2015

Journal of Applied Animal Research

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Energy use in the poultry sector has grown with increasing population and standard of living. The aim of this study was to determine energy use, energy efficiency and energy consumption for broiler and laying farms in the Bursa region of western Turkey. The data were collected using surveys with an interview-based questionnaire, which were administered at 29 broiler farms and 48 layer farms. An energy input-output analysis was performed, and the significance of energy consumption differences between broiler and layer farms was determined by an analysis of variance. The energy consumption of broiler and layer farms was found to be 438.568 and 516.848 MJ(1000 bird) −1 , respectively, whereas energy output was 250.401 MJ(1000 bird) −1 for broiler farms and 384.690 MJ(1000 bird) −1 for layer farms. The forms of indirect energy and renewable energy in the total average energy inputs were higher than the direct and nonrenewable energy forms for all the surveyed poultry farms. It was found that feed had the highest share among the energy inputs for both types of poultry farms. The results of energy use efficiency illustrate that energy in broiler farms was efficiently used, whereas energy use in layer farms was not efficient.

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“…Quantitative relations show how the system's benefits and costs are affected by energy consumptions. Determining energy efficiency has been the main subject of the majority of previous studies (e.g., Singh et al 2000;Karkacier and Goktolga 2005;Rafiee et al 2010;Zangeneh et al 2010;Pahlavan et al 2011;Bojacá et al 2012;Tabatabaie et al 2013). Some studies have published energy and economic analyses of rice crop alone (Pishgar-Komleh et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Efficient use of energy through organic rice–duck mutualism system

Pirdashti

Mohammadi

et al. 2015

Agron. Sustain. Dev.

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Industrial agriculture is not efficient anymore due in particular to the use of nonrenewable energy such as fossil fuels. Organic agriculture is an alternative system that aims to reduce the consumption of fossil fuels, pesticides, and mineral fertilizers. Animal-crop systems present potential mutual benefits, but such systems have been rarely studied quantitatively. Here, we compare energy inputs and outputs of organic riceduck farming with conventional rice production in Mazandaran province, Iran. Our results show that the global energy input of organic rice-duck, 79,307 MJ ha . The most used energy was fuel, amounting to 40 % of total energy for organic rice-duck and 33 % for conventional rice. Then, machinery consumed 25 % of total energy for organic rice-duck and 20 % for conventional rice. Then, fertilizers consumed 20 % of total energy in the form of organic fertilizers for organic rice-duck versus 16 % in the form of mineral fertilizers for conventional rice. Overall, our findings show that organic rice-duck farming has more energy efficiency than conventional rice farming.

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Input–output analysis of energy use in agriculture

Cited by 84 publications

References 3 publications

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

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

Analysis of the energy efficiency of poultry houses in the Bursa region of Turkey

Efficient use of energy through organic rice–duck mutualism system

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