A regression analysis of the effect of energy use in agriculture

Karkacıer, Osman; Göktolga, Ziya Gökalp; Çiçek, Adnan

doi:10.1016/j.enpol.2005.09.001

Cited by 70 publications

(44 citation statements)

References 5 publications

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“…At the current New Zealand energy demand (around 600 PJ), coal can produce enough energy for 250-300 years (Hood et al, 2007). 1 9 7 4 1 9 7 7 1 9 8 0 1 9 8 3 1 9 8 6 1 9 8 9 1 9 9 2 1 9 9 5 1 9 9 8 2 0 0 1 2 0 0 4 2 0 0 7 The existing link between the growth of fossil energy use and increase in biophysical productivity of modern economies implies that technical change has not provided any real 'emancipation' of production from the natural resources base (Karkacier et al, 2006;Mayumi, 1991). During the 1990s, most countries became energy efficient over time.…”

Section: Energy Consumption In New Zealandmentioning

confidence: 99%

“…However, the percentage of energy use in the agriculture sector in developing and developed countries is similar. Energy needs for agricultural production are about 3% of the national energy consumption in developed countries and about 3.6% in developing countries (Karkacier et al, 2006;Sauerbeck, 2001;Stout, 1990). However, the energy input per hectare in developing countries for agricultural production is about 7,700 MJ and in developed countries, it is about 37,900 MJ.…”

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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: Energy Consumption In New Zealandmentioning

confidence: 99%

mentioning

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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“…Karkacier et al (2006) investigate the energy use effects on agricultural productivity in Turkey. Strong relationship between energy use and agricultural productivity is proved and increasing energy use increases agricultural productivity.…”

Section: Uses Panel Cointegration Techniquesmentioning

confidence: 99%

The role of renewable energy and agriculture in reducing CO 2 emissions: Evidence for North Africa countries

Jebli

Youssef

2017

Ecological Indicators

390

130

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this paper uses panel cointegration techniques and Granger causality tests toinvestigate the dynamic causal links between per capita renewable energy consumption, agricultural value added (AVA), carbon dioxide (CO 2 ) emissions, and real gross domestic product (GDP) for a panel of five North Africa countries spanning the period 1980-2011. In the short-run, the Granger causality tests show the existence of a bidirectional causality between CO 2 emissions and agriculture, a unidirectional causality running from agriculture to GDP, a unidirectional causality running from GDP to renewable energy consumption, and a unidirectional causality running from renewable energy consumption to agriculture. In the long-run, there is bidirectional causality between agriculture and CO 2 emissions, a unidirectional causality running from renewable energy to both agriculture and emissions, and a unidirectional causality running from output to both agriculture and emissions. Long-run parameter estimates show that an increase in GDP and in renewable energy consumption increase CO 2 emissions, whereas an increase in agricultural value added reduces CO 2 emissions. As policy recommendation, North African authorities should encourage renewable energy consumption, and especially clean renewable energy such as solar or wind, as this improves agricultural production and help to combat global warming.

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“…energy consumption. Karkacier et al (2006) investigate the impacts of energy use on agricultural productivity in Turkey. They show the existence of a strong relationship between energy use and agricultural productivity.…”

Section: Literature Reviewmentioning

confidence: 99%

Renewable energy consumption and agriculture: evidence for cointegration and Granger causality for Tunisian economy

Jebli

Youssef

2016

International Journal of Sustainable Development & World Ec

179

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This paper uses the vector error correction model (VECM) and Granger causality tests to investigate short and long-run relationships between per capita carbon dioxide (CO 2 ) emissions, real gross domestic product (GDP), renewable and non-renewable energy consumption, trade openness ratio and agricultural value added (AVA) in Tunisia spanning the period 1980-2011. The Johansen-Juselius test shows that all our considered variables are cointegrated. Short-run Granger causality tests reveal the existence of bidirectional causalities between AVA and CO 2 emissions, and between AVA and trade; unidirectional causalities running from non-renewable energy and output to AVA and to renewable energy, and from CO 2 emissions to renewable energy. Interestingly, there are long-run bidirectional causalities between all considered variables. Our long-run parameters estimates show that non-renewable energy, trade and AVA increase CO 2 emissions, whereas renewable energy reduces CO 2 emissions. In addition, the inverted U-shaped environmental Kuznets curve (EKC) hypothesis is not supported. Our policy recommendations are to increase international economic exchanges because this gives new opportunities to the agricultural sector to develop and to benefit from renewable energy technology transfer. Subsidizing renewable energy use in the agricultural sector enables it to become more competitive on the international markets while polluting less and contributing to combat global warming.

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A regression analysis of the effect of energy use in agriculture

Cited by 70 publications

References 5 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”

The role of renewable energy and agriculture in reducing CO 2 emissions: Evidence for North Africa countries

Renewable energy consumption and agriculture: evidence for cointegration and Granger causality for Tunisian economy

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