Energy use and economical analysis of sugar beet production in Tokat province of Turkey

Erdal, Gülistan; Esengün, Kemal; Erdal, Hilmi; Gündüz, Orhan

doi:10.1016/j.energy.2006.01.007

Cited by 367 publications

(134 citation statements)

References 11 publications

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“…Generally, the rate of N application for growing pistachio trees in the targeted area is considerably lower compared with many other fruit trees and crops (for more details see Yilmaz et al, 2005;Pimentel, 2006;Erdal et al, 2007;Esengun et al, 2007). Over application of chemical fertilizers, especially nitrogen, Energy analysis of pistachio production 665 impose negative impacts on environmental quality and human health, as well as on energy use eff iciency.…”

Section: Energy Inputsmentioning

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: Energy Inputsmentioning

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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“…The error estimation indices of the represented ANN model during testing stage were calculated according to Equation (5) and (6). Mean absolute error and mean relative error were computed as 2.36 ton/ha and 5.59 %, respectively.…”

Section: Evaluation Of the Ann Potato Yield Prediction Modelmentioning

confidence: 99%

Prediction of Potato Yield Based on Energy Inputs using Artificial Neural Networks and C-sharp under Saudi Arabia Conditions

Al-Hamed¹,

Wahby²

2016

Biosci., Biotech. Res. Asia

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The objective of this study was to explore the relation between energy inputs and potato yield using artificial neural network (ANN) under Saudi Arabia conditions. Additionally, the extracted weights from ANN model were formulated using C-sharp language to develop interactive application for friendly and easy use. For this purpose, the energy use pattern was determined by collection data from two sources, the first source was actual field experiments in three sites belong to Riyadh region, Saudi Arabia and the second source from growers by using a face to face questionnaire method. The results indicated that total energy consumption and yield of potato production were different based on production pattern. In this study, for field experiment data, average the energy indices covering energy efficiency, specific energy and energy productivity were calculated at 2.25, 1.60 MJ/kg and 0.63 kg/MJ, respectively. For predicting of potato yield based on energy inputs, artificial neural network (ANN) with standard back propagation algorithm was employed. The results illustrated the ANN model with 6-15-22-1 architecture that had the best condition to the prediction of potato yield. With respect to ANN model, R 2 , mean absolute error and mean relative error were computed as 0.704, 2.36 ton/ha and 5.59%, respectively in testing stage. Moreover, contribution analysis was applied after training process of the ANN model. The results disclosed the water irrigation energy which had the highest contribution (24.75%) to potato yield among all inputs (machinery energy, diesel fuel energy, labor energy, chemicals energy and seeds energy). The developed C-sharp interactive application was tested and it can estimate potato yield. Soil and agronomy researchers, framers and agricultural engineers can use the developed C-sharp interactive application to explore the input variables that have more potential to increase potato yield on a farm. It is user-friendly and could be run on Windows desktop without C-sharp environment.

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“…farm machinery and tillage systems [35,36], fertilizer and herbicide application [37], energy utilization and sustainability [38][39][40], and postharvest technologies [41]. Nonetheless, a large number of studies on energy inputs for crop production have used a computational framework without any modeling [42,11,22,[43][44][45][46][47][48].…”

Section: Why Using Fuzzy Modeling and Dea Technique?mentioning

confidence: 99%

Sustainable and efficient energy consumption of corn production in Southwest Iran: Combination of multi-fuzzy and DEA modeling

Houshyar

Azadi

Almassi

et al. 2012

Energy

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Abstract:The goal of this study was to evaluate the sustainability and efficiency of corn production with regard to energy consumption in the Fars province, Southwest Iran. To reach the goal, fuzzy modeling and Data Envelopment Analysis (DEA) were employed. The fuzzy model included three sub-models which assessed the sustainability use of energy inputs, i.e. machinery, fuel, fertilizer, chemical, human power, seed, and electricity. Some DEA models like CCR (Charnes, Cooper and Rhodes), BCC (Banker, Charnes and Cooper) and SBM (Slack Based Measure) were applied in assessment of efficiency scores. Using non-hierarchical cluster analysis, 89 farmers were chosen in two clusters including 47 and 42 farmers as the first and second cluster, respectively. The farmers in cluster 2 used 44238.235 MJ/ha energy input which was 2500 MJ/ha more than the first cluster. The fuzzy modeling revealed that the sustainability indices were higher in the first cluster. However, the indices were in the ranges of very low to medium for all the farmers, representing that the current corn production system is not sustainable. Also, the DEA models revealed that the first cluster also held more efficient farmers. Since DEA defines a reference set of efficient for inefficient farmers to follow, lead inefficient farmers to improve their sustainability up to efficient ones. Therefore, efficient and inefficient farmers must change the trends of energy utilization for approaching even more sustainable production.

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Energy use and economical analysis of sugar beet production in Tokat province of Turkey

Cited by 367 publications

References 11 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

Prediction of Potato Yield Based on Energy Inputs using Artificial Neural Networks and C-sharp under Saudi Arabia Conditions

Sustainable and efficient energy consumption of corn production in Southwest Iran: Combination of multi-fuzzy and DEA modeling

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