Energy inputs and crop yield relationship in greenhouse tomato production

Hatırlı, Selim Adem; Özkan, Burhan; Fert, Cemal

doi:10.1016/j.renene.2005.04.007

Cited by 266 publications

(118 citation statements)

References 7 publications

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“…Renewable energy included human, labour, manure and seed, while non-renewable sources included diesel, electricity, chemicals, fertilizers, machinery. The energy coefficients of these sources are available in the papers (Croke 1979;Khan, Singh 1996;Ozkan et al 2004;Canakci et al 2005;Hatirli et al 2006). The energy coefficients used in this study are given in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…It enables researchers to calculate output-input ratio, relevant indicators, and energy use patterns in an agricultural activity (Adem Hatirli et al 2006). Also, the energy audit provides sufficient data to establish functional forms to inves-tigate the relationship between energy inputs and outputs.…”

mentioning

confidence: 99%

“…Also, the energy audit provides sufficient data to establish functional forms to inves-tigate the relationship between energy inputs and outputs. Estimating these functional forms is very useful for determining elasticity of inputs on yield and production (Adem Hatirli et al 2006).…”

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confidence: 99%

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Study and determination of energy consumption to produce conventional rice of the Guilan province

Alipour¹,

Veisi²,

Darijani³

et al. 2012

Res. Agric. Eng.

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Alipour A., Veisi H., Darijani F., Mirbagheri B., Behbahani A.G., 2012. Study and determination of energy consumption to produce conventional rice of the Guilan province. Res. Agr. Eng., 58: 99-106.The aim of this study was to determine the energy efficiency indices in the agro-ecosystems of the Guilan province in 2010. One hundred and twenty-seven farmers were interviewed using a particularly designed questionnaire. The inputs in the calculation of energy use in agro-ecosystems embraced labour, machinery, electricity, diesel oil, fertilizers, seeds, while rice and straw yield were included in the output. The results depicted that total input and output energy into these agro-ecosystems were about 47,604 and 90,680.04 MJ/ha, respectively. The highest energy input was related to water (38.84%), electricity (27.87%) and nitrogen fertilizer (17.5%). Energy efficiency and energy productivity in these agro-ecosystems was 2.19 and 0.064 kg/MJ, respectively, and water productivity was 0.11 kg/m 3 . The results also showed that due to application of flood irrigation in these agro-ecosystems and also water elicited from subterranean sources by electrical pump, the inputs had the largest portion among the energy inputs to agro-ecosystems that this matter increased energy use in the unit area and also reduced energy efficiency and productivity.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Study and determination of energy consumption to produce conventional rice of the Guilan province

Alipour¹,

Veisi²,

Darijani³

et al. 2012

Res. Agric. Eng.

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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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“…One of the most common production functions is the Cobb-Douglas production function, which was introduced at a macroeconomic level for the US manufacturing industries for the period 1899-1922 but has been widely applied to individual production processes at the microeconomic level, as well. For example, Shadbegian and Gray (2005) use the Cobb-Douglas production function to model production processes in the paper, steel and oil industries, Hatirli et al (2006) to model agricultural production, and Kogan and Tapiero (2009) to model logistics/supply chain operations. The Cobb-Douglas production function assumes that multiple (m) resources are needed for output, Q and they may be substituted to exploit the marginal cost advantages.…”

Section: Introductionmentioning

confidence: 99%

The dynamic lot-sizing problem with convex economic production costs and setups

Kian

Gürler

Berk

2014

International Journal of Production Economics

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a b s t r a c tIn this work the uncapacitated dynamic lot-sizing problem is considered. Demands are deterministic and production costs consist of convex costs that arise from economic production functions plus set-up costs. We formulate the problem as a mixed integer, non-linear programming problem and obtain structural results which are used to construct a forward dynamic-programming algorithm that obtains the optimal solution in polynomial time. For positive setup costs, the generic approaches are found to be prohibitively time-consuming; therefore we focus on approximate solution methods. The forward DP algorithm is modified via the conjunctive use of three rules for solution generation. Additionally, we propose six heuristics. Two of these are single-stepSilver-Meal and EOQ heuristics for the classical lot-sizing problem. The third is a variant of the Wagner-Whitin algorithm. The remaining three heuristics are two-step hybrids that improve on the initial solutions of the first three by exploiting the structural properties of optimal production subplans. The proposed algorithms are evaluated by an extensive numerical study. The two-step Wagner-Whitin algorithm turns out to be the best heuristic.

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Energy inputs and crop yield relationship in greenhouse tomato production

Cited by 266 publications

References 7 publications

Study and determination of energy consumption to produce conventional rice of the Guilan province

Study and determination of energy consumption to produce conventional rice of the Guilan province

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

The dynamic lot-sizing problem with convex economic production costs and setups

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