Cost Efficiency and Scope Economies of Crop and Livestock Farms in Missouri

Wu, Siqi; Prato, Anthony A.

doi:10.1017/s1074070800022604

Cited by 21 publications

(13 citation statements)

References 24 publications

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“…Therefore, the choice of which method to use appears to be arbitrary, as is pointed out by Dhungana, Nuthall, and Nartea (2004). We chose the DEA approach, because it imposes no a priori parametric restriction on the underlying technology (Chavas and Aliber, 1993;Fletschner and Zepeda, 2002;Lansink, Pietola, and Backman, 2002;Wu and Prato, 2006).…”

Section: Measurement Of Production Efficiencymentioning

confidence: 99%

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Measurement of Technical, Allocative, Economic, and Scale Efficiency of Rice Production in Arkansas Using Data Envelopment Analysis

Watkins

Hristovska

Mazzanti

et al. 2014

J. Agric. Appl. Econ.

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Data envelopment analysis is used to calculate technical, allocative, economic, and scale efficiencies for fields enrolled in the University of Arkansas Rice Research Verification Program. The results reveal most fields have high technical and scale efficiencies, implying inputs are used in minimum levels necessary to achieve given output levels and fields are close to optimal in size. However, most fields exhibit allocative and economic inefficiencies and do not use inputs in the right combinations necessary to achieve cost minimization. Tobit analysis indicated allocative and economic efficiencies could be improved with better variety selection and better irrigation management.

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Section: Measurement Of Production Efficiencymentioning

confidence: 99%

“…All other variables in equation (2) are as previously defined. The constraint ^Z A, , -= 1 in (2) again ensures that the minimum total costs for the field are calculated under the VRS assumption (Fletscher and Zepeda, 2002;Wu and Prato, 2006). Economic efficiency (EE n ) for each field is then calculated using the following equation:…”

Section: Min6"mentioning

confidence: 99%

Measurement of Technical, Allocative, Economic, and Scale Efficiency of Rice Production in Arkansas Using Data Envelopment Analysis

Watkins

Hristovska

Mazzanti

et al. 2014

J. Agric. Appl. Econ.

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show abstract

“…= the minimum total cost for firm ; = the price for input for firm ; * = the cost minimizing level of input for firm given its input price and output levels; all other variables are as previously defined. The constraint ∑ = 1 ensures that the total minimum costs for the field are calculated under VRS assumption [17,18]. The economic efficiency for each firm can then be calculated using Equation (11)…”

Section: Economic Efficiencymentioning

confidence: 99%

Data Envelopment Analysis Approach to Estimating Economic and Scale Efficiency in Processing Cassava into Gari in Ankpa Local Area, Kogi State, Nigeria

Haruna

Samuel

Amechima

2020

AJAEES

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This study examined the economic and scale efficiency in processing cassava into gari in Ankpa Local Government, Kogi State. Data were collected from 120 cassava processors through a multistage sampling technique in 2019 using questionnaire as the instrument for data collection. Data collected were analyzed through the use of Data Envelopment Analysis (DEA), ordinary Least squares regression analysis and simple descriptive statistics. The result of the study revealed that about 8.33% and 63.33% achieved full technical efficiency (TE = 1) under the CRS and VRS respectively while 12.50% achieved both full allocative and economic efficiency. About 8.33% achieved full scale efficiency. These efficiency scores revealed the presence of considerable level of inefficiency and room for improvement in order to become fully efficient. The returns to scale analysis revealed that majority of cassava processors (about 90%) are operating under increasing returns scale implying that most of the firms in the sample are too small and therefore would benefit from an increase in scale. The OLS result showed that household size, experience and education are the most important and significant factors affecting both technical and economic efficiency of the processors in the study area. We recommend that processors should be encouraged to form and join viable cooperatives where they can access credit, information, training and processing facilities in order to improve their efficiency.

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“…Therefore, the choice of which method to use appears to be arbitrary, as is pointed out by Dhungana et al (2004). We chose the DEA approach in this study, since it imposes no a priori (Chavas & Aliber, 1993;Fletschner & Zepeda, 2002;Wu & Prato, 2006).…”

Section: Defining Efficiencymentioning

confidence: 99%

Analysis of the technical efficiency, pure and scale efficiency of rained cereal farms: Case of the upper semi-arid

hassen¹,

Marwa²,

Hanen³

et al. 2017

JEBAS

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Tunisia is being faced with severe challenges in increasing wheat production in order to enhance the self-sufficiency ratio for wheat production. Using Data Envelopment Analysis (DEA), this paper provides an evaluation of the performance of a sample of rainy Durum Wheat producers in the region of Zaghouan (center of Tunisia) in upper semi-arid by means of overall technical, pure technical and scale efficiency and the performance of the average production unit. The factors which influence the efficiency scores in rainy wheat production, have been also determined using a Tobit model. Data were collected from 105 farms in 2015-2016. Results of study revealed that technical rather than scale inefficiency is the main source of productive inefficiency. The average overall technical, pure technical and scale efficiencies are 58%, 73% and 81% respectively, indicating that production efficiency can be improved by 42% without having to increase their input"s utilization. Most farms, 74.3% operate in increasing return to scale while 12.4 of them have decreasing returns to scale and only 13.3% are scale efficient. Hence, the farmers need some adjustments in their use of production inputs in order to operate on the optimal level. Results of Tobit model analysis showed a positive effect of the education level of farmers, farm size, main occupation, and technical package set including variety, nitrogen use, herbicides, pesticides and previous crops which are important policy variables and determinants of efficiency which can be integrated into the agricultural policy in Tunisia in order to raise the current level of productivity, food security and hence the sustainability development. KEYWORDS Technical efficiencyDurum wheat DEA food security Tunisia

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Cost Efficiency and Scope Economies of Crop and Livestock Farms in Missouri

Cited by 21 publications

References 24 publications

Measurement of Technical, Allocative, Economic, and Scale Efficiency of Rice Production in Arkansas Using Data Envelopment Analysis

Measurement of Technical, Allocative, Economic, and Scale Efficiency of Rice Production in Arkansas Using Data Envelopment Analysis

Data Envelopment Analysis Approach to Estimating Economic and Scale Efficiency in Processing Cassava into Gari in Ankpa Local Area, Kogi State, Nigeria

Analysis of the technical efficiency, pure and scale efficiency of rained cereal farms: Case of the upper semi-arid

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