scholarly journals DEA Performance Measurements in Cotton Production of Harran Plain, Turkey: A Single and Double Bootstrap Truncated Regression Approaches

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 108
Author(s):  
Tamer Işgın ◽  
Remziye Özel ◽  
Abdulbaki Bilgiç ◽  
Wojciech J. Florkowski ◽  
Mehmet Reşit Sevinç

A single and a double bootstrap of data envelopment analysis examines Harran Plain cotton farming in Turkey. The single bootstrap technique was employed to derive the bias-corrected efficiency values under both constant returns to scale (CRS) and versus variable returns to scale (VRS) technologies while discriminating between the two technologies using a smoothed bootstrap test statistic. Results indicated that the farms operated under VRS technology. Given that VRS technology prevailed across Harran Plain cotton farmers sampled, we then determined factors affecting the bias-corrected technical efficiencies using the double bootstrap technique. Another important finding in the single bootstrap analysis is that cotton farmers in the region have a U-shaped technical efficiency based on the input and output scale. Thus, small-scale farmers tend to use their resources more efficiently in cotton farming than that of both medium- and large-scale farmers. Interestingly, the medium-scale farmers with resource inefficiency are at the forefront of the other two types of farmers (i.e., small-scale and large-scale) on the Harran Plain in Turkey. The results also showed that most of the farm and farmer specific as well as economic factors play a significant role in explaining the technical efficiency values.

2002 ◽  
Vol 31 (2) ◽  
pp. 211-220 ◽  
Author(s):  
Kalyan Chakraborty ◽  
Sukant Misra ◽  
Phillip Johnson

Technical efficiency for cotton growers is examined using both stochastic (SFA) and nonstochastic (DEA) production function approaches. The empirical application uses farm-level data from four counties in west Texas. While efficiency scores for the individual farms differed between SFA and DEA, the mean efficiency scores are invariant of the method of estimation under the assumption of constant returns to scale. On average, irrigated farms are 80% and nonirrigated farms are 70% efficient. Findings show that in Texas, the irrigated farms, on average, could reduce their expenditures on other inputs by 10%, and the nonirrigated farms could reduce their expenditures on machinery and labor by 12% and 13%, respectively, while producing the same level of output.


2020 ◽  
Author(s):  
Benjamin Tetteh Anang ◽  
Hamdiyah Alhassan ◽  
Gideon Danso-Abbeam

Abstract The study explored the impact of improved variety adoption on technical efficiency of smallholder maize farmers in Tolon District of northern Ghana. Smallholder maize farmers in the study area were sampled using random sampling technique. Double bootstrap data envelopment analysis was applied to estimate technical efficiency and its determinants. The results indicate that producers in the study area have a bias-corrected technical efficiency of 57% under variable returns to scale (VRS) assumption and 52% under constant returns to scale (CRS) assumption. Controlling for potential endogeneity of the adoption variable, the results indicate that adoption of improved varieties enhance technical efficiency of maize farmers in the study area. Technical efficiency of the farmers increased with herd size but decreased with years of formal education, household size, extension contact, frequency of weeding, and farm size. Ensuring that improved seeds are made available and affordable to smallholder farmers and promotion of livestock rearing are policy measures likely to enhance technical efficiency of smallholder farmers.


1999 ◽  
Vol 2 (1) ◽  
pp. 54-76 ◽  
Author(s):  
S. Mbowa ◽  
W. L. Nieuwoudt ◽  
P. M. Despins

The analysis is based on survey data collected from small and large sugarcane farms during 1995 in the North Coast region of KwaZulu-Natal. A non-parametric research procedure to analyse farm efficiency was employed. Results indicate that farms smaller than eight hectares exhibit substantial economies of size; such economies tend to decline with size of enterprise; and farms larger than 10 hectares appear to have near constant returns to scale. This implies that efficiency of very small scale sugarcane farms can be enhanced by land consolidation while giving small scale farmers larger than 10 hectares access to the large scale commercial sector, may not lead to a loss in efficiency. Results are relevant as South Africa is embarking on settling small scale farmers on former large scale commercial farm land.


2021 ◽  
Vol 34 (1) ◽  
Author(s):  
MOHAMMED AL-SIYABI ◽  
SHEKAR BOSE ◽  
HUSSEIN AL-MASROORI

This paper investigates the extent, dynamics, and factors influencing technical efficiency (TE) and capacity utilisation (CU) in small-scale fisheries (SSF) using a two-stage data envelopment analysis (DEA) approach covering the period 2010–2012. A considerable extent of boat-level technical inefficiency, capacity underutilisation and scale inefficiency were evident. On average, TE and CU levels under the constant returns to scale (CRS) and variable returns to scale (VRS) models declined over time. The TE and CU scores of 2010 remained unaltered with the addition of ‘fishing time’ as an input to the model. The proportion of boats with unitary scale efficiency (SE) decreased from 26 % in 2010 to 12 % in 2012. The underutilisation rates of the inputs ‘crew’ and ‘fishing time’ ranged from 15.5 % to 31.6 % and 15.8 % to 28.6 %, respectively. Among the species category, the extent of excess capacity was 70 % to 156 % and 47 % to 119 % under the CRS and VRS models, respectively. The second-stage DEA results indicated that the explanatory variables ‘fishing location’, ‘catch per unit of effort’ (CPUE), ‘fuel costs’ and ‘crew share’ significantly influenced CU under the CRS model. In contrast, the significant influence of subsidies and other operating costs were noted under the VRS model. For the TE case, ‘age’, ‘education’, ‘subsidy’ and ‘CPUE’ were found to be significant under the CRS and VRS models. Other significant variables were found in the study under CRS and VRS models. Finally, the results from the descriptive and empirical analysis under the two-stage DEA model are discussed together with policy implications.


Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 246
Author(s):  
Markose Chekol Zewdie ◽  
Michele Moretti ◽  
Daregot Berihun Tenessa ◽  
Zemen Ayalew Ayele ◽  
Jan Nyssen ◽  
...  

In the past decade, to improve crop production and productivity, Ethiopia has embarked on an ambitious irrigation farming expansion program and has introduced new large- and small-scale irrigation initiatives. However, in Ethiopia, poverty remains a challenge, and crop productivity per unit area of land is very low. Literature on the technical efficiency (TE) of large-scale and small-scale irrigation user farmers as compared to the non-user farmers in Ethiopia is also limited. Investigating smallholder farmers’ TE level and its principal determinants is very important to increase crop production and productivity and to improve smallholder farmers’ livelihood and food security. Using 1026 household-level cross-section data, this study adopts a technology flexible stochastic frontier approach to examine agricultural TE of large-scale irrigation users, small-scale irrigation users and non-user farmers in Ethiopia. The results indicate that, due to poor extension services and old-style agronomic practices, the mean TE of farmers is very low (44.33%), implying that there is a wider room for increasing crop production in the study areas through increasing the TE of smallholder farmers without additional investment in novel agricultural technologies. Results also show that large-scale irrigation user farmers (21.05%) are less technically efficient than small-scale irrigation user farmers (60.29%). However, improving irrigation infrastructure shifts the frontier up and has a positive impact on smallholder farmers’ output.


Author(s):  
Tiziana Caliman ◽  
Paolo Nardi

The aim of this work is to introduce a first analysis concerning the relevance that ownership and financial structure, but also market dimension and business portfolios, have on the technical efficiency of Italian water utilities. Even though scholars have provided information on the influence of some dimensional or geographical variables, mono-utility character or ownership on efficiency, no paper, to the best of our knowledge, has ever considered the presence of all these hedonic variables as efficiency shifters or drivers. Antonioli and Filippini (2001) have not included ownership; Benvenuti and Gennari (2008) have included ownership and multi-utility strategy, but excluded the geographical dimension; Fabbri and Fraquelli (2000) have not included geographical location, business strategy or ownership; furthermore, most analyses of the Italian water sector have focused on the ATO level (investments, labour costs) and not on utility performances. We have estimated four heteroskedastic stochastic production frontiers: two different parametric models, where the hedonic dummy mono is either in the model as an additional variable or it is used to parameterize the variance of the inefficiency term; two competitive statistical formulations have also been introduced to specify the inefficiency component distribution, that is, the half normal and the exponential distributions. The most important findings of this paper can be summarized as follows. The labour, capital and other input elasticities are always highly significant, positive and quite stable in all the performed models, as expected for a well-behaved production function. The main results show that the mono-business strategy is not efficient; at the same time, operating water and sewerage together implies higher efficiency than water- only management. Theoretically, the population density can have an ambiguous effect on efficiency: on one hand, it could be more expensive to serve dispersed customers, but, on the other, it could generate congestion problems. It could be argued that the second effect prevails, therefore a higher density is accompanied by a higher inef- ficiency. The analysis points out that the variance of the idiosyncratic term is a function of the size of the firm, which is measured as the number of connected properties; the null hypothesis, that the firms use a constant returns-to-scale technology, has also been rejected. Considering the 1994 reform, it is possible to state that the integration of water and sewerage has substantially been positive; at the same time, the economies of scale and the ambiguity of density justify the division into provincial basins. The role of the private sector in the water industry, in agreement with previous literature, has neither a positive nor a negative impact on efficiency and ownership is simply not influent [obviously the quality of service should be considered, although the same indifference seems to emerge (Dore et al., 2001)]. Southern Italy suffers from a higher degree of inefficiency (also recently confirmed by Svimez, 2009), and this is probably the most important issue that has to be dealt with, because of the risks of drought and watering bans in those Regions during summer.


2019 ◽  
Vol 11 (24) ◽  
pp. 6974
Author(s):  
Nalun Panpluem ◽  
Adnan Mustafa ◽  
Xianlei Huang ◽  
Shu Wang ◽  
Changbin Yin

Rice production holds a significant position in the Thai economy. Although it is the world’s largest rice exporter, Thailand’s increase in rice production is the result of an expansion in the cultivation area rather than an increase in yield per unit area. The present study was designed to estimate the technical efficiency and its governing factors for certified organic rice-growing farms in Yasothon Province, Thailand. A data envelopment model was employed to assess the technical efficiency of 328 farmer groups. The data revealed that the average technical efficiency was 23% and 28% under constant returns to scale (CRS) and variable returns to scale (VRS) specifications, respectively. Farmers can reduce the use of machinery, fertilizer, seed, and labor as input factors by about 80.1%, 25.62%, 24.72%, and 19.15%, respectively, while still achieving the same level of output. Multiple regression analysis was applied to estimate factors that affect the pure technical efficiency score (PTES) in the test regions. Results show that household size, farm size, water source, market accessibility, health symptoms, income, and labor were highly related to the TES and the amount of organic rice production. The regression coefficients of the predictors show that the income was the best predictor of the PTES at a significance level of p < 0.05. It is concluded that the farmers can potentially increase their yields by up to 72%–77% under current management practices.


2014 ◽  
Vol 64 (2) ◽  
pp. 197-217 ◽  
Author(s):  
Štefan Bojnec ◽  
Imre Fertő ◽  
Attila Jámbor ◽  
József Tóth

Technical efficiency in agriculture of 10 new EU member states is analysed by Data Envelopment Analysis and econometric panel data analysis. Technical efficiency in agriculture is significantly positively associated with agricultural factor endowments, average farm size, farm specialisation, small-scale farms, and technological change. Foreign direct investments have an ambiguous effect. Reform and institutional developments, large-scale privatisation and price liberalisation, and urban- rural income gap are associated with technical efficiency in agriculture positively. An increase in technical efficiency in agriculture and the development of the rural economy are seen as a strategy to boost the level of living standards in agriculture and in rural areas.


2016 ◽  
Vol 13 (4) ◽  
pp. 470-482 ◽  
Author(s):  
Majed Alharthi

This study empirically estimates efficiency and its determinants in 190 Islamic (IBs), conventional (CBs), and socially responsible banks (SRBs) in 22 countries during the period 2005-2012. The study first uses non-parametric approaches to estimate the efficiency measures (scale efficiency (SE), technical efficiency-constant returns to scale (CRS), and technical efficiency-variable returns to scale (VRS)) and second employs ordinary least squares, fixed effects, random effects, and TOBIT models to get the efficiency determinants. The findings indicate that the average efficiency is 0.966, 0.952, and 0.983 for the SE, CRS, and VRS, respectively. However, efficiency measures show that the SRBs are most efficient banks whereas, the least efficiency scores archived by Islamic banks. Islamic bank efficiency is positively correlated with size, loan intensity, ROA, inflation rates, market capitalization and financial crisis. However, conventional banks’ TE and CRS efficiency are positively and significantly correlated with size, ROA, and market capitalization, while their VRS efficiency is negatively and significantly related to capital ratio, age and GDP. In addition, SRBs’ efficiency is increased by size, capital ratio, loan intensity, ROA, foreign ownership, domestic ownership, inflation and financial crisis. Furthermore, the financial crisis affects the SE and CRS efficiency measures in Islamic banks while socially responsible banks SE efficiency measure is positively affected by the financial crisis, which means that socially responsible banks were stabled and resisted during the crisis period. Finally, there is no significant correlation between financial crisis and efficiency indictors in conventional banks during the period


Author(s):  
Abebe Birhanu Ayele

This study measures the technical and scale efficiency of Micro and Small Enterprises (MSEs) and input slacks using Data Envelop Analysis (DEA) model and identifies the determinants of efficiencies of MSEs by employing ordinary least square (OLS) econometrics model. A sample of 375 randomly selected MESs are included in the study. The study found that the average technical and scale efficiency of MSEs are relatively low; technical efficiency averaged at 30 percent and 38.4 percent under constant returns to scale (CRS) and variable returns to scale (VRS) assumptions, respectively. Besides, the overall average scale efficiency score of MSEs was estimated at 77.8 percent. The highest mean technical and scale efficiencies were registered in the construction (71.8 percent) and manufacturing (85.7 percent) sectors, respectively. Whereas, the lowest technical and scale efficiency goes to urban agriculture sector and service sector, with 38.9 percent and 67.2 percent, respectively. The level of inputs, enterprise age and sector, human capital, labor productivity variables significantly affect relative technical efficiency level of MSEs with different directions while variables such as start-up capital, gender of the enterprise manager and availability of support from the government identified statistically not significant in determining the MSEs&rsquo; technical efficiency.


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