Differentials in technical efficiency among smallholder cassava farmers in Central Madagascar: A Cobb Douglas stochastic frontier production approach

Abstract

This study employed the Cobb-Douglas stochastic frontier production function to measure the level of technical efficiency among smallholder cassava farmers in Central Madagascar. A multi-stage random sampling technique was used to select 180 cassava farmers in the region and from this sample, input-output data were obtained using the cost route approach. The parameters of the stochastic frontier production function were estimated using the maximum likelihood method. The results of the analysis showed that individual farm-level technical efficiency was about 79%. The study found education, gender and age to be indirectly and significantly related to technical efficiency at a 1% level of probability, and to household size at a 5% level. The coefficient for occupational status was positive and highly significant at a 1% level. The results show that the study's cassava farmers are not fully technically efficient, showing a mean score of .79%, and suggesting that opportunities still exist for increasing efficiency among the farmers. There is a need, therefore, to ensure that these farmers have access to the appropriate inputs, especially land

Estimating Factors Affecting Technical Efficiency in Indian Manufacturing Sector

“…Stochastic frontier production function approach (SFPFA) and data envelopment analysis (DEA) are the two main techniques which can be used to estimate the technical efficiency (TE) of firms/industries and other sectors of the economy (Aigner, Lovell & Schmidt, 1977;Meeusen & Van den Broeck, 1977;Chavas & Aliber, 1993;Kumbhakar & Lovell, 2003;Rajesh, 2007;Faruq & Yi, 2010;Akpan et al, 2012;Pattnayak & Chadha, 2013;Kumar & Arora, 2012;Hamjah, 2014;Zhou, 2014;Mahajan, Nauriyal & Singh, 2014;Debnath & Sabastian, 2014;Bhatia & Mahendru, 2015;Munisamy, Fon & Khin, 2015;Sahu, 2015;Ng'ombe & Kalinda, 2015;Narwal & Pathneja, 2015;Ikram, Su & Sadiq, 2016); Okoye et al, 2016;Kea, Li & Pich, 2016;Vu, 2016;Fahmy-Abdullah et al, 2017;Gebresilassie, & Nyatanga, 2017;Singh, Narayanan & Sharma, 2018;Singh, Narayanan & Sharma, 2019). Efficiency is defined as the best allocation of resources to achieve the highest level of output (Bhatia & Mahendru, 2015).…”

“…SFPFA model produces regression coefficients of explanatory variables that have a significant association with output, including TE. It can be used in the case of parametric and non-parametric conditions (Ng'ombe & Kalinda, 2015;Okoye et al, 2016). Existing researchers have used SFPFA model to investigate the impact of various factors on output and to examine the TE of individual firm (Akpan et al, 2012;Pattnayak & Chadha, 2013;Zhou, 2014;Sahu, 2015;Ikram, Su & Sadiq, 2016); Vu, 2016;Fahmy-Abdullah et al, 2017).…”

“…In India, limited studies estimate the TE of firms in manufacturing sectors using SFPFA. SFPFA produces regression coefficients of explanatory variables that have a significant association with output including TE (Akpan et al, 2012;Pattnayak & Chadha, 2013;Zhou, 2014;Kea, Li & Pich, 2016;Okoye et al, 2016). Also, limited studies used primary information at the firm level to estimate the TE of firms in India.…”

Measuring the Climate Variability Impact on Cash Crops Farming in India: An Empirical Investigation

“…Existing researchers have used SFPFA to explore the effect of climatic and non-climatic factors on crop productivity and to estimate the technical efficiency (TE) in agricultural production analysis in different economies [31][32][33][34][35][36][37]. SFPFA model can be applied in parametric and non-parametric condition [34,36,37]. It comprises parameters of production and inefficiency function simultaneously in a model [38].…”

Technical Efficiency of Smallholder Tomato Production in Semi-Urban Farms in Cameroon: A Stochastic Frontier Production Approach

“…This model is appropriate for two reasons: (1) Agricultural production in general depends on climatic conditions and is affected by shocks, hence the need to identify and separate this (Abebe, 2014) and (2) stochastic frontier models specify noise separately from efficiency score and can test hypotheses since a functional form is specified. The outstanding advantage of this model is that it contains an inefficiency component which is used statistically to test for the degree of technical inefficiency of households (Abebe, 2014;Haji &Andersson, 2006;Malinga et al, 2015;Okoye et al, 2016;Wudineh&Endrias, 2016) and also less sensitive to outliers (Kavoi et al, 2016). Its main weakness however lies in its assumption of an explicit functional form for technology and frequency for the distribution of the inefficiency terms (Haji &Andersson, jms.ccsenet.org Journal of Management and Sustainability Vol.…”