Farm Management Information Systems (FMIS) and technical efficiency: An analysis of citrus farms in Brazil

Abstract This study aims to investigate the impacts of personal aspects and aspects of the decision-making process (Rougoor et al., 1998) on the technical efficiencies of citrus farms in Brazil. A variable that measures the adoption of Farm Management Information Systems (FMIS) was created, which is an innovative aspect of this paper. Primary data for the crop year of 2013/14 (cross sectional data) were collected from a sample of 98 farms located in the State of Sao Paulo, one of the largest citrus regions in the world. The single stage model developed by Battese and Coelli (1995) was used to estimate the stochastic production frontier translog and the determinants of the efficiency of farms. The results showed that the technical efficiency scores across citrus farms range from 28% to 97%, with a mean of 75%. The expectation formation (personal aspect), the use of long-term contracts and the adoption of Farm Management Information Systems (aspects of decision-making processes) were significant determinants of technical efficiency. This result confirms the hypotheses built around these variables.

[1]  F. Madau Parametric Estimation Of Technical And Scale Efficiencies In Italian Citrus Farming , 2010 .

[2]  Philip Garcia,et al.  Measuring the effect of risk attitude on marketing behavior , 2014 .

[3]  Ruud B.M. Huirne,et al.  How to define and study farmers' management capacity: theory and use in agricultural economics , 1998 .

[4]  F Kuhlmann,et al.  Information technology and farm management: developments and perspectives , 2001 .

[5]  M. Batalha,et al.  Optimization model of agricultural production system in grain farms under risk, in Sorriso, Brazil , 2014 .

[6]  Andrés J. Picazo‐Tadeo,et al.  Outsourcing and efficiency: the case of Spanish citrus farming , 2006 .

[7]  Peter L. Nuthall,et al.  Case studies of the interactions between farm profitability and the use of a farm computer , 2004 .

[8]  Jack P. C. Kleijnen,et al.  Economic value of management information systems in agriculture: a review of evaluation approaches. , 1995 .

[9]  D. Hadley,et al.  Measuring and Explaining Technical Efficiency in UK Potato Production , 1998 .

[10]  A. Mishra,et al.  Does the Milk Income Loss Contract program improve the technical efficiency of US dairy farms? , 2011, Journal of dairy science.

[11]  D. Aigner,et al.  P. Schmidt, 1977,?Formulation and estimation of stochastic frontier production function models,? , 1977 .

[12]  G. Battese,et al.  A model for technical inefficiency effects in a stochastic frontier production function for panel data , 1995 .

[13]  A. Alvarez,et al.  Technical efficiency and farm size: a conditional analysis , 2004 .

[14]  Ruud B.M. Huirne,et al.  Measuring managerial efficiency: the case of commercial greenhouse growers , 2002 .

[15]  Spyros Fountas,et al.  Farm management information systems: Current situation and future perspectives , 2015, Comput. Electron. Agric..

[16]  V E Cabrera,et al.  Determinants of technical efficiency among dairy farms in Wisconsin. , 2010, Journal of dairy science.

[17]  Hurvey Leibenstein Allocative efficiency vs. X-Efficiency , 1966 .

[18]  Paul Wilson,et al.  The influence of management characteristics on the technical efficiency of wheat farmers in eastern England , 2001 .

[19]  Dionysis Bochtis,et al.  Conceptual model of a future farm management information system , 2010 .

[20]  M. Fasiaben,et al.  Heterogeneidade da agricultura brasileira no acesso às tecnologias da informação , 2014 .