Factors influencing the adoption of Farm Management Information Systems (FMIS) by Brazilian citrus farmers

We analyzed the determining factors on adoption of computers and FMIS.The effects of personal, social and behavioral factors were analyzed.The institutional environment and characteristics of farms are also considered.The results of econometric models confirmed our main hypotheses.Suggestions to increase the diffusion of computers and FMIS were discussed. This paper examined the determining factors in decisions of citrus farmers on adoption of computers and Farm Management Information Systems (FMIS). Primary data were collected from a random representative sample of 98 citrus farmers from the state of So Paulo, Brazil. The data was analyzed using logit and count data (Poisson regression) models, which enabled testing hypotheses on the effect of ten variables on the decisions of farmers. The results of the logit model showed that education and production size had a positive and statistically significant effect on the adoption of computers, while experience had a negative effect. The adoption and intensity of use of FMIS were influenced positively by overconfidence in management, production size and use of technical assistance. Contract adjustments and farmers experience have a negative impact on the adoption of FMIS. The results confirmed the main hypotheses and can contribute to the development of new strategies for greater diffusion of FMIS in Brazilian citrus industry, which is relevant to increasing farm efficiency.

[1]  O. Williamson,et al.  The mechanisms of governance , 1996 .

[2]  Paulo Roberto de Albuquerque Bomfim,et al.  MICHEL ROCHEFORT E O INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA NA DÉCADA DE 1960 , 2015 .

[3]  A. Adrian,et al.  Producers' perceptions and attitudes toward precision agriculture technologies , 2005 .

[4]  Grigorios Emvalomatis,et al.  Factors affecting adoption of economic management practices in beef cattle production in Rio Grande do Sul state, Brazil , 2015 .

[5]  Peter L. Nuthall,et al.  Adoption of computer based information systems , 2006 .

[6]  Ashok K. Mishra,et al.  Farmers' perception of precision technology: The case of autosteer adoption by cotton farmers , 2012 .

[7]  Gerald F. Ortmann,et al.  Computer use and factors influencing computer adoption among commercial farmers in Natal Province, South Africa , 1994 .

[8]  Alberto Zezza,et al.  Meso-Economic Filters Along the Policy Chain: Understanding the links between policy reforms and rural poverty in Latin America , 2002 .

[9]  David Zilberman,et al.  Adoption of Agricultural Innovations in Developing Countries: A Survey , 1985, Economic Development and Cultural Change.

[10]  Hildo Meirelles de Souza Filho,et al.  Determinants of recognition of TRACES certification as valuable opportunity at the farm level in São Paulo, Brazil , 2015 .

[11]  M. Rosenzweig,et al.  Microeconomics of Technology Adoption. , 2010, Annual review of economics.

[12]  Jay T. Akridge,et al.  COMPUTER AND INTERNET ADOPTION ON LARGE U.S. FARMS , 2000 .

[13]  José Maria Ferreira Jardim da Silveira,et al.  CONDICIONANTES DA ADOÇÃO DE INOVAÇÕES TECNOLÓGICAS NA AGRICULTURA , 2011 .

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

[15]  David Zilberman,et al.  Marketing Institutions , Risk , and Technology Adoption , 2005 .

[16]  A. Bilgiç,et al.  Using count data models to determine the factors affecting farmers' quantity decisions of precision farming technology adoption , 2008 .

[17]  Ulrike Malmendier,et al.  Does Overconfidence Affect Corporate Investment? CEO Overconfidence Measures Revisited , 2005 .

[18]  Julie A. Caswell,et al.  Traceability adoption at the farm level: An empirical analysis of the Portuguese pear industry , 2009 .

[19]  Mário Otávio Batalha,et al.  Farm Management Information Systems (FMIS) and technical efficiency: An analysis of citrus farms in Brazil , 2015, Comput. Electron. Agric..

[20]  Hildo Meirelles de Souza Filho,et al.  Determinantes da demanda de crédito rural por pecuaristas de corte no estado de São Paulo , 2013 .

[21]  Kelvin Balcombe,et al.  The Determinants of Technology Adoption by UK Farmers Using Bayesian Model Averaging: The Cases of Organic Production and Computer Usage , 2011 .