Driving factors toward adoption of improved maize varieties in Mozambique. An approach based on generalized estimating equations for spatial structured data / Determinantes da adopção de variedades melhoradas de milho: Uma abordagem baseada em equações de estimação generalizadas para dados com estrutura espacial

• Autor(a) principal: Manuel, Lourenço
• Data de Publicação: 2022
• Outros Autores: da Silva, Jackelya Araujo, Scalon, João Domingos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Veras
• Texto Completo: https://ojs.brazilianjournals.com.br/ojs/index.php/BRJD/article/view/42802
• topic: Generalized estimating equations; spatial autocorrelation; adoption of maize varieties.

Maize is one of the main economic crops and staple food in Mozambique. However, despite the importance of the crop in the country, maize productivity is still low due to several factors including low adoption of improved agricultural technologies. This paper aimed to identify the main factors driving adoption of improved maize varieties applying generalized estimating equations (GEE). The motivation for this class of models is due to the fact that adoption of improved maize varieties is a spatial auto correlated variable and the traditional probit and logit models widely applied in studies of adoption of agricultural technologies do not take into account the structure of correlation existing in the response variable. The study uses data from Integrated Agrarian Survey of 2012 (IAI 2012). The proportion of small farmers who adopted improved maize varieties per district was used as response variable and a set of nine variables were used as covariates classified in social, economic, institutional and technologic factors. The spatial auto correlation of the dependent variable was assessed by global and local Moran indexes. Two classes of models were fitted: The traditional logistic regression (logit model) and the generalized estimating equations approach. The inclusion of spatial auto correlation in GEE was carried out inserting the Moran’s index in the working correlation matrix. The results have shown that the GEE approach for spatial lattice data was the best and all factors analysed in the study including the spatial dependency are the main factors driving adoption of improved maize varieties in Mozambique.