Generalized estimating equations approach for spatial lattice data: A case study in adoption of improved maize varieties in Mozambique

Manuel, Lourenço; Scalon, João Domingos

doi:10.1002/bimj.201800360

Cited by 1 publication

(2 citation statements)

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“…Thus, in order to overcome the limitation of such models, in this paper we study the proportion of farmers who adopted improved maize varieties in Mozambique applying generalized estimating equations (GEE) which are more appropriate to spatial and temporal correlated data (Liang and Zeger, 1986;Lin and Clayton, 2005). The inclusion of spatial dependency between observations of the response variable in GEE is accomplished inserting the Moran's index in the working correlation matrix (Manuel and Scalon, 2020). We also compare the results of GEE approach with the results of the traditional methods (logit model).…”

Section: Introductionmentioning

confidence: 99%

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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

Manuel¹,

Silva²,

Scalon³

2022

BJDV

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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.

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Section: Introductionmentioning

confidence: 99%

“…Source:Manuel and Scalon (2020) Brazilian Journal of Development, Curitiba, v.8, n.1, p. 4287-4302 jan. 2022.…”

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confidence: 99%