Modelling adoption intensity of improved soybean production technologies in Ghana - a Generalized Poisson approach

Abstract

Soybean is an important cash crop especially for farmers in the north of Ghana. However, cultivation of the commodity is dominated by smallholders equipped with traditional tools, coupled with low or no adoption of improved soybean production technologies. Using primary data collected from 300 soybean farmers across northern Ghana, the study employed count data modelling to estimate the determinants of adoption intensity of sustainable soybean production technologies. The study accounted for potential estimation errors due to underdispersion and over-dispersion, by using a model based on the generalized Poisson distribution. On the average, a farmer adopted 50% of the identified sustainable soybean production technologies. Age, education, extension visits, mass media through radio, and the perception of adoption of soybean production technologies being risky are significant with positive influence on the adoption intensity of sustainable soybean production technologies. The study therefore recommends among others, that various extension programmes should intensify education on the benefits of adopting sustainable soybean production practices. There is the need to set up many technology demonstration farms to give farmers hands-on training during field days.

Farmer adoption of sustainable intensification technologies in the maize systems of the Global South. A review

“…These models were developed in the first half of the twentieth century (Bliss 1934;Berkson 1944) and started attracting research interest in the 1970s (Zopounidis and Doumpos 2002). For an introduction of probit, logistic, and other limited dependent variable models, one may refer to the standard econometric textbooks such as Maddala (1986) and Wooldridge (2020). Soon, researchers started using these models to examine the determinants of technology adoption.…”

“…Alternatively, the extent of adoption or adoption intensity can be modeled using a Tobit regression framework, which was developed by Tobin (1958) to capture censored continuous outcome variables (e.g., the share of farmland area with a given technology, which is a variable with lower and upper limits of detection at 0 and 100%; e.g., Sinyolo (2020)). Count data models, such as Poisson or negative binomial regression, are used to capture farmer adoption of individual components of a technology portfolio (i.e., adoption intensity) or adoption of multiple complementary technologies in agriculture (Gido et al 2015;Krishna et al 2015;Mahama et al 2020). Extensions of binary variable models, such as multivariable probit/logit and multinomial logit, are used to capture the adoption complementarities between multiple technologies (Kassie et al 2015;Wainaina et al 2016).…”

Assessing farmers' contribution to greenhouse gas emission and the impact of adopting climate-smart agriculture on mitigation

“…The GPR has been studied by Famoye (1993) and has been used in modelling the number of accidents and some covariates by Famoye et al (2004). The GPR has also been recently used by Mahama et al (2020) to model underdispersed count-dependent variable. The mathematical formulation for count data models, and GPR in particular can be found in studies such as Famoye (1993), Famoye et al (2004), and Mahama et al (2020).…”

“…The GPR has also been recently used by Mahama et al (2020) to model underdispersed count-dependent variable. The mathematical formulation for count data models, and GPR in particular can be found in studies such as Famoye (1993), Famoye et al (2004), and Mahama et al (2020). These mathematical formulation and models were not presented in this study for brevity, and lack of space 1 .…”

Modelling Generalized Poisson Regression in the Number of Dengue Hemorrhagic Fever (DHF) in East Nusa Tenggara

“…One of the researches related to Generalized Poisson regression model is Mahama et. al (2020) [3] modeling technology adoption in increasing soybean production using the Generalized Poisson regression model. The dependent variable used is the number of technologies adopted, while significant independent variables include age, education, level of visits, and mass media via radio.…”