Table 12 Studies on Adoption Intensity in Agriculture
From: Intensity of crop and livestock insurance adoption: lessons from Mexico
Authors | Country | Focus | Statistical Models |
|---|---|---|---|
Mishra et al. (2018); Thompson et al. (2022); Yang et al. (2022); Bamire et al. (2010); Bopp et al. (2019) | USA/Europe/China/Borno/Chile | Sustainable agricultural practices | Negative Binomial Regression/ Structural Equation Modelling/Endogenous Switching Regression (ESR)/Tobit regression techniques/Poisson |
Kolady et al. (2021a); Mozambani et al. (2023); Palma-Molina et al. (2023) | South Dakota/Sao Paulo, Brazil/Ireland | Precision Agriculture Technologies/Precision livestock farming (PLF) technologies | Poisson Regression Model/Count Data Regression Model/Multinomial Logistic Regression |
Pedzisa et al. (2015); Ngaiwi et al. (2023); Arslan et al. (2014); Kunzekweguta et al. (2017); Akter et al. (2021) | Zimbabwe/ Eastern and Southern Regions of Cameroon/Zambia/ Bangladesh | Conservation Agriculture Practices | Poisson Regression Model/Multivariate Probit Model/Random effects Tobit and Pooled fractional Probit models/Double hurdle model |
Luh et al. (2023) | Taiwan | Organic farming | Spatial Tobit regression analysis |
Mujeyi et al. (2022); Mthethwa et al. (2022); Zakaria et al. (2020); Aryal et al. (2018); Sardar et al. (2021); Teklu et al. (2023) | Zimbabwe/South Africa/Northern Ghana/India/Pakistan/Ethiopia | Climate-Smart Agriculture | Poisson Regression Model/Multivariate Probit Model/Random effects Tobit and Pooled fractional Probit models/Double hurdle model |
Jara-Rojas et al. (2020); Mgendi et al. (2022); Miine et al. (2023) | Chile/Tanzania/Ghana | Technology Adoption | Cluster Analysis/Poisson Regression Model/Multivariate Probit Model |
Oladimeji et al. (2020) | Nigeria | Soil Conservation Practices | Pooled multivariate Probit and random effects ordered Probit |