| | Author (year) | Location | Method | Result |
| | [29] | Minjar shenkora woreda | Multinomial logit (MNL) model | The result from MNL analysis showed that age of the household, farmers’ experience, total annual income, access to credit, training, and perception are those variables that positively and significantly influenced the likelihood of adoption of teff row planting among farmers whereas education level, farming experience, training, access to technology input supply and perception towards row planting positively and significantly influenced the intensity of adoption of teff row planting. On the other hand, while landholding size negatively affected the intensity of adoption of teff row planting, the age of household head and land holding size negatively and significantly influenced the adoption of teff row planting. |
| | Fahad and Wang [1] | Charsadda, Peshawar, Mardan, and Nowshera of Khyber-Pakhtunkhwa (KP) province of Pakistan | Contingent valuation (CV) method | The study’s findings revealed that farm households in the study area faced a variety of challenges in adopting certain adaptation measures to deal with climate variability, including a labor shortage, insecure land tenure, lack of market access, poverty, lack of governmental support, lack of access to assets, a lack of water sources, a lack of credit sources, and a lack of knowledge and information. The findings of this study provide helpful information to those in charge of policy implementation. |
| | Dalango and Tadesse [30] | Southern Ethiopia, gena district in Dawro zone | Heckman two-stage model | In the first stage of probit regression, results of the study show that the adoption decision of chemical fertilizer use was driven by factors such as farm size, size of family, family labor, education, access to credit, access to information, and distance to the near market place. In the second stage, the intensification of chemical fertilizer application was influenced by the membership to cooperative, availability of extension service, access to credit, size of farm land, size of a family member, family labor, educational status, and sex of head. The policies that expand the accessibility of credit service, dissemination of productive agricultural technology information, and creating the opportunity of education for farm house hold have the potential to increase the chance of chemical fertilizer adoption decisions and strengthen the level of adoption among smallholder farmers. |
| | [31] | Mlali ward, Tanzania | Descriptive and regression analyses | Results revealed that respondents’ education level, family size, farming experience, availability of sunflower market, and frequency of contacting extension officers significantly influenced the adoption of sunflower farming innovations. However, the sex of the respondent, respondent’s age (years), respondent’s marital status, and livestock ownership did not significantly influence the adoption of sunflower farming innovations. |
| | [27] | Toke kutaye district, oromia regional state | Descriptive statistics and econometric model (tobit) | The model result revealed that variables such as farm size, off-farm income, and livestock asset were positively and significantly influenced agricultural adoptions. |
| | [32] | Duna district | Using binary logit regression | Results revealed that land holding, livestock ownership, and off-farm income use had all significant positive associations with households’ adoption decisions |
| | [21] | West wellega, gulliso district | Binary logit model and PSM model | The selection result showed that farm size, livestock asset, and the perception of farmers about the cost of inputs and off-farm income influence the adoption decision of farm households positively and significantly |
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