Author(s): Zekarias Bassa* and Abule Mechare
Adoption of improved climate smart food crop technologies is known to be the prerequisite for productivity improvement, assuring food security and enabling small scale farmers to widen income opportunities. However, due to different socioeconomic, demographic and institutional; factors the level of food crop technology adoption and utilization is not optimal. A meta-analysis is performed to review empirical estimates of Adoption factors of improved food crop technologies in Ethiopia. The objective of the study is to contribute to a better understanding of the factors that influence adoption of improved food crop technologies. A Critical review was done from data set of 150 significantly influential variables that merged in to 48 observations at different articles from 48 case studies. The synthesized data used in order to test if specific characteristics of the data and econometric specifications account for systematic differences in the adoption influencing factors. The data processed using Multinomial Logit model for estimating probability of food crop technology adoption choices that defined as higher, moderate and lower rate as dependant variable and study period, model type used, study district, sample size, data type and technology type introduced as explanatory variable. From these data and reviewed articles the results showed that using low adoption as bench mark, higher and moderate estimate of Adoption probability of food crop technology significantly affected by Sample size and technology type introduced. The synthesized information implies that larger sample size cannot be ultimate solution for accurate information and different technologies disseminated owned different value across small scale farmers that demands full packaged technologies and awareness creation. Using low adoption rate as base category (<40% adoption),the Results also showed that using the other than Probit model procedure indicate decrease in estimate of adoption probability that pointed out that model selection can play detrimental role in estimating the adoption probability, which also could result in wrong level of decision. The Meta analysis result also indicated that as number of sample size increase, the level of adoption decreases, which indicated existence of data management problem starting from data collection up to processing, which also could not be eased with increased sample size. Using low adoption as reference category, the result showed that Moderate adoption rate also significantly different across the study areas and affected by technology type and Model type applied. Other factors, including the study period and data type do not seem to significantly affect estimates of food crop technology adoption probability. The analysis result also confirmed that the mean size effect of food crop technology adoption estimate is function of training, extension service and credit access, oxen holding, TLU, labor force and income. This implied that through awareness creation, improving and credit, infrastructural development, livestock ownership and income earning opportunity improving, there is an opportunity for accelerating the speed of food crop technologies. The study result also justified that food crop technologies only focused on the specific technology type and quantity, not on how the technology implemented by farmers and how it scaled up, these assumed to be one of the most probable reason for low adoption of improved practices that resulted in low agricultural production and productivity the sector
