Factors Affecting Innovative Technologies Adoption by Livestock Holders in Arid Area of Tunisia

Abstract

Despite the efforts to enhance adoption of innovative technologies (IT) by the Tunisian Government through national and/or international development projects, the potential rate of adoption of these technologies has remained low among farmers. This study aims at shedding some light on the potential factors that influence IT adoption in the Tunisian arid areas. Technological, economic, institutional factors and human specific factors (social) are selected to be the determinants of agricultural technology adoption. A quantitative approach employing a cross-sectional design was used to gather data. Stratified random sampling was employed and a total of 200 small-scale farmers (100 adopters and 100 non-adopters) were sampled. Data analysis and assessment was done through descriptive and statistical inferential analysis, and econometric modeling using the binary logistic regression model. Empirical findings show that economic and socio-demographic factors such as farmer education, size of cattle flocks and off-farm income were statistically significant and had positive influence on technology adoption while age and farmer experience had significant and negative effects on IT adoption. The findings confirm the important role of institutional factors (being a member of an association, benefiting from extension services and source of technology knowledge) in the adoption decision of IT, particularly when such variables were found to be significant and positives. In contrast, labor and credit services do not significantly influence adoption of IT. Based on these results, Government should focus on educating young farmers with large cattle flock size and off-farm income to enhance the adoption of IT for livestock holders. It should also intensify training programs for farmers and for extension agents with the collaboration of the project managers and the involvement of the profession and the private sector. Finally, the open innovation strategy including all stakeholders during idea generation could be considered as a better way to decrease technology development costs and improve IT adoption.

TR22 Güney Marmara Bölgesindeki Süt Sığırı İşletmelerinde Yeniliklerin Benimsenmesini Etkileyen Faktörler

“…Similar results were reported by Boz et al (2011) and Boz (2014). However, these results are not congruent with those of Aksoy et al (2011) and Dhraief et al (2018), which concluded that there was a negative correlation between the farmers' age and the adoption of innovations. Thus, it can be said that it is important to make informative meetings about the innovations that can be applied in dairy farming activity to young and middle-aged farmers in the research area and striving towards increase their tendency to adopt innovations.…”

Factors influencing success of artificial insemination of pigs using extended fresh semen in rural smallholder pig farms of Rwanda

“…The household head with primary, secondary and university levels of education presented on average 2.26 ± 0.86, 1.85 ± 0.89 and 1.66 ± 0.61 AIs per conception. The present study results are consistent with the report by Bayei and Nache (2014) and Dhraief et al (2018) who suggest that the adoptions of new technologies are associated with education of the household head. Indeed, Ajala (2007) in Means in the same row with different superscripts differ (p < 0.05).…”

“…The lowest number of IC (1.59 ± 0.67) was observed in the farms managed by a group of 35 to 49 years old farmers while the highest number of IC (2.00 ± 0.90 and 2.04 ± 0.87) was observed in the farms managed by young and older farmers (Below 35 and above 50 years, respectively). This difference within age groups could be justified by the fact that age of the household head has a negative effect on the adoption of new technologies including AI (Dhraief et al, 2018;Bayei and Nache, 2014;Zanu et al, 2012) and innovations (Howley et al, 2012). The old farmers tend to stick to what they used to and hardly venture into new ways of doing things including AI (Bayei and Nache, 2014).…”

Factors Affecting Technology Adoption of Geoisolator on Solar Saltworks in East Java Province

“…Salt farmer participation in the extension had a positive and significant effect with p-value < 0.01 and marginal effect value of 0.2697, which means that the average probability of salt farmer to attend extension to decide on geoisolator implementation was 26.97 percent higher than that of farmers who did not attend the extension. This result is similar to study conducted by Beshir et al (2012) and Asmelash (2014) in Ethiopia, Dhraief et al (2018) in Tunisia, Kumar et al (2018) in aquaculture and Obiero et al (2019) in Kenya, Ahmed & Anang (2019) in Ghana that extension agents showed a significant effect on adoption level since the availability of information provided by extension agents is expected to stimulate adoption. The extension agent as a source of information related to geoisolator technology that has a strong influence on farmers' adoption decisions.…”