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.

The objective of this research study was to assess the sources of information on two improved agricultural and livestock technologies (barley variety and feed blocks) as well as the efficacy of numerous agricultural technology diffusion means introduced in the livestock–barley system in semi-arid Tunisia. The research used primary data collected from 671 smallholder farmers. A descriptive statistical analysis was conducted, and Kendall’s W-test and the chi-squared distribution test were deployed to categorize and evaluate the efficacy of the different methods of technology diffusion used by the Tunisian extension system. To address farmers’ perceived opinions and classify the changes from the use of the improved technologies, a qualitative approach based on the Stapel scale was used. Farmer training, demonstration, and farmer-to-farmer interactions were perceived as the most effective agricultural extension methods. The access to technology, know-how, adoption cost of that technology, and labor intensity for adoption influenced its adoption level. Farmers’ opinions about the changes resulting from the adoption of both technologies revealed that yield and resistance to drought were the most important impacts of the two technologies. The study recommends empowering the national extension system through both conventional and non-conventional technologies (ICT, video, mobile phones, etc.), given the cost-effectiveness and their impact on the farmers’ adoption decisions.

Due to the decrease of household incomes, the increase of food prices, and the negative effects of climate change on agricultural production, Tunisia faces a food insecurity challenge, especially in rural and arid areas. The purpose of our research is to understand and explore household resilience to food insecurity in two villages, Selta and Zoghmar, in central Tunisia. A cross-sectional survey of 250 sample households was conducted in the villages. Factor analysis and regression models were employed to analyze the data using SPSS version 21. The results indicate that only around 36% of the households were resilient at different levels. In Selta, 62.8% and in Zoghmar 66.7% of the households were vulnerable. As indicated by the factor loadings and beta coefficients, income and food access, adaptive capacity, and the social safety net were important dimensions of household resilience to food insecurity, being positively correlated with the resilience index. However, asset possession, and climate change negatively affect household resilience. Therefore, interventions must target strategies that address the different levels of resilience reflected by the resilience estimators. These estimators were generated by focusing mainly on building farmers' knowledge of how to face the different difficulties and challenges.

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