Back

Does improved (Limu) maize variety adoption improves rural households' food security: evidence from Dale Wabara district, Ethiopia.

Tesfa, B.; Eneyew, A.; Mitiku, F.

2023-06-07 ecology
10.1101/2023.06.06.543866 bioRxiv
Show abstract

Adopting improved agricultural technology is important instruments to ensure household food security. There is, however, limited empirical evidence on impacts of adoption of improved maize varieties grown by smallholder farmers. Consequently, the study examined the impact of Limu maize variety adoption on rural households food security in Dale Wabara District, Ethiopia. Quantitative and qualitative data were collected from both primary and secondary sources. Three-stage sampling technique was employed to select 319 households for primary data collection. Binary logit model regression model and propensity score matching model were used to investigate determinants of Limu maize variety adoption and its impact on rural households food security, respectively. Furthermore, daily calorie intake per adult equivalent was used to measure food security status of household in study area. According to the results of the binary logit regression model households head education, family size, total land size, frequency of extension visits, livestock ownership and availability of Limu maize seed increases the likelihood of households adoption of Limu maize variety. The result of propensity score matching model shows that adoption of improved Limu maize variety significantly improve household food security (daily calorie intake). Empirical evidence shows that adopter households are better-off than non-adopter households in terms of their daily calorie intake. Thus, it can be concluded that Limu maize variety adoptions significantly contribute to the economic and social development of smallholder farmers by increasing food security status in study areas. It is therefore recommended that wider distribution of Limu maize variety has to be prioritized to improve food security in study areas.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.