Adoption of improved agricultural technologies remains to be a promising strategy to achieve food security and poverty reduction in many developing countries. However, there are limited rigorous impact evaluations on the contributions of such technologies on household welfare. This paper investigates the impact of improved agricultural technology use on farm household income in eastern Ethiopia. Primary data for the study was obtained from a random sample of 248 rural households, 119 of which are improved technology users and the rest are non-users. The research employed the Propensity Score Matching (PSM) procedure to establish the causal relationship between adoption of improved crop and livestock technologies and changes in farm income. Results from the econometric analysis show that households using improved agricultural technologies had, on average, 23,031.28 Birr (Birr is the official currency of Ethiopia. The exchange rate according to the National Bank of Ethiopia (NBE) was 1 USD = 27.6017 Birr on 04 October 2018.) higher annual farm income compared to those households not using such technologies. Our findings highlight the importance of promoting multiple and complementary agricultural technologies among rural smallholders. We suggest that rural technology generation, dissemination and adoption interventions be strengthened. Moreover, the linkage among research, extension, universities and farmers needs to be enhanced through facilitating a multistakeholders innovation platforms.

/pdf/adoption-of-improved-agricultural-technology-and-its-impact-4nou79qrj2.pdf

Adoption of improved agricultural technology and its impact on household income: a propensity score matching estimation in eastern Ethiopia

This study analyzed the effects of climate change on rice farmers’ livelihoods vulnerability by using primary data elicited from 405 rice farming households in Can Tho, Dong Thap, and Tien Giang provinces in the Mekong Delta Region (MDR) of Vietnam. The Livelihood Vulnerability Index (LVI) showed that Can Tho province was the most vulnerable to climate change, followed by Dong Thap and Tien Giang provinces. In particular, the social index sub-indicator showed high vulnerability. The beta regression analysis identified seventeen significant factors influencing the susceptibility of rice farming households in the study area, such as weather information, flood occurrence, drought occurrence, access to extension services, access to credit, and cooperative membership as well as demographic variables and livelihoods related factors. The LVI result suggests the need for the government to consider raising the priority on households in Can Tho province through adaptation support to improve the resilience and adaptive capacity, especially by enhancing the social network in this area to stimulate support from local authorities and farmer groups. The regression results imply that extension services should provide adequate and timely weather information to equip the farmers to be more prepared for climatic shocks. Moreover, credit facilities with low interest rates should be made available, especially to those who are members of agricultural cooperatives.

Factors Influencing Smallholder Rice Farmers’ Vulnerability to Climate Change and Variability in the Mekong Delta Region of Vietnam

Impact of climate-smart agricultural technology on multidimensional poverty in rural Ethiopia

The ex-post impact of agricultural technology adoption on poverty: Evidence from north Shewa zone of Amhara region, Ethiopia

Kaffa, Sheka and Bench Maji zones of Southern Ethiopia are high honey production areas with dense forest coverage. However, due to the traditional production system, the productivity per hive is low. To exploit this production potential in these zones by increasing productivity per hive, different governmental and non-governmental organizations disseminate improved beehives technologies. Yet, the impact of these disseminated and adopted technologies on honey production has not been determined. Thus, this study aimed to estimate the impact of improved beehive adoption on honey production efficiency. A multistage sampling technique was used to select 360 sampled households. This study applied a stochastic frontier model to assess the technical efficiency (TE) of honey producers and propensity score matching (PSM) with probit model to analyze adoption decision of improved beehive and its impact on honey production efficiency. The finding of this study shows that the number hives, type of beehive used, and proximity to the available forest had a significant and positive effect on honey production. The mean TE was 77% that shows the deviation of actual from optimal production amounts. Beekeeping experience, education, level, cooperative membership, participation in improved beehive technology demonstration and extension contact had significant effects on TE. The result of PSM indicates that the adoption of improved beehive technology has a positive and significant effect on production efficiency, as measured by TE. Farmers who adopted improved beehive technology were less technically inefficient than those who did not adopt. Overall, the estimated 19.5% difference in TE among the two groups shows there is considerable room for the improvement of beekeeping by efficient utilization of inputs and also provides strong evidence for the diffusion of improved beehives in Kaffa Sheka and Bench Maji zones.

/pdf/impact-of-improved-beehives-technology-adoption-on-honey-2msm4epgp1.pdf

Impact of improved beehives technology adoption on honey production efficiency: empirical evidence from Southern Ethiopia

Many studies evaluating the impact of adoption on welfare focused on adoption of a single technology giving little attention on the complementarity/substitutability among agricultural technologies. Yet, smallholders commonly adopt several complementary technologies at a time and their adoption decision is best characterized by multivariate models. This paper, therefore, examines the impact of multiple complementary technologies adoption on consumption, poverty and vulnerability of smallholders in Ethiopia. The study used a balanced panel data obtained from a survey of 390 farm households collected in 2012, 2014 and 2016. A two stage multinomial endogenous switching regression model combined with the Mundlak approach and balanced panel data is employed to account for unobserved heterogeneity for the adoption decision and differences in household and farm characteristics. An ordered probit model is used to analyze the impact on poverty and vulnerability. We find that the adoption of improved technologies increases consumption expenditure significantly and the greatest impact is attained when farmers combine multiple complementary technologies. Similarly, the likelihood of households to remain poor or vulnerable decreased with the adoption of different complementary technologies. We therefore conclude that the adoption of multiple complementary technologies has substantial dynamic benefits that improve the welfare of smallholders in the study area, and given the observed low level of adoption rates, we suggest that much more intervention is warranted, with a special focus on poorer and vulnerable households, to ensure smallholders get support to improve their input use.

/pdf/the-impact-of-agricultural-technologies-on-poverty-and-53d1nhi4i2.pdf

Wubneshe Dessalegn Biru

Papers

The Impact of Agricultural Technologies on Poverty and Vulnerability of Smallholders in Ethiopia: A Panel Data Analysis