Messia Hagos

Bio: Messia Hagos is an academic researcher. The author has contributed to research in topics: Commercialization & Market integration. The author has an hindex of 1, co-authored 1 publications receiving 99 citations.

Agricultural technology adoption, seed access constraints and commercialization in Ethiopia

Abstract: This article examines the driving forces behind farmers’ decisions to adopt agricultural technologies and the causal impact of adoption on farmers’ integration into output market using data obtained from a random cross-section sample of 700 farmers in Ethiopia. We estimate a Double-Hurdle model to analyze the determinants of the intensity of technology adoption conditional on overcoming seed access constraints. We estimate the impact of technology adoption on farmers’ integration into output market by utilizing treatment effect model, regression based on propensity score as well as matching techniques to account for heterogeneity in the adoption decision, and for unobservable characteristics of farmers and their farm. Results show that knowledge of existing varieties, perception about the attributes of improved varieties, household wealth (livestock and land) and availability of active labor force are major determinants for adoption of improved technologies. Our results suggest that the adoption of improved agricultural technologies has a significant positive impact on farmers’ integration into output market and the findings are consistent across the three models suggesting the robustness of the results. This confirms the potential direct role of technology adoption on market participation among rural households, as higher productivity from improved technology translates into higher output market integration.

Understanding the agricultural input landscape in Sub-Saharan Africa : recent plot, household, and community-level evidence

Abstract: Conventional wisdom holds that Sub-Saharan African farmers use few modern inputs despite the fact that most growth-inducing and poverty-reducing agricultural growth in the region is expected to come largely from expanded use of inputs that embody improved technologies, particularly improved seed, fertilizers and other agro-chemicals, machinery, and irrigation. Yet following several years of high food prices, concerted policy efforts to intensify fertilizer and hybrid seed use, and increased public and private investment in agriculture, how low is modern input use in Africa really? This paper revisits Africa's agricultural input landscape, exploiting the unique, recently collected, nationally representative, agriculturally intensive, and cross-country comparable Living Standard Measurement Study-Integrated Surveys on Agriculture covering six countries in the region (Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda). The study uses data from more than 22,000 households and 62,000 plots to investigate a range of commonly held conceptions about modern input use in Africa, distilling the most striking and important findings into 10 key takeaway descriptive results.

Understanding the process of adaptation to climate change by small-holder farmers: the case of east Hararghe Zone, Ethiopia

Abstract: This study examined smallholder farmers' level of perception about climate change, source of information on climate change, types of adaptation strategies, factors influencing adaptation choices and barriers to adaptation in Eastern Hararghe Zone, Ethiopia. The surveyed farm households in the study area perceived at least one aspect of climate change primarily through their life experience. Planting trees is the major adaptation measure and 89.1 percent of the farmers took this adaptation strategy. Most farmers (96 percent) believe that deforestation is the main cause of climate change and the choice of farmers to plant trees as an adaptation strategy may be partly a mitigation strategy. However, the majority (49.6 percent) of the households employed at least one adaptation response on top of tree planting. The other adaptation strategies include: early planting, terracing, irrigation and water harvesting. The main source of information for these adaptation strategies for 58.4 percent of the respondents is from extension advice. Results of a multinomial logit model showed that non-farm income, farmer- to-farmer extension, access to credit, distance to selling markets, distance to purchasing markets, and income affect the choice of adaptation strategies. Finally, the study identified lack of information as the most important barrier to climate change adaptation. The other barriers include: lack of farm inputs, shortage of land, lack of money, lack of water and shortage of labor.

Welfare impact of adoption of improved cassava varieties by rural households in South Western Nigeria

Abstract: Low adoption of modern agricultural technologies amongst farmers in Nigeria has been identified as one of the main reasons for the low agricultural productivity and increase in poverty level. The general objective of this study is to examine the welfare impact of farm households adoption of improved cassava varieties in Southwestern (SW) Nigeria using poverty as an indicator. It utilizes cross-sectional farm household level data collected in 2013 from a randomly selected sample of 312 cassava producing households (186 in Ogun State and 126 in Osun State). The data obtained were subjected to descriptive and inferential statistical analysis such as Foster, Greer and Thorbecke (FGT) poverty measure and Logit regression model. The results revealed that adoption of improved cassava varieties increases the annual income and the annual consumption expenditure of producing households’ thus increasing welfare in the SW Nigeria. An analysis of the determinants of adoption with logistic regression model showed that access to improved cassava cuttings within the villages, use of radio, farming experience and farming as a major occupation are significant factors influencing adoption of improved cassava varieties in the study area. In order to achieve the much desired poverty reduction and generate an improvement in farming households’ welfare in SW Nigeria, efforts should be intensified in ensuring that farmers have access to adequate improved cassava cuttings at the right time and place. All programs, strategies and policies that would promote farmers’ education on the technology and consequently lead to improved adoption should be pursued.

Determinants and Impacts of Modern Agricultural Technology Adoption in West Wollega: The Case of Gulliso District

Abstract: This study analyzed factors affecting modern agricultural technology adoption by farmers and the impact of technology adoption decision on the welfare of households in the study area. The data used for the study were obtained from 145 randomly selected sample households in the study area. Binary logit model was employed to analyze the determinants of farmers’ decisions to adopt modern technologies. Moreover, the average effect of adoption on household incomes and expenditure were estimated by using propensity score matching method. The result of the logistic regression showed that household heads’ education level, farm size, credit accessibility, perception of farmers about cost of the inputs and off-farm income positively and significantly affected the farm households’ adoption decision; while family size affected their decision negatively and significantly. The result of the propensity score matching estimation showed that the average income and consumption expenditure of adopters are greater than that of non-adopters. Based on these findings it is recommended that the zonal and the woreda leaders extension agents farm and education experts, policy makers and other development oriented organizations have to plan in such a way that the farm households in the study area will obtain sufficient education, credit accessibilities and also have to train farmers to make them understand the benefits obtained from adopting the new technologies. These bodies have also to arrange policy issues that improve farm labour participation of household members and also to arrange the ways in which farmers obtain means of income outside farming activities. Keywords: Agriculture, Farm household, Technology Adoption Logit, Propensity Score Match