The study is an attempt to identify the type and channels of acquiring agricultural information by farmers; and whether this information helps them in their decisions to adopt new and improved technologies, which can then be translated into higher yield.  A unique two-period panel data sets that come from surveys conducted in 2011 and 2013 by the Central Statistical Agency in collaboration with Ethiopian Strategic Support Program were used to evaluate Agricultural Growth Program (AGP)

The objective of this paper is twofold. First, using a three rounds panel data of 7110 households, was investigate the adoption decisions and the complementarities among the four labor-intensive technologies (agricultural extension service, irrigation, soil conservation and planting seeds in a row) and a comprehensive use of four modern inputs (improved seed variates, inorganic fertilizer, pesticides, organic fertilizer) which have been frequently adopted by smallholder farmers.

This report is divided in 3 studies that acess the status of the agricultural innovation in Ethiopia. 

This brief describes the activies carried out by the project: South-South knowledge transfer strategies for scaling up pro-poor bamboo livelihoods, income generation and employment creation, and environmental management in Africa. The project, funded by the European Union and IFAD and implemented by the International Bamboo and Rattan Organisation (INBAR), targeted three countries – Ethiopia, Madagascar and Tanzania. This project aims to Contributing to higher productivity and incomes, it fully conformed to the strategy of the EU-IFAD agriculture research for development programme (AR4D).

This report brings the information about the capacity needs analysis carried out by CRP in five countries. Capacity development is a core enabling factor in the delivery of the 5 Livestock CRP flagships. One of the strategic capacity development actions for the Livestock CRP is to design evidence based capacity development interventions based on capacity needs analysis.

This paper assesses the role of economic, social, political and organizational processes on technology adoption in smallholder livestock production systems based on innovation systems perspective. Functions of the innovation systems framework was used to assess the missing links in the dairy sector value chains

The main goal of the study is to quantify the effects of a) change in nitrogen fertilization rate, b) adjustment of sowing date, c) implementation of new cultivars, and d) supplementary irrigation on maize cropping systems across six African countries including Ghana, Nigeria, Kenya, Malawi, Ethiopia and Burkina Faso. For this purpose, 30 years (1980-2010) of climate data are used as well as soil and management information obtained from global datasets at 0.5° x 0.5° spatial resolution.

The study analyzes the current state of Agricultural Technical and Vocational Education and Training (ATVET) in Africa and presents its challenges and opportunities. A review of the ATVET in selected Sub-Saharan Africa countries shows that there are far too few training opportunities for young people and that often, the training offered does not match the needs of the private sector and of local administrations. ATVET trainings focus primarily on production skills and on producers themselves with

This paper comparatively analyzes the structure of agricultural policy development networks that connect organizations working on agricultural development, climate change and food security in fourteen smallholder farming communities across East Africa, West Africa and South Asia.

The objective of the study was to identify a viable trade-off between low data requirements and useful household-specific prioritizations of advisory messages. At three sites in Ethiopia, Kenya, and Tanzania independently, we collected experimental preference rankings from smallholder farmers for receiving information about different agricultural and livelihood practices. At each site, was identified socio-economic household variables that improved model-based predictions of individual farmers’ information preferences.