Food insecurity remains a major challenge to rural households in Eastern Ethiopia. To improve food and nutrition security of vulnerable households in eastern Ethiopia, several agricultural technologies have been scaled-up by Haramaya University for more than six decades. However, the impact of these technologies on household nutritional outcomes was not systematically studied. This study examined the impact of selected agricultural technologies on household food and nutrition security. Cross-sectional data were generated from 248 randomly selected rural households.

The spatial and temporal variability of soil properties (fluid composition, structure, and water content) and hydrogeological properties employed for sustainable precision agriculture can be obtained from geoelectrical resistivity methods. For sustainable precision agricultural practices, site-specific information is paramount, especially during the planting season.

The digital transformation in agriculture introduces new challenges in terms of data, knowledge and technology adoption due to critical interoperability issues, and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.

Accurate and timely crop mapping is crucial for environment assessment, food security and agricultural production. However, for the areas with high landscape heterogeneity and frequent cloudy and rainy weather, the insufficient high-quality satellite images limit the accuracy of crop classification. The recently launched Chinese GF-6 wide field-of-view camera (WFV) with a revisit cycle of 4-day and spatial resolution of 16-meter shows great potential for agricultural monitoring.

This paper examines the impacts of access to extension services and cooperative membership on technology adoption, asset ownership and poverty using household-level data from rural Nigeria. Using different matching techniques and endogenous switching regression approach, we find that both extension access and cooperative membership have a positive and statistically significant effect on technology adoption and household welfare. Moreover, we find that both extension access and cooperative membership have heterogeneous impacts.

Much has been written on the determinants of technology adoption in agriculture, with issues such as input availability, knowledge and education, risk preferences, profitability, and credit constraints receiving much attention. This paper focuses on a factor that has been less well documented: the differential ability of households to take on risky production technologies for fear of the welfare consequences if shocks result in poor harvests. Building on an explicit model, this is explored in panel data from Ethiopia. Historical rainfall distributions are used to identify consumption risk.

Internet of Things (IoT) technology plays an important role in advancing the transformation of labor-intensive traditional agriculture into data-driven smart farming by providing technological and theoretical support to address the challenges of food shortages caused by rapid population growth, as well as the sustainable development challenges posed by the scarcity of natural resources due to climate warming. IoT smart farming is a multidisciplinary field encompassing agricultural knowledge, communication technologies, automation, artificial intelligence, and computing.

In this study, a novel government-backed agri-food supply chain model (GBASM) is introduced among farmers and agricultural enterprises in their respective market. In the GBASM, the government provides subsidies to each member of the supply chain to encourage them to collaborate on a common economic platform. A game-theoretic perspective is established among farmers and agricultural enterprises to determine whether or not they should participate in this new model for their economic benefits.