In recent years, the agricultural industry has been experiencing an ever-increasing application of information and communication technologies globally. This new revolution has been touted to impact efficiency and productivity in the agricultural extension services within the agriculture sector. Notwithstanding this, empirical research need to be carried out amongst its users in the sector to ascertain these assertions.
ICT-driven digital tools to support smallholder farmers are arguably inevitable for agricultural development, and they are gradually evolving with promising outlook. Yet, the development and delivery of these tools to target users are often fraught with non-trivial, and sometimes unanticipated, contextual realities that can make or mar their adoption and sustainability. This article unfolds the experiential learnings from a digital innovation project focusing on surveillance and control of a major banana disease in East Africa which is being piloted in Rwanda.
This study provides a model that supports systematic stakeholder inclusion in agricultural technology. Building on the Responsible Research and Innovation (RRI) literature and attempting to add precision to the conversation around inclusion in technology design and governance, this study develops a framework for determining which stakeholder groups to engage in RRI processes. We developed the model using a specific industry case study: identifying the relevant stakeholders in the Canadian digital agriculture ecosystem.
Contract farming has gained in importance in many developing countries. Previous studies analysed effects of contracts on smallholder farmers’ welfare, yet mostlywithout considering that different types of contractual relationships exist. Here, we examine associations between contract farming and farm household income in the oilpalm sector of Ghana, explicitly differentiating between two types of contracts,namely simple marketing contracts and more comprehensive resource-providing contracts.
Individuals from a diverse range of backgrounds are increasingly engaging in research and development in the field of artificial intelligence (AI). The main activities, although still nascent, are coalescing around three core activities: innovation, policy, and capacity building. Within agriculture, which is the focus of this paper, AI is working with converging technologies, particularly data optimization, to add value along the entire agricultural value chain, including procurement, farm automation, and market access.
Women play a key role in agriculture and food security, making up around 48 percent of the agricultural labour force in low-income countries. Despite this, their important contribution is hardly visible and largely unrecognized. Gender equality regards human rights but gender-based constraints in the sector cause also major inefficiencies in value chains, and are a key impediment for rural development, food security, and social and environmental sustainability. Moreover, the severe and multidimensional constraints faced by women hamper their productive potential and livelihoods.
The overall objective of the technical workshop was to present the guidelines on AIS and EAS assessments, the results at country level and to design and develop a framework of indicators to complement those assessments. Specific objectives were to:
The national assessment of the agricultural innovation system (AIS) in Malawi was conducted using a framework of four types of analyses: functional, structural, capacity and enabling environment analysis. The approach included five case studies that addressed three methods including the use of indigenous methods for fall armyworm (FAW) control in Farmer Field Schools (FFS), livestock transfer programs, and a horticulture marketing innovation platform in Mzimba, Ntchisi, Balaka, and Thyolo districts.
Considering the new opportunities that ICT innovations bring to improve performance of financial and extension services, this study looks at the potential contribution of financial and extension services to the Sustainable Development Goals (SDGs). The approach used extends the standard Data Envelopment Analysis (DEA) model to include longer-term management goals and find a solution that balances the efficient use of innovation investments and the achievement of policy goals, making this approach well suited for the analysis of the SDGs.
This toolbox has been developed to collate different tools and methods that can be used for food system analysis.
It is specifically based on systems thinking for food system analysis, with the aim to formulate actionable recommendations that can bring about systemic change.It describes both the process of a food system analysis, as well as a set of tools that can be used at different stages.