Agricultural research continues to be a good investment. The studies show that investments in international and national agricultural research account for almost all of the total factor productivity (TFP) growth in SSA and large shares of agricultural growth globally. The existing agricultural research institutions have, on average, delivered rates of return to public investment above 30-40%, which is much higher than the 5-10% available to other public investments or the 2-5% cost of borrowing public funds.
Agricultural Innovation System (AIS) is a collection of institutions enabling agricultural and food system transformation in a country. Any attempt to engage in emergency interventions by institutions and bounce back with higher levels of resilience requires strong organizational and human capacity as a prerequisite. What role do these institutions play in emergencies such as COVID-19 and how can they bounce back after such a crisis is over? What can be done to help these institutions build resilience capacity for such recovery?
Agricultural innovation in low-income tropical countries contributes to a more effective and sustainable use of natural resources and reduces hunger and poverty through economic development in rural areas. Yet, despite numerous recent public and private initiatives to develop capacities for agricultural innovation, such initiatives are often not well aligned with national efforts to revive existing Agricultural Innovation Systems (AIS).
Grown in Jamaica since the days of slavery, food yams are major staples in local diets and a significant non-traditional export crop. The cultivation system used today is the same as 300 years ago, with alleged unsustainable practices. A new cultivation system called minisett was introduced in 1985 but the adoption rate twenty four years later is extremely low.
The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one.
Indicator-based tools are widely used for the assessment of farm sustainability, but analysts still face methodological and conceptual issues, including data availability, the complexity of the concept of sustainability and the heterogeneity of agricultural systems. This study contributes to this debate through the illustration of a procedure for farm sustainability assessment focussed on the case study of the South Milan Agricultural Park, Italy. The application is based on a set of environmental, social and economic indicators retrieved from the literature review.
The process of adopting innovation, especially with regard to precision farming (PF), is inherently complex and social, and influenced by producers, change agents, social norms and organizational pressure. An empirical analysis was conducted among Italian farmers to measure the drivers and clarify “bottlenecks” in the adoption of agricultural innovation. The purpose of this study was to analyze the socio-structural and complexity factors that affect the probability to adopt innovations and the determinants that drive an individual’s decisions.
In recent decades, the confluence of different global and domestic drivers has led to progressive and unpredictable changes in the functioning and structure of agri-food markets worldwide.
Although the benefits of genetically modified (GM) crops have been well documented, how do farmers manage the risk of new technology in the early stages of technology adoption has received less attention. We compare the total factor productivity (TFP) of cotton to other major crops (wheat, rice, and corn) in China between 1990 and 2015, showing that the TFP growth of cotton production is significantly different from all other crops. In particular, the TFP of cotton production increased rapidly in the early 1990s then declined slightly around 2000 and rose again.
Familiar mixed dairy sheep farm is the most widespread system in the Mediterranean basin, in Latin America and in developing countries (85%). There is a strong lack of technological adoption in packages of feeding and land use in small-scale farms. To increase competitiveness, it would be of great interest to deepen the knowledge of how innovation was selected, adopted, and spread. The objective of this research was to select strategic feeding and land use technologies in familiar mixed dairy sheep systems and later assess dairy sheep farms in Spain.