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.
Past studies showing that barriers to farmers’ adaptation behaviors are focused on their socio-economic factors and resource availability. Meanwhile, psychological and social considerations are sparingly mentioned, especially for the related studies in developing countries. This study investigates the impact of psychological factors and social appraisal on farmers’ behavioral intention to adopt adaptation measures for the aforementioned reason, due to climate change and not to anthropogenic climate change.
Agricultural production is a crucial and fundamental aspect of a stable society in China that depends heavily on the climate situation. With the desire to achieve future sustainable development, China’s government is taking actions to adapt to climate change and to ensure food self-sufficiency.
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.
The creation of commercialization opportunities for smallholder farmers has taken primacy on the development agenda of many developing countries. Invariably, most of the smallholders are less productive than commercial farmers and continue to lag in commercialization. Apart from the various multifaceted challenges which smallholder farmers face, limited access to extension services stands as the underlying constraint to their sustainability.
Common Agricultural Policy (CAP) proposes environmental policies developed around action-based conservation measures supported by agri-environment schemes (AES). High Nature Value (HNV) farming represents a combination of low-intensity and mosaic practices mostly developed in agricultural marginalized rural areas which sustain rich biodiversity. Being threatened by intensification and abandonment, such farming practices were supported in the last CAP periods by targeted AES.
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.
This study explores the properties of innovation systems and their contribution to increased eco-efficiency in agriculture. Using aggregate data and econometric methods, the eco-efficiency of 79 countries was computed and a range of factors relating to research, extension, business and policy was examined. Despite data limitations, the analysis produced significant results.
The study focuses on how levels of innovation, measured by complexity and investments requirements of the adopted technologies, relates to innovative behavior and complying with social responsibility practices, as two indicators of the farmer's behavior towards innovation. A typology of farmers with different technological
levels was constructed based on multivariate techniques, according to the adoption of seven technologies. The main objective of the study was to relate SR and innovative behavior to the technology clusters