AFINET is one of the seventeen thematic networks that the European Union has financed under the H2020 framework and it is supervised by the EIP-Agri in order to foster innovation in Europe. The main topic of AFINET is agroforestry a practice of deliberately integrating woody vegetation with crops and/or animal systems and the promotion of this practice to foster climate changes. AFINET follows a multi-actor approach linked to the nine Regional Innovations Networks created to identify main challenges and develop main innovations about agroforestry.
This document represents a collaborative effort by the Secretariat of the Global Forum on Agricultural Research (GFAR) and the Regional Forums to agree on a common policy for advocacy on Information and Communication Management. The recognition of knowledge as a critical resource for practising efficient farming and developing agriculture makes sharing and exchange of knowledge globally vital for agricultural development.
These advanced training materials have been produced to foster the capacity of practitioners from private, nongovernmental and public sectors on one hand, and academics and scientists on the other, to practically implement cost-efficient RWHI technologies and practices in arid and semi-arid areas. Therefore, these training materials intend to provide the required information to support proper planning, design and construction of cost-efficient RWHI technologies and practices, with special emphasis on the specific problems encountered in Ethiopia, Kenya, Mozambique and Zimbabwe.
Social learning processes can be the basis of a method of agricultural innovation that involves expert and empirical knowledge. In this sense, the objective of this study was to determine the effectiveness and sustainability of an innovation process, understood as social learning, in a group of small farmers in the southern highlands of Peru. Innovative proposals and its permanence three years after the process finished were evaluated. It was observed that innovation processes generated are maintained over time; however, new innovations are not subsequently generated.
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.
The adoption of innovations and Precision Agriculture Technologies (PAT) is fundamental for establishing the patterns of agricultural production. However, the dynamics of adoption of PAT by farmers differs by regions. Although there is large number of related researches, there are considerable gaps in the literature: studies on adoption of PAT can be systematically reviewed and integrated in a conceptual model of technology adoption by rural producers, which still lacking in the literature.
Precision agriculture (PA) is growing considerably in Brazil. However, there is a lack of information regarding to PA adoption and use in the country. This study sought to: (i) investigate the perception of growers and service dealership about PA technologies; (ii) identify constraints to PA adoption; (iii) obtain information that might be useful to motivate producers and agronomists to use PA technologies in the crop production systems.
Relying on cross-sectional data from 300 smallholder rice farmers, the study examined the effects of agricultural extension on improved rice variety adoption and farm income in northern Ghana. A recursive bivariate probit (RBP) model was used to assess the effect of agricultural extension on adoption while regression with endogenous treatment effect model (RETEM) was adopted to evaluate the effect of agricultural extension on farm income. The results indicate a statistically significant effect of agricultural extension on both adoption and farm income.
CONTEXT: Adoption and diffusion of digital farming technologies are expected to help transform current agricultural systems towards sustainability. To enable and steer transformation we need to understand the mechanisms of adoption and diffusion holistically. Our current understanding is mainly informed by empirical farm-level adoption studies and by agent-based models simulating systemic diffusion mechanisms. These two approaches are weakly integrated.
Mission-oriented agricultural innovation systems (MAIS) are becoming more prevalent in view of tackling the challenges of agri-food systems transformation. In this perspective, we argue that the politics of MAIS requires more comprehensive and considerable attention in the field, given the contested and deeply normative nature of the direction of innovations in agri-food systems transformation. Literature from development studies, policy sciences, and transition studies is reviewed to inform the perspective.