The profound changes in European policy for farms advisory services (FAS) require a period of experimentation and results observation before the new CAP 2021-2027. This paper focuses on Measure 2 of Rural Development Programme (RDP) 2014-2020. The paper is focused on the description of case studies in three Italian regions: Campania, Emilia-Romagna and Veneto. Different Measure 2 – sub-measure 2.1 models are analyzed through a qualitative approach, using a conceptual framework adapted by Birner et al. (2009).
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The Fall Armyworm first landed in West Africa in 2016 and has now spread over the whole continent. It has been recently reported in Yemen and India, and is most likely to spread in South east Asia and South China. This pest invades fields and cause significant damage to crops, if not well managed. FAO’s efforts to support farmers in the affected areas include amongst others the FAO Programme for Action, a global coordination project that brings together development and resource partners to maximize coordinated results and minimize duplications.
Innovation is the main driver of agricultural and rural transformation. This video highlights support provided by FAO to countries in adopting and scaling-up sustainable practices, particularly by promoting agricultural innovation to smallholder farmers. FAO has developed and deployed a Fall Armyworm Monitoring and Early Warning System (FAMEWS).
This study presents a quasi-experimental analysis of the impact of FairTrade certification on the commercial performance of coffee farmers in Tanzania. In doing so the study emphasises the importance of a well-contextualised theory of change as a basis for evaluation design. It also stresses the value of qualitative methods to control for selection bias. Based on a longitudinal (pseudo-panel) dataset comprising both certified and conventional farmers, it shows that FairTrade certification introduced a disincentive to farmers’ commercialisation.
This study analyzed the determinants of ICT usage in agricultural value chains among rural youth in Busia County, Kenya. A total of 213 young farmers were randomly selected and interviewed using semi-structured questionnaires. Descriptive statistics and Poisson regression model were applied in data analysis. Findings showed youth participation using ICTs was concentrated at the marketing level of the agricultural chain activities.
The primary aim of this research was to examine the factors influencing behavioral intention of farmers to use ICTs for agricultural risk management. The past research reveals that many researchers had tried to determine factors affecting behavioural intentions of the respondents and TPB has been applied as technology acceptance model in various contexts. However, predicting behavioral intentions to use ICTs for agricultural risk management has not been evaluated from the actual field. Therefore, the data were collected from 360 farmers through multistage cluster sampling technique.
The privatization of agricultural advisory and extension services in many countries and the associated pluralism of service providers has renewed interest in farmers’ use of fee-for-service advisors. Understanding farmers’ use of advisory services is important, given the role such services are expected to play in helping farmers address critical environmental and sustainability challenges. This paper aims to identify factors associated with farmers’ use of fee-for service advisors and bring fresh conceptualization to this topic.
The objectives of this study twofold (i) First to assess farmer's perceptions of IT and secondly (ii) to determine the major factors influencing farmer's adoption decisions. This study offers for policy makers important considerations that could stimulate and sustain adoption of these IT in Tunisian arid agricultural areas. The present study is based on the hypothesis that the farm adoption decision of farmers has no relationship with the type of technology
Diffusion of innovations has gained a lot of attention and concerns different scientific fields. Many studies, which examine the determining factors of technological innovations in the agricultural and agrifood sector, have been conducted using the widely used Technology Accepted Model, for a random sample of farmers or firms engaged in agricultural sector. In the present study, a holistic examination of the determining factors that affect the propensity of firms to innovate or imitate, is conducted