This report provides a synthesis of all findings and information generated through a “stocktaking” process that involved a desk study of Prolinnova documents and evaluation reports, a questionnaire to 40 staff members of international organizations in agricultural research and development (ARD), self-assessment by the Country Platforms (CPs) and backstopping visits to five CPs. In 2014, the Prolinnova network saw a need to re-strategise in a changing context, and started this process by reviewing the activities it had undertaken and assessing its own functioning.
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.
In this paper the authors provide climate smart agriculture (CSA) planners and implementers at all levels with a generic framework for evaluating and prioritising potential interventions. This entails an iterative process of mapping out recommendation domains, assessing adoption potential and estimating impacts. Through examples, related to livestock production in sub-Saharan Africa, they demonstrate each of the steps and how they are interlinked. The framework is applicable in many different forms, scales and settings.
The Newsletter of the Tropical Agriculture Platform (TAP) provides regular updates on global activities by TAP and its partners, on the CDAIS projects and on upcoming related events. This issue specifically refers to the period from November 2018 to September 2019.
The Newsletter of the Tropical Agriculture Platform (TAP) provides regular updates on global activities by TAP and its partners, on the CDAIS projects and on upcoming related events. This issue specifically refers to the period from October 2019 to December 2019.
During the period 2013-2019, the Agricultural Extension in South Asia (AESA) Network has served as a platform for collating the voices, insights, concerns, and experiences of people in the extension sphere of South Asia. Diverse professionals shared their concerns on the present and future of Extension and Advisory Services (EAS) in the form of blog conversations for AESA. Together, all of these individuals who are involved, interested and passionate about EAS, discussed ways to move beyond some of the seemingly intransigent problems that are hindering the professionalization of EAS.
Ce guide est principalement destiné aux membres des services opérationnels des organismes de développement internationaux et régionaux et des administrations nationales qui sont chargés de concevoir et d’exécuter des projets de financement, ainsi qu’aux praticiens qui conçoivent des programmes thématiques et des programmes d’assistance technique.
This paper develops a quantitative, graph-theoretic method for analysing systems of institutions. With an application to the agricultural innovation system of Azerbaijan, the method is illustrated in detail. An assessment of existing institutional linkages in the system suggests that efforts should be placed on the development of intermediary institutions to facilitate quick and effective flow of knowledge between the public and the private components of the system.
This study identifies systemic problems in the New Zealand Agricultural Innovation System (AIS) that affect the ability of participants in the agricultural sectors to co-develop technologies. We integrate structural and functional streams of innovation system enquiry, gathering data through 30 semi-structured interviews with individuals in Government, industry and research. Interviews explored perceptions of the influence of actors, interactions, institutions, infrastructure, and market structure on the effectiveness of AIS functions.
According to the authors of this paper, actual methods of scaling are rather empirical and based on the premise of ‘find out what works in one place and do more of the same, in another place’. These methods thus would not sufficiently take into account complex realities beyond the concepts of innovation transfer, dissemination, diffusion and adoption. As a consequence, scaling initiatives often do not produce the desired effect.