A bilateral project between the Swiss Agency for Cooperation and Development (SDC) and the Nepalese government, which ran from 2016 to 2020 and covered 61 municipalities in provinces 1, 3 (Bagmati) and 6 (Karnali), with technical support from the Swiss NGO Helvetas, aimed to promote a multi-stakeholder approach to agricultural services in Nepal.
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.
This report summarizes studies conducted in a framework of TAP-AIS project implemented by FAO’s Research and Extension Unit, and funded by the European Union as a component of the European Union initiative on “Development Smart Innovation through Research in Agriculture” (DeSIRA).
La participación de los pequeños productores en procesos de investigación asociados a los sistemas productivos agrícolas ha sido difícil de lograr. Por esto el objeto de la presente investigación fue el de lograr la vinculación de pequeños productores de yuca (Manihot esculenta Crantz) a procesos de investigación en la región caribe de Colombia. Por lo anterior, se implementaron ensayos de campo en los que se empleó un método de investigación participativa a través de modelos integrados de producción.
In recent years, the agricultural industry has been experiencing an ever-increasing application of information and communication technologies globally. This new revolution has been touted to impact efficiency and productivity in the agricultural extension services within the agriculture sector. Notwithstanding this, empirical research need to be carried out amongst its users in the sector to ascertain these assertions.
Due to the increasing gap between input costs and the final prices they receive for their produce, Indian farmers have been increasingly affected by the current agrarian crisis. It is within this context that Zero Budget Natural Farming (ZBNF) - a farming method promising low to zero input costs - has been gaining momentum.
This study provides a model that supports systematic stakeholder inclusion in agricultural technology. Building on the Responsible Research and Innovation (RRI) literature and attempting to add precision to the conversation around inclusion in technology design and governance, this study develops a framework for determining which stakeholder groups to engage in RRI processes. We developed the model using a specific industry case study: identifying the relevant stakeholders in the Canadian digital agriculture ecosystem.
This paper addresses how co-producing knowledge can assist local farmers in reshaping their territories into sustainable farming systems. We describe the emergence and consolidation of an agroforestry system in an Eastern Amazon forest frontier, unpacking the co-production of a new farming system over recent decades. Instead of assuming pre-defined categories (e.g., traditional/technical, local/external), the analysis focuses on interactions among knowledge holders and how multiple knowledge sources are intercalated.
This paper contends that the exclusion of millions of poor from agricultural development gains is inexorably linked to the innovation system features that have evolved over time. An oft repeated lament of the Government of India about the inadequacy of reforms in agricultural research and extension, is used to explore the structure and institutions of agricultural innovation. Three main components of the agricultural innovation system, are the agricultural research and extension actors, the farming communities, and policy making agencies.
Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies.