This research delves into the underlying impacts of farmers' innovative entrepreneurship on agricultural and rural economic development in China, adopting a dynamic and spatio-temporal perspective. The study utilizes panel data encompassing 30 provinces (cities and autonomous regions) from 2015 to 2020, with a systematic consideration of diversified spatial weight matrices.
While intrapreneurship and scaling are key themes in the International Business (IB) discussion, our research is the first to show how these concepts manifest in the context of the United Nations and how learnings from IB may be transferred. The United Nations (UN) organizations are tasked with solving the world’s pressing and difficult problems. These organizations are major players in international governance and are characterized by bureaucratic, globally dispersed and politically driven structures, but are hardly ever considered in IB research.
The importance of extension services in helping smallholder farmers to address the many challenges of agricultural production cannot be over-emphasized. However, relatively few studies have been conducted that investigate how the capacities of agricultural extension agents can be built to more effectively assist smallholder farmers in managing climate risks and impacts. As climate change is a key threat to smallholder food production, addressing this issue is increasingly important.
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.
Conventional approaches to agricultural extension based on top–down technology transfer and information dissemination models are inadequate to help smallholder farmers tackle increasingly complex agroclimatic adversities. Innovative service delivery alternatives, such as field schools, exist but are mostly implemented in isolationistic silos with little effort to integrate them for cost reduction and greater technical effectiveness.
The study was conducted in Thakurgaon sadar Upazila to determine farmers’ perception of the extent and factors of ICTs effectiveness in transferring farming information. A total of 250 people who were already been taken services from different ICT center was selected as sample respondents following a random sampling technique. Primary data were collected using a predesigned interview schedule.
Technology and innovation are important in addressing complex problems in the agricultural sector in many developing communities. However, ways and mechanisms to integrate them in the agricultural sector are still a challenge due to the lack of clear pathways and trajectories. Value chains are seen as a strong policy instrument to increase profitability in the agricultural sector; there is also debate around whether value chains can be a potential option to organize technology and innovation trajectories in agriculture.
Le projet RIVAGE veut favoriser l’adoption de pratiques alternatives pour gérer les impacts de la pollution diffuse dans le bassin versant de la rivière Pérou en Guadeloupe. Son objectif est de produire et partager les connaissances sur les processus, les impacts et les pratiques innovantes avec les acteurs du territoire. Pour faciliter la prise en compte des résultats, le projet a créé une « école-acteurs ». L’école-acteurs est un espace d’échanges autour des thématiques liées à la pollution diffuse agricole.
L’objectif de cet article est d’explorer les défis et les contraintes de l’adoption des technologies de l’agriculture de précision, en se plaçant du point de vue des entreprises opérant dans le secteur des nouvelles technologies agricoles. L’étude s’appuie sur une approche qualitative moyennant une analyse de contenu issue de huit entretiens semi-directifs.
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.