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.
This chapter assesses the potential of farmer-to-farmer extension (F2FE) as a low-cost approach for promoting CSA. It is based on surveys of extension program managers and farmer-trainers in Cameroon, Kenya and Malawi who are involved in promoting a wide range of agricultural practices, including CSA. In the F2FE approach, extension programs provide education for farmer-trainers, who in turn educate other farmers, typically 17–37 per year. Extension program managers find this approach to be effective in boosting their ability to reach large numbers of farmers.
The TOWS Matrix is derived from the SWOT Analysis model. The SWOT analysis is based on two factors; internal factors (Strengths and Weakness) and external factors (Opportunities and Threats). For an organisation to function at the best of its potential, these tools should be utilised at the beginning of the year. This article shows how important these tools are important in an organisation.
Recent research on agricultural innovation has outlined social networks’ role in diffusing agricultural knowledge; however, so far, it has broadly neglected the socio-spatial dimensions of innovation processes. Against this backdrop, the authors applies a spatially explicit translocal network perspective in order to investigate the role of migration-related translocal networks for adaptive change in a small-scale farming community in Northeast Thailand.
Kenya has emerged as a frontrunner in information and communication technologies (ICT) in Sub-Saharan Africa. The government has been actively supporting the ICT sector as one of the key drivers of economic growth. In addition to large international firms that are setting up offices in Nairobi, such as Nokia, IBM and Google, local start-ups have also been expanding rapidly.
Agricultural information is transferred through social interactions; therefore, ties to agricultural informants and network structures within farmers’ local neighborhoods determine their information-gathering abilities. This paper uses a spatial autoregressive model that takes account of spatial autocorrelation to examine such network connections, including friendship networks and advice networks, upon farmers’ knowledge-gathering abilities during formal agricultural training.
Most of today’s information services on the web are designed for PC users. There are few services fit to be accessed by mobile devices. In the countryside of China, most of the mobile phone users can not access the Internet. For this reason, was developed a General Agriculture Mobile Service Platform. The Platform is designed to make these information services fit to be accessed by mobile users, and to make those mobile phone users can use these services without Internet connection.
In order to solve the problems of low efficiency and backward methods in the agro-technical extension activities, this paper designed an agro-technical extension information system based on cloud storage technology. This paper studied the key technologies, such as cloud storage service engine, cloud storage management node and cloud storage data node and designed the overall architecture of the agro-technical extension information system based on cloud storage technology.
Timely availability of reliable information on weather conditions, agro-advisories, and market information can help to minimize losses in agriculture. This paper presents a scientific and integrated approach to identify areas of high agriculture vulnerability to climate change and availability of ICT services for dissemination of Climate Smart Agriculture (CSA) information in the vulnerable areas. This study was illustrated for India where the majority of the population depends on agriculture for their livelihoods, and this sector is highly vulnerable to climate change.
In this paper, a novel method to collect symptoms of the disease, as observed by the farmers, using a mobile phone application has been presented. A cumulative composite risk index (CCRI) obtained from more than one existing disease forecast models is validated from the actual late blight queries received from the farmers. The main contribution of the paper is a protocol that combines the symptoms based diagnostic approach along with the plant disease forecasting models resulting in detection of Potato late blight with higher accuracy.