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.

Building on previous research, the purpose of this study was to describe the needs of the extension agents, in the Riyadh Region of Saudi Arabia, for training on Organic Agriculture (OA). This knowledge will be used to develop organic educational programs for extension agents. The specific objectives were to:

This presentation sets out a future research agenda for research on agricultural extension and advisory services, under influence of sustainability transitions and disruptive technologies such as digital agriculture technology, and synthetic foods. For a recording of the presentation see: https://www.youtube.com/watch?v=03V7zSD63pw

Participatory agricultural extension programmes aimed at encouraging knowledge transfer and the adoption of new technology and innovation at the farm level are a novel approach to advisory service provision. In order to drive sustainable agricultural production systems that address farm-level economic and environmental objectives, the College of Agriculture, Food and Rural Enterprise (CAFRE) in November 2015, developed a new participatory extension programme for farmers in Northern Ireland, the Business Development Groups (BDGs).

The national assessment of the agricultural innovation system (AIS) in Malawi was conducted using a framework of four types of analyses: functional, structural, capacity and enabling environment analysis. The approach included five case studies that addressed three methods including the use of indigenous methods for fall armyworm (FAW) control in Farmer Field Schools (FFS), livestock transfer programs, and a horticulture marketing innovation platform in Mzimba, Ntchisi, Balaka, and Thyolo districts. 

Since 2017, in line with COAG’s recommendation, the Research and Extension Unit engaged in the development of a participatory AIS assessment framework including a customizable toolbox for countries with a totally new capacity development perspective. The assessment framework is meant for actors of the national agricultural innovation systems, i.e.

Extension and advisory services (EAS) play a key role in facilitating innovation for sustainable agricultural development. To strengthen this role, appropriate investment and conducive policies are needed in EAS, guided by evidence. It is therefore essential to examine EAS characteristics and performance in the context of modern, pluralistic and increasingly digital EAS systems. In response to this need, the Food and Agriculture Organization of the United Nations (FAO) has developed guidelines and instruments for the systematic assessment of national EAS systems.

Extension and advisory services (EAS) play a key role in facilitating innovation processes, empowering marginalized groups through capacity development, and linking farmers with markets. Advisory services are increasingly provided by a range of actors and funded from diverse sources. With the broadened scope of EAS and the growing complexity of the system, the quantitative performance indicators used in the past (e.g. related to investment, staffing or productivity) are not adequate anymore to understand whether the system is well-functioning.