The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one.
Este documento recoge los principales avances y logros de innovación de los más de ochocientos agricultores y agricultoras, “Héroes de la Alimentación”, y representantes institucionales, que participaron desde el año 2018 en el proyecto “Desarrollo de Capacidades para Sistemas de Innovación Agrícola en El Salvador” (CDAIS, por sus siglas en inglés).
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.
The Food and Agriculture Organization of the United Nations (FAO) has developed a web-based Rift Valley fever (RVF) Early Warning Decision Support Tool (RVF DST), which integrates near real-time RVF risk maps with geospatial data, historical and current RVF disease events from EMPRES Global Animal Disease Information System (EMPRES-i) and expert knowledge on RVF eco-epidemiology.
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.
Evaluation provides effective feedback for development plans and programs. In this respect, it is of utmost importance to ensure that the outputs of agricultural extension and education projects are compatible with the ones expected. Therefore, the main purpose of this study was to evaluate agricultural extension model sites approach from actors’ perspectives and to analyze their gaps via the context, input, process, and product (CIPP) evaluation model.
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.
Common Agricultural Policy (CAP) proposes environmental policies developed around action-based conservation measures supported by agri-environment schemes (AES). High Nature Value (HNV) farming represents a combination of low-intensity and mosaic practices mostly developed in agricultural marginalized rural areas which sustain rich biodiversity. Being threatened by intensification and abandonment, such farming practices were supported in the last CAP periods by targeted AES.
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.