Undoubtedly, high demands for food from the world-wide growing population are impacting the environment and putting many pressures on agricultural productivity. Agriculture 4.0, as the fourth evolution in the farming technology, puts forward four essential requirements: increasing productivity, allocating resources reasonably, adapting to climate change, and avoiding food waste.
Monitoring animal performance is a challenge due to lack of systematic recording in the smallholder dairy sector in Malawi. A mobile recording system using short messaging service (SMS) was therefore trialled for data capturing and subsequent feedback provision to farmers following analyses and interpretation. This study aimed at drawing lessons regarding use of SMS recording system among dairy farmers. Of the 210 participants, 85% were farmers and 25% were other dairy value chain players.
Internet of things (IoT) results in massive amount of streaming data, often referred to as “big data”, which brings new opportunities to monitor agricultural and food processes. Besides sensors, big data from social media is also becoming important for the food industry. In this review we present an overview of IoT, big data, and artificial intelligence (AI) and their disruptive role in shaping the future of agri-food systems.
This report presents and reflects on the opportunities that new technological developments related to automation and precision agriculture (e.g. robotics) can offer to agriculture in developing countries. These technologies are mainly targeted to support farmers that struggle with the cost of labour when harvesting crops and to tackle the declining availability of manpower for general cropping operations.
Le présent rapport étudie les possibilités que les nouvelles avancées technologiques liées à l’automatisation et à l’agriculture de précision (la robotique, par exemple) peuvent offrir à l’agriculture dans les pays en développement. Ces technologies visent principalement à aider les agriculteurs appelés à faire face au coût de la main-d’oeuvre nécessaire pour les opérations de récolte, ainsi qu’à répondre au problème de la raréfaction de la main-d’oeuvre disponible pour l’ensemble des travaux agricoles.
Digitalisation is an integral part of modern agriculture. Several digital technologies are available for different animal species and form the basis for precision livestock farming. However, there is a lack of clarity as to which digital technologies are currently used in agricultural practice. Thus, this work aims to present for the first time the status quo in Swiss livestock farming as an example of a highly developed, small-scale and diverse structured agriculture.
L’étude propose une série de mesures qui s’avèrent nécessaires et urgentes pour sauver, consolider et revoir le secteur agricole dans cette situation entourant le coronavirus en Tunisie, et ce, afin de pouvoir penser l’après-Covid-19.
En Tunisie, l’agriculture fait sa révolution numérique: Capteurs installés dans les conduits d’irrigation et dans le sol, colliers émetteurs au cou des vaches… Le monde agricole tunisien est en pleine mutation.
This webinar on Precision Agriculture for Smallholders was organized in 2020 by the Forum for Agricultural Research in Africa (FARA) in collaboration with the partners Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA), Centre for Coordination of Agricultural Research and Development for Southern Africa (CCARDESA, West and Central African Council for Agricultural Research and Development (CORAF) and financial support of the European Union and the International Fund for Agricultural Development (IFAD).
Outlines of the presentation:
Genetic improvement on local breeds kept by small farmers in developing countries is challenging. Even though good pedigree and performance recording is crucial and an important component of breeding programs, it remain difficult or next to impossible under conditions of subsistence livestock farming. This means that standard genetic evaluations, as well as selection and planning of mating based on estimates of the animals' genotypes, cannot be done at any level in the population of the target breed or genetic group.