Dans de nombreux pays, les décideurs ont besoin d'informations pertinentes sur les systèmes d'innovation agricole (SIA) pour guider la formulation des stratégies et des instruments politiques de soutien à l'innovation.
The globalisation of agrifood systems is a mega-trend with potentially profound nutritional implications. This paper describes various facets of this globalisation process and reviews studies on nutritional effects with a particular focus on developing countries. Results show that global trade and technological change in agriculture have substantially improved food security in recent decades, although intensified production systems have also contributed to environmental problems in some regions.
Modern agriculture and food production systems are facingincreasing pressures from climate change, land and wateravailability, and, more recently, a pandemic. These factors arethreatening the environmental and economic sustainability ofcurrent and future food supply systems. Scientific andtechnological innovations are needed more than ever to secureenough food for a fast-growing global population. Scientificadvances have led to a better understanding of how variouscomponents of the agricultural system interact, from the cell tothe field level.
L’une des avancées les plus importantes dans le domaine de l’observation de la terre est la découverte des indices spectraux, ils ont notamment prouvé leur efficacité dans la caractérisation des surfaces agricoles, mais ils sont généralement définis de manière empirique. Cette étude basée sur l’intelligence artificielle et le traitement du signal, propose une méthode pour trouver un indice optimal. Et porte sur l’analyse d’images issues d’une caméra multi-spectrale, utilisée dans un contexte agricole pour l’acquisition en champ proche de végétation.
Mobiliser les approches issues de l’Intelligence Artificielle (IA) en Santé Animale (SA) permet d’aborder des problèmes de forte complexité logique ou algorithmique tels que rencontrés en épidémiologie quantitative et prédictive, en médecine de précision, ou dans l’étude des relations hôtes × pathogènes.
Individuals from a diverse range of backgrounds are increasingly engaging in research and development in the field of artificial intelligence (AI). The main activities, although still nascent, are coalescing around three core activities: innovation, policy, and capacity building. Within agriculture, which is the focus of this paper, AI is working with converging technologies, particularly data optimization, to add value along the entire agricultural value chain, including procurement, farm automation, and market access.
In eastern Zambia, population growth has reduced per-capita land availability to such an extent that traditional bush fallows can no longer be practiced, and low soil fertility is a major constraint to crop production. Improved fallows (IF) based on leguminous trees are a low cash-input agroforestry practice to restore soil fertility. The objective of the study reported here was to assess the adoption of IF by farmers who tested the technology, including the extent to which the technology is practiced relative to its potential scale.
This paper addresses how co-producing knowledge can assist local farmers in reshaping their territories into sustainable farming systems. We describe the emergence and consolidation of an agroforestry system in an Eastern Amazon forest frontier, unpacking the co-production of a new farming system over recent decades. Instead of assuming pre-defined categories (e.g., traditional/technical, local/external), the analysis focuses on interactions among knowledge holders and how multiple knowledge sources are intercalated.
Fall Armyworm (Spodoptera frugiperda), or FAW, is an insect native to tropical and subtropical regions of the Americas. In the absence of natural controls or good management, it can cause significant damage to crops. It prefers maize, although it can feed on more than 80 additional species of crops including rice, sorghum, millet, sugarcane, vegetable crops and cotton.
This article provides an overview of some of the recent research in agriculture involving remote sensing and GIS. Attention focuses on application of remote sensing and GIS specially in agriculture including geography, land surveying, most Earth Science disciplines, parent child relationship, unique identification, attributes, technical parameters, 2D/3D view and any other requirement customized. These advances have been made over recent years and foundations for future research established and can be efficiently used in Agriculture for better results.