A central concern about achieving global food security is reconfiguring agri-food systems towards sustainability. However, historically-informed trajectories of agri-food system development remain resistant to a change in direction. Through a systematic literature review, the authors identify three research domains exploring this phenomenon and six explanations of resistance: embedded nature of technologies, misaligned institutional settings, individual attitudes, political economy factors, infrastructural rigidities, research and innovation priorities.
Two major agricultural transformations are currently being promoted worldwide: digitalisation and ecologisation, that include different practices such as organic farming and sustainable intensification. In literature and in societal debates, these two transformations are sometimes described as antagonistic and sometimes as convergent but are rarely studied together. Using an innovation system approach, this paper discusses how diverse ecologisation pathways grasp digitalisation in the French agricultural sector; and do not discriminate against organic farming.
CONTEXT
Big data applications in agriculture evolve fast, as more experience, applications, good practices and computational power become available. Actual solutions to real-life problems are scarce. What characterizes the adoption of big data problems to solutions and to what extent is there a match between them?
OBJECTIVE
We aim to assess the conditions of the adoption of big data technologies in agricultural applications, based on the investigation of twelve real-life practical use cases in the precision agriculture and livestock domain.
METHODS
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.
A fragmented digital agriculture ecosystem has been linked to the slow scale-out of digital platforms and other digital technology solutions for agriculture. This has undermined the prospects of digitalizing agriculture and increasing sectoral outcomes in sub-Saharan African countries. We conceptualized an aggregator platform for digital services in agriculture as a special form of digital platforms that can enhance the value and usage of digital technologies at the industry level. Little is known about how such a platform can create value as a new service ecology in agriculture.
Le projet RIVAGE veut favoriser l’adoption de pratiques alternatives pour gérer les impacts de la pollution diffuse dans le bassin versant de la rivière Pérou en Guadeloupe. Son objectif est de produire et partager les connaissances sur les processus, les impacts et les pratiques innovantes avec les acteurs du territoire. Pour faciliter la prise en compte des résultats, le projet a créé une « école-acteurs ». L’école-acteurs est un espace d’échanges autour des thématiques liées à la pollution diffuse agricole.
Animal-source foods (ASF), such as fish, provide a critical source of nutrients for dietary quality and optimal growth of children. In sub-Saharan Africa, children often consume monotonous cereal-based diets, a key determinate of malnutrition such as stunting. Identifying existing sources of ASF for children’s diets will inform the development of nutritious food systems for vulnerable groups.
Fish is a key source of income, food, and nutrition in Zambia, although unlike in the past, capture fisheries no longer meet the national demand for fish. Supply shortfalls created an opportunity to develop the aquaculture sector in Zambia, which is now one of the largest producers of farmed fish (Tilapia spp.) on the continent. In its present form, the aquaculture sector exhibits a dichotomy.
Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies.
L’herbe pâturée est l’aliment qui coûte le moins cher dans une ration et la bonne gestion de l’herbe passe entre autre par une connaissance des quantités disponibles. Afin de simplifier et d’automatiser ces mesures d’herbe, et ainsi contribuer au maintien voire au développement du pâturage, le projet HERDECT s’est attaché à construire des méthodes d’estimation de la biomasse des prairies à partir d’outils de télédétection (d’acquisition à distance) et à en estimer la faisabilité opérationnelle.