This study explores one of the most important questions for alleviating poverty in sub-Saharan Africa, why are advancements in agricultural technology not taking root in this region? Using data from deep interviews of 42 small-scale farmers in Ghana and Cameroon, a conceptual analysis of drivers and factors of agricultural technology adoption in this region is made and represented as causal loop diagrams. Interviews also provide a basis for weighting factors that farmers consider before adopting a new technology.
Given the search for new solutions to better prepare cities for the future, in recent years, urban agriculture (UA) has gained in relevance. Within the context of UA, innovative organizational and technical approaches are generated and tested. They can be understood as novelties that begin a potential innovation process. This empirical study is based on 17 qualitative interviews in the U.S. (NYC; Philadelphia, PA, USA; Chicago, IL, USA).
To meet global demands towards food security, safety as well as sustainable agriculture and food systems innovative approaches are inevitable. Despite the growing body of literature in both innovation research and in values and aims, what has been explored to a lesser extent is the bridging link between these areas. This study represents a first step in addressing this relationship.
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.
Indicator-based tools are widely used for the assessment of farm sustainability, but analysts still face methodological and conceptual issues, including data availability, the complexity of the concept of sustainability and the heterogeneity of agricultural systems. This study contributes to this debate through the illustration of a procedure for farm sustainability assessment focussed on the case study of the South Milan Agricultural Park, Italy. The application is based on a set of environmental, social and economic indicators retrieved from the literature review.
The process of adopting innovation, especially with regard to precision farming (PF), is inherently complex and social, and influenced by producers, change agents, social norms and organizational pressure. An empirical analysis was conducted among Italian farmers to measure the drivers and clarify “bottlenecks” in the adoption of agricultural innovation. The purpose of this study was to analyze the socio-structural and complexity factors that affect the probability to adopt innovations and the determinants that drive an individual’s decisions.
Familiar mixed dairy sheep farm is the most widespread system in the Mediterranean basin, in Latin America and in developing countries (85%). There is a strong lack of technological adoption in packages of feeding and land use in small-scale farms. To increase competitiveness, it would be of great interest to deepen the knowledge of how innovation was selected, adopted, and spread. The objective of this research was to select strategic feeding and land use technologies in familiar mixed dairy sheep systems and later assess dairy sheep farms in Spain.
African agriculture is currently at a crossroads, at which persistent food shortages are compounded by threats from climate change. But, as this book argues, Africa can feed itself in a generation and help contribute to global food security. To achieve this Africa has to define agriculture as a force in economic growth by: advancing scientific and technological research; investing in infrastructure; fostering higher technical training; and creating regional markets.
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.
Cet article présente une nouvelle approche à base de logique floue pour évaluer le risque phytosanitaire dans une serre produisant des roses. Le but de cette étude est de fournir à l’agriculteur un indice représentant le risque de présence de nuisible : Western Flower Thrips (WFT) ou Frankliniella Occidentalis, et d’enlever la phase decomptage manuel. Un systéme d’aide à la décision modulaire basé sur la connaissance d’experts a été conçu. Le systéme proposé fournit un facteur de risque en fonction des données météorologiques et statiques.