L’érosion hydrique est une préoccupation agronomique, économique et environnementale importante en milieu semi-aride méditerranéen. Le choix de la méthode pour l’estimer dépend des données disponibles et des conditions de la zone d’étude. Cet article vise à quantifier et spatialiser l’érosion hydrique en utilisant l’équation universelle des pertes en sol (Universal Soil Loss Equation [USLE]) dans ses versions modifiée (Modified − MUSLE) et révisée (Revised − RUSLE) et couplées à un Système d’information géographique (SIG) : MUSLE-SIG et RUSLE-SIG.
This article examines how research on the agriculture and agrifood systems mobilizes the concept of Innovation System (IS). A literature review on the IS provides an analytical framework for determining its theoretical frame of reference, its area of application and its uses.
Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low.
L’objectif de cet article est d’explorer les défis et les contraintes de l’adoption des technologies de l’agriculture de précision, en se plaçant du point de vue des entreprises opérant dans le secteur des nouvelles technologies agricoles. L’étude s’appuie sur une approche qualitative moyennant une analyse de contenu issue de huit entretiens semi-directifs.
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.
La diminution du nombre de prairies, que l’on observe à l’échelle mondiale depuis plusieurs décennies, s’est accompagnée de l’évolution de leur mode de gestion dans un contexte d’intensification de l’usage des terres. Face aux enjeux que ces changements impliquent, tant sur le plan environnemental qu’économique, il est nécessaire d’identifier et de caractériser les dynamiques spatiotemporelles des prairies, afin notamment d’évaluer les impacts du changement climatique sur ces dernières et leur capacité à s’y adapter.
Le drone est un outil de plus en plus utilisé dans de nombreux domaines et en particulier en agriculture. La méthode présentée permet d’estimer la hauteur de plantes fourragères à partir de photos prises d’un drone. Cette méthode revêt un intérêt tout particulier pour la sélection végétale.
Many of the world’s food-insecure and undernourished people are smallholder farmers in developing countries. This is especially true in Africa. There is an urgent need to make smallholder agriculture and food systems more nutrition-sensitive. African farm households are known to consume a sizeable part of what they produce at home. Less is known about how much subsistence agriculture actually contributes to household diets, and how this contribution changes seasonally. We use representative data from rural Ethiopia covering every month of one full year to address this knowledge gap.
The recent proliferation of mobile phones in rural Africa has also led to increased interest in mobile financial services (MFS), such as mobile money and mobile banking. Such services are often portrayed as promising tools to improve agricultural finance, especially among smallholders who are typically underserved by traditional banks. However, empirical evidence on the actual use of MFS for agricultural activities is thin. Here, we use nationally representative data from Kenya to analyze the use of mobile payments, mobile savings, and mobile credit among the farming population.
Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually.