Potato is the third most widely-cultivated food crop in the world and one of the most profitable food crops. Therefore, it allows producers to improve their living conditions with
La crise liée au SARS-CoV2 (syndrome respiratoire aigu sévère–coronavirus 2) a donné lieu à une profusion de documents et webinaires sur la sécurité alimentaire au niveau international, ce qui tend à brouiller la compréhension des dynamiques à l’œuvre sur le terrain. Cet article se propose de faire le point sur la situation des secteurs agricole et agroalimentaire, à partir des informations relayées par un réseau d’experts du Cirad et de leurs partenaires dans une diversité de pays en Afrique subsaharienne.
Dans le cadre du développement durable et des innovations dans les systèmes agroalimentaires, les systèmes mixtes horticoles (vergers et maraîchage) visent à répondre aux enjeux actuels auxquels l’agriculture est confrontée, à savoir une diminution de la pollution des sols, une meilleure gestion des ressources (eau, énergies) et un enrichissement de la biodiversité, tout en continuant d’assurer des fonctions alimentaires. Ils combinent des productions à la fois diversifiées et relativement intensifiées, leur permettant de s’insérer en périphérie urbaine.
The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse.
Agricultural innovation has played a critical role in the economic transformation of developing East Asian countries over the past half century. The Green Revolution—in the form of modern seed varieties, chemical fertilizers, pesticides, and modern machinery—has contributed to increased crop yields and farm incomes, and decreased poverty across the region. Although policy makers’ traditional focus on expanding and intensifying agricultural production has brought many benefits, the focus on productivity has come at a rising cost.
Improvements in the sustainability of agricultural production depend essentially on advances in the efficient use of nitrogen. Precision farming promises solutions in this respect. Variable rate technologies allow the right quantities of fertilizer to be applied at the right place. This helps to both maintain yields and avoid nitrogen losses. However, these technologies are still not widely adopted, especially in small-scale farming systems. Recent developments in sensing technologies, like drones or satellites, open up new opportunities for variable rate technologies.
The impact of global warming on crop growth periods and yields has been evaluated by using crop models, which need to provide various kinds of input datasets and estimate numerous parameters before simulation. Direct studies on the changes of climatic factors on the observed crop growth and yield could provide a more simple and intuitive way for assessing the impact of climate change on crop production.
The aim of this survey is to identify and characterize new products in plant biotechnology since 2015, especially in relation to the advent of New Breeding Techniques (NBTs) such as gene editing based on the CRISPR-Cas system. Transgenic (gene transfer or gene silencing) and gene edited traits which are approved or marketed in at least one country, or which have a non-regulated status in the USA, are collected, as well as related patents worldwide. In addition, to shed light on potential innovation for Africa, field trials on the continent are examined.
Soil texture is a key soil property influencing many agronomic practices including fertilization and liming. Therefore, an accurate estimation of soil texture is essential for adopting sustainable soil management practices. In this study, we used different machine learning algorithms trained on vis–NIR spectra from existing soil spectral libraries (ICRAF and LUCAS) to predict soil textural fractions (sand–silt–clay %). In addition, we predicted the soil textural groups (G1: Fine, G2: Medium, and G3: Coarse) using routine chemical characteristics as auxiliary.