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.
Productivity growth in smallholder agriculture is an important driver of rural economic development and poverty reduction. However, smallholder farmers often have limited access to information, which can be a serious constraint for increasing productivity. One potential mechanism to reduce information constraints is the public agricultural extension service, but its effectiveness has often been low in the past.
The 2021 Global Report on Food Crises (GRFC 2021) highlights the remarkably high severity and numbers of people in Crisis or worse (IPC/CH Phase 3 or above) or equivalent in 55 countries/territories, driven by persistent conflict, pre-existing and COVID-19-related economic shocks, and weather extremes. The number identified in the 2021 edition is the highest in the report’s five-year existence. The report is produced by the Global Network against Food Crises (which includes WFP), an international alliance working to address the root causes of extreme hunger.
The study assesses the farmers’ use of Global System for Mobile (GSM) for communication among farmers in agricultural extension programs in Taraba State, Nigeria. Specifically, the objectives include: identify key areas in which GSM are used for communication in agricultural extension programs activities, determine the frequency of usage of GSM for information exchange between farmers and extension personnel; and ascertain farmers’ satisfaction in the use of GSM in obtaining information in the various agricultural extension programs.
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.
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.
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.
Recent Society 5.0 efforts by the Government of Japan are aimed at establishing a sustainable human-centered society by combining new technologies such as sensor networks, edge computing, Internet of Things (IoT) ecosystems, artificial intelligence (AI), big data, and robotics. Many research works have been carried out with an increasing emphasis on the fundamentals of wireless sensor networks (WSN) for different applications; namely precision agriculture, environment, medical care, security, and surveillance.