Limiting warming to 1.5 degrees Celsius and transitioning the planet to an equitable climate and nature-positive future by 2050 will require systemic shifts in how food is produced and consumed.
With the current realities of the food systems, the fusion of innovation with purpose becomes not just a choice but a necessity.
In rural areas of developing countries, more than 70% of the population still depends on agriculture. However, economic crises, unscientific land allocation and climate change issues have hindered attempted gains in agricultural productivity and related rural development outcomes. Technology-driven breakthrough has usually pushed agriculture to the brink of another development that can affect not only plant diversity and yield, but also climatological and socio-economic outcomes.
The global food supply is increasingly facing disruptions from extreme heat and storms. It is also a major contributor to climate change, responsible for one-third of all greenhouse gas emissions from human activities.This tension is why agriculture innovation is increasingly being elevated in international climate discussions.
Providing farmers with essential agricultural information and training in the era of COVID-19 has been a challenge that has prompted a renewed interest in digital extension services. There is a distinct gender gap, however, between men’s and women’s access to, use of, and ability to benefit from information and communication technologies (ICTs).
La notion de service écosystémique est devenue incontournable dans les discours institutionnels et académiques en dépit des controverses et des critiques. Initialement portée par les acteurs de la conservation de la biodiversité, elle connaît depuis plusieurs années un déploiement dans les milieux agricoles. Si l’idée selon laquelle les fonctionnalités des écosystèmes sont déterminantes dans la production agricole n’est pas nouvelle, cette notion permet de mettre en évidence les nouveaux enjeux liés aux changements climatiques et aux besoins alimentaires croissants.
Accurate and operational indicators of the start of growing season (SOS) are critical for crop modeling, famine early warning, and agricultural management in the developing world. Erroneous SOS estimates–late, or early, relative to actual planting dates–can lead to inaccurate crop production and food-availability forecasts. Adapting rainfed agriculture to climate change requires improved harmonization of planting with the onset of rains, and the rising ubiquity of mobile phones in east Africa enables real-time monitoring of this important agricultural decision.
Climate smart agriculture (CSA) technologies are innovations meant to reduce the risks in agricultural production among smallholder farmers. Among the factors that influence farmer adoption of agricultural technologies are farmers' risk attitudes and household livelihood diversification. This study, focused on determining how farmers' risk attitudes and household livelihood diversification influenced the adoption of CSA technologies in the Nyando basin. The study utilized primary data from 122 households from two administrative regions of Kisumu and Kericho counties in Kenya.
The potential beneficial and harmful social impacts generated by the introduction of novel technologies, in general, and those concerning nutrient recovery and the improvement of nutrient efficiency in agriculture, in particular, have received little attention, as shown in the literature. This study investigated the current social impacts of agricultural practices in Belgium, Germany and Spain, and the potential social impacts of novel technologies introduced in agriculture to reduce nutrient losses.
The spatial and temporal variability of soil properties (fluid composition, structure, and water content) and hydrogeological properties employed for sustainable precision agriculture can be obtained from geoelectrical resistivity methods. For sustainable precision agricultural practices, site-specific information is paramount, especially during the planting season.
The determination of bunch features that are relevant for bunch weight estimation is an important step in automatic vineyard yield estimation using image analysis. The conversion of 2D image features into mass can be highly dependent on grapevine cultivar, as the bunch morphology varies greatly. This paper aims to explore the relationships between bunch weight and bunch features obtained from image analysis considering a multicultivar approach.