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 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.
La transition agroécologique requiert de transformer la manière d’accompagner les agriculteurs dans leurs changements de pratiques. Les champs-écoles sont des dispositifs participatifs pertinents pour cela, car ils accroissent les capacités des agriculteurs à expérimenter, à produire des connaissances et à construire eux-mêmes des innovations. Il est toutefois nécessaire de veiller à la qualité de mise en œuvre de ces dispositifs, ce qui a des implications pour les acteurs de la recherche et du développement.
Global Open Data for Agriculture and Nutrition (GODAN) and The Haller Foundation joined forces in 2016 when the UK based charity released version one of the Haller Farmers App.
Innovation for sustainable agricultural intensification (SAI) is challenging. Changing agricultural systems at scale normally means working with partners at different levels to make changes in policies and social institutions, along with technical practices. This study extracts lessons for practitioners and investors in innovation in SAI, based on concrete examples, to guide future investment.
A huge increase in investment in innovation for agricultural systems is critical to meet the Sustainable Development Goals and Paris Climate Agreement. Most of this increase needs to come from reorienting existing funding for innovation. However, understanding whether an investment will fully promote environmentally sustainable and equitable agri-food systems can be difficult.
This training manual, which is based on a methodology developed by FAO’s Research and Extension Unit (OINR), presents a training course on assessing AIS consisting of eight modules.