L’An deux mille vingt du 15 au 16 décembre 2020 s’est tenu à Bobo Dioulasso au Centre Agricole Polyvalent de Matourkou un séminaire académique sur le thème : Rôles des services de vulgarisation agro-sylvo-pastorales dans la mise à échelle des Pratiques climatointelligentes. Ce séminaire est Co-organisé par l’Union Internationale pour la Conservation de la Nature (UICN), l’Institut de l’Environnement et de Recherches Agricoles (INERA), World Agroforestry (ICRAF) et le Centre Agricole Polyvalent de Matourkou (CAP). La rencontre s’est tenue dans l’amphithéâtre 540 du CAP Matourkou.
This study draws on social-psychology in an attempt to identify the various motivations for technology adoption (TA), including both economic and non-economic, and to gain insights into how and why Brazilian innovative beef farmers make decisions about whether or not to adopt particular technologies.
This work examined the determinants of the adoption of improved Irish potato technologies by farmers in three divisions of the Western Region of Cameroon. Data were collected from 170 farmers from 14 villages in our study area using a mixed-method approach—structured questionnaires, focus group discussion, key informant interviews, and participatory observations with individual farmers and farmers belonging to cooperative and common initiative groups. The study employed descriptive statistics and regression analysis to assess the adoption status of farmers and its determinants.
Mobile phones fit well into the lives of pastoralists in low-income countries. The technology is firmly integrated into most pastoralist communities, affecting and transforming several core activities. Most studies concerned with this relationship, however, have narrow regional and thematic foci. The complementarity or discrepancy between relevant research is unknown, and a critical assessment of the current state of research is lacking.
This paper reviews the empirical literature on the determinants of farmer adoption of sustainable intensification technologies in maize agri-food systems of the Global South. The attributes of the technology and the dissemination institutions interact with farm/farmer-specific variables, leading to heterogeneous impacts, making the prediction of technology adoption challenging.
The tools of biotechnology present an opportunity to infuse a new round of technology into Indian agriculture, which has been going through "technology fatigue" in recent period. These technologies follow from the conventional plant breeding techniques and complement them in improving crops to resist biotic and abiotic stresses, break yield barriers, and sustain yields in the face of resource degradation and climatic change.
One of the key challenges for agriculture today is feeding an increasing population without contributing to climate change. Increasingly, digital agriculture is discussed as a new sociotechnical regime that could help limit emissions for farmers worldwide. While sustainability is an important issue, recent papers in the field of digital agriculture do not address the problem directly. After a literature review, this paper will focus on the importance of shared perspectives as enablers in socio-technical transitions.
The Digital Innovation Strategy (DIS) of the Regional Office for Africa (RAF) of FAO has been prepared to respond to critical challenges facing inclusive and sustainable agrifood system transformation in sub-Saharan Africa. It is enshrined in the new Strategic framework 2022–2030 that aims to accelerate the "transformation to more efficient, inclusive, resilient and sustainable agri-food systems for better production, better nutrition, a better environment and a better life, leaving no one behind".
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.
Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization.