In this study, it is applies a participatory scenario modelling framework to assess potential societal responses to the impacts of climate change by the mid-21st century, and model consequent land use and land cover change scenarios under different livelihood futures as guided by communities’ members in the areas under investigation. The authors focused their analysis on two montane sites of the Eastern Afromontane Biodiversity Hotspot , the Taita Hills, Kenya, and a montane area north-west of Jimma, Ethiopia
Many initiatives to address contemporary complex challenges require the crossing of sector, domain, and level boundaries, which policy entrepreneurs are believed to facilitate. This study aims to enhance our understanding of how, why, and with what effect such entrepreneurs operate to cross boundaries. As this requires an account of both entrepreneurial strategy and the surrounding policy environment, we embed entrepreneurship in the policy frameworks of multiple streams, advocacy coalitions, and punctuated equilibrium.
It has long been recognized that investment is needed to build capacity in Science Technology and Innovation (STI) particularly in low and medium income (LMI) countries. Yet there is little understanding as to how to do this.
Recently, increasing attention has been paid to intermediaries, actors connecting multiple other actors, in transition processes. Research has highlighted that intermediary actors (e.g. innovation funders, energy agencies, NGOs, membership organisations, or internet discussion forums) operate in many levels to advance transitions. The authors argue that intermediation, and the need for it, varies during the course of transition. Yet, little explicit insight exists on intermediation in different transition phases.
Fall Armyworm (Spodoptera frugiperda), or FAW, is an insect native to tropical and subtropical regions of the Americas. In the absence of natural controls or good management, it can cause significant damage to crops. It prefers maize, although it can feed on more than 80 additional species of crops including rice, sorghum, millet, sugarcane, vegetable crops and cotton.
L’étude a contribué à améliorer la chaîne de valeur (CVA) maïs grain blanc grâce à des liens et relations plus structurés entre ses acteurs. L’Approche Recherche Intégrée pour le Développement a été utilisée à travers le suivi strict de toutes ses étapes pour installer une plateforme d’innovations dans la commune de Matéri et promouvoir les relations inter-acteurs. Les résultats de l’application de l’approche multi-acteurs ont fait ressortir le maïs grain blanc comme chaîne de valeur prioritaire dans la zone d’étude.
Weather risk is a serious issue in the African small farm sector that will further increase due to climate change. Farmers typically react by using low amounts of agricultural inputs. Low input use can help to minimize financial loss in bad years, but is also associated with low average yield and income. Increasing small farm productivity and income is an important prerequisite for rural poverty reduction and food security. Crop insurance could incentivize farmers to increase their input use, but indemnity-based crop insurance programs are plagued by market failures.
La demande croissante de denrées alimentaires, à la fois en quantité et en qualité, a accru la nécessité d'intensifier et d'industrialiser le secteur agricole. L'Internet des objets est une famille de technologies très prometteuse, capable d'offrir de nombreuses solutions pour la modernisation de l'agriculture.
Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems.
For an intelligent agricultural robot to reliably operate on a large-scale farm, it is crucial to accurately estimate its pose. In large outdoor environments, 3D LiDAR is a preferred sensor. Urban and agricultural scenarios are characteristically different, where the latter contains many poorly defined objects such as grass and trees with leaves that will generate noisy sensor signals. While state-of-the-art methods of state estimation using LiDAR, such as LiDAR odometry and mapping (LOAM), work well in urban scenarios, they will fail in the agricultural domain.