This study provides a model that supports systematic stakeholder inclusion in agricultural technology. Building on the Responsible Research and Innovation (RRI) literature and attempting to add precision to the conversation around inclusion in technology design and governance, this study develops a framework for determining which stakeholder groups to engage in RRI processes. We developed the model using a specific industry case study: identifying the relevant stakeholders in the Canadian digital agriculture ecosystem.
The paper aims at finding out how significantly stakeholders are consulted and involved by preparers, Ukrainian publicly-listed agricultural companies, while compiling sustainability reporting (SR) and by assurance providers, during assurance processes of SR. The paper’s main research question may be formulated as follows ‘How deeply stakeholders are involved at Ukrainian agricultural companies in the preparation of their sustainability reporting and assurance?’
Networks and partnerships are commonly-used tools to foster knowledge sharing between actors and organisations in the Agricultural Knowledge and Innovation System (AKIS), but in Europe the policy emphasis on including users, such as farmers and foresters, is relatively recent. This paper assesses user involvement in a diverse set of European Union (EU)-funded and non-EU (formal and informal) multi-actor partnerships. This research used a common methodology to review several forms of multi-actor partnerships involving users and other actors.
In Mali, agricultural activities such as seeding, harvesting, and irrigation play a significant role in productivity. These activities must be carried out appropriately and above all, at an appropriate time to achieve excellent performance. Unfortunately, most farmers are unaware of the impact of these activities on the yield of their crops. This study aims to help farmers and youth people wishing to gather information needed in the field of agriculture entrepreneurship through a mobile application (mobile app).
CONTEXT
Big data applications in agriculture evolve fast, as more experience, applications, good practices and computational power become available. Actual solutions to real-life problems are scarce. What characterizes the adoption of big data problems to solutions and to what extent is there a match between them?
OBJECTIVE
We aim to assess the conditions of the adoption of big data technologies in agricultural applications, based on the investigation of twelve real-life practical use cases in the precision agriculture and livestock domain.
METHODS
The Progress towards Sustainable Agriculture initiative (PROSA) is a framework that seeks to complement ongoing efforts on the Sustainable Development Goals (SDGs), and particularly indicator 2.4.1, to support country-level assessments using data already available at the national level. Making agriculture more sustainable – productive, environmentally friendly, resilient and profitable is fundamental, as agriculture remains the main source of livelihood for the majority of the world’s poor and hungry.
The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities.
FAO Eritrea, in partnership with the Ministry of Agriculture is implementing the national component of a global project entitled “Developing capacity in Agriculture Innovation System project: Scaling up the Tropical Agriculture Platform Framework”.
L’une des avancées les plus importantes dans le domaine de l’observation de la terre est la découverte des indices spectraux, ils ont notamment prouvé leur efficacité dans la caractérisation des surfaces agricoles, mais ils sont généralement définis de manière empirique. Cette étude basée sur l’intelligence artificielle et le traitement du signal, propose une méthode pour trouver un indice optimal. Et porte sur l’analyse d’images issues d’une caméra multi-spectrale, utilisée dans un contexte agricole pour l’acquisition en champ proche de végétation.
Individuals from a diverse range of backgrounds are increasingly engaging in research and development in the field of artificial intelligence (AI). The main activities, although still nascent, are coalescing around three core activities: innovation, policy, and capacity building. Within agriculture, which is the focus of this paper, AI is working with converging technologies, particularly data optimization, to add value along the entire agricultural value chain, including procurement, farm automation, and market access.