The creation of Competitive Research Grants (CRGs) is globally recognized as an institutional innovation for improving the effectiveness of agricultural research. Unlike block grants for research, CRGs are expected to bring in many top-quality proposals from a wide range of actors, selecting the best out of them and thus getting more value for money.
This research note explains the results of social experiment designed with three primary objectives. These include (1) to mitigate the digital divide concerning the accessibility of forecasted weather information and crop advisories for women farmers in Bangladesh and (2) to assess the potential impact of a digital climate advisory tool on the agricultural practices of climate-smart agriculture facilitated by digital advisory tools for stakeholders in the value chain, such as microfinance institutions offering crop loans in areas facing higher weather-related risks.
Familiar mixed dairy sheep farm is the most widespread system in the Mediterranean basin, in Latin America and in developing countries (85%). There is a strong lack of technological adoption in packages of feeding and land use in small-scale farms. To increase competitiveness, it would be of great interest to deepen the knowledge of how innovation was selected, adopted, and spread. The objective of this research was to select strategic feeding and land use technologies in familiar mixed dairy sheep systems and later assess dairy sheep farms in Spain.
Many countries are facing growing levels of food insecurity, reversing years of development gains, and threatening the achievement of Sustainable Development Goals by 2030. Even before COVID-19 reduced incomes and disrupted supply chains, chronic and acute hunger were on the rise due to various factors, including conflict, socio-economic conditions, natural hazards, climate change and pests.
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 digital transformation in agriculture introduces new challenges in terms of data, knowledge and technology adoption due to critical interoperability issues, and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.
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.
Africa–Europe Cooperation and Digital Transformation explores the opportunities and challenges for cooperation between Africa and Europe in the digital sphere.
Over the past few decades, some countries in Asia have been more successful than others in addressing poverty and malnutrition. The key question is what policies, strategies, legislation and institutional arrangements have led to a transformed agricultural sector, effectively contributing to poverty alleviation and addressing malnutrition. The great majority of national policymakers within and outside the Asia-Pacific region are keen to understand the causes of agricultural development and transformation in successful countries in Asia.
The Food and Agriculture Organization of the United Nations (FAO) has developed a web-based Rift Valley fever (RVF) Early Warning Decision Support Tool (RVF DST), which integrates near real-time RVF risk maps with geospatial data, historical and current RVF disease events from EMPRES Global Animal Disease Information System (EMPRES-i) and expert knowledge on RVF eco-epidemiology.