This collection of posters from the TAP-AIS project illustrates key achievements of the project towards strengthening national agricultural innovation systems (AIS) in Africa (Burkina Faso, Eritrea, Malawi, Rwanda, Senegal), Latin America (Colombia), Asia and the Pacific (Cambodia, Lao PDR, Pakistan). For each of these nine countries, and for their respective regions, the posters provide: i) thematic focus and context; ii) constraints in the AIS; iii) capacity development interventions; iv) outcomes; v) the way forward.
This study aims to investigate blockchain technology for agricultural supply chains during the COVID-19 pandemic. Benefits and solutions are identified for the smooth conduction of agricultural supply chains during COVID-19 using blockchain. This study uses interviews with agricultural companies operating in Pakistan. The findings discover the seven most commonly shared benefits of applying blockchain technology, four major challenges, and promising solutions.
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.
Ornamental plants are constantly being improved by new technologies and cultivation systems to provide new, high-quality plant material for one of the most demanding markets in the horticulture sector. In addition, the ornamental production sector faces several challenges, such as an increase in costs of production, new and old pests and diseases, climate change and the need to adapt to environmental stresses, the need for conservation and environmental protection, and competition with other food and energy crops in terms of areas and natural resources.
This brochure presents the five-year TAP-AIS project (2019-2024) funded by the European Union under the DeSIRA Initiative and implemented by the Food and Agriculture Organization (FAO) of the United Nations. The project has the main objective to strengthen capacities to innovate in national agricultural innovation systems (AIS) in the context of climate-relevant, productive, and sustainable transformation of agriculture and food systems in Africa, Latin America, Asia and the Pacific.
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 2021 Global Report on Food Crises (GRFC 2021) highlights the remarkably high severity and numbers of people in Crisis or worse (IPC/CH Phase 3 or above) or equivalent in 55 countries/territories, driven by persistent conflict, pre-existing and COVID-19-related economic shocks, and weather extremes. The number identified in the 2021 edition is the highest in the report’s five-year existence. The report is produced by the Global Network against Food Crises (which includes WFP), an international alliance working to address the root causes of extreme hunger.
The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse.
The impact of global warming on crop growth periods and yields has been evaluated by using crop models, which need to provide various kinds of input datasets and estimate numerous parameters before simulation. Direct studies on the changes of climatic factors on the observed crop growth and yield could provide a more simple and intuitive way for assessing the impact of climate change on crop production.
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.