Providing economic opportunities for youth in agriculture is essential to securing the future of agriculture in Africa, addressing poverty, unemployment, and inequality. However, barriers limit youth participation in agriculture and the broader food system. This scoping review aimed to investigate the opportunities and challenges for youth in participating in agriculture and the food system in Africa. This review conducted a scoping review using the PRISMA guideline. Published studies were retrieved from online databases (Web of Science, Cab Direct, and Science Direct) for 2009 to 2019.
Cette document (Note méthodolgique pour l'analyse des chaines de valeur agricoles) combine:
- les réponse aux quatre Questions Structurantes en relation avec l'analyse des chaines de valeurs agricoles.
Question 1: Quelle est la contribution de la chaine de valeur à la croissance économique ?
Question 2: Cette croissance économique est-elle inclusive ?
Question 3: La chaine de valeur est-elle durable du point de vue social ?
Question 4: La chaine de valeur est-elle durable du point de vue environnemental ?
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”.
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.
Agricultural innovation has played a critical role in the economic transformation of developing East Asian countries over the past half century. The Green Revolution—in the form of modern seed varieties, chemical fertilizers, pesticides, and modern machinery—has contributed to increased crop yields and farm incomes, and decreased poverty across the region. Although policy makers’ traditional focus on expanding and intensifying agricultural production has brought many benefits, the focus on productivity has come at a rising cost.
The national assessment of the agricultural innovation system (AIS) in Malawi was conducted using a framework of four types of analyses: functional, structural, capacity and enabling environment analysis. The approach included five case studies that addressed three methods including the use of indigenous methods for fall armyworm (FAW) control in Farmer Field Schools (FFS), livestock transfer programs, and a horticulture marketing innovation platform in Mzimba, Ntchisi, Balaka, and Thyolo districts.
This report introduces the reader to the concept of agricultural innovation systems (AIS) and the TAP-AIS project being implemented by FAO in nine countries, including Lao People's Democratic Republic (Lao PDR). The results of the AIS assessment for Lao PDR are presented, highlighting key barriers and opportunities for agricultural innovation in the country.
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.