A4NH releases strategic briefs highlighting key program achievements, lessons learned
- From
-
Published on
20.02.21
- Impact Area

Throughout its 11-year history, the CGIAR Research Program on Agriculture for Nutrition and Health (A4NH) has contributed to the knowledge and evidence base with innovative, strategic research linking these critical areas of work. As the program’s second phase draws to a close, some of these learnings and achievements have been captured in a series of strategic briefs, touching on work conducted across the five research flagships by researchers from seven managing partners, representing work from across the globe.
Topic include
- Measuring the impact of agriculture programs on diets and nutrition
- Catalyzing the scale-up of crop biofortification
- Lessons learned on scaling Aflasafe through commercialization in sub-Saharan Africa
- Food systems research to support sustainable impact
- Supporting consumer choices toward healthy, safe and sustainable diets in low- and middle-income countries
- Mitigating health risks in sustainable agricultural intensification
- Intersectionality and addressing equity in agriculture, nutrition and health
Photo credit: Roadside market. Mozambique, Angonia province (ILRI/Stevie Mann)
Related news
-
National workshop brings together key actors to co-design resilient livestock and aquaculture strategies for the Kafue River Basin in Zambia
Sustainable Animal and Aquatic Foods Science Program15.09.25-
Climate adaptation & mitigation
-
Food security
-
Nutrition
-
Poverty reduction, livelihoods & jobs
As Southern Africa faces mounting climate pressures and food system vulnerabilities, Zambia is stepp…
Read more -
-
Pathways of change: Schools as building blocks towards nurturing biodiversity and resilient agricultural and food systems
Multifunctional Landscapes Science Program04.09.25-
Biodiversity
-
Environmental health
-
Environmental health & biodiversity
-
Nutrition
When we think of schools, we often imagine classrooms, textbooks, and examinations. Yet, schools hol…
Read more -
-
Machine learning algorithms are helping scientists analyze and predict grain quality and nutritional content for rice breeding
International Rice Research Institute (IRRI)27.08.25-
Nutrition
The technology promises to accelerate the development of rice varieties that meet consumer preferen…
Read more -