Project

A review to document and characterize the aggregation models used in agricultural value chains for biofortified crops globally and within the Commercialisation of Biofortified Crops (“CBC”) programme.

Acronym
GAIN 2022
Code
41Y06722
Duration
21 November 2022 → 28 February 2023
Funding
European funding: various
Research disciplines
  • Social sciences
    • Agricultural and natural resource economics, environmental and ecological economics
    • Business management
    • Logistics and supply chain management
    • Marketing channels and retailing
    • Marketing models
Keywords
Biofortification Scoping review in-depth interviews Agricultural value chains Farmers
 
Project description

Through the Commercialising Biofortified Crops (CBC) programme, GAIN and HarvestPlus share an ambition to expand coverage of biofortified nutrient dense foods to at least 190 million consumers by 2022. The current focus will be on biofortified varieties of six highly promising crops, developed by HarvestPlus and its partners. These crops form the frontline cluster of sentinel nutritious staple crops to be considered for commercialisation at scale by the partnership. 

in the frame of the CBC-programme, the main objective of this project is to undertake a review to document and characterise the aggregation models used in agricultural value chains for biofortified crops globally and within the CBC programme.
The specific research questions are:
1. What types of aggregation models are used in agricultural value chains for biofortified crops globally and what are their characteristics (e.g., category/type, actors involved, resources required) and aims (e.g., to improve production, supply, efficiencies, profitability)?
2. Within these models, what are the barriers and enablers to achieving the intended effects?
3. Which aggregation models would be most applicable to the commercialisation of biofortified crops?
4. To what extent did the aggregation models used under the CBC programme fit into these categories and achieve the intended effects, and if not, how could they be improved moving forward?