Of more than 50,000 known edible plant species, the United Nations’ Food and Agriculture Organization estimates that only three crops – rice, wheat and maize – account for two-thirds of the world’s food supply.
As a result, many nutritious, resilient crops remain underutilized, contributing to poor dietary diversity and health outcomes.
FFAR partnered with the Global Alliance for Improved Nutrition (GAIN) to launch Harvest for Health initiative to accelerate the development of underutilized crops, increasing the diversity of foods in the marketplace. The model developed through this initiative will predict underutilized crops’ potential as sources of functional and nutritious ingredients that could replace, complement or aid in reformulating the existing food products or developing new ones.
Why Underutilized Crops?
Underutilized crops can help ensure nutritional security in the face of climate change and provide diverse economic opportunities to growers. Additionally, consumers are increasingly seeking healthier, sustainable food products and global flavor-driven food experiences. Introducing new and exciting nutritious foods with various tastes and flavors will expand the food and agriculture industry’s consumer base and contribute to our food system’s health and environmental sustainability.
Breakthrough Crop Challenge
While underutilized crops have incredible functional and nutritional potential, the development of such crops for consumption or use in other products is prohibitively expensive and time-intensive. To attract more private sector investment in underutilized crop development, Harvest for Health is launching the Breakthrough Crop Challenge to develop a predictive model that can screen underutilized crops to determine a crop’s usefulness as a source of functional ingredients or nutrients.
Who is Eligible?
Any US and non-US public or private institution, consortium, non-profit organization, for-profit company, tribal government entity or any combination of the above is eligible to apply. Contestants do not need to participate in Seed Funding to participate in the Challenge or potentially receive $1 million.
The Challenge winner will be involved with FFAR in the utilization of the predictive model in Phase II of the Harvest for Health program, which will be used to prioritize underutilized crops for commercial development based on their potential for increased public and private investments.
Application Requirements
The Breakthrough Crop Challenge is requesting concept notes for predictive models to identify crops with market potential as a source of one or more of the following functional ingredients:
- Thickeners, emulsifiers, and stabilizers
- Bulking agents
- Taste and flavor enhancers
- High nutrient density (specific nutrients to be proposed by applicants)
Proposed models will be assessed and prioritized based on the number of functionalities addressed and the accuracy of their predictive abilities.
Ingredient functionality is dependent on specific physicochemical properties of molecules and, for less refined ingredients, the complex mixtures from which they are derived. For example, hydrocolloids are a class of compounds that include polysaccharides, such as starches used as thickening agents that can be found in a wide variety of crops, including potatoes, cassava, corn, lentils and cereals. Different hydrocolloids, often derived from different sources, have distinct physicochemical properties (e.g., solubility in cold or hot water, gelation, viscosity) that determine how well they function as thickeners, in addition to other functional ingredient properties such as stabilizers or emulsifiers.
For seed funding, applicants must state their hypothesis and outline a detailed plan to develop a predictive model using data that currently exists or is likely to exist for underutilized crops as model inputs. This includes, but is not limited to, compositional, genetic, and biochemical data. Applicants must provide descriptions of the volume and accessibility of data that will be used to build the predictive model. The predictive model may also consider advancements in processing and product development. Ideally, the predictive model will provide limited, reasonable predictions or inferences of functional properties using publicly available data on known ingredients that correlate the measured properties (see section on Examples of Important Measurements for Ingredients with Different Functional Properties below) with the potential applications and processing methods used to predict the desired quality and composition.
Seed Funding Submission Requirements
To submit concepts, applicant(s) must submit an online application using FFAR’S Grant Management System. Concepts must include:
- Principal Investigator (PI) contact information
- Title of concept note
- Clear description of which functionality(-ies) is/are being considered (up to 750 words)
- Approach to developing the predictive model and rationale for this approach (up to 3,000 words):
- Description of the modeling technique
- Model development methodology
- Hypothesis of candidate variables that will be important aspects of the predictive model
- Set(s) of data that will be used for calibration and internal validation of the predictive model, including a justification for the use of this/these data set(s)
- Validation methodology plan using a range of crops and measured properties
- Describe possible barriers and approaches for overcoming them.
- Qualifications of the Research Team
- Budget request amount (up to $75 000) and itemized budget justification
Examples of Important Measurements for Ingredients with Different Functional Properties
Thickeners, Emulsifiers, and Stabilizers
- Solubility in aqueous and non-aqueous solution
- Viscosity at standard concentration and shear rates
- Particle size
- Texture and gelation
- Volatiles (aroma), non-volatiles, taste components (sweetness, bitterness, saltiness, umami, sourness)
- Emulsification
- Ingredient compatibility and unique interaction/mechanism in product formation
- pH, acid/base compatibility, and functionality
- Quality of protein – amino acid composition
- Fiber (both soluble and insoluble)
Bulking Agents
- Solubility in aqueous and non-aqueous solution
- Melting range
- pH
- Saponification value, and ester value
- Organoleptic qualities
- Total sugars and reducing sugars
- Ingredient compatibility and interaction/mechanism in product formation
Taste and Flavor Enhancers
- Organoleptic qualities; Flavonoids
- Solubility; ethanol, water, etc.
- Molecular weight
- Melting range and boiling point
- Volatiles (aroma), non-volatiles, taste components (sweetness, bitterness, saltiness, umami, sourness)
- pH
- Refractive Index
- Specific gravity
High Nutrient Density
- Macronutrient (amount present. e.g., quality of protein [amino acid composition, bioavailability, etc.])
- Fiber (both soluble and insoluble)
- Micronutrient (vitamin and mineral composition)
4. Known or novel phytochemicals of value, e.g., anthocyanins
Seed Funding Review Criteria
Applicant submissions will undergo expert review. Reviewers will consider the following criteria when evaluating each Concept Note:
- Technical merit,
- Technical approach and validation strategy,
- Expertise and qualifications of the team, and
- Appropriateness of data sets selected for developing the predictive model.
- Technical merit (25%)
- Does the concept note clearly describe functional properties to be considered?
- Are the goals and objectives of the concept note clear?
- Are possible barriers addressed and approaches for overcoming them proposed?
- Technical approach and validation strategy (30%)
- Is the technical approach to developing the predictive model reasonable and scientifically feasible?
- Does the concept note describe the modeling technique to be used?
- Does the concept note hypothesize candidate crop variables that will form important aspects of the predictive model?
- Does the concept note describe the model development methodology?
- Are the validation strategies described reasonable and scientifically feasible?
- Expertise and qualifications of the team (15%)
- How well qualified is the individual, team, or organization to conduct the proposed activities?
- Does the concept note demonstrate that the applicant would have adequate resources to build a predictive model?
- Selection of data sets (30%)
- Are the data sets appropriate for developing the predictive model?
- Can the data set(s) be used to predict the selected properties of interest?
What Can I Apply For ?
Seed funding of up to $75,000 to develop a predictive model of underutilized crops. The submission portal for the predictive model development and validation will open in the spring of 2023.
How Do I Apply?
To submit concepts, applicant(s) must submit an online application using FFAR’S Grant Management System.
