Project Brief
Some of those key factors are;
1. Access to Finance
Youth in rural areas struggle to secure financing for agricultural ventures. The EFInA 2020 survey found that only 27% of rural adults have access to formal financial services, limiting the potential for young agripreneurs.
Source: EFInA, 2020
URL: https://efina.org.ng/wp-content/uploads/2021/02/A2F-2020-Final-Report.pdf
2. Post-Harvest Losses
Nigeria experiences 30%-50% post-harvest losses due to poor storage and logistics, costing the economy $9 billion annually. The FMARD highlights that this waste discourages young people from engaging in agriculture.
Source: FMARD, 2022
URL: https://fmard.gov.ng/
By focusing on these critical barriers, this hackathon aims to inspire innovative solutions that can enhance productivity, reduce waste, and create new opportunities for young agripreneurs in Nigeria.
You have been presented the opportunity to develop a solution to any one of these three key barriers using data and innovative technology.
Process
Access to affordable finance remains one of the biggest barriers preventing young Nigerians from entering agriculture.
According to the EFInA Access to Financial Services in Nigeria 2020 survey, only 27% of rural adults have access to formal financial services. Among young people aged 18–25, access to loans for business purposes is even lower at 6%.
Meanwhile, agriculture employs about 35% of Nigeria’s workforce (World Bank, 2022), but contributes less than 25% to the GDP — suggesting underinvestment.
Commercial banks often consider agriculture “too risky” because of factors like climate variability, land tenure issues, and lack of credit history. Without access to credit, young farmers cannot invest in seeds, technology, irrigation, or machinery needed to boost yields and incomes.
How can we use data to bridge this finance gap and make agriculture a bankable, attractive industry for youth?
Develop a data-driven solution that helps young agripreneurs obtain access to affordable finance.
You are expected to collect, process, analyze, and model data to:
- Identify factors that make farmers “creditworthy.”
- Predict future repayment behavior or business success.
- Design tools that banks, investors, or grant bodies could trust to fund young farmers.
Deliverables:
Each team must submit:
Data Strategy:
-
- Description of datasets used and sources.
- Steps taken for cleaning and analyzing the data.
Farmer Finance Profile:
-
- Visualizations or scoring models that assess the likelihood of young farmers to succeed or repay loans.
- Insights into what makes a farmer “low risk” or “investment-ready.”
Predictive Model (Optional but bonus points):
-
- A credit scoring model or farm business success prediction.
Solution Proposal:
-
- A concept or prototype (e.g., an app/dashboard for loan applications, farmer credit scorecards, microfinance risk dashboards).
- How the solution builds trust between farmers and financiers.
Bonus Points:
- Design an app prototype.
- Include farmer education features (e.g., “how to improve your credit score” tips).
- Build a dashboard that lenders or agricultural investors can easily use.
Important Notes:
- Teams can be 3–5 people.
- Projects must be completed within the hackathon timeline.
- Solutions should prioritize financial inclusion, fairness, and scalability for rural youth.
2. Post-Harvest Losses Problem: Reducing Post-Harvest Losses to Build Youth-Led Agri-Businesses
Background:
Post-harvest losses (PHL) — the loss of crops between harvest and market — are one of the biggest drains on Nigeria’s agricultural productivity and a major disincentive for young entrepreneurs.
According to the Federal Ministry of Agriculture and Rural Development (FMARD), Nigeria loses between 30% to 50% of its perishable agricultural produce annually due to poor storage, transportation, and processing infrastructure.
The World Bank estimates that these losses cost Nigeria about $9 billion annually — food that could feed millions and create profitable businesses.
Young people who want to enter agriculture are discouraged when they see their hard work wasted and profits lost. Without innovations around storage, logistics, processing, and market access, farming remains unattractive.
How can we use data to predict, prevent, and reduce post-harvest losses — and help youth build successful agri-businesses?
Objective:
Develop a data-driven solution that helps young agripreneurs minimise post-harvest losses and maximise profit.
You are expected to collect, process, analyse, and model data to:
- Identify risk factors for post-harvest loss based on crop type, region, or season.
- Predict high-loss areas or periods.
- Design smart systems for storage, distribution, or market linkage.
Deliverables:
Each team must submit:
Data Strategy:
-
- Description of datasets used and sources.
- Steps taken for cleaning and analyzing the data.
PHL Risk Map or Dashboard:
-
- Visualize high-risk areas, crops, seasons for losses.
- Insight into where interventions are most needed.
Predictive Model (Optional but bonus points):
-
- Predict likelihood of loss by crop/region/handling method.
Solution Proposal:
-
- A concept or prototype (e.g., smart storage alert systems, digital marketplaces, transportation apps).
- How the solution encourages youth participation in the agricultural value chain.
Bonus Points:
- Create a solution for smallholder farmers.
- Integrate logistics providers into your solution (e.g., linking farmers with cold storage truck owners).
- Build a dashboard that identifies top investment needs (e.g., storage hubs, processing plants).
Important Notes:
- Teams can be 3–5 people.
- Projects must be completed within the hackathon timeline.
- Solutions should prioritize youth entrepreneurship, digital innovation, and scalability.
3. Greenhouse Farms: Pest Effects on Crop Yield
To be populated shortly
Benefits
Application Dealine May 15, 2025
How To Apply
Interested and Qualified candidates should Go:
Click Here To Apply
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