Hello everyone I'm a final year business intelligence and data analytics student exploring an agricultural financial intelligence platform. Is this worth a short ?

[u/Actual-Key7383]

as mentioned already I am a Business Intelligence and Data Analytics student from Botswana, and I've been exploring the idea of building an agricultural financial intelligence and decision-support platform.

The idea came from researching the challenges farmers and agribusinesses face, particularly access to financing, production planning, profitability, climate risks, market volatility and poor data management.

One thing realized is that agricultural data solutions often focus on crop detection, disease classification, yield prediction and satellite monitoring you know the surface level stuff . I'm considering a financial and business intelligence perspective instead.

what i am thinking

I want to develop a system that combines agricultural, financial, economic, environmental and logistics data to help farmers and agribusinesses understand their operations and evaluate business decisions.

It would have three main components:

1. Historical agricultural and economic knowledge base

A Botswana-focused repository combining historical agricultural production, commodity prices, rainfall, inflation, exchange rates, interest rates, trade statistics and other relevant information.

Potential sources include Statistics Botswana, Bank of Botswana, FAOSTAT, agricultural marketing organisations and satellite datasets.

The objective is to provide historical context for financial analysis, forecasting and business simulations.

2. Financial intelligence and calculation engine

A system where farmers can input their operational and financial information to calculate:

• Production costs and profitability.
• Cash flow and working capital requirements.
• Break-even points.
• Production-cycle financing requirements.
• Financial exposure to changing input costs and commodity prices and more.( due to limited domain knowledge this is all i can confidently state)

Rather than relying entirely on machine learning, the engine would use established financial formulas and business rules, ideally validated by agricultural economists and finance professionals.

3. Predictive modelling and interactive simulations

Users could explore questions such as:

• What happens to profitability if fertiliser or feed prices increase by 20% ?
• How would lower production affect cash flow and debt repayments ?
• How much working capital would be required to expand production ?
• How could exchange-rate movements affect imported agricultural inputs ?
• What would happen if transport costs increased or market prices declined ?

I would initially experiment with interpretable regression models for forecasting selected variables, alongside deterministic financial calculations and scenario simulations.

Eventually, I'd like to integrate climate information, geospatial indicators and more sophisticated risk models where sufficient reliable data exists.

The bigger idea

I'm considering developing reusable analytical infrastructure that could support different applications: farm profitability assessments, agricultural investment feasibility studies, market intelligence, risk analysis and financial planning.

The platform wouldn't necessarily be limited to individual farmers. Agricultural distributors, cooperatives, lenders and other agribusinesses could potentially benefit.

I'm considering collaborating with agricultural economics, accounting, crop science and animal science students to improve the domain knowledge behind the models.

I'd appreciate feedback on a few things:

1. Does this address a genuine agricultural business problem, or am I overestimating the need?
2. Which financial or operational decisions would be most valuable to support first?
3. Is building a historical agricultural-economic knowledge base worthwhile, or am I introducing unnecessary complexity?
4. What would be a sensible MVP for a student project?
5. Are there existing tools or platforms doing something similar that I should study?
6. If you've worked with farmers or agribusinesses, what are the biggest obstacles to collecting reliable operational and financial data?
   

ALL comments are greatly appreciated . Thank you so much in advance.....

[u/Relevant-Resist9878]

the problem is real, but the scope is huge lol. that’s 3 startups, not one student project

I’d start with one thing: a simple profitability calculator for one farm type. cattle makes sense in Botswana. costs in, profit and break-even out, plus a slider for “what if feed goes up 20%”. skip the knowledge base for now, add only the 2-3 datasets the calculator actually needs

before building anything, talk to 5-10 farmers. you’ll probably find the biggest problem is that most don’t keep records at all, so data entry has to be super easy or nobody will use it

also lenders and cooperatives might be your real users, not farmers. they need exactly this to approve loans