Three Observable Variables That Define a Coffee Farm Credit Score
Farm risk varies. Learn which observable variables best indicate a coffee grower's repayment capacity.
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Farm credit and agricultural data research.
Culttivo team analysis of Brazil's smallholder coffee credit market, plot-level risk scoring, and the rural financing gap.
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Smallholder Coffee Data and the Rural Lending Gap in Brazil
The evidence for underwriting coffee smallholder loans sits at plot level. Banks lack the pipeline to interpret it.
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Research from the archive
Why Brazilian Coffee Belt Farmers Lack Access to Formal Credit
Brazil grows a third of the world's coffee, yet many farmers cannot obtain formal loans. This explains how that financing gap is structured.
How an Automated Data Pipeline Makes 48-Hour Agricultural Credit Possible
Traditional agricultural credit may take weeks. Our automated pipeline reaches a decision in 48 hours without cutting risk review.
Where Conventional Credit Scoring Breaks Down in Smallholder Agriculture
Generic scores penalize missing formal records. For smallholder agriculture, that gap is normal and does not by itself indicate risk.
Sul de Minas 2025 Harvest Outcomes: Which Farm Variables Flagged Risk Early
Reviewing 2025 Sul de Minas harvest results showed which farm inputs most consistently signaled yield shortfalls.
From Farm to Plot: Why the Unit of Analysis Changes Agricultural Credit
Treating a farm as one unit hides differences between plots. Two hectares of high-altitude shade-grown arabica carry different risk from two hectares of lowland cerrado.
The Rural Credit Landscape in Minas Gerais: Coverage, Gaps, and Regional Data
Minas Gerais supplies half of Brazil's specialty coffee but receives a disproportionately small share of formal agricultural credit.
Frost, Rainfall, and the Biennial Cycle: Modeling Coffee Yield Risk in Minas Gerais
Frost, rainfall shifts, and biennial bearing complicate coffee yield forecasts in Minas Gerais. Here is how we account for them.
Using Plot-Level Data to Lower Agricultural Loan Default Rates
Satellite imagery, soil maps, and cooperative yield records already support stronger rural underwriting. The challenge is bringing these sources together and scoring them at scale.
How Sentinel-2 Satellite Imagery Feeds Agricultural Credit Risk Models
Sentinel-2 imagery provides 10-metre NDVI canopy readings every five days. For farm credit, those observations become a real-time underwriting signal.
Rural Credit Rejection in Brazil: An Information Problem, Not a Risk Problem
Rural credit denials often reflect incomplete information rather than excessive risk. The data banks use may not show what a productive farm can actually produce.