Farm Management and Data

The Spreadsheet Problem

Most coffee farms in Colombia run on spreadsheets, paper notebooks, and the administrator's memory. The Diario de Labores (daily labor diary) is a massive Excel file. The budget lives in another. Pest monitoring is noted on paper. Soil tests sit in PDF reports from the lab. Fertilizer applications are recorded somewhere else. None of these systems talk to each other, and the farmer makes decisions based on intuition and experience rather than integrated data.

This is not a criticism -- it is reality. And it is the starting point for understanding why data-driven farm management matters.

What We Track

Across our 6 farms and 179 plots, we maintain integrated data on:

• Labor -- every task assignment, every day, every plot. Over 6,300 task records covering who did what, where, when, and at what cost per jornal (daily labor unit)
• Budgets -- over 8,200 budget line items tracking planned vs actual spending by farm, plot, crop, and category
• Fertilization -- every fertilizer application tracked by product, quantity, date, and plot. Over 2,700 agrochemical movements with nutrient compositions for 269 products
• Production -- 342 budget forecasts, 2,100+ wet mill lots, conversion factors, quality scores
• Pest and disease -- 800+ pest records and 1,300+ weed assessments from field monitoring
• Soil and foliar -- lab results linked to specific plots for nutrient planning
• Phenology -- the current physiological stage of every plantation (75 crop cycles) updated weekly
• Photos -- over 4,100 geotagged field photos linked to plots and dates

From Data to Decisions

Raw data is noise. The value comes from cross-referencing:

Plot Intelligence

By combining soil tests, fertilization records, production data, pest monitoring, photos, and phenological stage for a single plot, we can generate a holistic health assessment. A plot showing low potassium in soil tests, below-average yield, and high broca pressure tells a different story than any single data point would suggest.

Budget vs. Execution

Comparing planned spending against actual execution by category reveals where resources are being misallocated. When the fertilization budget is underspent but the labor budget is over, it signals a management issue -- not just a financial one.

Phenological Alignment

Knowing that a plot is in the grain fill stage (high potassium and calcium demand) while the fertilization records show no recent K application creates an actionable alert. Timing nutrient delivery to match the plant's physiological needs is more effective than calendar-based scheduling.

Labor Productivity

Tracking task completion rates, yields per jornal, and contractor performance across plots identifies both efficiency opportunities and quality risks. A contractor with consistently high picking speed but high conversion factor (poor cherry selection) is costing the farm in quality, not saving it in labor.

Technology in Traditional Farming

The challenge is not building the technology -- it is integrating it into farm operations without disrupting the human relationships and institutional knowledge that make farms work. Our approach:

• Data enters through existing workflows -- the administrator's Excel diary, the agronomist's field reports, the quality manager's WhatsApp messages
• Analysis happens in the background -- automated ingestion, parsing, cross-referencing
• Insights are delivered in the language the team speaks -- Spanish-language reports with familiar metrics (jornales, arrobas, lotes)
• Decisions remain human -- the system recommends, the administrator decides

The goal is not to replace the experienced farmer's judgment. It is to give that judgment better inputs.

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This post is adapted from Module 18 of our Advanced course. Want to see how data-driven management works on a real 221-hectare operation? Join the free community at [skool.com/particular-3064](https://skool.com/particular-3064) for discussions on technology, decision-making, and farm management systems.