Harvest Predictions: How to avoid the divorce between Field and Sales

There is a silent "Civil War" in almost every agribusiness in the US. It is not fought with weapons, but with urgent emails and angry phone calls on Monday mornings.

It is the total disconnect between what Production believes it will harvest and what Sales has already promised the Retail buyer. This lack of precision is one of the biggest capital leaks in the sector.

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The Million-Dollar Cost of the "Crystal Ball" (Guesswork)

Continuing to operate with estimates based on empirical experience ("I think about 5,000 boxes are coming") is an unacceptable financial risk in modern agriculture. Calculation errors have two disastrous commercial consequences:

• 📉 Scenario A (Shortage): You breach signed contracts. You have to go to the Spot Market to buy fruit from third parties (more expensive and of unknown quality) to fill the gap, or face severe fines from retailers for shortages.
• 📉 Scenario B (Oversupply): You harvest 30% more than planned and Sales wasn't ready. You have to push the excess fruit to the wholesale terminal markets at "dumping" prices, or worse, it stays in the cooler losing shelf life and value.

The Science of Prediction: Historical Curves + Real Data

How is this conflict resolved? By stopping the guessing game and measuring with data science. ERPagro (Request Prediction Demo) uses an advanced hybrid model to fine-tune your estimates week by week:

The system doesn't do magic; it combines three critical sources of information to give you a reliable number:

1. Historical Curve: "How exactly did this lot (variety/rootstock) behave last year?" The system learns from the past.
2. Digital Scouting: The Field Scout counts fruits on representative plants (marked with GPS) and enters the data into the App. The system extrapolates statistically.
3. Agrometeorological Variables: If the system detects fewer light hours or fewer Growing Degree Days (GDD), it automatically adjusts the probable ripening date.

Excel vs. Agricultural Intelligence (BI)

Success Story: Costa de Huelva and Total Planning 🍓

If prediction is hard on a single farm, imagine doing it for hundreds of partners. That was the challenge for Costa de Huelva (Coophuelva), one of the largest berry cooperatives in Europe (comparable to large Californian Berry groups).

They didn't have the problem of "not knowing how to sell", they had the problem of "not knowing what was entering the cooler". They operated as a passive receiving center, which generated logistical chaos and lost commercial opportunities.

The Digital Transformation: They implemented the Crop Planning and Intake Forecasting module of ERPagro. Now, the cooperative has a "digital map" of all its partners' acres.

• ✅ The system calculates the forecast (Boxes/Pallets) per week based on variety and planting date.
• ✅ The grower confirms their harvest estimate from the Mobile App.
• ✅ The Cooperative closes deals with Retailers knowing exactly the available volume.

The result: They went from being a "warehouse" to being a planned industry, maximizing the return price to the grower.

Discover how Costa de Huelva manages harvest forecasting for hundreds of partners.

Frequently Asked Questions about Harvest Prediction