The future of predictive analytics in crop pricing

Why are agricultural prices so volatile?

Agricultural prices are volatile because supply depends on harvests that cannot be adjusted quickly. A drought, a frost or a change in demand from a large importer can move the world price within weeks, while planting or removing a crop takes months or years. That mismatch in timing explains much of the sudden rises and falls.

The food price crises of 2007-2008 and 2010-2011 showed how deeply that volatility affects producers and consumers. In response, the G20 promoted the Agricultural Market Information System (AMIS), officially launched in Rome in September 2011 to bring transparency to the markets for wheat, maize, rice and soybeans.

Which public data help anticipate prices?

The best-known public source is the FAO Food Price Index, which measures the monthly change in international prices of a basket of food commodities. FAO publishes monthly series from 1990 and annual series from 1961, so any current movement can be compared with decades of history.

The index averaged 136.0 points in September 2026, 2 points above August and 5.8 percent higher than a year earlier, although 15.1 percent below its peak of March 2022. Alongside it, AMIS market reports and FAO commodity reviews provide production, trade and stock data that any model can use as a baseline.

What does the avocado case show?

Avocado shows how quickly the value of a single product can change. According to FAO, the average export unit value of avocado rose by 24 percent in 2024, to USD 2 789 per tonne. In 2025 it hovered around USD 3 000 in the first quarter and, from May onwards, fell steadily to USD 2 100 in September.

Countries do not move in step either. In 2025 the unit value for Mexico rose by 18 percent, to USD 3 806 per tonne, while Peru’s fell by 19 percent, to USD 1 760. FAO explains that Peruvian avocados are mostly sold through contracts agreed in advance rather than on the spot market. It also warns that these unit values are an approximation and not actual market prices.

What can predictive analytics add?

Predictive analytics can provide an estimate of the harvest and the price weeks in advance, which is when there is still time to decide when and how to sell. FAO describes an example in its 2022 report on automation: in the Netherlands, an AI-powered robot counts tomatoes, measures their ripeness and produces harvest forecasts one to four weeks ahead.

Applied to trading, a model of this kind combines several layers of information:

• Market data: price series and unit values such as those published by FAO.
• Production data: harvest forecasts by area and variety.
• Field data: sensors for weather, humidity and crop condition.
• Demand data: imports into the main destination markets.

What are the limits of any price forecast?

No price forecast is a guarantee, because agricultural markets depend on weather and on policy decisions that are hard to anticipate. A model can estimate likely ranges and flag risks, but it cannot remove uncertainty. That is why forecasting works best as decision support, combined with tools that fix the price in advance, such as forward contracts.

The quality of the result also depends on the quality of the data. Official sources themselves are revised over time, and a model fed with incomplete or mislabelled data produces estimates that are just as unreliable.

How does Avocate apply it?

Avocate’s roadmap includes an AI-based prediction engine to assess risk and anticipate market trends. The project is building its minimum viable product, and the prediction engine belongs to the following phase.

The idea is to combine the traceability data recorded by the platform, such as origin, volumes and IoT sensor readings, with public market series, so that growers and buyers can negotiate with more information.