Butter
Butter usually reacts with a lag to feed, pasture, energy and local inflation because contracts and processing buffer short-term shocks.
The score measures directional pressure. It is not the expected percentage price change. Current market coverage: 100%
What the current data says
Current outlook
Butter price pressure in Germany is currently 52/100. The current score is at least as high as 88% of recorded observations.
What changed recently?
Across the latest 4 recorded observations, the score was unchanged.
What is driving it?
The largest upward contribution is Wholesale prices · butter (+1.9 points), while Food inflation is the strongest offset (-0.2 points).
What could change the outlook?
All else equal, pressure would weaken if Wholesale prices · butter eases. The current cushion from Food inflation would fade if that signal moves back toward neutral.
This explanation updates automatically from the latest stored score, recorded history and current driver contributions.
Why this score?
What could change it?
Butter usually reacts with a lag to feed, pasture, energy and local inflation because contracts and processing buffer short-term shocks.
Scores can reverse when commodity momentum, local weather, crop conditions, currency, logistics or inflation signals change. The model estimates directional pressure rather than an exact shelf price.
Market
Advanced analysis
A deeper view of score history, dispersion and driver concentration. This layer is structured so it can become a Pro feature later without changing the core forecast.
Historical score
160 observationsDriver contribution map
Bars show the current score-point contribution. Prior weight is the maximum structural sensitivity before signal value, quality and freshness.
Cross-market distribution
See how the same product’s current pressure differs across all ten markets.
Weight provenance for this product
The table shows the effective prior weights used for this market before signal value, quality and freshness are applied. A larger number means the signal can move the score more when it is extreme and high quality.
Research determines the hierarchy and safeguards; exact point values remain bounded product priors pending empirical calibration.
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