What type of predictive model is most useful for a farmer who increases wheat production by a consistent number of bushels each year?

In this scenario, a linear model is the most appropriate option because the farmer is increasing wheat production by a consistent number of bushels each year. A linear model describes a relationship in which one variable increases or decreases by a fixed amount when another variable increases.

In the context of the question, as time progresses (each year), the production of wheat increases by a constant amount. This behavior aligns perfectly with the characteristics of a linear equation, which takes the form of (y = mx + b), where (m) represents a constant rate of change (the number of bushels added each year).

By utilizing a linear model, the farmer can easily predict future wheat production over the years based on this consistent increase, making it a straightforward and effective choice for understanding this particular growth pattern. Other models, like exponential, polynomial, or logarithmic, would not accurately represent a situation where growth is steady and uniform over time.