  Precision agriculture, also known as site-specific crop management, plays a
crucial role in modern agriculture. Yield maps are an essential tool as they
help identify the within-field variability that forms the basis of precision
agriculture. If farmers could obtain yield maps for their specific site based
on their field's soil and weather conditions, then site-specific crop
management techniques would be more efficient and profitable. However,
forecasting yield and producing reliable yield maps for an individual field can
be challenging due to limited historical data. This paper proposes a novel
two-stage approach for site-specific yield forecasting based on short-time
series and high-resolution yield data. The proposed approach was successfully
applied to predict yield maps at three different sites in Nebraska,
demonstrating the method's ability to provide fine resolution and accurate
yield maps.
