Background: Sound agronomic recommendations are crucial for today's agronomists as they strive for improved yields, profits, and sustainability. Determining the spatial relationships between yield and landscape variation including soil properties, soil texture, and terrain attributes may improve management decisions, particularly with regards to proper nitrogen application for minimizing both costs to farmers and environmental impacts.
Methods: Here we investigate relationships between landscape features and corn yield as part of a preliminary study to model corn yield with variations in landscape attributes, soil properties, and weather. We used yield monitor data collected from 2010- 2015 at a 12 ha field at the Davis Purdue University Agricultural Research Center in Randolph County, IN, USA We obtained 15 digital elevation-based models of terrain attributes that describe morphometric and hydrologic characteristics of the field. For each year we used the random forest method to select terrain attributes that were most important for predicting corn yield across the field. We performed cluster analysis with these variables to select the terrain attributes for our spatial regression models. Models, either the spatial error or the spatial lag model, were selected based on the lowest Akaike Information Criterion (AIC) score for the model.
Results: The most important terrain attributes for predicting corn yield were topographic wetness index, topographic position index, relative slope position, catchment slope, and catchment area.
Discussions: These results demonstrate that models for predicting corn yield in Indiana need to include landscape features for increased model performance.
Conclusion: This analysis met one objective of a larger investigation that will incorporate soil properties, soil texture, and weather patterns into models of corn yield across Indiana landscapes
