Principal soil series Blount series -On till planes of Wisconsin age, formed in silty clay loam or clay loam till -Fine, illitic, mesic Aeric Epiaqualfs Pewamo series -On moraines, near-shore zones,and lake planes, formed in till -Fine, mixed, active, mesic Typic Epiaqualfs Obtain and prepare yield monitor data. Original estimates from yield monitor -2 0 2 4 6 8 10 % IncMSE Relationships between crop yield and landscape features Ashley Kissick, James Camberato, and Robert Nielsen Purdue University, West Lafayette, IN, USA U N I V E R S I T Y R Introduction 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 applicaiton for minimizing both costs to farmers and environmental impacts. 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. Use cluster analysis to select unique terrain attributes. Select most important variables for model using Random Forest. Variable importance 1. Topographic position index 5. Vertical distance to channel network 4. Valley depth 6. Convergence index 7. Flow accumulation 8. SAGA topographic wetness index 9. Catchment area 10. Topographic wetness index 11. Longitudinal curvature 12. Slope 13. Catchment slope 14. Cross-sectional curvature 15. Diurnal anisotropic heating 3. Modified catchment area 2. Relative slope position -2 0 2 4 6 8 10 %IncMSE 0 10 20 30 40 50 Height 6. Convergence Index 2. Relative Slope Position 1. Topographic Position Index 5. Vertical Distance to Channel Network 3. Modified Catchment Area 4. Valley Depth 8. SAGA Topographic Wetness Index 9. Catchment area 7. Flow Accumulation Field site Questions? Ashley L. Kissick, Ph.D. akissick@purdue.edu 5m raster of yield Calculate terrain attributes from digital elevation model. Catchment area Catchment slope Convergence index Cross-sectional curvature Diurnal anisotropic heating Flow accumulation Longitudinal curvature Modified catchment area Relative slope position Slope Topographic position index Topographic wetness index SAGA topographic wetness index Valley depth Vertical distance to channel network 657200 657300 657400 4456850 4456950 4457050 293.0 293.5 294.0 294.5 295.0 295.5 296.0 Elevation, m 293.0 293.5 294.0 294.5 295.0 295.5 296.0 Lag distance (m) Yield residuals 0.8 1.0 0.4 0.6 0.0 0.2 0 100 150 200 50 250 Semivariance range SAGA GIS Use variogram range as distance for spatial weight matrix Run two models for spatial processes: Predicted Yield 1. Spatial lag model: Υ = ρWΥ + Xβ + ε 2. Spatial error model: Υ = Xβ + η, η = λWη + ε Lowest AIC (Akaike Information Criterion) Predictors W Results Discussion The most important terrain attributes for predicting corn yield were relative slope position, topographic wetness index, topographic position index, and catchment slope. These results demonstrate that models for predicting corn yield in Indiana need to include landscape features for increased model performance. Since this preliminary study, we have added soil texture and soil properties to the models at the Davis Purdue Agricultural Center. This has substantially improved all models. 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. The focus of future work will investigate relationships between yield and terrain attributes, soil properties, and weather patterns aross Indiana, USA. Standardized coefficient Estimate p-value Topographic position index -0.49 5.51e-05 Catchment slope -0.19 0.03 Silt 0.70 2.20e-16 Topographic wetness index 0.30 9.00e-04 Longitudin