336-3 Science-Based Zone Mapping for Site-Specific N Management in Dryland Wheat-Based Cropping Systems On Complex, Pacific Northwest Palouse Landscapes.

See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: Symposium--Understanding Yield Variability
Wednesday, October 24, 2012: 1:30 PM
Duke Energy Convention Center, Room 263, Level 2
Share |

Emily A. Bruner1, David Brown1, David Huggins2, Erin Brooks3, Jan U. Eitel4, Troy Magney5, Lee Vierling6, Matteo Poggio1 and Tabitha T. Brown7, (1)Washington State University, Pullman, WA
(2)Land Management and Water Conservation Research Unit, USDA-ARS, Pullman, WA
(3)Biological and Agricultural Engineering, University of Idaho, Moscow, ID
(4)Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID
(5)University of Idaho, Moscow, ID
(6)Rangeland Ecology and Management, University of Idaho, Moscow, ID
(7)Crop & Soil Sciences, Washintgon State University, Pullman, WA
Increasing N use efficiency (NUE) through precision management of agricultural N in space and time will play a central role in the reduction of agricultural N2O emissions and NO3 losses. Precision N management requires a greater understanding of the spatio-temporal variability of factors supporting N management decisions such as crop yield, water and N availability, utilization and losses.

In this presentation, we outline initial zone mapping approaches as part of a five-year Site-Specific Climate-Friendly Farming (SCF) project. Using a combination of stratification and space-filling techniques, we selected 36 representative and distributed measurement locations each in four grower fields that span the precipitation gradient across the Pacific Northwest Palouse region. At these locations we have monitored soil moisture dynamics and N availability. We stratify grower fields into zones with the objective of maximizing the statistical explanation of these target variables using terrain indices, remote sensing indices, and proximal soil sensing data (e.g. from electromagnetic induction and VisNIR penetrometer).

See more from this Division: ASA Section: Agronomic Production Systems
See more from this Session: Symposium--Understanding Yield Variability