Post on 06-Aug-2020
transcript
Mark Lundy Assistant CE Specialist UCCE-UC Davis
Tools for Selecting Small Grain Varieties from UCCE Statewide Trials
January 12, 2018
Statewide testing program Research Question: What is the agronomic potential of new and existing small grain cultivars in California’s diverse growing regions?
Objective: Measure small grain productivity, quality, disease resistance and agronomic characteristics across a range of environmental and management conditions that represent California agroecosystems.
Objective: Apply multi-level statistical models to trial data to understand and communicate varietal differences due to genotypic, environmental and management effects.
Delta, 1/6/17 Delta, 3/16/17
Objective: Apply multi-level statistical models to trial data to understand and communicate varietal differences due to genotypic, environmental and management effects.
Objective: Apply multi-level statistical models to trial data to understand and communicate varietal differences due to genotypic, environmental and management effects.
Objective: Report results in formats that are useful and accessible to multiple types of users.
http://smallgrains.ucanr.edu/Variety/
Objective: Report results in formats that are useful and accessible to multiple types of users.
Annual field day at Davis in May
Overview of reporting and summaries for disease and agronomic trait data based on the statewide testing data
Summary of 2017 disease incidence from UC sites
Overview of reporting and summaries for disease and agronomic trait data based on the statewide testing data
96-100%
85-95%
70-84%
50-69%
30-49%
15-29%
4-14%
0-3%
Overview of reporting and summaries for disease and agronomic trait data based on the statewide testing data
96-100%
85-95%
70-84%
50-69%
30-49%
15-29%
4-14%
0-3%
Overview of reporting and summaries for disease and agronomic trait data based on the statewide testing data
96-100%
85-95%
70-84%
50-69%
30-49%
15-29%
4-14%
0-3%
90th Percentile value determined for variety-specific measurements over the past 5 seasons
96-100%
85-95%
70-84%
50-69%
30-49%
15-29%
4-14%
0-3%
90th Percentile value determined for variety-specific measurements over the past 5 seasons
96-100%
85-95%
70-84%
50-69%
30-49%
15-29%
4-14%
0-3%
Variety-specific values are sorted to determine classification on a relative basis
96-100%
85-95%
70-84%
50-69%
30-49%
15-29%
4-14%
0-3%
Variety-specific values are sorted to determine classification on a relative basis
96-100%
85-95%
70-84%
50-69%
30-49%
15-29%
4-14%
0-3%
Variety-specific values are sorted to determine classification on a relative basis
96-100%
85-95%
70-84%
50-69%
30-49%
15-29%
4-14%
0-3%
Objective: Manipulate crop water and nitrogen availability and measure variability in genotypic reactions to these varying management conditions.
High water, High N High water, Low N Low water, High N
Genotype x Environment x Management
Objective: Measure in-season changes and variety-specific differences in growth directly and via crop phenotyping platforms.
High-throughput phenotyping
Objective: Measure in-season changes and variety-specific differences in growth directly and via crop phenotyping platforms.
High-throughput phenotyping
Nitrogen Management In-season applications improve wheat yield and quality
Pre-plant only
Tillering-Flowering
16% higher yield; 1% increase in protein
DIY calibration?
DIY calibration?
Significant contributions to this work from members of the UC Davis Grain Cropping Systems Lab: Nic George, Michael Rodriguez, Taylor Nelsen, Leah Puro
Thanks as well to the efforts, cooperation and participation from:
Michelle Leinfelder-Miles, Konrad Mathesius,, Steve Wright, Steve Orloff, Bob Hutmacher, Nick Clark, Brian Marsh, Ethan McCullough, Jonathan Slocum, Paul Martinez, Peter Murphy, Rozana Moe, Jessica Henriquez, Quinn Levin, Jim Jackson, Fred Stewart, Lalo Banuelos, Francisco Maciel, Darrin Culp, Rob Wilson, Ryan Byrnes, Jason Tsichlis, Phil Mayo, Gerry Hernandez, Israel Herrera, Emma Torbert, Rika Fields, Katy Mulligan, Eric Lin, Dan Putnam, Chris de Ben, Israel Herrera, Jessica Schweiger, Lee Jackson, Josh Hegarty, Jorge Dubcovsky, and the California Grain Foundation and these sources of funding and support:
Thank you!