Water use efficiency and barley production on the Canadian Prairies Dr. Anthony Anyia Senior Scientist & Acting Manager, Bioresource Technologies, Alberta Innovates – Tech Futures June 23, 2010 Development of barley with improved nitrogen and water use efficiencies 21st BMBRI Triennial Barley Improvement Meeting held in Guelph

Challenges of Crop Production on the Prairies –demonstrated through News Releases by CWB June 11, 2010: Wet weather severely impairs crop prospects across the Prairies June 11, 2009: Cold spring, dry fields lower 2009 crop prospects in Western Canada June 12, 2008: Rains help boost 2008 crop estimates, cold spring a concern June 14, 2007: Wet spring lowers Prairie wheat acres, increases barley June 10, 2004: Moisture conditions improve across Western Canada but dry pockets remain June 12, 2003: Improved moisture conditions good news for prairie farmers August 6, 2003: Hot, dry July plays havoc with crops across the prairies

Characteristics of Canadian Prairies Vegreville AB, April 2007 Short and dry growing season Insufficient growing season rainfall Drought and heat stress in summer Long and cold winter Spring and fall frost common Occasional flooding and water logging in spring Seeding delayed due to water logged field Fields may be abandoned due to water logged soils or drought Vegreville AB, 2002, courtesy AAFC

Canada is a major producer of barley Despite the challenges, Canada is a major world producer of barley Canada is a major producer of barley Canadian yields are lower than most other leading producers

Canadian barley and wheat yields in comparison to yields in China China: W = 50%; B = 60% Canada: W = 5%; B = 0% Source: FAOStat Severe drought year in Alberta Breeding progress is masked by genotype by location variation in yield Low yields can be attributed to poor growing conditions prevalent on the prairies

Barley yield depend on both moisture and temperature Yield (data label = tonnes/ha) Weather conditions, Vegreville Low moisture + high temp = very low yield Good moisture + high temp = below average yield Good moisture + moderate temp = above average yield

Can we further improve yield and yield stability? Improved management of the cropping systems (Agronomic research still essential) Genetic improvement (direct vs. indirect selection) Experience show that targeting of underlying physiological traits that limit yield can contribute to substantial yield improvements (there are only few successful examples) To be useful, physiological traits should be easy to score and have no yield penalty under favorable conditions Many breeders are already taking advantage of advances in genomics and genetic mapping in breeding programs (more still need to be done) Bridging the gap between breeders, physiologists & ‘omics

Life-cycle of a typical cereal crop Phase: Vegetative Reproductive Stages: Establishment & Growth Foundation for future yield Pre-Anthesis Formation of yield potential Post-Anthesis determinant of actual yield Growth Conditions Usually good moisture Moisture is limiting Drought and heat stress Adapted from Anyia et al., 2008 Adapted from Anyia et al., 2008

Genetic improvement of crops Can we design new smart varieties that: Use more of the water supply - Increase water use - Decrease soil evaporation Early seedling vigour (Leaf area, SLA, LAI) Better exchange of water for CO2 -Increase water use efficiency Increase TE (carbon isotope discrimination) Increase stem reserves (non structural CH2O) Convert more biomass into grain - Increase harvest index

Wheat lines selected for low CID (Rebetzke et al. 2002)

Relationship between WUE and CID (Adapted from Anyia et el., 2007) Well watered Well watered Well stressed Well stressed Two-row barley Six-row barley

Rank stability of leaf-CID across locations & years Data from Chen et al. 2010, in-press Two years Two-row barley Two locations Six-row barley

CID & protein distribution of F5 RIL population

Discriminant Analyses on Merit x H93174006 RIL population Variables; DM and Seed Weight and HI Variables: Protein, DM, and Seed weight Variables; DM and Seed Weight Canonical correlations were 0.58 and 0.55 for the first and second canonical discriminate variates, respectively. The first canonical variate accounted for 42% of the variance, whereas the second canonical discriminate accounted for 36% of the variance. Because of the variance attributed to both canonical variates, we conclude that genetic variance was detected by the first and second canonical variates. The loadings on both canonical discriminant functions indicated that lines differed in seed yield and harvest index. *** Protein had a significant –ve corr with HI

Merit x H93174006 RIL population Summary DArT diversity in a population of 188 RILs and the two parental lines Wheat DH population Parent 1 Parent 2 # markers 1 184 6 - 5 221 2   418 Merit x H93174006 RIL population Parent 1 Parent 2 # markers 1 193 254 ?? -   140 ?? 146 6 4 743 Canonical correlations were 0.58 and 0.55 for the first and second canonical discriminate variates, respectively. The first canonical variate accounted for 42% of the variance, whereas the second canonical discriminate accounted for 36% of the variance. Because of the variance attributed to both canonical variates, we conclude that genetic variance was detected by the first and second canonical variates. The loadings on both canonical discriminant functions indicated that lines differed in seed yield and harvest index.

Relationship between water/nitrogen use efficiencies & protein We tested the following hypotheses For the same nitrogen supply, higher levels of soil moisture will lower protein content, whereas drier conditions lead to higher protein content. When moisture is limiting, water use efficient varieties will improve yield and hence decrease nitrogen concentration leading to lower protein content (implies a negative correlation)

The Results of greenhouse studies Two N levels under WW and WD conditions

Correlations amongst WUE, NUE and protein under drought in GH Two N levels under WD conditions

Results of field studies with 7 varieties

Conclusions To maintain/improve on the yield progress already made by our breeders, new tools are needed to target specific traits and growth conditions that limit yield The new tools must be complementary to existing tools and easy to deploy in existing breeding programs Although several physiological traits have been proposed, only a few have been successfully used to improve yield Improvement in one trait can have the unintended consequence of leading to a decline in another

Pyramiding of several traits such as WUE and NUE related traits may lead to progress in achieving yield stability Narrow genetic base of modern varieties may impede progress (new sources of variations are necessary to overcome this) Advances in genomics and genetic mapping are making it faster and cheaper to combine several polygenetic traits in new varieties Identifying QTLs and their linked markers will potentially reduce time and cost to make the use of physiological traits more attractive in barley breeding

Acknowledgements Collaborators/Institutions: Funding: Brewing & Malting Barley Res. Institute Alberta Agricultural Research Institute Alberta Crop Industry Development Fund Alberta Barley Commission Project Staff: Jing Chen Ludovic Capo-Chichi Sharla Eldridge Collaborators/Institutions: FCDC Lacombe Dr. Pat Juskiw Dr. Joseph Nyachiro Jennifer Zantinge University of Alberta Dr. Scott Chang