Breeding and genetics for improved productivity and resilience - Claire Domoney, John Innes Center, UK
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www.fao.org/pulses-2016/en/ International Year of Pulses - Global Dialogue - IDRC 10 year research strategy
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Breeding and genetics for improved productivity and resilience - Claire Domoney, John Innes Center, UK
- 1. A 10-YEAR RESEARCH STRATEGY FOR PULSE CROPS CHAPTER 1: BREEDING & GENETICS FOR IMPROVED PRODUCTIVITY & RESILIENCE FAO, Rome 22-23 November, 2016claire.domoney@jic.ac.uk
- 2. 1. Defining research objectives 2. Tools & Approaches 3. Current capacities & competencies
- 3. Genetics to underpin breeding • Yield, resilience & other agricultural production objectives • End uses ………… exploiting synergies Franke et al (2016)
- 4. Genetics to underpin breeding • End uses - Nutrition - Processing & cooking preferences - On-farm use Nature Rev Neurosci 14: 551-564 Normal villi Villi in coeliac disease Yield vs quality: increasing delivery of high quality nutrition per hectare Beta-oxalylamino-L-alanine
- 5. Genetics to underpin breeding • End uses - Nutrition - Processing & cooking preferences - On-farm use Weed suppression Standing ability Mihailović et al. (2016) Legume Perspectives 13 Intercropping
- 6. Genetics to underpin breeding ………… exploiting synergies CoolseasonWarmseason Adapted from Lavin et al (2005) Syst Biol 54: 575–594 by Foyer et al (2016) Nature Plants
- 7. 1. Defining research objectives 2. Tools & Approaches 3. Current capacities & competencies
- 8. • Conserving & using genetic resources • Using sequence data in breeding programmes • Crop modelling & foresight 1.1 1.2 1.3 1.4 1.5 1.6 1U 2.1 2.2 U 3 Understanding germplasm & mutant resources - Novel mutant alleles of genes: fundamental & breeding TTCGCCGCAAA--------------GTTTTGA
- 9. Sampson & Weiss (2013) Bioessays 36:34-38 Lawrenson et al. (2015) Genome Biol. 16:258 - MAS/ SNP platforms/ KASPar assays - CRISPR/Cas9 genome editing (speed breed) • Targeted mutagenesis • Gene insertion or replacement • Ease of design, flexible, affordable & efficient Non-homologous end joining Homology- directed repair • Conserving & using genetic resources • Using sequence data in breeding programmes • Crop modelling & foresight
- 10. • Conserving & using genetic resources • Using sequence data in breeding programmes • Crop modelling & foresight Yield: integration of many traits • measure traits affecting yield in many agro-ecological scenarios Ghanem et al (2015) Agric Syst 137: 24–38 Probability of grain yield increase of simulating different sowing day- of-year (A) 201, (B) 229, (C) 243, and (D) 264 as compared to a sowing day of 152 • Crop modelling & foresight
- 11. 1. Defining research objectives 2. Tools & Approaches 3. Current capacities & competencies
- 12. Diamond (2002) Nature 418: 700-707 Centres of pulse domestication Implementing coherent breeding & genetics programmes to deliver productivity Data from FAO (www.faostat.fao.org) & Foyer et al (2016) Nature Plants GL species/category
- 13. severe drought risk normal low drought risk Implementing coherent breeding & genetics programmes to deliver resilience Dai (2013) Nature Climate Change 3: 52-58
- 14. THANK YOU
Hinweis der Redaktion
- Credit = CGIAR via Noel; Bernard Vanlauwe (B.Vanlauwe@cgiar.org) for N benefit to cereal following pulse; permission requested
- Uses a guide RNA to direct the nuclease. Very efficient in barley and Brassica oleracea: Lawrenson et al. (2015). The paper describes, for the first time, successful genome editing using CRISPR / Cas9 in barley and Brassica oleracea. The mutations generated were inherited by progeny plants in the absence of the T-DNA. Specific opportunities to target members of multigene families in crops. CRISPR/Cas system (clustered regularly interspaced short palindromic repeats)
- These simulations indicated that selection and breeding for lentil accessions in East Africa should consider changes in plant phenology and/or sowing dates. Ghanem et al: Production potential of Lentil (Lens culinaris Medik.) in East Africa. Yield probability vs planting date shows potential for integration of information on physiology and genetics of This emphasises the geographical component. Genetics is mostly absent and relies on physiological model. Genetic variation of the model parameters can be assessed (e.g. in RIL populations). That would couple knowledge of allelic variation to location (and predicted climate) through the crop physiology.
- 1, bambara bean; 2, broad bean and faba bean; 3, chickpea; 4, cowpea; 5, groundnut; 6, lentil; 7, lupin; 8, miscellaneous grain legumes; 9, pea; 10, Phaseolus spp.; 11, pigeonpea; and 12, string bean. Top three grain legumes (excluding soybean) were groundnut (42.8 Mt), chickpea (13.3 Mt) and pea (11.5 Mt). Phaseolus spp. is a significant category of grain legumes by production (23.7 Mt). Figure 3: World grain legume production in 2013. a, 121 million tonnes (Mt) of grain legumes (excluding soybean) were produced globally in 2013. Data comprises the grain legumes as cited in Fig. 1 plus string bean. Production of the 12 categories are presented as a stacked column graph by the ten net highest-producing countries (inset). 1, bambara bean; 2, broad bean and faba bean; 3, chickpea; 4, cowpea; 5, groundnut; 6, lentil; 7, lupin; 8, miscellaneous grain legumes; 9, pea; 10, Phaseolus spp.; 11, pigeonpea; and 12, string bean. Of these, the top three grain legumes (excluding soybean) were groundnut (42.8 Mt), chickpea (13.3 Mt) and pea (11.5 Mt). Phaseolus spp. is a significant category of grain legumes by production (23.7 Mt). b, Global soybean production was 278 Mt in 2013, accounting for 70% of global grain legumes produced. The top five soybean-producing countries were the USA (91.4 Mt), Brazil (81.7 Mt), Argentina (49.3 Mt), China (12.0 Mt) and India (11.9 Mt). Data from FAO (www.faostat.fao.org accessed 30/01/2016). The maps were generated using R ver. 3.1.3 (R Core Team 2015) with extension packages, rworldmap80 and RColorBrewer81. Countries indicated in white are where data are unavailable.
- The Palmer drought index is based on a supply-and-demand model of soil moisture. Supply is comparatively straightforward to calculate, but demand is more complicated, as it depends on many factors: not just temperature and the amount of moisture in the soil but also hard-to-calibrate factors including evapotranspiration and recharge rates. Palmer tried to overcome such difficulties by developing an algorithm that approximated them based on the most readily available data, precipitation and temperature. The index has been most effective in determining long-term drought, a matter of several months, but it is not as good with conditions over a matter of weeks.