Sorghum production quality in SAT; biology & technology G E M S team System Analysis for Climate Smart Agriculture Theme by Keerthi Chandalavada, Jana Kholová et al. (ICRISAT) (+ILRI, QUAAFI, IIMR, Fraunhofer, HarvestMaster) Sorghum_21 st century_2018
How to enhance SAT systems production? M management G genotype YIELD QUALITY E environment Turning complexity into opportunity!!!
C O Dual purpose; stover/grain PY Rabi sorghum usage, SAT; Sorghum bread Livestock feed Current breeding product profile O T 0.5 Resistance/tolera Other criteria nce required Post-rainy season sorghum for food and feed Shoot fly, aphid and charcoal rot resistance; Post-flowering drought tolerance N Livestock per capita Product concept O Vikraman et al., in prep 12.3 Plant height: 2.0 to 2.2 m; Must have traits: White, globular, bold, lustrous grains; high biomass; high quality) stover Nice to have traits: high grain Zn and Fe; >2.8 g (100 grain weight Grain/stover qualities restrain cvs. adoption? Product development goals 5% increase in grain yield and 10% increase in stover yield over best check (M 35-1; Parbhani Moti, Phule Vasudah, Phule Revti)
Maldandi (M35-1) Optimal input Optimal input Limited input Limited Input Environment of cultivation reversed acceptability of products!
Overview: 1. Plant functions linked to WU;.yield quantity in SAT 2. Plant functions linked to WU;.yield quality in SAT? 3. Tools necessary to accelerate research on grain qualities
Plant functions linked to WU=> STG => yield QUANTITY (see V.Vadez ppt) M Grain Number vpd G Grain Yield A >A Biomass Crop production Grain Size & N RADN TE T RUE R int θ kl Root s SLN Hammer et al. APSIM Generic Crop Template k LAI N LNoT E Traits with high heritability, genetically and functionally correlated to yield in TPE Grain Yield (g plant -1 ) 20 16 12 8 4 0 WU-related traits R² = 0.7108 drought tolerance (Vadez et al. 2011) (Murugesan et al. 2017) 0.0 1.0 2.0 3.0 4.0 WU in week 3 after panicle emergence
TR (g/m-2/2h) 0.0120 0.0100 0.0080 0.0060 0.0040 0.0020 0.0000 Grain Number vpd Grain Yield A >A Biomass Grain Size & N RADN TE T RUE R int kl θ K359 R16 Roots SLN N k LAI LNo T 9.00 11.00 13.00 15.00 17.00 Time during the day Crop WU => STG CANOPY growth & conductivity underlie stay-green ILs Leaf size (cm2) 450 400 350 300 250 200 150 100 50 0 7001 predicted (7001) S35 predicted (S35) 0 5 10 Leaf No 15 20 25
Stover price (IR/kg DM) 4.2 4.0 3.8 3.6 3.4 3.2 3.0 STG_QTLs alter stover quality y = -4.9 + 0.17x; R 2 = 0.75; P = 0.03 2.8 44 45 46 47 48 49 50 51 52 53 54 55 Stover in vitro digestibility (%) Blümmel and Parthasarathy, 2006 STG_QTLs affect grain qualities WHAT are the functional processes & its genetics? (Chandalavada et al., in prep) Plant WU => STG => yield QUALITY stoverivod(%) 60 55 50 45 40 35 30 R16 Blümmel et al., 2015 proteins(%) 16 14 12 10 8 6 4 2 0 K359W 3 units/qtl 6040 R16 K648 K648 WW S35 2%/QTL WW S35 K359W stoverivod 6026 7001 6008 7001 6008 6026 6040 3 units/qtl WS R16 K648 K359W 6026 6040 S35 6008 7001 Protein in grains 2%/QTL WS K359W R16 K648 6008 S35 7001 6026 6040
grain size [g/100] 4.5 4.0 3.5 3.0 2.5 y = -0.0314x + 4.497 R² = 0.6374 2.0 10 20 30 40 50 Stem/stover ratio WHAT are the functional processes & its genetics? Senescence [%] => STG=> WU 0.90 0.85 0.80 0.75 0.70 0.65 0.60 0.55 y = 0.1883x + 0.5242 R² = 0.4126 0.50 0.5 0.7 0.9 1.1 1.3 1.5 grain AA [w/w%] grain AA [w/w%] Δ senescence 13 12 y = 1.6134x + 4.4771 11 R² = 0.5819 10 9 8 7 6 5 4 3 1.0 2.0 3.0 4.0 grain size [g/100] Δ Plant WU Δ grain qualities => genetic background dependency! (Chandalavada et al., in prep) Δ TE, photosynthesis
Canopy size [cm2] WHAT are the functional processes & its genetics? => Independent RIL populations with Δ grain size; 600 500 400 300 200 100 0 50% variability LA grain size small lower plants bulk LA grain size big higher plants bulk 4 3.5 3 2.5 2 1.5 1 Grain size [g/100] grain size QTL co-localize with early vigor QTLs on chromosome 5 LOD N13 x E36-1. Grain size Canopy size Chromosome 5 (Chandalavada et al., in prep)
How to process so many samples? XRF prediction R 2 = 0.89 Zn content [ICP_MS] XRF+NIRS; calibration curves NIRS prediction (%) NIRS prediction (%) 17 15 13 11 9 7 5 13 12 11 10 9 8 7 6 Protein y = 0.9653x + 0.3761 R² = 0.9563 6 8 10 12 Wet- LAB(%) [Kjeldahl] Multi-cereals protein y = 0.9159x + 0.8962 R² = 0.933 5 7 9 11 13 15 17 Wet- LAB(%) [Kjeldahl]
Sample-processing...in the field??? => Adapt existing equipment for new usage => Physically combine pieces Processing quality Grain size, moisture, Bulk density Product definition Protein, Starch, Oil, AA. Value for human health Fe, Zn, Ca, As, Cd => Part of CG-EiB effort, multi-crop
Conclusion: 1. Plant functions linked to WU affect yield quantity and quality 2. More research needed to dissect causal mechanisms affecting quality 3. Breeding & research on crop qualities require reliable HTP tools!!!
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